Neuro-Oncology

doi: 10.1093/neuonc/noz104pmid: 31175826

Abstract Many factors contribute to the poor survival of malignant brain tumor patients, some of which are not easily remedied. However, one contributor to the lack of progress that may be modifiable is poor clinical trial accrual. Surveys of brain tumor patients and neuro-oncology providers suggest that clinicians do a poor job of discussing clinical trials with patients and referring patients for clinical trials. Yet, data from the Cancer Action Network of the American Cancer Society suggest that most eligible oncology patients asked to enroll on a clinical trial will agree to do so. To this end, the Society for Neuro-Oncology (SNO) in collaboration with the Response Assessment in Neuro-Oncology (RANO) Working Group, patient advocacy groups, clinical trial cooperative groups, including the Adult Brain Tumor Consortium (ABTC), and other partners are working together with the intent to double clinical trial accrual over the next 5 years. Here we describe the factors contributing to poor clinical trial accrual in neuro-oncology and offer possible solutions. brain tumors, clinical trial accrual, neuro-oncology The prognosis for adult patients with malignant brain tumors remains poor, with only minor improvements in survival over the past few decades. While many factors contribute to this lack of progress, a major impediment to improving outcomes is poor clinical trial accrual. In general, when cancer patients are eligible and offered a clinical trial, more than 50% of patients enroll.1–5 Yet, the percentage of patients who actually enroll on clinical trials is much lower. In 2002, Chang et al reported that only 21.3% of malignant glioma patients participated in a clinical trial.6 Unfortunately, accrual to neuro-oncology trials has remained stagnant over the past decade. A 2016 survey of brain tumor patients by the National Brain Tumor Society (NBTS) revealed that only 21% of patients participated in a clinical trial and only 24% of patients were informed of clinical trials at the time of diagnosis.7 Similarly, a 2018 paper on the clinical trials landscape for glioblastoma (GBM) estimated that only 8–11% of newly diagnosed GBM patients enroll in clinical trials.8 Based on this information, the Society for Neuro-Oncology (SNO), NBTS, and the Neuro-Oncology Branch of the National Cancer Institute (NCI) partnered on a survey of over 350 neuro-oncology providers in an effort to identify challenges and barriers to clinical trial referral and participation.9 According to results from this survey, less than 30% of all patients are referred for clinical trials, but over one-third of participants noted that their institution did not track clinical trial referral, making accurate estimates difficult.9 SNO, in collaboration with the Response Assessment in Neuro-Oncology (RANO) Working Group, patient advocacy groups, clinical trial cooperative groups, including the Adult Brain Tumor Consortium (ABTC), and other partners are working together with the intent to double clinical trial accrual over the next 5 years. There have been previous efforts to identify barriers to clinical trial accrual in cancer.10–13 Here, we discuss the factors contributing to poor trial accrual specifically to neuro-oncology trials and offer possible solutions. We will focus on patient and community factors, disparities, physician and provider factors, clinical trial factors, and site and organizational factors (Table 1, Figure 1). Table 1 Factors impacting accrual and potential solutions Challenges Potential Solutions A: Patient and Community Factors Hindrances to patient’s decision-making ability, including low level of education, limited understanding of clinical trials, and impact of disease on neurocognitive function • Provider education to improve communication with patients regarding clinical trials (including written and online materials and videos) • Engagement of patients and providers with advocacy groups • Use of clinical trial patient navigator Patient’s limited awareness of clinical trial opportunities • Patient education campaigns focused on reducing barriers to trial participation • Brain tumor registry to help providers identify trial eligible patients and to provide an opportunity for outreach to those patients • Simplify the ability to identify local and national trials through improved online search tools, smart phone apps, or clinical trial patient navigators • Engagement with patient advocacy groups, brain tumor support groups, and use of patient navigators Patient misconceptions about research study involvement including negative personal and family attitudes about clinical trials and perceived lack of personal benefit • Patient education campaigns focused on demystifying clinical trials and dispelling rumors about them • Engagement with patient advocacy groups which can promote the existence and benefits/ risks of joining a clinical trial Suboptimal (ie, poorly timed or rushed) discussion of clinical trial opportunities with overwhelmed patients/caregivers • Heighten empathy for patients/caregivers • Incorporate multimedia materials to effectively describe in layman’s terms the study rationale, potential benefits/risks and logistics • Improve informed consent documents and processes to facilitate greater understanding of the issues involved in clinical trial participation • Provide enough time for patient/caregivers concerns to be addressed; incorporate additional research team input from nurses, mid-levels, and navigators • Refer patients to brain tumor patient advocacy organizations for additional support and access to patient navigators Concerns about the complexity and difficulty of complying with protocols, cost, and time/convenience • Design trials that are more patient friendly by obtaining their input • Resources to facilitate travel and reduce costs of trial participation such as parking, housing, and absence from work • Ensure coverage of routine patient care costs in clinical trials by both federal and private payers • Open larger number of trials at smaller centers (a role especially for the National Clinical Trials Network in the United States) • Use novel technologies such as telemedicine to minimize trips for clinical trial assessments • Trial-design changes aimed at reducing the number of clinical trial visits B: Disparities Access and referral patterns • Establish standard paradigm for referral of patients to neuro/medical oncology • Implementation of patient navigators dedicated to support of URM/vulnerable patients • Establish partnerships with community-based organizations serving URM Unconscious bias • Unconscious bias training with regular evaluation of its efficacy and relevance Lack of diversity in oncology workforce • Strengthen pipeline of URM candidates into neurology, oncology, neurosurgery, and radiation oncology C: Physician and Provider Factors Failure to discuss clinical trials as an option with patients Change standard practice to one where clinical trials are always discussed as an option in addition to existing standard therapies, particularly at two timepoints: • When formulating the initial plan of care. • At the time of disease progression or disease recurrence. • Early on, dispel potential patient and caregiver attitudes that clinical trials are only for a time when all other options have run out, especially since patients who are heavily pre- treated are less likely to qualify for trials Time and inconvenience • Trial navigators and/or electronic tools (eg, apps) to allow for rapid, real-time trial matching • Brief trial summaries pitched at physicians and at patients to provide a high-level overview of the trial rationale, design, risks/benefits, and visit schedule can distill complex information into some of the key elements • Optimizing and streamlining referral processes on both the referring and receiving ends to reduce the barriers to referring patients to outside institutions for trials. Lack of knowledge • For complex cases scheduled in advance, care team members could prepare ahead of the encounter to gather information on the best course of action • If preparing ahead of the encounter is not possible, an alternative could be to acknowledge the complexity of the patient’s case, and explain that further discussion with colleagues will take place (ie, multidisciplinary tumor board) to identify the best course of action Lack of information about available clinical trials including eligibility criteria • Take advantage of resources such as a clinical research navigator and on-line matching tools (eg, apps), who can help identify clinical trials available at the institution that may be appropriate for each patient • When a patient is a good candidate for clinical trials and there is not a trial available at the provider’s institution, consider searching for studies available at referring center(s) • Efforts to develop trial search engines which can provide accurate and appropriate potential trial matches while minimizing manual data input from the provider, and which can filter for key factors (geography, stage, disease status, lines of therapy, relevant biomarkers) are underway and should continue Lack of willingness to refer a patient to another center for study (including financial incentives) • Encourage a change in culture to always consider referring patients to centers with trials if one is not available locally, if practically feasible. • Allow patients to receive some of their evaluations and treatments with the referring physician to reduce the sense that the physician is “losing” their patients; to validate the importance of a continued connection between the referring physician and the patient; to reduce the financial disincentive to refer patients; and to support stronger collaborations between oncology teams at the referring and trial sites Lack of Incentive • Consider a research RVU system that compensates for clinical trial related activities • Increase possibility of authorship for physicians who enroll patients into clinical trials Concerns regarding a patient’s interest and ability to participate • A candid discussion with the patient and the research staff, ideally during the encounter to discuss treatment options, should be conducted to address any source of concerns for participation Concern about the interference in the physician-patient relationship • Discussions about goals of care, patient’s preferences and expectations should be done during the clinical trial in the same manner as they are done during routine clinical care Conflict between the physician’s role as caregiver versus scientist There are at least three strategies to help mitigate this concern: • To place the patient’s needs and preferences first • To enroll or refer to trials which are scientifically valid and designed in a clinically justifiable manner • To explain the differences between the role of the primary oncologist and the role of the clinical investigator, particularly when they are embodied in the same person • Ask patients for any source of concerns about conflicts between the roles, and address them Physician burnout • Understand and acknowledge the effect that clinical trials can have on physician burnout • Work with institutional leadership to emphasize the value of access to clinical trials and the need for resources to facilitate clinical research. • Work with the research staff, research nurses, clinical research coordinators and other personnel to address challenges to distribute the burden across the trial team. • Future platforms, such as artificial intelligence may assist with curating clinical trial options. • Shared electronic medical records across institutions may improve access. D: Clinical Trial Factors Patient/caregiver hardships due to frequent study center visits limit enthusiasm for trial participation • Incorporate patient/caregiver/advocate feedback into early drafts of clinical trial during development • Allow study assessments and treatments, especially those considered “standard of care” to be done locally; • Require study center visits only when critically relevant to the study therapy; • Proactively incorporate patient/caregiver considerations into need for regulatory requirements for source documentation Inefficient clinical trial design features • Incorporate multi-stage, multi-arm trials with adaptive randomization • Incorporate careful toxicity and efficacy stopping rules • Consider lower statistical power thresholds for non-registration efficacy trials • Involve at an early stage of clinical trial design patient advocacy organizations, patients and caregivers Excessively stringent eligibility criteria limit trial participation • Limit inclusion/exclusion to criteria critically relevant to study primary endpoint • Ensure eligibility criteria do not preferentially exclude a demographic or racial group, eg, upper or lower age limits, or excluding comorbidities more highly associated with demographic or socioeconomic subgroup unless specific rationale for exclusion exists. • Include patients with primary and metastatic brain tumors in early phase oncology clinical trials Technology trials ignore clinical equipoise challenges and incorporate traditional trial endpoints • Allow non-traditional primary endpoint/s that addresses key clinically meaningful objective of technology being assessed E: Site and Organizational Factors Clinical research requires specialized personnel, training, infrastructure, resources • Effective leadership of a multidisciplinary team and organization culture to promote accrual • Adequate infrastructure to allow clinical research • Cancer care accreditation to incentivize trial enrollment • Hire physicians and research staff committed to clinical research Slow activation of trials due to IRB issues, contracting issues, etc. • Centralize IRB operations with harmonization across countries Limited resources at community centers to support clinical research • Greater partnership between academic and community oncology centers • Enhanced incentives for patient enrollment in clinical trials or referrals to academic centers for clinical trials • Education programs for community physicians emphasizing the importance of clinical trials Challenges Potential Solutions A: Patient and Community Factors Hindrances to patient’s decision-making ability, including low level of education, limited understanding of clinical trials, and impact of disease on neurocognitive function • Provider education to improve communication with patients regarding clinical trials (including written and online materials and videos) • Engagement of patients and providers with advocacy groups • Use of clinical trial patient navigator Patient’s limited awareness of clinical trial opportunities • Patient education campaigns focused on reducing barriers to trial participation • Brain tumor registry to help providers identify trial eligible patients and to provide an opportunity for outreach to those patients • Simplify the ability to identify local and national trials through improved online search tools, smart phone apps, or clinical trial patient navigators • Engagement with patient advocacy groups, brain tumor support groups, and use of patient navigators Patient misconceptions about research study involvement including negative personal and family attitudes about clinical trials and perceived lack of personal benefit • Patient education campaigns focused on demystifying clinical trials and dispelling rumors about them • Engagement with patient advocacy groups which can promote the existence and benefits/ risks of joining a clinical trial Suboptimal (ie, poorly timed or rushed) discussion of clinical trial opportunities with overwhelmed patients/caregivers • Heighten empathy for patients/caregivers • Incorporate multimedia materials to effectively describe in layman’s terms the study rationale, potential benefits/risks and logistics • Improve informed consent documents and processes to facilitate greater understanding of the issues involved in clinical trial participation • Provide enough time for patient/caregivers concerns to be addressed; incorporate additional research team input from nurses, mid-levels, and navigators • Refer patients to brain tumor patient advocacy organizations for additional support and access to patient navigators Concerns about the complexity and difficulty of complying with protocols, cost, and time/convenience • Design trials that are more patient friendly by obtaining their input • Resources to facilitate travel and reduce costs of trial participation such as parking, housing, and absence from work • Ensure coverage of routine patient care costs in clinical trials by both federal and private payers • Open larger number of trials at smaller centers (a role especially for the National Clinical Trials Network in the United States) • Use novel technologies such as telemedicine to minimize trips for clinical trial assessments • Trial-design changes aimed at reducing the number of clinical trial visits B: Disparities Access and referral patterns • Establish standard paradigm for referral of patients to neuro/medical oncology • Implementation of patient navigators dedicated to support of URM/vulnerable patients • Establish partnerships with community-based organizations serving URM Unconscious bias • Unconscious bias training with regular evaluation of its efficacy and relevance Lack of diversity in oncology workforce • Strengthen pipeline of URM candidates into neurology, oncology, neurosurgery, and radiation oncology C: Physician and Provider Factors Failure to discuss clinical trials as an option with patients Change standard practice to one where clinical trials are always discussed as an option in addition to existing standard therapies, particularly at two timepoints: • When formulating the initial plan of care. • At the time of disease progression or disease recurrence. • Early on, dispel potential patient and caregiver attitudes that clinical trials are only for a time when all other options have run out, especially since patients who are heavily pre- treated are less likely to qualify for trials Time and inconvenience • Trial navigators and/or electronic tools (eg, apps) to allow for rapid, real-time trial matching • Brief trial summaries pitched at physicians and at patients to provide a high-level overview of the trial rationale, design, risks/benefits, and visit schedule can distill complex information into some of the key elements • Optimizing and streamlining referral processes on both the referring and receiving ends to reduce the barriers to referring patients to outside institutions for trials. Lack of knowledge • For complex cases scheduled in advance, care team members could prepare ahead of the encounter to gather information on the best course of action • If preparing ahead of the encounter is not possible, an alternative could be to acknowledge the complexity of the patient’s case, and explain that further discussion with colleagues will take place (ie, multidisciplinary tumor board) to identify the best course of action Lack of information about available clinical trials including eligibility criteria • Take advantage of resources such as a clinical research navigator and on-line matching tools (eg, apps), who can help identify clinical trials available at the institution that may be appropriate for each patient • When a patient is a good candidate for clinical trials and there is not a trial available at the provider’s institution, consider searching for studies available at referring center(s) • Efforts to develop trial search engines which can provide accurate and appropriate potential trial matches while minimizing manual data input from the provider, and which can filter for key factors (geography, stage, disease status, lines of therapy, relevant biomarkers) are underway and should continue Lack of willingness to refer a patient to another center for study (including financial incentives) • Encourage a change in culture to always consider referring patients to centers with trials if one is not available locally, if practically feasible. • Allow patients to receive some of their evaluations and treatments with the referring physician to reduce the sense that the physician is “losing” their patients; to validate the importance of a continued connection between the referring physician and the patient; to reduce the financial disincentive to refer patients; and to support stronger collaborations between oncology teams at the referring and trial sites Lack of Incentive • Consider a research RVU system that compensates for clinical trial related activities • Increase possibility of authorship for physicians who enroll patients into clinical trials Concerns regarding a patient’s interest and ability to participate • A candid discussion with the patient and the research staff, ideally during the encounter to discuss treatment options, should be conducted to address any source of concerns for participation Concern about the interference in the physician-patient relationship • Discussions about goals of care, patient’s preferences and expectations should be done during the clinical trial in the same manner as they are done during routine clinical care Conflict between the physician’s role as caregiver versus scientist There are at least three strategies to help mitigate this concern: • To place the patient’s needs and preferences first • To enroll or refer to trials which are scientifically valid and designed in a clinically justifiable manner • To explain the differences between the role of the primary oncologist and the role of the clinical investigator, particularly when they are embodied in the same person • Ask patients for any source of concerns about conflicts between the roles, and address them Physician burnout • Understand and acknowledge the effect that clinical trials can have on physician burnout • Work with institutional leadership to emphasize the value of access to clinical trials and the need for resources to facilitate clinical research. • Work with the research staff, research nurses, clinical research coordinators and other personnel to address challenges to distribute the burden across the trial team. • Future platforms, such as artificial intelligence may assist with curating clinical trial options. • Shared electronic medical records across institutions may improve access. D: Clinical Trial Factors Patient/caregiver hardships due to frequent study center visits limit enthusiasm for trial participation • Incorporate patient/caregiver/advocate feedback into early drafts of clinical trial during development • Allow study assessments and treatments, especially those considered “standard of care” to be done locally; • Require study center visits only when critically relevant to the study therapy; • Proactively incorporate patient/caregiver considerations into need for regulatory requirements for source documentation Inefficient clinical trial design features • Incorporate multi-stage, multi-arm trials with adaptive randomization • Incorporate careful toxicity and efficacy stopping rules • Consider lower statistical power thresholds for non-registration efficacy trials • Involve at an early stage of clinical trial design patient advocacy organizations, patients and caregivers Excessively stringent eligibility criteria limit trial participation • Limit inclusion/exclusion to criteria critically relevant to study primary endpoint • Ensure eligibility criteria do not preferentially exclude a demographic or racial group, eg, upper or lower age limits, or excluding comorbidities more highly associated with demographic or socioeconomic subgroup unless specific rationale for exclusion exists. • Include patients with primary and metastatic brain tumors in early phase oncology clinical trials Technology trials ignore clinical equipoise challenges and incorporate traditional trial endpoints • Allow non-traditional primary endpoint/s that addresses key clinically meaningful objective of technology being assessed E: Site and Organizational Factors Clinical research requires specialized personnel, training, infrastructure, resources • Effective leadership of a multidisciplinary team and organization culture to promote accrual • Adequate infrastructure to allow clinical research • Cancer care accreditation to incentivize trial enrollment • Hire physicians and research staff committed to clinical research Slow activation of trials due to IRB issues, contracting issues, etc. • Centralize IRB operations with harmonization across countries Limited resources at community centers to support clinical research • Greater partnership between academic and community oncology centers • Enhanced incentives for patient enrollment in clinical trials or referrals to academic centers for clinical trials • Education programs for community physicians emphasizing the importance of clinical trials Abbreviations: IRB institutional review board, RVU relative value unit, URM underrepresented minority Open in new tab Table 1 Factors impacting accrual and potential solutions Challenges Potential Solutions A: Patient and Community Factors Hindrances to patient’s decision-making ability, including low level of education, limited understanding of clinical trials, and impact of disease on neurocognitive function • Provider education to improve communication with patients regarding clinical trials (including written and online materials and videos) • Engagement of patients and providers with advocacy groups • Use of clinical trial patient navigator Patient’s limited awareness of clinical trial opportunities • Patient education campaigns focused on reducing barriers to trial participation • Brain tumor registry to help providers identify trial eligible patients and to provide an opportunity for outreach to those patients • Simplify the ability to identify local and national trials through improved online search tools, smart phone apps, or clinical trial patient navigators • Engagement with patient advocacy groups, brain tumor support groups, and use of patient navigators Patient misconceptions about research study involvement including negative personal and family attitudes about clinical trials and perceived lack of personal benefit • Patient education campaigns focused on demystifying clinical trials and dispelling rumors about them • Engagement with patient advocacy groups which can promote the existence and benefits/ risks of joining a clinical trial Suboptimal (ie, poorly timed or rushed) discussion of clinical trial opportunities with overwhelmed patients/caregivers • Heighten empathy for patients/caregivers • Incorporate multimedia materials to effectively describe in layman’s terms the study rationale, potential benefits/risks and logistics • Improve informed consent documents and processes to facilitate greater understanding of the issues involved in clinical trial participation • Provide enough time for patient/caregivers concerns to be addressed; incorporate additional research team input from nurses, mid-levels, and navigators • Refer patients to brain tumor patient advocacy organizations for additional support and access to patient navigators Concerns about the complexity and difficulty of complying with protocols, cost, and time/convenience • Design trials that are more patient friendly by obtaining their input • Resources to facilitate travel and reduce costs of trial participation such as parking, housing, and absence from work • Ensure coverage of routine patient care costs in clinical trials by both federal and private payers • Open larger number of trials at smaller centers (a role especially for the National Clinical Trials Network in the United States) • Use novel technologies such as telemedicine to minimize trips for clinical trial assessments • Trial-design changes aimed at reducing the number of clinical trial visits B: Disparities Access and referral patterns • Establish standard paradigm for referral of patients to neuro/medical oncology • Implementation of patient navigators dedicated to support of URM/vulnerable patients • Establish partnerships with community-based organizations serving URM Unconscious bias • Unconscious bias training with regular evaluation of its efficacy and relevance Lack of diversity in oncology workforce • Strengthen pipeline of URM candidates into neurology, oncology, neurosurgery, and radiation oncology C: Physician and Provider Factors Failure to discuss clinical trials as an option with patients Change standard practice to one where clinical trials are always discussed as an option in addition to existing standard therapies, particularly at two timepoints: • When formulating the initial plan of care. • At the time of disease progression or disease recurrence. • Early on, dispel potential patient and caregiver attitudes that clinical trials are only for a time when all other options have run out, especially since patients who are heavily pre- treated are less likely to qualify for trials Time and inconvenience • Trial navigators and/or electronic tools (eg, apps) to allow for rapid, real-time trial matching • Brief trial summaries pitched at physicians and at patients to provide a high-level overview of the trial rationale, design, risks/benefits, and visit schedule can distill complex information into some of the key elements • Optimizing and streamlining referral processes on both the referring and receiving ends to reduce the barriers to referring patients to outside institutions for trials. Lack of knowledge • For complex cases scheduled in advance, care team members could prepare ahead of the encounter to gather information on the best course of action • If preparing ahead of the encounter is not possible, an alternative could be to acknowledge the complexity of the patient’s case, and explain that further discussion with colleagues will take place (ie, multidisciplinary tumor board) to identify the best course of action Lack of information about available clinical trials including eligibility criteria • Take advantage of resources such as a clinical research navigator and on-line matching tools (eg, apps), who can help identify clinical trials available at the institution that may be appropriate for each patient • When a patient is a good candidate for clinical trials and there is not a trial available at the provider’s institution, consider searching for studies available at referring center(s) • Efforts to develop trial search engines which can provide accurate and appropriate potential trial matches while minimizing manual data input from the provider, and which can filter for key factors (geography, stage, disease status, lines of therapy, relevant biomarkers) are underway and should continue Lack of willingness to refer a patient to another center for study (including financial incentives) • Encourage a change in culture to always consider referring patients to centers with trials if one is not available locally, if practically feasible. • Allow patients to receive some of their evaluations and treatments with the referring physician to reduce the sense that the physician is “losing” their patients; to validate the importance of a continued connection between the referring physician and the patient; to reduce the financial disincentive to refer patients; and to support stronger collaborations between oncology teams at the referring and trial sites Lack of Incentive • Consider a research RVU system that compensates for clinical trial related activities • Increase possibility of authorship for physicians who enroll patients into clinical trials Concerns regarding a patient’s interest and ability to participate • A candid discussion with the patient and the research staff, ideally during the encounter to discuss treatment options, should be conducted to address any source of concerns for participation Concern about the interference in the physician-patient relationship • Discussions about goals of care, patient’s preferences and expectations should be done during the clinical trial in the same manner as they are done during routine clinical care Conflict between the physician’s role as caregiver versus scientist There are at least three strategies to help mitigate this concern: • To place the patient’s needs and preferences first • To enroll or refer to trials which are scientifically valid and designed in a clinically justifiable manner • To explain the differences between the role of the primary oncologist and the role of the clinical investigator, particularly when they are embodied in the same person • Ask patients for any source of concerns about conflicts between the roles, and address them Physician burnout • Understand and acknowledge the effect that clinical trials can have on physician burnout • Work with institutional leadership to emphasize the value of access to clinical trials and the need for resources to facilitate clinical research. • Work with the research staff, research nurses, clinical research coordinators and other personnel to address challenges to distribute the burden across the trial team. • Future platforms, such as artificial intelligence may assist with curating clinical trial options. • Shared electronic medical records across institutions may improve access. D: Clinical Trial Factors Patient/caregiver hardships due to frequent study center visits limit enthusiasm for trial participation • Incorporate patient/caregiver/advocate feedback into early drafts of clinical trial during development • Allow study assessments and treatments, especially those considered “standard of care” to be done locally; • Require study center visits only when critically relevant to the study therapy; • Proactively incorporate patient/caregiver considerations into need for regulatory requirements for source documentation Inefficient clinical trial design features • Incorporate multi-stage, multi-arm trials with adaptive randomization • Incorporate careful toxicity and efficacy stopping rules • Consider lower statistical power thresholds for non-registration efficacy trials • Involve at an early stage of clinical trial design patient advocacy organizations, patients and caregivers Excessively stringent eligibility criteria limit trial participation • Limit inclusion/exclusion to criteria critically relevant to study primary endpoint • Ensure eligibility criteria do not preferentially exclude a demographic or racial group, eg, upper or lower age limits, or excluding comorbidities more highly associated with demographic or socioeconomic subgroup unless specific rationale for exclusion exists. • Include patients with primary and metastatic brain tumors in early phase oncology clinical trials Technology trials ignore clinical equipoise challenges and incorporate traditional trial endpoints • Allow non-traditional primary endpoint/s that addresses key clinically meaningful objective of technology being assessed E: Site and Organizational Factors Clinical research requires specialized personnel, training, infrastructure, resources • Effective leadership of a multidisciplinary team and organization culture to promote accrual • Adequate infrastructure to allow clinical research • Cancer care accreditation to incentivize trial enrollment • Hire physicians and research staff committed to clinical research Slow activation of trials due to IRB issues, contracting issues, etc. • Centralize IRB operations with harmonization across countries Limited resources at community centers to support clinical research • Greater partnership between academic and community oncology centers • Enhanced incentives for patient enrollment in clinical trials or referrals to academic centers for clinical trials • Education programs for community physicians emphasizing the importance of clinical trials Challenges Potential Solutions A: Patient and Community Factors Hindrances to patient’s decision-making ability, including low level of education, limited understanding of clinical trials, and impact of disease on neurocognitive function • Provider education to improve communication with patients regarding clinical trials (including written and online materials and videos) • Engagement of patients and providers with advocacy groups • Use of clinical trial patient navigator Patient’s limited awareness of clinical trial opportunities • Patient education campaigns focused on reducing barriers to trial participation • Brain tumor registry to help providers identify trial eligible patients and to provide an opportunity for outreach to those patients • Simplify the ability to identify local and national trials through improved online search tools, smart phone apps, or clinical trial patient navigators • Engagement with patient advocacy groups, brain tumor support groups, and use of patient navigators Patient misconceptions about research study involvement including negative personal and family attitudes about clinical trials and perceived lack of personal benefit • Patient education campaigns focused on demystifying clinical trials and dispelling rumors about them • Engagement with patient advocacy groups which can promote the existence and benefits/ risks of joining a clinical trial Suboptimal (ie, poorly timed or rushed) discussion of clinical trial opportunities with overwhelmed patients/caregivers • Heighten empathy for patients/caregivers • Incorporate multimedia materials to effectively describe in layman’s terms the study rationale, potential benefits/risks and logistics • Improve informed consent documents and processes to facilitate greater understanding of the issues involved in clinical trial participation • Provide enough time for patient/caregivers concerns to be addressed; incorporate additional research team input from nurses, mid-levels, and navigators • Refer patients to brain tumor patient advocacy organizations for additional support and access to patient navigators Concerns about the complexity and difficulty of complying with protocols, cost, and time/convenience • Design trials that are more patient friendly by obtaining their input • Resources to facilitate travel and reduce costs of trial participation such as parking, housing, and absence from work • Ensure coverage of routine patient care costs in clinical trials by both federal and private payers • Open larger number of trials at smaller centers (a role especially for the National Clinical Trials Network in the United States) • Use novel technologies such as telemedicine to minimize trips for clinical trial assessments • Trial-design changes aimed at reducing the number of clinical trial visits B: Disparities Access and referral patterns • Establish standard paradigm for referral of patients to neuro/medical oncology • Implementation of patient navigators dedicated to support of URM/vulnerable patients • Establish partnerships with community-based organizations serving URM Unconscious bias • Unconscious bias training with regular evaluation of its efficacy and relevance Lack of diversity in oncology workforce • Strengthen pipeline of URM candidates into neurology, oncology, neurosurgery, and radiation oncology C: Physician and Provider Factors Failure to discuss clinical trials as an option with patients Change standard practice to one where clinical trials are always discussed as an option in addition to existing standard therapies, particularly at two timepoints: • When formulating the initial plan of care. • At the time of disease progression or disease recurrence. • Early on, dispel potential patient and caregiver attitudes that clinical trials are only for a time when all other options have run out, especially since patients who are heavily pre- treated are less likely to qualify for trials Time and inconvenience • Trial navigators and/or electronic tools (eg, apps) to allow for rapid, real-time trial matching • Brief trial summaries pitched at physicians and at patients to provide a high-level overview of the trial rationale, design, risks/benefits, and visit schedule can distill complex information into some of the key elements • Optimizing and streamlining referral processes on both the referring and receiving ends to reduce the barriers to referring patients to outside institutions for trials. Lack of knowledge • For complex cases scheduled in advance, care team members could prepare ahead of the encounter to gather information on the best course of action • If preparing ahead of the encounter is not possible, an alternative could be to acknowledge the complexity of the patient’s case, and explain that further discussion with colleagues will take place (ie, multidisciplinary tumor board) to identify the best course of action Lack of information about available clinical trials including eligibility criteria • Take advantage of resources such as a clinical research navigator and on-line matching tools (eg, apps), who can help identify clinical trials available at the institution that may be appropriate for each patient • When a patient is a good candidate for clinical trials and there is not a trial available at the provider’s institution, consider searching for studies available at referring center(s) • Efforts to develop trial search engines which can provide accurate and appropriate potential trial matches while minimizing manual data input from the provider, and which can filter for key factors (geography, stage, disease status, lines of therapy, relevant biomarkers) are underway and should continue Lack of willingness to refer a patient to another center for study (including financial incentives) • Encourage a change in culture to always consider referring patients to centers with trials if one is not available locally, if practically feasible. • Allow patients to receive some of their evaluations and treatments with the referring physician to reduce the sense that the physician is “losing” their patients; to validate the importance of a continued connection between the referring physician and the patient; to reduce the financial disincentive to refer patients; and to support stronger collaborations between oncology teams at the referring and trial sites Lack of Incentive • Consider a research RVU system that compensates for clinical trial related activities • Increase possibility of authorship for physicians who enroll patients into clinical trials Concerns regarding a patient’s interest and ability to participate • A candid discussion with the patient and the research staff, ideally during the encounter to discuss treatment options, should be conducted to address any source of concerns for participation Concern about the interference in the physician-patient relationship • Discussions about goals of care, patient’s preferences and expectations should be done during the clinical trial in the same manner as they are done during routine clinical care Conflict between the physician’s role as caregiver versus scientist There are at least three strategies to help mitigate this concern: • To place the patient’s needs and preferences first • To enroll or refer to trials which are scientifically valid and designed in a clinically justifiable manner • To explain the differences between the role of the primary oncologist and the role of the clinical investigator, particularly when they are embodied in the same person • Ask patients for any source of concerns about conflicts between the roles, and address them Physician burnout • Understand and acknowledge the effect that clinical trials can have on physician burnout • Work with institutional leadership to emphasize the value of access to clinical trials and the need for resources to facilitate clinical research. • Work with the research staff, research nurses, clinical research coordinators and other personnel to address challenges to distribute the burden across the trial team. • Future platforms, such as artificial intelligence may assist with curating clinical trial options. • Shared electronic medical records across institutions may improve access. D: Clinical Trial Factors Patient/caregiver hardships due to frequent study center visits limit enthusiasm for trial participation • Incorporate patient/caregiver/advocate feedback into early drafts of clinical trial during development • Allow study assessments and treatments, especially those considered “standard of care” to be done locally; • Require study center visits only when critically relevant to the study therapy; • Proactively incorporate patient/caregiver considerations into need for regulatory requirements for source documentation Inefficient clinical trial design features • Incorporate multi-stage, multi-arm trials with adaptive randomization • Incorporate careful toxicity and efficacy stopping rules • Consider lower statistical power thresholds for non-registration efficacy trials • Involve at an early stage of clinical trial design patient advocacy organizations, patients and caregivers Excessively stringent eligibility criteria limit trial participation • Limit inclusion/exclusion to criteria critically relevant to study primary endpoint • Ensure eligibility criteria do not preferentially exclude a demographic or racial group, eg, upper or lower age limits, or excluding comorbidities more highly associated with demographic or socioeconomic subgroup unless specific rationale for exclusion exists. • Include patients with primary and metastatic brain tumors in early phase oncology clinical trials Technology trials ignore clinical equipoise challenges and incorporate traditional trial endpoints • Allow non-traditional primary endpoint/s that addresses key clinically meaningful objective of technology being assessed E: Site and Organizational Factors Clinical research requires specialized personnel, training, infrastructure, resources • Effective leadership of a multidisciplinary team and organization culture to promote accrual • Adequate infrastructure to allow clinical research • Cancer care accreditation to incentivize trial enrollment • Hire physicians and research staff committed to clinical research Slow activation of trials due to IRB issues, contracting issues, etc. • Centralize IRB operations with harmonization across countries Limited resources at community centers to support clinical research • Greater partnership between academic and community oncology centers • Enhanced incentives for patient enrollment in clinical trials or referrals to academic centers for clinical trials • Education programs for community physicians emphasizing the importance of clinical trials Abbreviations: IRB institutional review board, RVU relative value unit, URM underrepresented minority Open in new tab Figure 1. Open in new tabDownload slide Potential barriers to clinical trial accrual. Figure 1. Open in new tabDownload slide Potential barriers to clinical trial accrual. Patient and Community Factors Patient and community factors impeding clinical trial participation of brain tumor patients are poorly understood. Indeed, disease-specific research regarding these factors is critically important due to the influence of the illness on cognitive function, behavior, and motor skills, which can impair patients’ medical decision making, employment, and sense of control. This section evaluates patient and community factors that might impede trial participation with the goal of identifying opportunities for developing interventions to improve participation. Patient-specific factors A wide range of brain tumor patient factors could influence clinical trial participation. Some of the factors which negatively affect participation in brain tumor trials, such as social disparities,14,15 language or cultural barriers, and older age,6 are common across oncology. Some factors are unique to the population given the impact of the disease on neurocognitive function. The timing and way in which clinical trials are discussed with patients may impact accrual, particularly depending on the patient’s level of understanding and the health care provider’s ability to communicate trial information. Lower educational achievement is a known barrier to clinical trial enrollment; previous studies have identified associations between education and patients’ ability to make decisions about clinical trial participation in other cancer types.1,16–19 Regardless of their educational background, the explanation of complex trial-related procedures by providers and lengthy consent forms may overwhelm patients and caregivers with no prior knowledge of clinical research methodology or terminology. The nature of clinical research and procedures such as randomization, blinding, and biospecimen collection—as well as the extra time involved as a study participant due to added travel for clinic appointments—all add complications that may be perceived as unwanted interventions beyond routine care. Additionally, clinical trials are typically presented during the emotional and psychosocial upheaval of a new or recurrent cancer diagnosis. In prior studies, the quality of communication, a strong patient–physician relationship fostering a sense of trust, and a patient-friendly presentation of clinical trial information were factors associated with improved oncology trial participation,4 highlighting the importance of communication and providing appropriate patient education. Verbal, written, video, and online review materials, carefully prepared in layman’s terms, as well as adequate time to address questions and concerns can prove invaluable to patients/caregivers pondering varied and complex treatment options. Sensitive attention to trial complexities, such as use of a placebo or blinded trial design, as well as availability of financial or other supportive resources (such as travel compensation, discounted lodging, etc) can also help encourage trial participation. Some neurologic symptoms associated with a brain tumor may be negative predictors of trial enrollment. Based on neurocognitive testing and standardized research consent capacity measures, patients with malignant gliomas perform significantly below healthy controls with respect to appreciation, reasoning, and understanding of consents.20 Not surprisingly, patients with a greater degree of cognitive impairment are less likely to enroll onto clinical trials.21 Higher symptom burden may be associated with a lower performance score such as Karnofsky performance status (KPS), precluding enrollment on a trial due to traditional inclusion criteria requiring good performance status. Physicians may also be unwilling to expose patients with a borderline KPS to the burden of a clinical trial. Additionally, patients with cognitive impairment or a greater neurologic symptom burden may feel less independent; loss of autonomy itself has been inversely correlated with participation in clinical trials in other cancers.22 Receptive or global aphasia raise concerns about a patient’s ability to provide informed consent and to understand all treatment information.20 Given the high incidence of cognitive impairment in primary brain tumor patients, additional studies are warranted to investigate the influence of cognitive function and symptom burden on trial enrollment and determine strategies to mitigate this barrier. Of course, some patients may not be suitable for trials, but there should be a relatively low threshold to consider trials for brain tumor patients given the poor results with standard therapies. Limited awareness of clinical trials Another important patient-related barrier to clinical trial enrollment is limited awareness of clinical trials and limited understanding of the overall goals of clinical research. Based on a survey of cancer patients and caregivers, most reported that their physicians did not discuss clinical trials as a treatment option and most believed that their physicians would have recommended clinical trials if trials were appropriate.23 Indeed, in the 2016 NBTS survey of brain tumor patients, only 42% of patients were informed about clinical trials at any time during the course of their illness by their medical team.7 Although many centers do not have trials to offer patients, the crucial importance of clinical research in improving care for brain cancer is a message worthy of discussion with all patients regardless of stage of treatment (thus also helping dispel the myth that clinical trials are only for those with advanced disease). Unfortunately, increased awareness itself may not be adequate to expand patient enrollment in clinical trials.7 The most common incentive for clinical trial participation cited in the NBTS survey was to not only help “me” but also help future brain tumor patients.7 This altruistic motivation is common in cancer patient trial participants, particularly in phase III clinical trials.24 A better understanding of the critical role of clinical research in advancing patient outcomes—in addition to the overall value of clinical research to society—deserves greater public dissemination. Misconceptions about research study involvement Another barrier to clinical trial participation is the misconception about risks and benefits of research study involvement. For example, patient randomization to the control arm of a study might be perceived as a lost opportunity to derive clinical benefit, leading to the patient’s non-enrollment. A recent study found that low trial enrollment might be related to a lack of understanding of details and availability of clinical trials by physicians in addition to their lack of familiarity with the principal investigators and sites where trials are available.25 A patient may feel that a physician is offering a clinical trial for his or her own selfish reasons rather than to provide the best care for the patient; this may lead to a perceived lack of personal benefit by the patient and limit engagement in clinical trials. Importantly, the design of a clinical trial can dissuade some patients from enrolling. All efforts to enhance trial enrollment will be enabled by greater involvement of patients and advocacy groups via their input into the design of clinical trials at an early stage of a trial’s development. One uncommon barrier (<10% of respondents) identified by the NBTS survey is the fear of experimental therapy toxicity, including life-threatening events.7 Clear, honest communication of realistic risks and benefits of therapeutic interventions may reduce these barriers and enhance brain tumor clinical trial enrollment. Patient-related logistical and cost constraints Another prominent barrier to trial enrollment is the distance required to travel to participate and the cost of participation.21,26,27 Travel can be a barrier for many reasons, including a patient’s inability to drive due to seizures, financial burden linked to travel and lodging expenses, time away from work for patients and caregivers, childcare costs, and overall stress related to travel. Opening trials at more centers, allowing delivery of radiation therapy or other standard of care (SOC) treatments closer to home, greater involvement of local oncologists, use of novel technologies such as telemedicine,28,29 collection of patient-reported data through mobile devices such as the MyStudies app from the US Food and Drug Administration (FDA),30 trial-design changes such as reducing the number of clinical trial visits, and resources to facilitate travel and reduce lost income may all be factors which enhance trial participation. The FDA recently updated its guidance to clinical investigators allowing reimbursements to patients in clinical trials for lodging and travel.31,32 When feasible, investigators should consider budgeting such reimbursements to patients, particularly for complex trials requiring frequent and extended visits. Education campaigns, brain tumor registries, and social media may reduce barriers to trial participation Education campaigns should focus on reducing barriers to trial participation. In addition, research is needed to identify the greatest barriers specific to brain tumor patients. The focus should be on the impact of neurologic deficits, symptom burden, cognitive dysfunction, and feelings of loss of control in addition to known general factors such as dispelling myths associated with clinical research (eg, clinical trials are only a last-resort treatment). Development of a brain tumor registry to quickly identify newly diagnosed cases with specific molecular profiles across the country could enable targeted education for these patients and physicians for early intervention, highlighting the potential benefits of precision medicine trials. Similar efforts are in place using the Screening Patients for Efficient Clinical Trial Access (SPECTA) platform by the European Organisation for Research and Treatment of Cancer (EORTC)33 (NCT02307604). Social media can also help disseminate credible information about clinical trials to a broader audience (including rural communities, younger patients, patients with rare tumors, and underrepresented minorities), improve patient understanding about research, and provide a platform for communication between researchers, clinicians, patients, caregivers, and patient advocates.34,35 Approximately 70% of online adults in the United States are regularly on social media, and usage only continues to increase.36 Social media may help engage patients in clinical research, particularly those from underserved populations. In an examination of recruitment strategies for studies of young adult female cancer survivors, internet-based recruitment resulted in the highest number and yield of participants.37 Because social media is inherently different from traditional print media, including privacy concerns in this less-controlled environment, the US National Institutes of Health (NIH) provides guidance to investigators on social media strategies for clinical trials.38–40 The internet, particularly Clinicaltrials.gov, is a frequent resource for patients and caregivers seeking treatment options, but searches for clinical trials can be cumbersome and overwhelming. Efforts to simplify identification of local studies could include development of easily accessible clinical trial matching tools available through a website or a smartphone app, increased use of clinical trial navigators, and engagement with advocacy groups and provision of patient navigators to help find clinical trial opportunities (Table 2). Table 2. Patient Advocacy Groups Name of Organization Headquarters Contact Information Clinical Trial Related Services for Patients/Caregivers Accelerate Brain Cancer Cure (ABC2) Washington, DC, USA abc2.org Provides links to clinical trial resources through their website 1-202-419-3140 American Brain Tumor Association (ABTA) Chicago, IL, USA www.abta.org Clinical trial finder and navigators available through TrialConnect app.emergingmed.com/abta/home or phone 1-877-769-4833 1-800-886-2282 Brain Tumor Network (BTN) Ponte Vedra Beach, FL, USA www.braintumornetwork.org Professional staff conduct personalized clinical trial searches and provide treatment-related navigation throughout the continuum of care 1-844-286-6110 International Brain Tumour Alliance (IBTA) Tadworth, Surrey, UK theibta.org Links to international clinical trial portals available through their website +44 (0) 1737 813872 Musella Foundation for Brain Tumor Research and Information Hewlett, NY, USA virtualtrials.com Copayment assistance program; Clinical trial finder available through their website 1-888-295-4740 National Brain Tumor Society (NBTS) Newton, MA, USA braintumor.org Clinical trial finder and navigators available through their website or phone 1-877-769-4812 1-617-924-9997 Name of Organization Headquarters Contact Information Clinical Trial Related Services for Patients/Caregivers Accelerate Brain Cancer Cure (ABC2) Washington, DC, USA abc2.org Provides links to clinical trial resources through their website 1-202-419-3140 American Brain Tumor Association (ABTA) Chicago, IL, USA www.abta.org Clinical trial finder and navigators available through TrialConnect app.emergingmed.com/abta/home or phone 1-877-769-4833 1-800-886-2282 Brain Tumor Network (BTN) Ponte Vedra Beach, FL, USA www.braintumornetwork.org Professional staff conduct personalized clinical trial searches and provide treatment-related navigation throughout the continuum of care 1-844-286-6110 International Brain Tumour Alliance (IBTA) Tadworth, Surrey, UK theibta.org Links to international clinical trial portals available through their website +44 (0) 1737 813872 Musella Foundation for Brain Tumor Research and Information Hewlett, NY, USA virtualtrials.com Copayment assistance program; Clinical trial finder available through their website 1-888-295-4740 National Brain Tumor Society (NBTS) Newton, MA, USA braintumor.org Clinical trial finder and navigators available through their website or phone 1-877-769-4812 1-617-924-9997 Open in new tab Table 2. Patient Advocacy Groups Name of Organization Headquarters Contact Information Clinical Trial Related Services for Patients/Caregivers Accelerate Brain Cancer Cure (ABC2) Washington, DC, USA abc2.org Provides links to clinical trial resources through their website 1-202-419-3140 American Brain Tumor Association (ABTA) Chicago, IL, USA www.abta.org Clinical trial finder and navigators available through TrialConnect app.emergingmed.com/abta/home or phone 1-877-769-4833 1-800-886-2282 Brain Tumor Network (BTN) Ponte Vedra Beach, FL, USA www.braintumornetwork.org Professional staff conduct personalized clinical trial searches and provide treatment-related navigation throughout the continuum of care 1-844-286-6110 International Brain Tumour Alliance (IBTA) Tadworth, Surrey, UK theibta.org Links to international clinical trial portals available through their website +44 (0) 1737 813872 Musella Foundation for Brain Tumor Research and Information Hewlett, NY, USA virtualtrials.com Copayment assistance program; Clinical trial finder available through their website 1-888-295-4740 National Brain Tumor Society (NBTS) Newton, MA, USA braintumor.org Clinical trial finder and navigators available through their website or phone 1-877-769-4812 1-617-924-9997 Name of Organization Headquarters Contact Information Clinical Trial Related Services for Patients/Caregivers Accelerate Brain Cancer Cure (ABC2) Washington, DC, USA abc2.org Provides links to clinical trial resources through their website 1-202-419-3140 American Brain Tumor Association (ABTA) Chicago, IL, USA www.abta.org Clinical trial finder and navigators available through TrialConnect app.emergingmed.com/abta/home or phone 1-877-769-4833 1-800-886-2282 Brain Tumor Network (BTN) Ponte Vedra Beach, FL, USA www.braintumornetwork.org Professional staff conduct personalized clinical trial searches and provide treatment-related navigation throughout the continuum of care 1-844-286-6110 International Brain Tumour Alliance (IBTA) Tadworth, Surrey, UK theibta.org Links to international clinical trial portals available through their website +44 (0) 1737 813872 Musella Foundation for Brain Tumor Research and Information Hewlett, NY, USA virtualtrials.com Copayment assistance program; Clinical trial finder available through their website 1-888-295-4740 National Brain Tumor Society (NBTS) Newton, MA, USA braintumor.org Clinical trial finder and navigators available through their website or phone 1-877-769-4812 1-617-924-9997 Open in new tab Disparities The 2002 Institute of Medicine report entitled “Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care” first defined the concept of health care disparity as racial or ethnic differences in the quality of health care that are not due to access-related factors or clinical needs, preferences, and appropriateness of intervention.41 Disparities with respect to prevention, early intervention, and survival have been documented in multiple diseases, including cancers42–46 and in clinical trial enrollment for cancer.47–49 There is also evidence that patients from subsets of ethnic minority groups may not be afforded the same opportunity to participate in clinical trials and thus benefit from advanced therapies offered via clinical trials.50–52 Additionally, given the advent and emphasis on advanced genomics and personalized medicine, biomarkers from diverse populations are less likely captured, thus limiting our ability to determine if true differences exist in mutational profiles across various ethnic groups.46 While the incidence of primary brain tumors is higher in non-Hispanic white patients, there is conflicting evidence regarding survival outcomes as they relate to race.53–55 However, as in other cancers, clinical trial enrollment among minority populations remains poor. In this section, we will discuss challenges in accrual to clinical trials for underrepresented ethnic minorities (URM), as well as possible approaches to addressing them. The Association of American Medical Colleges (AAMC) has defined URMs to include people from the following groups: black/African American, Hispanic/Latino, Native American, Pacific Islander, and mainland Puerto Ricans.56 It is important to note that disparities of care also exist for populations based on age, sex, socioeconomic status, and sexual orientation, and the impact of these on care of neuro-oncology patients warrants further investigation. Access and referral patterns Patient-specific factors underlying limitations to accessing proper health care for URMs include lack of insurance or underinsurance, transportation issues, cultural and language barriers, and opportunity cost from income lost from work and other practical obstacles.47,48 Lack of insurance or underinsurance can directly impact access to clinical trials, especially if care at larger academic institutions with more trial options such as NCI-designated cancer centers is not covered.48 Perhaps as an extension of insurance status, access to institutions dedicated to cancer care and clinical trials may also be dependent on referral patterns for care, which in turn may be contingent upon geography, the patient’s medical condition and preference, the preferences of the referring medical team, and patients’ awareness of trials, among other factors. Treatment planning may be interrupted and delayed especially in instances where initial surgery or biopsy is obtained at an institution separate from that of the specialist care providers.57 For patients from URM groups with wariness or unfamiliarity of the health care system as well as those with limited English or medical literacy, clinical trials may only be briefly discussed or deferred due to time considerations.48 While patient resources such as patient navigators and advocacy groups such as the American Brain Tumor Association (ABTA), the Brain Tumor Network (BTN), and NBTS are in place to guide patients and families through their treatment process (Table 2), greater attention and support is needed for the most vulnerable patients or for those who may not have an awareness of the existence of these programs. To address larger issues such as insurance coverage and other socioeconomic determinants of health (eg, income, education, habitat), a multidisciplinary effort by stakeholders including physicians, hospital administrators, policy makers, and legislators is needed. These factors are outside the scope of this current paper. A solution to this challenge in the immediate term would be to first establish a standardized management algorithm with proposed timing and steps in care for management of patients with brain tumors (Figure 2).58 Ideally, each step of the algorithm functions as a trigger or reflex, such that this may eliminate any uncertainty or hesitation in moving the patient through treatment in a smooth and uninterrupted manner. The algorithm would provide a framework for treatment planning within and across institutions ranging from large academic centers to smaller community settings and allow patients to better understand the necessary steps in their treatment course. Fig. 2 Open in new tabDownload slide Recommended management algorithm for newly diagnosed primary brain tumor patients and potential pitfalls, particularly for underrepresented minority populations. HGG = high-grade glioma. Fig. 2 Open in new tabDownload slide Recommended management algorithm for newly diagnosed primary brain tumor patients and potential pitfalls, particularly for underrepresented minority populations. HGG = high-grade glioma. Drawing on work which already exists through internal institutional and patient advocacy groups, dedicated patient navigator programs specific to clinical trials participation can be expanded, perhaps through patient advocacy, for vulnerable patients with limited literacy of health care systems or those most at risk to being lost to follow-up. As noted earlier, a specific patient’s care may cross various institutions; however, at the time of an initial diagnosis, the patient should be provided by the initial treating care provider with an overall framework of the treatment course, including options for treatment, the various roles of the treatment team, and opportunities for psychosocial support. Bias and mistrust The history of medicine is notable for transgressions with respect to race and ethnicity, which has laid groundwork for mistrust among URMs toward larger institutions including health care systems.41 In medical and research education, keen attention is paid to training and retraining to avoid errors in designing and carrying out treatment protocols. In a routine clinical encounter, however, unconscious (or implicit) bias has been determined to be a source of discrimination which may impact the treatment relationship. Communication may be poorer in what have been identified as “racially discordant interactions” between a racial minority patient and a non–racial minority physician.61–63 The medical care provider displays uncertainty as a result of misbeliefs about the patient (or his/her identity), and the patient reacts in turn. This may result in shorter patient encounters, with the patient being less willing to follow instructions and the physician less likely to recommend a course of action or clinical trial, based on an assumption that the patient may not choose to participate.41,61 Although the impact of unconscious bias may be more challenging to quantify as it relates to overall survival outcome, this still represents an important key to establishing a treatment relationship between patient and physician. Whether through larger SNO/RANO-led efforts or at a local/institutional level, there should be a commitment to ensuring that all members of a treatment team (physician, nurse, patient navigators) are trained in unconscious bias and receive updated training at intervals. Similar to medical school programs of cultural competency,64 institutions should establish metrics for success of the program and continually evaluate for efficacy and relevance. Lack of exposure to diverse workforce Black/African Americans are 13% of the US population and Hispanic Americans are 18% of the population, yet 2.8% of US oncologists identify as African American and 5.8% as Hispanic.65 The weakening of this training pipeline is likely due to concerns around education, income, and opportunity. However, it is also compounded by a decline in URM physicians and medical students, owing to reduction in medical school recruitment and pipeline programs; limited exposure to oncology and oncology subspecialties in medical school, insufficient URM role models, and implicit bias in candidate selection.61,65,66 Through an American Society of Clinical Oncology (ASCO)‒led taskforce established to increase racial and ethnic diversity in the oncology workforce, a strategic plan was developed to this end. As the functional and daily challenges faced by primary brain tumor patients are unique, SNO and partnering organizations should consider a parallel or separate pipeline program, aimed at recruitment of physicians from URM groups into neuro-oncology. URM student organizations such as the Student National Medical Association (SNMA) may provide a worthy and enthusiastic pool of the next generation of clinicians, researchers, and leaders in neuro-oncology. Similar efforts can be made with respect to resident trainees. Efforts for pairing current medical students and resident trainees with faculty mentors or representation of SNO at national conferences may be a small step in establishing a longitudinal pipeline critical to ensuring equal, high-quality care for our patients. Physician and Provider Factors Physicians and clinical providers play a key role in the clinical trial process. This includes the identification of patients for appropriate trials, discussion regarding the option of participation, assessment of patients on study, and compliance with trial conduct. In general, there are two main options for physicians to enroll patients into clinical trials: enrolling onto trials that are open at the physician’s own institution and referring patients for open trials at other institutions. The latter option is particularly important when good SOC options do not exist, when there are no available or appropriate trials at the physician’s own institution, and when patients are motivated to participate in a clinical trial at another institution. In this section, we focus on the interaction between patients and their treating physician. Rogers and colleagues report that the most frequent barriers clinicians face include factors related to trial location (eg, difficulty finding trials in the patient’s geographic area, patient being unable to stay for treatment at the academic site), lack of available slots on an existing trial, patient not meeting eligibility criteria, and limited staff resources.9 The importance of the physician’s role in trial accrual is strongly supported by the variance in accrual between physicians within a practice.67 The difference between “high” accruers and “low” accruers is unlikely due to different patient populations within the same practice and likely due to individual physician-related factors. Here we identify and provide solutions to the common challenges that physicians and providers experience with clinical trials. Clinical care The option of participation in a clinical trial is a major part of professional practice guidelines such as those from the National Comprehensive Cancer Network (NCCN).60 As a scientific community, the review and discussion about local and regional clinical trial options should become part of the standard workflow for good clinical practice. If this discussion is missed, patients may not have the opportunity to participate in a trial that may be of interest to them.68 There are several potential barriers that limit this discussion. From a practical perspective, the provider may feel that there is insufficient time in an appointment to discuss clinical trials in addition to the standard therapies.69 Another challenge is the lack of knowledge regarding the potential trials that patients may be eligible for either at the provider’s own institution or at another site.19 This may be due to lack of time to search and identify potential studies for the individual patient. In fact, the burden of the clinical trial process has been identified as a major barrier for referring patients for clinical trials.70 However, most patients are unaware of available clinical trials, and therefore the role of the physician in identifying clinically appropriate studies is critical.71 In addition, provider attitudes toward clinical trials are an essential factor for successful trial enrollment. Patients whose physicians are motivated to present the trial in detail, explain the relevance of the study intervention in their medical management, and encourage them to participate are more likely to enroll. This is also perceived by patients who view their physicians as the most trusted health care professional source of health information.72 Another barrier is the unwillingness to refer patients to an outside institution where a trial is identified. When the referral does not happen, it is a missed opportunity to both enhance patient care through clinical trial participation and collaborate with referral centers. There are other challenges to clinical trial enrollment. Some physicians may not want to enroll patients in randomized studies if they feel uncomfortable with the presence of a control arm, as discussed further in the clinical trial factors section. In addition, the physician’s perception about risk of toxicity in an individual patient with factors such as older age, worse KPS, extensive comorbidities, and a high level of symptom burden may negatively influence a physician’s decision to recommend a clinical trial. Age is a significant influence on recruitment of patients to clinical trials73 and is a strong independent predictor of trial participation in glioma patients.6 The interaction between age and risk of toxicity perceived by the physician has been shown to influence enrollment of elderly women in breast cancer clinical trials.74 Given the importance of clinical research in improving outcomes, it is critical to minimize physician bias and its influence on clinical trial participant selection. Some physicians may have the perception that the patient is not interested in participating and may be concerned about an adverse effect on the physician–patient relationship.75 Both of these challenges may be addressed by allocating time for specific discussions with patients. These discussions are actually similar to discussions that clinicians have with patients during standard treatments, where clinicians will discuss with patients about risks, benefits, and candidacy for any standard therapy. Additional time is also spent in the care of patients enrolled on clinical trial, including additional documentation, assessment of adverse events, tumor measurements, and regulatory activities. Whether physicians should be compensated for the additional time required to address clinical trial questions (similar to the billing mechanism in the US that supports the additional time required for end of life discussions) is unclear; however, what is clear is that identifying and presenting clinical trials to patients does take time. Another factor that may also influence trial enrollment that is closely related to the physician–patient relationship is the potential conflict physicians perceive as their role as caregiver versus scientist.69 In addition, the physician may have an interest in enrolling patients on a particular study over another concurrently run trial, which can skew accrual and may or may not be the best fit for the patient. Potential solutions to these challenges are shown in Table 1. Physician-related logistical barriers The interest and enthusiasm for research participation by physicians may vary between specialties, and between clinical practice models, such as academic versus private practice versus hospital based. The physician financial compensation model may influence the likelihood of recruitment to trials available within a physician’s practice as well as the likelihood of referral to another institution for trial consideration. In the US, physician compensation is often tied to relative value unit (RVU) production, a measure of value used in the reimbursement formula for physician services. As detailed above, trial accrual and participation increase the time and complexity of patient care that may reduce physician efficiency, with a subsequent reduction in RVU production. Therefore, many physicians regard research participation as a “volunteer” activity. If a patient is referred to another center for trial participation, the result is a loss of revenue for the referring physician and, often more importantly, his or her institution. Allocation of RVUs for research-related activities (research RVUs76,77) may be a strategy to incentivize greater participation, although it requires a concerted effort between physicians, institutions, and payors to provide direct and indirect incentives for participation in research activities. Expanding authorship to more physicians who accrue to trials78 is another strategy, particularly if recognized by institutions for academic promotion. Clinical operations play an important role to support physicians in enrolling patients in clinical trials. For example, adequate staff effort is critical to secure timely referrals and to ensure safe continuity of care in cases where a trial is not available on site. Potential solutions to encourage trial accrual related to logistical concerns are shown in Table 1. Physician burnout Physician burnout related to clinical research is a complex problem.79 As outlined above, physicians can experience significant challenges regarding accrual of patients to clinical trials. These challenges can be frustrating and can harm the motivation of providers to enroll, or even to discuss clinical trials as an option with their patients. Potential solutions can include supporting research navigators who can alleviate the workload of curating and reviewing clinical trials for patients and addressing the financial constraints that physicians face.75 In looking into the future, new technologies may be able to assist in decreasing the physician’s burden—for example, artificial intelligence may help screen patients for eligibility to clinical trials and may help identify clinical trial options that are available at other institutions. However, the affordability and the integration of such technologies will be key elements for their access and use. Electronic medical records, which are widely available today, make clinical information easier to access and may assist in clinical research, particularly for trials that are available at the same institution. Clinical Trial Factors The development and implementation of clinical trials may adversely affect trial accrual and enrollment. Clinical trial factors that serve as barriers include the lack of input from patients, caregivers, and advocates in clinical trial design; lack of engagement with local health care providers in clinical trial implementation; overly stringent participant eligibility, and challenges associated with incorporation of novel technologies into neuro-oncology practice. Here, we discuss general principles across different types of clinical trials. There are additional factors affecting accrual depending on the type of clinical trial (ie, early phase trials versus late phase trials, randomized versus single arm studies, sponsored versus consortium versus investigator-initiated studies) which are worth further evaluation. Trial factors affecting patient participation and health care provider engagement Incorporation of feedback from key stakeholders including patients, caregivers, and advocates early in the developmental process of a clinical trial can provide invaluable perspectives that may markedly enhance how user-friendly and attractive a trial is to patients. Often such input addresses practical and logistical issues not appreciated by investigators focused on the trial’s ability to address its key objectives. An important example, repeatedly raised by patients and caregivers, is that clinical trials should incorporate flexibility to allow study assessments that are considered SOC, particularly laboratory, imaging, and physical examination evaluations, to be performed locally whenever possible. Similarly, allowing standard therapies, such as radiation therapy or approved systemic therapies, to be administered locally according to established guidelines rather than at the trial center would ensure continuity of care, enhance quality of life, and reduce financial and logistical burdens of travel. Such considerations will also likely heighten enthusiasm among local clinicians who value remaining key contributors for their patients enrolled on a trial and lessen the financial disincentives of referring to another institution. On the other hand, evaluations and treatments are typically required to be performed at the study center, even those that are SOC because of perceived and actual regulatory requirements specifying that data be incorporated into a study database, and hence subject to audit, and be obtained from a validated, quality controlled, and certified source. For example, in the US, sponsors/investigators for studies under an investigational new drug agreement are compelled to add laboratories, imaging centers, and local treatment providers to the FDA 1572 form with appropriate credential certifications filed in the participant’s study record. Rather than bother with this significant administrative effort and associated risks of potentially missed or inaccurate data, many sponsors/investigators simply opt to require patients to travel to the study center for all evaluations and treatments. Although such regulatory considerations are ultimately based on ensuring patient safety during trial participation, they can impose a significant burden on patients/caregivers by requiring an inordinate frequency of study center visits. Better clarification of study-specific regulatory requirements between investigators, sponsors, and auditors as well as a willingness to allow standard assessments and treatments to be performed locally by investigators for trials, despite the additional effort, will be important to lessen the impact of this issue on clinical trial accrual. In this way, visits to the study center are reserved for evaluations that are critically relevant to the investigational therapy. Eligibility criteria Excessively stringent eligibility criteria may also impede accrual. Although a growing number of trials appropriately restrict eligibility to a subset of patients with tumors exhibiting a specific mutation or biologic feature being targeted by an investigational agent, many other eligibility criteria are unnecessarily restrictive, excluding patients who could effectively contribute to addressing the study’s primary question. For example, many phase I trials of CNS tumors limit participation to patients with histopathologic grade IV GBMs, those at first or second recurrence, and those who have not progressed on prior bevacizumab, while in reality, patients with lower-grade, isocitrate dehydrogenase (IDH) wildtype gliomas or even uncommon primary malignant CNS tumors (for whom clinical trials rarely exist) or those with unlimited number of recurrences or who have progressed on prior bevacizumab could contribute to determine safety and a maximum tolerated dose (MTD)/recommended phase II dose (RP2D). For these early trials where safety is the primary outcome and treatment is not specifically targeted at a specific histology, specified laboratory criteria and performance status should be considered the main eligibility criteria for participation. An exploratory cohort of specific histology or treatment history could be incorporated once the MTD/RP2D is defined. The definition of the most appropriate SOC for each and every patient is beyond the scope of this article, but concurrent use of tumor treating fields in combination with adjuvant temozolomide for newly diagnosed GBM is a common exclusion criterion in many current trials. This may unnecessarily exclude some patients from trial participation. The decision to exclude concomitant use of tumor treating fields in trials for newly diagnosed GBM should depend on whether there is a scientific reason to do so (ie, potential interference with the therapy being evaluated, with the evaluation of side effects from therapy, or with the endpoints of the study). For randomized clinical trials, if tumor treating field use is to be allowed for patients on study, proper stratification should be utilized to control confounding effects. Some specific eligibility criteria can also be inappropriate for some phase II and III trials. For example, trials with a primary efficacy endpoint of progression-free survival (PFS) or overall survival (OS) should not exclude participants who have undergone a gross total resection prior to study enrollment, including trials on recurrent glioma. Although patients without measurable disease cannot be included in the determination of the objective response rate,59 they can contribute to evaluation of a PFS or OS primary endpoint. US regulatory bodies recognize the importance of loosening overly restrictive eligibility criteria when appropriate.80 Indeed, ASCO, Friends of Cancer Research (FOCR), and the FDA examined specific eligibility criteria for population groups historically excluded from trials, including patients with brain metastases, and recommended ways to broaden eligibility criteria for these populations in cancer clinical trials. The RANO Brain Metastases Working Group have made similar recommendations.81 Subsequently, the National Cancer Institute (NCI) pledged to utilize these broadened eligibility criteria in clinical trials going forward. A remaining barrier is the continued exclusion of glioma patients from most phase I trials in cancer based on outdated concerns.82,83 An ongoing effort is needed to ensure that eventually glioma patients will routinely be included in phase I trials if there is biologic rationale and the drug has reasonable access across the blood–brain barrier. Novel technology development Comparative Studies. In neuro-oncology as well as other cancer indications, trials assessing novel technology interventions pose further challenges for accrual associated with clinical equipoise. Do we have the clinical equipoise of “first do no harm” prior to embarking on randomized comparisons of technology? The example of proton versus photon radiation therapy underscores such a challenge. For this technical improvement comparison, the treating physician and the patient are both aware that one technology can reduce unwanted and unnecessary radiation dose to normal tissues. Patients and treating physicians may disregard such trials due to concern of randomizing to the “less desirable” arm. This lack of equipoise in technology testing serves as a major impediment to accrual and is underscored by several languishing proton versus photon therapy or intensity-modulated radiation therapy (IMRT) versus standard radiotherapy phase III trials (eg, NCT021790860). In this context, third party coverage decisions, resulting in inordinate delays in initiating treatment, also substantially impede trial accrual.84 Endpoint Selection. Conventional trials incorporate traditional endpoints such as PFS, OS, and response rate. In contrast, technology trials may be best suited to provide results focused on “unconventional endpoints.” For example, the fluorescent dyes used to enhance surgical resection of gliomas categorically demonstrate the ability to produce a more complete tumor resection, which translates to short-term, but not long-term, survival benefit, and therefore FDA approval of such agents lagged Europe’s approval by more than a decade. Endpoints for technology trials need to be tailored to the expected outcome to be tested. For example, craniospinal irradiation with protons might permit a greater ability to provide dose-intense chemotherapy for medulloblastoma patients, or volume-sparing radiation techniques might cause less lymphopenia, or preoperative stereotactic radiosurgery (SRS) might reduce the local failure rate and pattern observed with postoperative SRS of brain metastases. Consideration of unconventional endpoints may be appropriate beyond technology trials, such as the use of seizure control, change in neurocognition, or preservation of neurologic function as endpoints for lower-grade glioma trials.85 However, these endpoints are “non-traditional,” and therefore often not considered germane, leading to incorporation of traditional yet less appropriate endpoints that may impact enthusiasm for accrual. Site and Organizational Factors Clinical research is distinct from routine clinical care. Centers with clinical research capabilities have specialized personnel, training, infrastructure, resources, dedicated research nurses, specialized institutional facilities for specimen collection and analysis, infusion centers, and imaging. Clinical trials for brain tumor patients have become increasingly complicated in recent years, having evolved into more subgroup-focused studies, and therefore enroll smaller numbers of patients per trial. Genomic profiling, patient screening, and added procedures require greater infrastructure support. At many larger centers, additional sources of research support are used, such as philanthropy and funding from not-for-profit foundations; but these are mostly available to research-focused clinical centers. The critical need for funding is highlighted by the fact that centers in the US with NCI- or industry-sponsored clinical trials have greater trial enrollment,86,87 whereas unfunded sites have found a lack of specialized resources to be a barrier to trial enrollment.88 It follows, therefore, that health care organizations are critical to the successful conduct of neuro-oncology clinical trials. Even if physicians are engaged in research and eligible patients are available, accrual to clinical trials is dependent upon the proper stage being set by the organization. If health care organizations focus on setting the stage with infrastructure, clinical practice models, and appropriate staff necessary for clinical trial conduct, then trial accrual to neuro-oncology studies will improve. At present, many clinical centers do not view providing access of their patients to clinical trials as a goal of high or even modest priority. Internationally, this is also true for academic centers, since their reimbursement systems usually do not differ from those of non-academic centers and since patients enrolled onto clinical trials incur more administrative effort, but often less income. Thus, organizations are often faced with difficult decisions when funding different aspects of patient care. At a different level, publicity generated by the participation in clinical trials is usually limited and clinical centers tend to be sensitive to activities that raise national or local publicity. The major administrative and financial burdens may differ somewhat between countries and individual sites but commonly include the institutional review boards (IRBs) or ethics committees, challenges related to contracting between trial sponsors and the institutions, maintaining compliance with research billing, and financially supporting research support staff. Cancer care accreditation as an incentive for trial enrollment Accreditation is a useful tool to recognize sites for clinical excellence. In the US, the Commission on Cancer (CoC) recognizes cancer care programs for their commitment to provide comprehensive, high-quality, and multidisciplinary patient-centered care. Research participation is an integral component of the CoC standards. To meet accreditation standards, cancer centers must therefore place an emphasis on research participation, which supports the financial investment cancer centers put toward research activities. The development of accreditation for neuro-oncology centers through the Society for Neuro-Oncology (SNO) would spur organizational and site investment in research infrastructure, similar to what is seen for the general oncology practice through CoC accreditation. IRB issues Centralizing operations may also relieve some of the administrative and financial burdens for individual sites. Moving toward a central IRB (eg, on a country or trial level) could be an important step that remains yet to be realized in most countries; moreover, issues remain on an international level, notably with European involvement, since processes of harmonization among different countries in Europe remain slow. However, even when a central IRB is available, not all institutions will recognize that approval in lieu of their own internal processes. In the US, a central IRB is now required for all multi-site, NIH-funded studies, which has relieved a significant burden from individual sites and has led to more rapid trial conduct. Contracting issues Contracting challenges more often come from the institution than from external trial sponsors. Both industry and cooperative study groups prefer to institute a single contract per site, which facilitates trial conduct and money flow, but this requires contract review, cooperation, and communication at the various levels within each institution. Supporting disciplines like pathology, radiology, radiation oncology, and pharmacy often request separate contracts, which drives administrative burden and cost. Contracting issues can be especially problematic for investigator-initiated multicenter trials and greater use of master contracts would reduce the timelines involved. Another and often underrecognized challenge relates to the determination of which study-related events or procedures are considered to be “standard of care” versus “experimental.” It is generally recognized that SOC procedures should be covered by traditional means (eg, insurance or national health care plan) and that experimental procedures, including additional imaging, blood tests, etc, should be covered by the sponsor. Often there is some disagreement as to which medical tests, measures, and tasks should be considered SOC and which should be considered trial related. While some of these costs may be covered, at least in part, by the sponsors, there are certain costs (for example, the primary investigator’s professional time) that are not recognized. The level of necessary insurance coverage for clinical trial conduct may add complexity in some countries. In the US, these coverage determinations come in the form of a Medicare Coverage Analysis (MCA). It has been recognized that for each individual site to complete a separate MCA is unnecessary. The development of central MCAs which are provided for NCI cooperative group trials is ongoing. Another significant expense relates to the processing of adverse event (AE) reports. These reports are as important to the evaluation of a new treatment as are the primary outcome measures, and the amount of time spent on recognizing and reporting AEs is typically not covered in clinical trial contracts. These reports require the input of the investigators, research nurses, protocol managers, and other administrative personnel; and they often require multiple revisions in collaboration with the study sponsor. The most significant cost of these activities is time, particularly that of the primary investigator. The primary investigator is also expected to attend steering committee meetings (in person or by telephone) and other internal research regulatory meetings, which also deplete the finite resource of time and are usually not reimbursed. Especially if an investigational agent is investigated in several parallel large trials, the number of serious AE reports may prevent adequate reading. Clinical practice models Organizational support of a clinical practice model that fosters research participation is also essential to improve neuro-oncology clinical trial accrual. Practice models may vary between academic and community centers. In academic institutions, there are centralized models (eg, central core units including phase I sections where cancer trials across tumor entities are conducted) as opposed to decentralized models where small dedicated teams take care of brain tumor patients, both outside and within clinical trials. The former units are commonly more sustainable, better capable of more complex trial supportive measures, and often preferred by institutions, whereas the latter are more likely to meet specific requirements addressing the specific needs of brain tumor patients, notably their impairment including aphasia, paresis, or seizures. Practice models in community centers vary widely and relate to various factors, including whether neuro-oncology patients are seen in a neurology clinic versus a general medical oncology practice, or whether they are seen in a rural or urban setting. In general, research subjects in community practices are not seen in a separate unit. Thus, any guidance toward developing criteria for a clinical practice supporting research activities must be broad to include multiple practice models. The value of a dedicated assessment tool to benchmark clinical trial infrastructure is currently being explored.89 For several reasons, enrolling patients into clinical trials becomes even more difficult if the respective disease is diagnosed and treated in a multidisciplinary fashion. Tailoring diagnosis and treatment strategies to individual patients with brain tumors is commonly believed to be done best in a multidisciplinary tumor board, which a priori requires additional resources from all departments involved. Tumor boards represent the ideal setting for research staff to gather feedback about upcoming trials and to educate all departments about open studies. Such boards also appear to be the best platform to ensure that eligible patients for each open trial are readily identified. Still, since disease experts cannot be expected to universally agree with all trials of the institution’s portfolio, it is important that once a decision to join a trial is made, the whole team of disciplines supports this trial, notwithstanding potential reservations of individual team members. This is particularly relevant for clinical trials which may be perceived to challenge one’s own discipline (eg, trials which examine withholding surgery or radiotherapy). Also, requirements for time-consuming molecular testing can imply that eligibility for a trial becomes confirmed shortly before treatment is to start, which requires additional efforts from departments that bring their services to the trial without being involved and without formal recognition. To overcome this threat requires strong leadership of the multidisciplinary team and a broad institutional mindset that considers a strong clinical trial portfolio an asset to an academic medical unit. Tumor boards, with involvement of outside experts or use of virtual tumor boards, are particularly useful for community practices as their input may represent the sole point in time when the full multidisciplinary team is together. Physicians and research staff Specific challenges associated with physician engagement in clinical research were discussed previously in this article. Physician engagement with research varies within each clinical trial site, and thus accrual suffers if particular physicians are not engaged.67 This is particularly true in community settings as opposed to academic settings. Non-research-focused clinical centers, including community centers, may have fewer incentives to enroll patients in clinical trials. Physicians in larger academic centers are evaluated and incentivized with promotion, tenure, and academic credit for developing—and enrolling patients in—clinical trials. In the US, the same incentives are typically absent at smaller community hospitals and smaller academic centers, where success of the provider and center might be based on revenue-generating procedures and overall patient volume (eg, RVU-based incentives). Usually, due to pressures to increase patient volume activity, protected time is limited for community physicians to open clinical trials and spend the time necessary to explain the value of clinical research to patients. A greater collaboration between research-based centers and community partners in local or statewide networks as well as enhancing incentives for patient enrollment in clinical trials (discussed previously in the “Physician and Provider Factors” section) could greatly improve trial accrual and patient satisfaction. In the US, up to 80% of cancer patients are treated in community settings, highlighting the importance of greater partnership between academic centers and community physicians. Educational efforts focused on community physicians to emphasize the importance of clinical research would be helpful. Although benefits from state-specific awareness campaigns have been short lived,90,91 it is imperative that the brain tumor community identifies optimal strategies to increase clinical trial awareness. Closer academic–community collaboration is essential. Realigning incentives within the community is important to facilitate patient referrals to centers of excellence for enrollment. Medicare, Medicaid, and third-party insurance reimbursements could be linked to patient trial enrollment. Additionally, opening more clinical trials in community centers may lead to greater patient participation. Nonetheless, each health care organization maintains the ultimate responsibility to hire physicians engaged in research activities and to maintain engagement by developing the infrastructure and clinical practice model necessary to facilitate physician involvement in research. If sites place an emphasis on research, they will seek to hire new physicians who share similar goals. To maintain engagement, sites may develop strategies to recognize and financially reward physicians for research activities, such as a research RVU system. In addition to physicians, research staff and support staff at an institution, including nurse practitioners, nurses, study coordinators, social workers, and translators, play a critical role in trial accrual. These personnel are crucial in establishing patient trust in otherwise complex and impersonal medical systems and provide a more compassionate and human touch to necessary medical encounters and decision making. At many research institutions, especially community sites, the research nurse is the primary point of contact for patients to learn details about clinical trial participation. This interaction plays a key role in whether a patient elects to participate in a particular trial. Development of education materials and accreditation specific for neuro-oncology will improve each site’s ability to hire and maintain support staff necessary for the conduct of trials and to enhance accrual. Plans for the Future More research is needed to better define the factors specific to neuro-oncology affecting accrual. To better understand this issue, clinical trial investigators should systemically document the reasons why patients do not enroll on trials. However, an analysis alone of the complex factors contributing to poor trial accrual to neuro-oncology trials is insufficient. The next step is to determine a roadmap to overcome these barriers (see Table 1 for summary). Borrowing from the field of behavioral economics, we must find “nudges” 92 that can effect significant, long-lasting change and promote clinical trial participation. Provider and patient educational campaigns as well as engagement with advocacy groups may help change the mindset of physicians, patients, and caregivers to one where enrollment in clinical trials is the best option for most patients. This is now reflected in the NCCN guidelines for central nervous system tumors, where clinical trials are now the preferred option for most tumors.60 We must also harness modern technology to spread information about clinical trials through social media campaigns, smartphone apps, or internet-based clinical trial matching services that simplify clinical trial searches for patients and caregivers, increasing the number of patient navigators to guide patients through the process, as well as creation of national brain tumor registries where patients can contribute tumor tissues for sequencing which may help pair them with clinical trial options. Until we can expand clinical trial opportunities to more community-based settings (perhaps through a more efficient national clinical trial network, allowance of SOC components of the trials to be performed locally, and use of telemedicine for virtual visits), any patient with a GBM or uncommon brain cancer should be offered a referral to a major neuro-oncology center(s) in their relative proximity for consideration of clinical trials. We must also critically evaluate clinical trial design that fosters accrual and removes excessive hindrances for participants. Continuing the momentum of work by ASCO/FOCR in collaboration with NCI, we will plan to work with the neuro-oncology community and clinical trial consortia to modernize eligibility criteria for brain tumor trials. However, these efforts to increase clinical trial accrual are likely to have limited impact if there are insufficient good quality trials. It will be critical that parallel efforts be undertaken in neuro-oncology to address this issue as well. How this can be accomplished is beyond the scope of this paper, although the neuro-oncology community must work together to increase the number of high quality clinical trials, especially in the NCI National Clinical Trials Network in the US and the EORTC in Europe, and to develop more trials utilizing efficient clinical trial designs (ie, adaptively randomized trials such as AGILE93 and INSIGhT94). Funding This work was supported by Adult Brain Tumor Consortium (ABTC) NIH/NCI UM1 CA137443. Conflict of interest statement. Brian Alexander: Personal fees from Abbvie, Schlesinger Associates, Precision Health Economics, BMS. Research support from Celgene, Eli Lilly, Puma. Employment by Foundation Medicine, Inc. John de Groot: Grant or Research Support from Sanofi-Aventis, Astrazeneca, EMD-Serono; Eli Lilly, Novartis, Deciphera Pharmaceuticals, Mundipharma. Paid Consultant: Celldex; Deciphera Pharmaceuticals, AbbVie, FivePrime Therapeutics, Inc., GW Pharma, Carthera, Eli Lilly, Kadmon, Boston Biomedical Inc.,Taiho Pharmaceuticals, Kairos Venture Investments, Syneos Health, Monteris, Agios, Mundipharma Research, GenomiCare, Blue Earth Diagnostics. Advisory Boards: Genentech, Celldex, Foundation Medicine, Inc., Novogen, Deciphera, Astrazeneca, Insys Therapeutics, Merck, Eli Lilly, Novella Clinical, Kiyatec, Blue Earth Diagnostics. Other Relevant Financial or Material Interests: DSMB: VBL Therapeutics; DSMB: Novella; VBI Vaccines, Inc. Stock Ownership: Ziopharm Oncology, Gilead. Company Employment (Spouse): Ziopharm Oncology. Maryam Fouladi: Grant or research funding from Pfizer, PTC Therapeutics, Merck, and BMS Eudocia Q. Lee: Consulting for Eli Lilly. Contributor to Up to Date, Inc. and Medlink. Jose Pablo Leone: Research support (paid to institution) from Merck Nancy Lin: Research funding: from Pfizer, Genentech, Novartis, Seattle Genetics. Consulting for Genentech, Seattle Genetics, Puma, Daichii. Minesh Mehta: Consulting relationships with honoraria with Varian, Agenus, Insys, Remedy, IBA, Astra-Zeneca, Celgene, Tocagen, Abbvie; Board of Directors position with stock options with Oncoceutics; and DSMB for Monteris. Kathy Oliver: Mission support funding and grants (paid to the International Brain Tumour Alliance/IBTA from 2005 to date) from AbbVie, Accuray, Antisense Pharma, Apogenix, Archimedes, Ark Therapeutics, Astra Zeneca, Bayer, Boehringer Ingelheim, Brain Tumor Network (USA), Brain Tumor Resource and Information Network (USA), Bristol-Myers Squibb Celldex Therapeutics, Crusade, Dijon Designs (UK), Elekta, Eli Lilly, Gerry & Nancy Pencer Brain Trust (Canada), Gosling Foundation (UK), GlaxoSmithKline (GSK), Ivy Foundation (USA), Lilly, Link Pharmaceuticals, MagForce, Medac, Merck Serono, Merck, MGI Pharma, MSD Oncology, NeoPharm, Neuroendoscopy (Australia), Northwest Biotherapeutics, Novartis, Novocure, Pediatric Brain Tumor Foundation (USA), Pfizer, Photonamic, Roche, Schering-Plough (Global), Sontag Foundation (USA), Spink (UK), to-BBB, Vane Percy (UK), VBL Therapeutics and the Wallerstein Foundation (USA). Honoraria for advisory board work and conference speaking from AbbVie, Bayer, Bristol-Myers Squibb, GSK, Lilly, Novartis, Sarcoma Patients EuroNet David Reardon: Research support (paid to institution): Acerta Phamaceuticals; Agenus; Celldex; EMD Serono; Incyte; Inovio; Midatech; Omniox; Tragara. Advisory/consultation (paid to self): Abbvie; Advantagene; Agenus; Bristol-Myers Squibb; Celldex; EMD Serono; Genentech/Roche; Inovio; Merck; Merck KGaA; Monteris; Novocure; Oncorus; Oxigene; Regeneron; Stemline; Taiho Oncology, Inc. Honoraria (paid to self): Abbvie; Advantagene; Agenus; Bristol-Myers Squibb; Celldex; EMD Serono; Genentech/Roche; Inovio; Merck; Merck KGaA; Monteris; Novocure; Oncorus; Oxigene; Regeneron; Stemline; Taiho Oncology, Inc. Solmaz Sahebjam: Research support (paid to institution) from BMS and Merck. Martin van den Bent: Consultanting for Cellgene, BMS, Agios, Boehringer, Abbvie. Michael Vogelbaum: Indirect equity and patient royalty interests from Infuseon Therapeutics. Honoraria from Celgene, Tocagen, and Blue Earth Diagnostics. Michael Weller: Research grants from Abbvie, Bayer, Merck, Sharp & Dohme (MSD), Dracen, Merck (EMD), Novocure, OGD2, Piqur, Roche and Adastra. Honoraria for lectures or advisory board participation or consulting from Abbvie, Basilea, Bristol Meyer Squibb, Celgene, Merck, Sharp & Dohme (MSD), Merck (EMD), Novocure, Orbus, Roche, Teva and Tocagen. Patrick Wen: Research support from Agios, Astra Zeneca, Beigene, Eli Lily, Genentech/Roche, Karyopharm, Kazia, MediciNova, Merck, Novartis, Oncoceutics, Sanofi-Aventis, VBI Vaccines. Participated on advisory boards for Abbvie, Agios, Astra Zeneca, Blue Earth Diagnostics, Eli Lilly, Genentech/Roche, Karyopharm, Kiyatec, Puma, Vascular Biogenics, Taiho, Deciphera, VBI Vaccines, Tocagen. Speaker for Merck, Prime Oncology. No other authors report conflicts of interest. 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For permissions, please e-mail: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

doi: 10.1093/neuonc/noz079pmid: 31063549

Abstract Background Hyperactivation of the RAS-RAF-MEK-ERK signaling pathway is exploited by glioma cells to promote their growth and evade apoptosis. MEK activation in tumor cells can increase replication of ICP34.5-deleted herpes simplex virus type 1 (HSV-1), but paradoxically its activation in tumor-associated macrophages promotes a pro-inflammatory signaling that can inhibit virus replication and propagation. Here we investigated the effect of blocking MEK signaling in conjunction with oncolytic HSV-1 (oHSV) for brain tumors. Methods Infected glioma cells co-cultured with microglia or macrophages treated with or without trametinib were used to test trametinib effect on macrophages/microglia. Enzyme-linked immunosorbent assay, western blotting, and flow cytometry were utilized to evaluate the effect of the combination therapy. Pharmacokinetic (PK) analysis of mouse plasma and brain tissue was used to evaluate trametinib delivery to the CNS. Intracranial human and mouse glioma-bearing immune deficient and immune competent mice were used to evaluate the antitumor efficacy. Result Oncolytic HSV treatment rescued trametinib-mediated feedback reactivation of the mitogen-activated protein kinase signaling pathway in glioma. In vivo, PK analysis revealed enhanced blood–brain barrier penetration of trametinib after oHSV treatment. Treatment by trametinib, a MEK kinase inhibitor, led to a significant reduction in microglia- and macrophage-derived tumor necrosis factor alpha (TNFα) secretion in response to oHSV treatment and increased survival of glioma-bearing mice. Despite the reduced TNFα production observed in vivo, the combination treatment activated CD8+ T-cell mediated immunity and increased survival in a glioma-bearing immune-competent mouse model. Conclusion This study provides a rationale for combining oHSV with trametinib for the treatment of brain tumors. glioblastoma (GBM), oncolytic herpes simplex virus-1 (oHSV), RAS-RAF-MEK-ERK signaling, trametinib, tumor necrosis factor α (TNFα) Key Points 1. HSV therapy significantly increases blood–brain barrier penetration of trametinib. 2. HSV treatment could revert trametinib-induced feedback reactivation of MEK signaling. 3. Trametinib treatment reduces oHSV therapy–induced inflammatory TNFα secretion and enhances T-cell dependent antitumor immunity. Importance of the Study Glioblastoma (GBM) has limited therapeutic options, primarily due to poor drug penetration into the CNS. Oncolytic viruses are now being evaluated in clinical trials for GBM. Although activation of the RAS-RAF-MEK-ERK signaling pathway is common in GBM, trametinib, the FDA-approved MEK kinase inhibitor, has limited utility for brain tumors due to its poor CNS penetrance. Here we show that oHSV treatment increases trametinib penetration into the brain of glioma-bearing mice and enhances animal survival. Trametinib treatment in conjunction with oHSV therapy also activated T-cell dependent antitumor immunity. Our study highlights the effectiveness utilizing MEK kinase inhibitors in conjunction with oHSV-based therapy for the treatment of GBM. Compared with other cancers which have considerably benefited from therapeutic advancements, glioblastomas (GBMs) remain resistant to traditional therapies and patient outcome remains dismal.1 Oncolytic HSV (oHSV) therapy has emerged as an attractive treatment option for therapy-resistant cancers, and several oncolytic vectors are currently being investigated for safety and efficacy in patients diagnosed with GBM.2,3 In the wake of these advancements, investigating the impact of virotherapy in the setting of chemotherapy is important to guide researchers and clinicians on how to maximize therapeutic benefit while minimizing toxicity.4 Trametinib is an FDA-approved MEK1/2 kinase inhibitor for the treatment of metastatic melanoma patients harboring a BRAFV600E/K mutation, either as a monotherapy or in combination with dabrafenib.5 While RAF mutations are a rare event in GBM, activation of the RAS-RAF-MEK-ERK signaling cascade via loss of or mutations in neurofibromin 1 (NF1) or upstream receptor tyrosine kinases are a frequent event in these tumors,6 thus targeting this pathway could be beneficial. However, poor CNS penetration of trametinib has limited its clinical use for intracranial malignancies.7 In addition to tumor cells, macrophages have also been shown to rely on an activated MEK/ERK signaling pathway during infection and specifically in the context of an lipopolysaccharide-induced pro-inflammatory response. Interestingly, oHSV therapy for glioma has been shown to induce macrophage infiltration to the tumor bed, resulting in their activation and release of pro-inflammatory cytokines such as tumor necrosis factor alpha (TNFα). In this context, macrophages have shown the potential to promote tumor growth, invasion, and angiogenesis, as well as result in virus clearance.8 The impact of combining trametinib with oHSV as a treatment for intracranial GBM has not been explored to our knowledge. In this study, we found that oHSV therapy increased blood–brain barrier (BBB) penetration of trametinib. Combination of trametinib and oHSV improved antitumor efficacy in 3 different animal models. Interestingly, despite a reduction in pro-inflammatory TNFα secretion, the combination therapy improved antitumor immunity in a syngeneic mouse model. To our knowledge, this is the first report investigating the impact of oHSV therapy on CNS drug penetration and implies the utilization of oncolytic viruses to enhance the delivery of BBB impermeable agents. Materials and Methods Cells and Viruses Human glioma cell lines (U251T3 and U87ΔEGFR), Vero, murine BV2 microglia cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM; Gibco BRL) supplemented with 10% fetal bovine serum. Murine RAW264.7 macrophages were maintained in Roswell Park Memorial Institute medium supplemented with 5% fetal bovine serum. Patient-derived primary GBM cells (GBM30, GBM8, GBM12, GBM28, and GBM43) were maintained as tumor spheres in DMEM/F-12 supplemented with 2% B27 without vitamin A, human epidermal growth factor (EGF) (20 ng/mL), and basic fibroblast growth factor (20 ng/mL). The details about source and short tandem repeat profiling of cells is described in the Supplementary Material. For oHSV, we used rHSVQ and rHSVQ-IE4/5-luciferase (rHSVQ-Luc), which are both disrupted in the UL39 locus and deleted for both copies of the ICP34.5 gene; rHSVQ-Luc also contains the luciferase transgene under the HSV-1 IE4/5 promoter.9 Viral stocks were generated and titered on Vero cells via standard plaque forming unit assays as previously described.9 Primary murine bone marrow–derived macrophages (BMDMs) were isolated as previously described8 and details of BMDM generation are described in the Supplementary Material. Quantitative Virus Spread by Cytation 5 Live Imaging Virus-encoded green fluorescent protein (GFP) expression was monitored every 2 hours for 24 hours using the Cytation 5 live plate reader/imager (BioTek Instruments) (details are described in the Supplementary Material). Viral Replication Assay The number of infectious particles present was determined by performing a standard plaque formation assay on Vero cells as described8 (details are in the Supplementary Material). Western Blot and ELISA Assays Cell lysates were fractionated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis and transferred to polyvinylidene difluoride membranes. Blocked membranes were then incubated with antibodies against phosphorylated MEK, total MEK, phosphorylated extracellular signal-regulated kinase (ERK), total ERK, caspase-8, cleaved poly(ADP-ribose) polymerase (PARP) (Cell Signaling Technology), glyceraldehyde 3-phosphate dehydrogenase (Abcam), horseradish peroxidase (HRP)–conjugated secondary anti-mouse antibody (GE Healthcare), and HRP-conjugated secondary goat anti-rabbit antibody (Dako). All antibodies were diluted 1:1000 and the immunoreactive bands were visualized using enhanced chemiluminescence (GE Healthcare). Co-culture assay and quantification of murine TNFα by enzyme-linked immunosorbent assay (ELISA) was performed as previously described8 and murine TNFα was quantified in cell supernatants using the murine TNFα DuoSet ELISA kit (R&D Systems) per manufacturer’s recommendations. Animal Surgery All animal experiments were housed and performed in accordance with and approval of the Subcommittee on Research Animal Care of the Ohio State University and the Animal Welfare Committee at University of Texas Health Science Center. Intracranial glioma cells were implanted as previously described8 (details are in the Supplementary Material). In vivo virus propagation was measured by the IVIS Lumina Series III Pre-clinical In Vivo Imaging System (Perkin Elmer) as previously described8 (details are in the Supplementary Material). Pharmacokinetic Analysis Pharmacokinetic (PK) analyses of mouse plasma and brain tissue in glioma-bearing mice were conducted using a liquid chromatography–tandem mass spectrometry (LC-MS/MS) system (Thermo Fisher Scientific). PK analysis of the whole blood data was performed using Phoenix 7.0 (Certara) modeling software. Detailed methods for LC-MS/MS are provided in the Supplementary Material. Statistics To compare 2 independent treatments for continuous endpoints such as viral titers and cell viability assay, a Student’s t-test was used. When multiple pairwise comparisons were made, one-way ANOVA was used. To evaluate the synergistic interaction between trametinib and oHSV, an interaction contrast or two-way ANOVA model was applied. Synergistic effect indicates that the combined treatment produced an effect (vs control) greater than the additive effect of 2 single treatments (vs control, respectively). A log-rank test was used to compare survival curves for survival data and a Cox regression model was used to evaluate the synergistic interaction between trametinib and oHSV on survival data. P-values were adjusted for multiple comparisons by Holms’s procedure. A P-value of 0.05 or less was considered significant. Results Trametinib Treated Macrophages and Microglial Cells Have a Reduced Pro-Inflammatory Antiviral Response We have previously shown that macrophages and microglia (collectively referred to as effector cells here) secrete TNFα in response to infected glioma cells.8 To evaluate if trametinib treatment modulated the TNFα response in the context of oHSV therapy, patient derived primary GBM cells and glioma cell lines infected with or without oHSV for 1 hour were overlaid with macrophages or microglial cells pretreated with dimethyl sulfoxide (DMSO) or trametinib for 1 hour. Twenty-four hours later, TNFα secretion by primary murine BMDMs, Raw264.7 macrophages, or BV2 microglial cells was measured by ELISA. Measurement of TNFα secretion in the co-cultures revealed a significant reduction when effector cells were pretreated with trametinib (Fig. 1A–C), without any evidence of toxicity toward treated effector cells (Supplementary Fig. 1). Fig. 1 Open in new tabDownload slide Trametinib treatment modulates macrophage and microglia anti-viral responses. (A‒C) TNFα release from BMDM (A), Raw264.7 (B), and BV2 (C) pretreated with or without trametinib (100 nM for 1 h), and then overlaid on the indicated primary GBM or glioma cell lines treated with saline or rHSVQ (MOI = 0.1), n = 3/group. (D‒E) Impact of trametinib on macrophage mediated virus clearance. (D) Fluorescent microscopy images of GFP-positive U251T3 human glioma cells, infected with rHSVQ (MOI = 0.01) for 1 h. After thorough washing to remove unbound virus particles, glioma cells were overlaid with DMSO or trametinib pretreated macrophage for 24 hours. Data shown are bright field and fluorescence microscopic images (bottom) of GFP-positive oHSV-infected cells 48 hours post infection. All bars are 100 μm for ×10 magnification images. (E) Quantification of virus yield from patient derived primary GBM and glioma cell lines infected with oHSV alone or co-cultured with primary BMDM cells. Briefly both cells and media were harvested 48 hours after overlay with DMSO or trametinib treated BMDM cells and viral titers were determined by standard plaque forming assay. Data shown are mean virus titer ±SD. *P < 0.05; n = 3/group. (F) Western blot analysis of co-cultures of U251T3 glioma cells with Raw264.7 macrophage cells pretreated with trametinib. U251T3 glioma cells were treated with/without rHSVQ (MOI = 0.5) for 1 h and then overlaid with macrophages pretreated with DMSO or trametinib overnight. Twenty-four hours post overlay, cells were harvested and cell lysates were probed with antibodies against caspase-8 and cleaved PARP. Βeta-tubulin was used as a loading control. Caspase-8 antibody can detect both total and cleaved forms of caspase-8. Arrow head indicates cleaved active form of caspase-8. Fig. 1 Open in new tabDownload slide Trametinib treatment modulates macrophage and microglia anti-viral responses. (A‒C) TNFα release from BMDM (A), Raw264.7 (B), and BV2 (C) pretreated with or without trametinib (100 nM for 1 h), and then overlaid on the indicated primary GBM or glioma cell lines treated with saline or rHSVQ (MOI = 0.1), n = 3/group. (D‒E) Impact of trametinib on macrophage mediated virus clearance. (D) Fluorescent microscopy images of GFP-positive U251T3 human glioma cells, infected with rHSVQ (MOI = 0.01) for 1 h. After thorough washing to remove unbound virus particles, glioma cells were overlaid with DMSO or trametinib pretreated macrophage for 24 hours. Data shown are bright field and fluorescence microscopic images (bottom) of GFP-positive oHSV-infected cells 48 hours post infection. All bars are 100 μm for ×10 magnification images. (E) Quantification of virus yield from patient derived primary GBM and glioma cell lines infected with oHSV alone or co-cultured with primary BMDM cells. Briefly both cells and media were harvested 48 hours after overlay with DMSO or trametinib treated BMDM cells and viral titers were determined by standard plaque forming assay. Data shown are mean virus titer ±SD. *P < 0.05; n = 3/group. (F) Western blot analysis of co-cultures of U251T3 glioma cells with Raw264.7 macrophage cells pretreated with trametinib. U251T3 glioma cells were treated with/without rHSVQ (MOI = 0.5) for 1 h and then overlaid with macrophages pretreated with DMSO or trametinib overnight. Twenty-four hours post overlay, cells were harvested and cell lysates were probed with antibodies against caspase-8 and cleaved PARP. Βeta-tubulin was used as a loading control. Caspase-8 antibody can detect both total and cleaved forms of caspase-8. Arrow head indicates cleaved active form of caspase-8. The presence of TNFα in the tumor microenvironment has been previously identified as a major barrier to oncolytic virus replication and therapeutic efficacy.8 Therefore, we next evaluated the impact of trametinib on oHSV infection in glioma cells co-cultured with macrophages in vitro. In this study, we used HSV-1 derived oHSV, rHSVQ.9 Human glioma cells infected with oHSV (multiplicity of infection [MOI] = 0.01) were overlaid with control or trametinib-treated murine Raw264.7 macrophages and cultured for 48 hours. Fluorescence microscopy was used to image oHSV-infected cells based on GFP expression (Fig. 1D). Consistent with previous reports, the addition of macrophages to infected glioma cells visibly reduced GFP+ cells. However, this effect was significantly rescued when macrophages were pretreated with trametinib. Similarly, quantification of oHSV-infected glioma cells co-cultured with BMDMs pretreated with or without trametinib revealed a significant increase in oHSV replication upon trametinib treatment (P < 0.001; Fig. 1E). Infected cell apoptosis by macrophage- or microglia-secreted TNFα is one of the major pathways exploited by human cells to limit HSV-1 spread.8 Thus we examined the effect of trametinib and oHSV treatment on glioma cell apoptosis. Consistent with increased TNFα secretion, when infected glioma cells were co-cultured with macrophages there was an increase in cleaved PARP and cleaved caspase-8 (Fig. 1F). When macrophages were treated with trametinib, however, the apoptotic response in infected glioma cells was diminished. Collectively, these findings suggest that the reduction in TNFα expression and secretion by macrophages treated with trametinib resulted in the suppression of macrophage-mediated apoptotic cell death in oHSV-infected glioma cells, thus increasing virus replication in vitro. Trametinib Treatment of Glioma Cells Inhibits MEK Signaling and Suppresses Virus Spread In order to evaluate the impact of trametinib on infected glioma cells in the absence of effector cells, we measured changes in MEK signaling by western blot. Glioma cells were infected with oHSV for 1 h and then treated with the indicated dose of trametinib. Inhibition of MEK1/2 activity by trametinib resulted in an inhibition of the phosphorylation and activation of the downstream ERK kinase pro-survival pathway (Fig. 2A). Interestingly, trametinib treatment resulted in a feedback reactivation of the MEK kinase signaling pathway, as evidenced by increased phosphorylation of MEK following trametinib treatment. This rebound MEK activation has been implicated in the development of resistance to MEK inhibition.10 Importantly, glioma cells co-treated with trametinib and oHSV reduced this feedback activation in a dose-dependent manner (Fig. 2A). Fig. 2 Open in new tabDownload slide The combination effect of trametinib and oHSV on glioma cells. (A) Western blot analysis of the indicated cells treated with rHSVQ in the presence or absence of trametinib. The indicated patient derived primary GBM and glioma cells were infected with 0.1 MOI of rHSVQ. One hour post oHSV infection, unbound virus was removed and treated with the indicated concentration of trametinib (0.1‒10 μM). Sixteen hours post treatment, cells were harvested and cell lysates were probed with antibodies against pERK, pMEK1/2, total MEK1/2, total ERK, and cleaved PARP. Glyceraldehyde 3-phosphate dehydrogenase was used as a loading control. (B) Effects of trametinib and oHSV on glioma cell killing. The indicated cells were infected with oHSV or PBS for 1 hour, followed by treatment with trametinib (10 μM) or DMSO. Seventy-two hours later, cell viability was measured via standard MTT assay. Data shown are mean % of cell death ±SD and analyzed by the paired Student’s t-test (n = 3/group). Dotted line indicates the predicted additive cell killing. (C) Comparison of virus spread in cultures of the indicated glioma cells treated with rHSVQ in the presence or absence of trametinib over time. The indicated glioma cells were infected with rHSVQ and GFP expression was monitored every 2 hours for 24 hours utilizing the Cytation 5 live imaging system. GFP object count was quantified and graphed as an average of 4 wells per treatment group. Data shown are average counts of GFP positive cells ±SD over time. (D) Both cells and media were collected 48 hours post virus infection and virus titers were measured by standard plaque forming assay. Data shown are median titers ±SD (n = 3/group). (N.D. is not detectable). Fig. 2 Open in new tabDownload slide The combination effect of trametinib and oHSV on glioma cells. (A) Western blot analysis of the indicated cells treated with rHSVQ in the presence or absence of trametinib. The indicated patient derived primary GBM and glioma cells were infected with 0.1 MOI of rHSVQ. One hour post oHSV infection, unbound virus was removed and treated with the indicated concentration of trametinib (0.1‒10 μM). Sixteen hours post treatment, cells were harvested and cell lysates were probed with antibodies against pERK, pMEK1/2, total MEK1/2, total ERK, and cleaved PARP. Glyceraldehyde 3-phosphate dehydrogenase was used as a loading control. (B) Effects of trametinib and oHSV on glioma cell killing. The indicated cells were infected with oHSV or PBS for 1 hour, followed by treatment with trametinib (10 μM) or DMSO. Seventy-two hours later, cell viability was measured via standard MTT assay. Data shown are mean % of cell death ±SD and analyzed by the paired Student’s t-test (n = 3/group). Dotted line indicates the predicted additive cell killing. (C) Comparison of virus spread in cultures of the indicated glioma cells treated with rHSVQ in the presence or absence of trametinib over time. The indicated glioma cells were infected with rHSVQ and GFP expression was monitored every 2 hours for 24 hours utilizing the Cytation 5 live imaging system. GFP object count was quantified and graphed as an average of 4 wells per treatment group. Data shown are average counts of GFP positive cells ±SD over time. (D) Both cells and media were collected 48 hours post virus infection and virus titers were measured by standard plaque forming assay. Data shown are median titers ±SD (n = 3/group). (N.D. is not detectable). To evaluate the effect of trametinib in combination with oHSV on glioma cell survival, we measured the viability of the indicated glioma cells treated with either agent alone or in combination. Consistent with the significantly reduced reactivation of the pro-survival MEK signaling pathway, glioma cells treated with both trametinib and oHSV showed significantly greater cytotoxicity compared with cells treated with either agent alone. The combination of the 2 agents was either additive or greater than the predicted additive effect of the 2 individual agents (dotted line) (Fig. 2B). To evaluate if trametinib treatment increased virus replication directly in tumor cells, glioma cells were infected with oHSV and treated with either DMSO or the indicated concentration of trametinib. Viral spread was followed in cultures through quantification of virus-encoded GFP using Cytation 5 Live Cell Imaging over time (Fig. 2C). Additionally, viral replication was measured by a standard plaque forming assay 48 hours post infection (Fig. 2D). Consistent with the known significance of hyperactivated MEK signaling in promoting viral spread,11 we observed reduced virus spread and replication in glioma cells treated with trametinib (Fig. 2C–D). Together, these results suggest that trametinib treatment increases glioma cell sensitivity to trametinib-mediated cell death (Fig. 2). Combination Treatment of Trametinib and oHSV Increases Therapeutic Efficacy In Vivo Independently of Its Effects on Macrophages While trametinib treatment reduced macrophage inflammatory response to infected cells and increased virus replication in glioma cells co-cultured with macrophage treated cells, it also reduced virus spread when glioma cells were treated with trametinib directly. Since trametinib is thought to poorly penetrate the BBB, we rationalized that its treatment would reduce antiviral inflammatory signaling and provide an oncolytic benefit in vivo. To examine the effect of this combination on tumor growth in vivo, athymic nude mice bearing intracranial tumors (U87ΔEGFR or GBM 30) were treated as shown in the schematic in Fig. 3A. Kaplan–Meier curves revealed a significant improvement in median survival of mice treated with trametinib plus oHSV compared with either agent alone for both tumor models (Fig. 3B–C). Next we investigated the impact of trametinib treatment on the tumor microenvironment of virus-treated glioma-bearing mice. Briefly, mice bearing intracranial glioma were treated as shown in Fig. 3A, brains were harvested, and the tumor-bearing hemispheres were analyzed for infiltrating monocyte derived macrophages (CD45+CD11bhi) by flow cytometry. Consistent with the reduced antiviral response of trametinib treated effector cells (Fig. 1), mice treated with oHSV showed a significant increase in tumor infiltrating monocyte derived macrophages compared with control mice (Fig. 3D, bottom left panel compared with top left panel). Additionally, while trametinib alone had only a modest effect on this tumor infiltrating population compared with control (Fig. 3D, top right panel compared with top left panel), mice receiving both trametinib and oHSV had a significant reduction in the total number of tumor infiltrating monocyte derived macrophages (Fig. 3D, bottom right panel compared with bottom left panel). Consistent with its anti-inflammatory properties, there was a 3.67-fold reduction of TNFα expression in tumors treated with both oHSV and trametinib (Fig. 3E). Reduced antiviral TNFα production along with reduced macrophage infiltration in oHSV treated tumors in nude mice can result in increased viral propagation.12 To evaluate the changes in virus propagation after trametinib treatment in vivo over time, we measured virus-encoded luciferase expression using bioluminescence imaging13 (Fig. 3F–G). Surprisingly, despite a significant reduction in macrophage infiltration and TNFα production (Fig. 3D–E), we observed no significant difference in virus propagation in response to trametinib treatment (Fig. 3F–G), indicating that the effect of trametinib on glioma-bearing mouse survival was independent of increased viral propagation in trametinib treated mice. Fig. 3 Open in new tabDownload slide Impact of trametinib on antitumor efficacy of oHSV in vivo. (A) Schematic representation of animal survival study. (B‒C) Kaplan‒Meier survival curve of mice bearing intracranial tumors treated with DMSO or trametinib and treated with phosphate buffered saline (PBS) or oHSV. Briefly, athymic nude mice bearing intracranial human glioma cells (B‒C) were treated with either DMSO or trametinib (1 mg/kg) by daily oral gavage. Two days after the initiation of gavage (10 days post tumor implantation for U87ΔEGFR) and 9 days post tumor implantation for GBM30), mice were treated with PBS or rHSVQ-Luc (1 × 105 pfu) by intratumoral injection (n = 10/each groups for B‒C). Data shown are Kaplan‒Meier survival curves of mice treated with oHSV and/or trametinib. *P < 0.05 compared with mice receiving single treatment of oHSV. The arrows indicate the time of treatment with oHSV. (D‒G) Intracranial glioma (U87ΔEGFR) bearing mice were treated with DMSO or trametinib (1 mg/kg) by daily oral gavage, starting from day 8 post tumor cell implantation. Ten days post tumor cell implantation, mice were injected with rHSVQ-Luc by direct intratumoral injection. (D) Three days post virus injection, tumor-bearing hemispheres were analyzed for CD11bhigh/CD45+ monocyte derived macrophage infiltration and activation by flow cytometry. Data shown are a representative scatter plot showing CD11bhigh/CD45+ monocyte derived macrophage infiltration in vivo. (E) TNFα levels in brain hemispheres of mice treated with oHSV and trametinib. Twenty-four hours post oHSV injection, tumor-bearing brain hemispheres were homogenized and the supernatants were analyzed for murine TNFα by ELISA (DMSO and trametinib, n = 6; oHSV and trametinib plus oHSV, n = 10). Combination treatment with trametinib and oHSV significantly suppressed murine TNFα compared with oHSV alone. (F‒G) Trametinib effect on virus replication over time. (F) Representative bioluminescence images of mice treated with rHSVQ-Luc showing virus-encoded luciferase activity in mice treated with trametinib or DMSO at the indicated days post virus injection. (G) Quantification of images shown in F, Data shown are mean ±SD of quantification of total flux in each mouse, (n = 9/group). Fig. 3 Open in new tabDownload slide Impact of trametinib on antitumor efficacy of oHSV in vivo. (A) Schematic representation of animal survival study. (B‒C) Kaplan‒Meier survival curve of mice bearing intracranial tumors treated with DMSO or trametinib and treated with phosphate buffered saline (PBS) or oHSV. Briefly, athymic nude mice bearing intracranial human glioma cells (B‒C) were treated with either DMSO or trametinib (1 mg/kg) by daily oral gavage. Two days after the initiation of gavage (10 days post tumor implantation for U87ΔEGFR) and 9 days post tumor implantation for GBM30), mice were treated with PBS or rHSVQ-Luc (1 × 105 pfu) by intratumoral injection (n = 10/each groups for B‒C). Data shown are Kaplan‒Meier survival curves of mice treated with oHSV and/or trametinib. *P < 0.05 compared with mice receiving single treatment of oHSV. The arrows indicate the time of treatment with oHSV. (D‒G) Intracranial glioma (U87ΔEGFR) bearing mice were treated with DMSO or trametinib (1 mg/kg) by daily oral gavage, starting from day 8 post tumor cell implantation. Ten days post tumor cell implantation, mice were injected with rHSVQ-Luc by direct intratumoral injection. (D) Three days post virus injection, tumor-bearing hemispheres were analyzed for CD11bhigh/CD45+ monocyte derived macrophage infiltration and activation by flow cytometry. Data shown are a representative scatter plot showing CD11bhigh/CD45+ monocyte derived macrophage infiltration in vivo. (E) TNFα levels in brain hemispheres of mice treated with oHSV and trametinib. Twenty-four hours post oHSV injection, tumor-bearing brain hemispheres were homogenized and the supernatants were analyzed for murine TNFα by ELISA (DMSO and trametinib, n = 6; oHSV and trametinib plus oHSV, n = 10). Combination treatment with trametinib and oHSV significantly suppressed murine TNFα compared with oHSV alone. (F‒G) Trametinib effect on virus replication over time. (F) Representative bioluminescence images of mice treated with rHSVQ-Luc showing virus-encoded luciferase activity in mice treated with trametinib or DMSO at the indicated days post virus injection. (G) Quantification of images shown in F, Data shown are mean ±SD of quantification of total flux in each mouse, (n = 9/group). Oncolytic HSV Therapy Enhances Trametinib CNS Penetration Trametinib has been shown to be a poor BBB permeant drug, with limited brain distribution in mouse models.14 Treatment of brain tumors by oHSV increases endothelial activation in vivo, implying that it could increase penetration of drugs otherwise inaccessible to the central nervous system (CNS).15 Thus, we next evaluated the effect of oHSV infection on CNS penetration of trametinib by LC-MS/MS (Fig. 4A–C). While there was no obvious difference in plasma concentrations of trametinib in mice treated with or without oHSV (Fig. 4A), a significant increase in trametinib concentrations was observed in tumor-bearing brain hemispheres 24 hours after the final trametinib treatment (trametinib concentration in tumor-bearing brain hemisphere: mean values of 20.03 nM vs 74.38 nM; 3.71-fold increase, P < 0.0001, at 24 h) (Fig. 4B). Importantly, this effect was limited to the tumor-bearing brain hemisphere, and there were no significant differences in the distribution of trametinib in the non–tumor bearing brain hemispheres of mice (mean values of 16.56 nM vs 19.89 nM between trametinib alone and trametinib plus oHSV) (Fig. 4C). We next performed a PK analysis of the whole blood data using Phoenix 7.0 Modeling Software; comparative analysis of the PK parameter outputs are summarized in Table 1. The brain-to-plasma percent ratios were calculated using the equation AUCBrain / AUCPlasma *100. These results demonstrate that the addition of oHSV to the trametinib dosing regimen increased the percentage of trametinib distributed to the glioma-bearing brain hemisphere from 18.9% to 132% of plasma exposure (Table 1). There was no difference in trametinib distribution in the non–tumor bearing hemisphere when oHSV was added to the dosing regimen; however, oHSV does appear to shift the PK profile, where the maximum brain-to-plasma concentrations (Cmax) are increased by 49.6% in the tumor bearing hemisphere and 91.2% in the non-tumor bearing hemisphere (Table 1). Additionally, this PK analysis shows there is no difference in trametinib exposure when comparing normal versus tumor burdened tissue in the absence of oHSV; however, when oHSV is added, the trametinib brain-to-plasma percent exposure in the tumor increases by nearly 600%. Table 1 Relevant comparative analysis of the PK parameters of trametinib. Parameters were estimated by curve fitting of all data and show trametinib brain-to-plasma % ratios for estimated area under the concentration-time curve (AUC) and maximum concentration (Cmax) PK parameters (n = 5, observation per time point) Brain-to-Plasma % Ratios for Trametinib PK parameters Trametinib Trametinib+oHSV PK Parameter Non-Tumor Bearing Mice Brain Hemisphere Tumor-Bearing Mice Brain Hemisphere Non-Tumor Bearing Mice Brain Hemisphere Tumor-Bearing Mice Brain Hemisphere AUC (h*fmol/μL) 17.8 18.9 18.7 132 Cmax(fmol/mg) 5.12 10.0 9.79 15.0 Brain-to-Plasma % Ratios for Trametinib PK parameters Trametinib Trametinib+oHSV PK Parameter Non-Tumor Bearing Mice Brain Hemisphere Tumor-Bearing Mice Brain Hemisphere Non-Tumor Bearing Mice Brain Hemisphere Tumor-Bearing Mice Brain Hemisphere AUC (h*fmol/μL) 17.8 18.9 18.7 132 Cmax(fmol/mg) 5.12 10.0 9.79 15.0 Open in new tab Table 1 Relevant comparative analysis of the PK parameters of trametinib. Parameters were estimated by curve fitting of all data and show trametinib brain-to-plasma % ratios for estimated area under the concentration-time curve (AUC) and maximum concentration (Cmax) PK parameters (n = 5, observation per time point) Brain-to-Plasma % Ratios for Trametinib PK parameters Trametinib Trametinib+oHSV PK Parameter Non-Tumor Bearing Mice Brain Hemisphere Tumor-Bearing Mice Brain Hemisphere Non-Tumor Bearing Mice Brain Hemisphere Tumor-Bearing Mice Brain Hemisphere AUC (h*fmol/μL) 17.8 18.9 18.7 132 Cmax(fmol/mg) 5.12 10.0 9.79 15.0 Brain-to-Plasma % Ratios for Trametinib PK parameters Trametinib Trametinib+oHSV PK Parameter Non-Tumor Bearing Mice Brain Hemisphere Tumor-Bearing Mice Brain Hemisphere Non-Tumor Bearing Mice Brain Hemisphere Tumor-Bearing Mice Brain Hemisphere AUC (h*fmol/μL) 17.8 18.9 18.7 132 Cmax(fmol/mg) 5.12 10.0 9.79 15.0 Open in new tab Fig. 4 Open in new tabDownload slide Impact of oHSV therapy on trametinib brain tumor penetration in vivo. Nude mice bearing intracranial U87ΔEGFR human gliomas were treated as described above. Three days post virus injection, mice were sacrificed at the indicated time following the fifth trametinib treatment and plasma and both brain hemispheres were harvested (n = 5 mice/group). Trametinib concentration in plasma and brain was quantified by LC-MS/MS assay. Concentration-time plot of main PK trametinib in mouse plasma (A), tumor-bearing brain hemisphere (B), and non-tumor-bearing brain hemisphere (C). *P < 0.05 compared with mice treated with single treatment with oHSV. Fig. 4 Open in new tabDownload slide Impact of oHSV therapy on trametinib brain tumor penetration in vivo. Nude mice bearing intracranial U87ΔEGFR human gliomas were treated as described above. Three days post virus injection, mice were sacrificed at the indicated time following the fifth trametinib treatment and plasma and both brain hemispheres were harvested (n = 5 mice/group). Trametinib concentration in plasma and brain was quantified by LC-MS/MS assay. Concentration-time plot of main PK trametinib in mouse plasma (A), tumor-bearing brain hemisphere (B), and non-tumor-bearing brain hemisphere (C). *P < 0.05 compared with mice treated with single treatment with oHSV. Combining Trametinib with oHSV Therapy Enhances T-cell Mediated Antitumor Efficacy Collectively, our results showed increased brain penentration of trametinib and enhanced survival benefit of glioma-bearing immune deficient mice treated with trametinib and oHSV. Trametinib treatment also reduced macrophage-induced TNFα, a cytokine that has been implicated in neuroinflammation and glioma growth. However, TNFα has also been described as an important player in the development of antitumor immunity.16 Therefore, we evaluated therapeutic benefit of combining oHSV therapy with trametinib in immune competent mice bearing glioma. Fig. 5A shows Kaplan–Meier survival curves of mice bearing intracranial GL261-N4 glioma treated with oHSV and trametinib. Combination of trametinib with oHSV resulted in a significant improvement in survival compared with either therapy alone, with several mice surviving longer than 100 days post-tumor implantation (median survival: complete regression of 84.5% vs 66 days between oHSV + trametinib and oHSV, P = 0.0194; undetermined vs 53 days between oHSV + trametinib and trametinib alone, P = 0.0184; undetermined vs 38 days between oHSV + trametinib and PBS, P < 0.0001). Fig. 5 Open in new tabDownload slide Combination treatment with trametinib and oHSV enhances T-cell mediated antitumor efficacy. (A) C57BL/6 mice bearing syngeneic murine glioma GL261-N4 cells were treated with either DMSO or trametinib (1 mg/kg) by daily oral gavage. Two days after initiation of gavage (10 days post tumor implantation), mice were treated with phosphate buffered saline or rHSVQ-Luc (1 × 105 pfu) by intratumoral injection (n = 17/each groups). Data shown are Kaplan‒Meier survival curves of mice treated with oHSV and/or trametinib. *P < 0.05 compared with mice receiving single treatment with oHSV. The arrows indicate the time of treatment of oHSV. (B) Representative MRI image of a C57BL/6 mouse treated with oHSV and trametinib in Fig. 5A 110 days after tumor cell implantation. (C) Mice treated with the combination trametinib and oHSV and surviving more than 100 days were rechallenged with a second intracranial GL261-N4 injection into the left hemisphere and compared with age matched naïve C57BL/6 mice (n = 7). Mice were monitored for up to 100 days and survival was analyzed by Kaplan–Meier. *P < 0.05. (D) Intracranial GL261-N4-bearing C57BL/6 mice were treated with trametinib and oHSV as described above as well as with isotype control, anti-CD4, or anti-CD8 depleting antibodies by intraperitoneal injection 2, 4, and 7 days post virus therapy (isotype control, n = 13; anti-CD4, n = 14; anti-CD8, n = 14). *P < 0.05 compared with CD4 or CD8 depletion. Fig. 5 Open in new tabDownload slide Combination treatment with trametinib and oHSV enhances T-cell mediated antitumor efficacy. (A) C57BL/6 mice bearing syngeneic murine glioma GL261-N4 cells were treated with either DMSO or trametinib (1 mg/kg) by daily oral gavage. Two days after initiation of gavage (10 days post tumor implantation), mice were treated with phosphate buffered saline or rHSVQ-Luc (1 × 105 pfu) by intratumoral injection (n = 17/each groups). Data shown are Kaplan‒Meier survival curves of mice treated with oHSV and/or trametinib. *P < 0.05 compared with mice receiving single treatment with oHSV. The arrows indicate the time of treatment of oHSV. (B) Representative MRI image of a C57BL/6 mouse treated with oHSV and trametinib in Fig. 5A 110 days after tumor cell implantation. (C) Mice treated with the combination trametinib and oHSV and surviving more than 100 days were rechallenged with a second intracranial GL261-N4 injection into the left hemisphere and compared with age matched naïve C57BL/6 mice (n = 7). Mice were monitored for up to 100 days and survival was analyzed by Kaplan–Meier. *P < 0.05. (D) Intracranial GL261-N4-bearing C57BL/6 mice were treated with trametinib and oHSV as described above as well as with isotype control, anti-CD4, or anti-CD8 depleting antibodies by intraperitoneal injection 2, 4, and 7 days post virus therapy (isotype control, n = 13; anti-CD4, n = 14; anti-CD8, n = 14). *P < 0.05 compared with CD4 or CD8 depletion. Approximately 80% (14/17) of mice treated with both trametinib and oHSV survived longer than 100 days post tumor cell implantation with no visible signs of tumor burden, suggesting a complete eradication of the tumor. MRI of the surviving mice confirmed a complete response (Fig. 5B). To evaluate the possibility of memory T-cell generation to oHSV infection, we rechallenged the surviving mice with tumor cells administered to the contralateral hemisphere (Fig. 5C). While age matched control mice died of tumor burden, 100% of the treated mice that were rechallenged remained alive. This indicated a complete rejection of tumors in the surviving mice, and implied the development of a memory T-cell antitumor immunity. To evaluate the role of T cells in tumor rejection, we evaluated the therapeutic efficacy of combining trametinib and oHSV in mice with CD4 or CD8 T-cell depletion. Survival advantage of combination therapy with both trametinib and oHSV was reduced when mice were treated with T-cell depleting antibodies (Fig. 5D). Collectively, these results indicate that the survival benefit achieved by the combination of trametinib plus oHSV is dependent on both CD4+ and CD8+ T-cell subsets, which have the capacity to form a strong effector memory activation in the event of antigen re-exposure. Discussion GBM is the most common and deadly primary brain malignancy in adults; patients diagnosed have a median survival of less than 2 years.1,17 Nearly all tumors recur and are often resistant to chemotherapy, leaving few options available to delay disease progression.18 The RAS-RAF-MEK-ERK pathway is one of the most frequently dysregulated signal transduction pathways in cancer and plays a critical role in tumorigenesis, cell proliferation, and inhibition of apoptosis. Although RAF mutations are a rare event in GBM, these tumors are known to be addicted to this mitogenic signaling cascade,7 and thus targeting this pathway for GBM tumors is a relevant choice. Inhibition of MEK signaling is an attractive therapeutic target and is currently under investigation. Reactivation of MEK signaling after treatment with trametinib, through the relief of a feedback control regulated by ERK kinase, remains a significant mechanisim of acquired resistance.19,20 Here we examined trametinib in combination with oHSV therapy and found that treatment with oHSV could resist the increased MEK activation that occurs in tumor cells after treatment with trametinib and result in a significant increase in tumor cell sensitization to MEK inhibition. Trametinib treatment also resulted in a significant suppression of macrophage/microglia infiltration to the tumor bed, along with a reduction in TNFα secretion. Macrophages are among the first line of defense to an invading pathogen such as a virus,21 and are a major source of the pro-inflammatory cytokine, TNFα. While low dose TNFα can enhance antitumor efficacy,22,23 at high doses it is associated with toxicity.24 In a phase I clinical trial, systemic administration of TNFα in patients with advanced malignancy refractory to previous standard therapy was associated with a significant toxicity profile, with only 3 out of 57 evaluable patients demonstrating a partial tumor regression.22 Thus, the modulation of TNFα by trametinib might also control inflammation associated toxicity. Interestingly, despite a reduction in inflammation, combination therapy significantly enhanced antitumor immunity in immune competent mice. Several studies have shown that activated mitogen-activated protein kinase signaling in cancer cells downregulates the expression of major histocompatibility complex I (MHC-I) on the tumor cell surface, enabling immune escape.25 MEK inhibition can circumvent this effect, resulting in increased MHC-I cell surface expression and activation of CD8+ cytotoxic lymphocytes.26 Therapy with oHSV also induces immunogenic activation in the tumor microenvironment, resulting in an antitumor immune response.27 Here we showed that combining trametinib with oHSV resulted in complete tumor regression in almost 80% of the mice. These long-term survivors then rejected subsequent tumor implantation. We went on to show that this survival benefit was dependent on both CD4+ and CD8+ T cells, which also conveyed long-term protection as tumor growth was completely inhibited upon rechallenge in the surviving mice. MEK is an emerging target in cancer immunotherapy research, and preclinical studies have suggested that MEK inhibitors potentiate the antitumor T-cell response when used as both a monotherapy and in combination.26 For example, the combination of BRAF and MEK inhibition induces programmed cell death protein 1 (PD-1) expression on melanoma cells, sensitizing them to anti–PD-1 checkpoint blockade.28 Additional preclinical studies have also shown that trametinib combined with the BRAF inhibitor dabrafenib enhances antitumor immunity28 and synergizes with other immunotherapies. Thus, it would be interesting to test a triple combination including trametinib, oHSV, and immune checkpoint inhibition for the treatment of glioma. Trametinib is known to have poor CNS penetrance and limited brain distribution in the intracranial glioma-bearing mice xenograft model.14 Thus, despite activation of RAS signaling in brain tumors, its clinical usage has been limited. Here we identified that oHSV therapy could enhance CNS penetration of trametinib, resulting in increased therapeutic efficacy. PK analysis of brain tissue from mice uncovered a significant increase in trametinib penetration and distribution in tumor bearing hemisphere after oHSV therapy. We recently showed that mice bearing intracranial gliomas treated with oHSV have increased endothelial cell activation and vascular leakiness in the brain.15 This is the first report to our knowledge that has shown increased BBB penetration of a drug with oHSV therapy. The results from this study are therefore directly applicable to current clinical testing, and will help guide researchers and clinicians for the development of future clinical programs. Funding This work was supported by R01 NS064607, R01 CA150153, and P01CA163205 (to Balveen Kaur) and the American Cancer Society/Joe & Jessie Crump Foundation and North Texas Pay-if Group (to Jessica Swanner). Acknowledgments We would like to acknowledge the Pharmacoanalytical Shared Resource center (PhASR) within the James Comprehensive Cancer Center, all at The Ohio State University, for their services. Conflict of interest statement. The authors disclose no potential conflicts of interest. Authorship statement. Conceptualization, J.Y.Y. and B.K. Methodology and investigation, J.Y.Y., J.S., Y.O., M.N., F.P., Y. B., J.L, A.C.L. B.H., F.G., D.B., and H.Q. Data analysis, J.Y.Y., J.S., Y.O., M.N., F.P., J.L, A.C.L. D.B., M.P., D.G., T.J.L., and B.K. Writing, review, and editing, J.Y.Y. and B.K. All authors edited and reviewed manuscript. References 1. Jo J , Wen PY . Antiangiogenic therapy of high-grade gliomas . Prog Neurol Surg. 2018 ; 31 : 180 – 199 . WorldCat 2. Saha D , Wakimoto H , Rabkin SD . Oncolytic herpes simplex virus interactions with the host immune system . Curr Opin Virol. 2016 ; 21 : 26 – 34 . Google Scholar Crossref Search ADS WorldCat 3. 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Abstract Background Predictive molecular biomarkers to select optimal treatment for patients with glioblastoma and other cancers are lacking. New strategies are needed when large randomized trials with correlative molecular data are not feasible. Methods Gene signatures (GS) were developed from 31 orthotopic glioblastoma patient-derived xenografts (PDXs), treated with standard therapies, to predict benefit from radiotherapy (RT-GS), temozolomide (Chemo-GS), or the combination (ChemoRT-GS). Independent validation was performed in a heterogeneously treated clinical cohort of 502 glioblastoma patients with overall survival as the primary endpoint. Multivariate Cox analysis was used to adjust for confounding variables and evaluate interactions between signatures and treatment. Results PDX models recapitulated the clinical heterogeneity of glioblastoma patients. RT-GS, Chemo-GS, and ChemoRT-GS were correlated with benefit from treatment in the PDX models. In independent clinical validation, higher RT-GS scores were associated with increased survival only in patients receiving RT (P = 0.0031, hazard ratio [HR] = 0.78 [0.66–0.92]), higher Chemo-GS scores were associated with increased survival only in patients receiving chemotherapy (P < 0.0001, HR = 0.66 [0.55–0.8]), and higher ChemoRT-GS scores were associated with increased survival only in patients receiving ChemoRT (P = 0.0001, HR = 0.54 [0.4–0.74]). RT-GS and ChemoRT-GS had significant interactions with treatment on multivariate analysis (P = 0.0009 and 0.02, respectively), indicating that they are bona fide predictive biomarkers. Conclusions Using a novel PDX-driven methodology, we developed and validated 3 platform-independent molecular signatures that predict benefit from standard of care therapies for glioblastoma. These signatures may be useful to personalize glioblastoma treatment in the clinic and this approach may be a generalizable method to identify predictive biomarkers without resource-intensive randomized trials. biomarker, Glioblastoma, patient-derived xenograft, radiation, signature, temozolomide Key Points 1. Predictive biomarkers for glioblastoma are lacking. 2. We developed a novel patient-derived xenograft approach. 3. We developed biomarkers for response to standard glioblastoma treatments. Importance of the Study Developing predictive genomic biomarkers to guide cancer therapy typically requires large randomized trials with correlative molecular data. We developed a novel alternative approach utilizing a panel of orthotopic patient-derived glioblastoma xenografts to identify gene signatures that predict benefit from radiotherapy, temozolomide, or the combination of the two. We then independently validated these signatures in a clinical cohort of 502 glioblastoma patients. These signatures may be useful to select the optimal treatment for patients with glioblastoma or to select patients for clinical trials. Furthermore, this approach represents a potentially generalizable method to develop predictive biomarkers without resource-intensive randomized trials. Following surgery, most patients with glioblastoma (GBM) are treated with a combination of temozolomide (TMZ) and radiotherapy (RT), while some receive either RT or TMZ alone. Molecular information to select from these treatment options is lacking. While promoter methylation of the gene encoding O6-methylguanine-DNA methyltransferase (MGMT) may predict for treatment benefit from TMZ, there are no biomarkers to inform response to RT or multi-modality therapy.1–4 To date, few predictive genomic biomarkers have been developed to guide treatment decisions for patients with cancer. A notable exception is breast cancer, where a 21-gene expression recurrence score guides decision making regarding adjuvant cytotoxic chemotherapy.5 Similar efforts are ongoing in prostate cancer to determine which men should receive adjuvant hormonal therapy6 or radiation following surgery.7 These efforts to personalize treatment have benefited from the high incidence of breast and prostate cancers, which facilitates the large number of patients and randomized trials necessary for predictive biomarker development.8 Rarer cancers, such as GBM, have more limited patient numbers and randomized trial data, and require alternative approaches to develop predictive biomarkers. Orthotopic patient-derived xenografts (PDXs), in which tumor tissue directly from patients is implanted into the relevant body site in mice, recapitulate much of the biology of human tumors, including the microenvironment, intratumoral heterogeneity, and, in GBM, the blood–brain barrier.9 Orthotopic PDXs typically maintain the treatment responsiveness of their founder tumors10 and can be used to assess individual biomarkers.11–13 However, to our knowledge, there are no reported studies utilizing large numbers of PDXs combined with high-throughput gene expression profiling as a strategy to identify predictive biomarkers for treatment response. In the first study of its kind, we performed RNA sequencing on a large cohort of GBM PDXs at baseline. We treated these PDXs with RT, TMZ, or RT+TMZ and developed gene signatures (GS) predicting treatment response (termed RT-GS, Chemo-GS, and ChemoRT-GS). We then independently validated the gene signatures in a clinical GBM cohort to assess the performance of the GS as predictive biomarkers, and compared our results with MGMT promoter methylation and gene expression. Methods Patient-Derived Xenografts Data on 31 orthotopic GBM PDXs with baseline RNAseq data were obtained from the Mayo Clinic PDX National Resource (https://www.mayo.edu/research/labs/translational-neuro-oncology/mayo-clinic-brain-tumor-patient-derived-xenograft-national-resource. Accessed June 4, 2019). Clinical characteristics and genomic data on these PDXs are summarized in Supplementary Table 1 and are publicly available at (http://www.mayo.edu/research/documents/7-mayo-pdx-clinical-data/doc-20339608. Accessed June 4, 2019) and (http://www.mayo.edu/research/documents/1-mayo-pdx-genotype/doc-20339599. Accessed June 4, 2019). These PDX models have been used to evaluate various treatments, including RT (n = 67 experiments on 31 PDXs), TMZ (n = 137 experiments on 31 PDXs), and RT+TMZ (n = 79 experiments on 29 PDXs), as previously described.14,15 Gene expression was log2 transformed, then centered and scaled using the “scale” function in R. Additional details on the analyses in the PDXs can be found in the Supplementary Methods. Clinical Validation Independent clinical validation was performed in The Cancer Genome Atlas (TCGA)16 GBM cohort because of the large numbers of patients and the availability of gene expression, treatment data, and clinical outcomes. While other published GBM cohorts exist, they lack one or more of these required data.17,18 Gene expression data were downloaded from the University of California Santa Cruz cancer browser.19 Affymetrix U133A microarray data were selected for analysis, rather than RNAseq data, as microarray data were available on more samples. Expression, treatment, and outcomes were available for 502 patients. Gene expression was centered and scaled as above. Patients were classified as having received chemotherapy if they had received TMZ or other alkylating chemotherapy during their treatment course and were classified as having received ChemoRT if they had received both modalities of chemotherapy and radiation. Additional details on the analyses in TCGA can be found in the Supplementary Methods. Gene Signature Development The primary endpoint for each PDX experiment was the ratio of survival time with treatment relative to survival time without treatment. Spearman’s correlation was calculated for each gene to this ratio. Gene expression signatures for treatment response were developed using the genes with the highest absolute correlation coefficients. A score was created from the top genes by averaging20,21 their expression. For any gene selected for signature development with a negative correlation coefficient, the expression was multiplied by −1 such that a higher value always corresponded with increased benefit from treatment. All model development was performed exclusively in the PDXs. To identify biological pathways associated with treatment response, we used gene set enrichment analysis (GSEA).22 Additional details on the PDXs, RNAseq, formulas for the signatures, and GSEA can be found in the Supplementary Methods. Statistical Analysis The primary endpoint for clinical validation was overall survival. Once signatures were defined in the PDX data, independent clinical validation was performed without further modification. To assess for predictive potential, Cox regression was performed to test the interaction between the signatures and treatment.23 Multivariate analysis (MVA) of interactions was used to adjust for treatment selection bias as previously described.24 Gene signatures, MGMT promoter methylation, and gene expression were treated as continuous variables in Cox regression. This allowed the results to be comparable to each other, and is suggested by Janes et al23 for treatment selection biomarker evaluation. Therefore, all statistical inference was performed using gene signatures as continuous variables. Continuous variables are categorized into tertiles within Kaplan–Meier curves only for the purposes of visualization within the main text. The pre-specified analyses were the assessments of the 3 treatment signatures, MGMT promoter methylation, and MGMT expression for treatment benefit. P-values <0.05 were considered significant. Results The overall study schema is depicted in Fig. 1. We utilized the gene expression and treatment response data from the PDXs to develop gene signatures for RT, chemotherapy, and ChemoRT response, which were then independently validated in a clinical cohort. Fig. 1 Open in new tabDownload slide Flow diagram detailing the analysis pipeline. Fig. 1 Open in new tabDownload slide Flow diagram detailing the analysis pipeline. Patient-Derived Xenografts The PDXs recapitulated the clinical heterogeneity of human GBMs. Sixty-one percent were from male patients and 39% from females. The median age at diagnosis was 63 years, with a range from 38 to 83 years. MGMT promoter methylation occurred in 45% of samples.1 All PDXs were isocitrate dehydrogenase 1 (IDH1) wild-type (ie, derived from primary GBMs), but mutations in EGFR, PTEN, and TP53 were all present.16 Clinical and molecular characteristics are further summarized in Supplementary Table 1, with all data publicly available online at the Mayo Clinic PDX National Resource website. Treatment benefit was greatest with TMZ + RT combined, followed by TMZ, and then by RT (Supplementary Fig. 1). There was limited overlap between the top 100 genes that were positively and negatively correlated with response to RT, TMZ, and RT+TMZ (Supplementary Fig. 2, Supplementary Table 2). Within the top 10 pathways correlated with resistance to RT, GSEA revealed that several were related to epithelial-mesenchymal transition and extracellular matrix interactions (Supplementary Fig. 3A). RAS signaling pathways were represented in the top pathways correlated with TMZ resistance (Supplementary Fig. 3B). For pathways correlated with resistance to RT+TMZ, 9 out of the top 10 pathways were associated with DNA replication (Supplementary Fig. 3C). Clinical Validation Patients in the clinical validation cohort (Supplementary Table 3, N = 502) were treated with combined ChemoRT (65%), RT alone (16%), or chemotherapy alone (3%) or received no treatment (16%). Patients treated with ChemoRT had the best outcomes, followed by single modality treatment (RT or chemotherapy), and patients receiving no treatment had the worst outcomes (Supplementary Fig. 4). MGMT promoter methylation was highly inversely correlated with MGMT gene expression (Spearman’s rho = −0.54, P < 0.0001), as expected, since promoter methylation silences MGMT, and is consistent with the literature.25 Alkylating Chemotherapy Response Signature We rank genes for correlation to TMZ response in the PDX models and found that MGMT had the second-highest ranked absolute correlation coefficient (Spearman’s rho: −0.47). Because of the known biology of MGMT promoter methylation and increased sensitivity to alkylating chemotherapy,26 the fact that it was highly correlated with TMZ response in the PDXs supported the validity of our methodology. Therefore, we hypothesized that a gene signature consisting of the average of MGMT and the only gene ranked higher GPRASP1, Spearman’s rho = −0.48) would predict response to chemotherapy. The absolute correlation of the 2-gene Chemo-GS was higher than MGMT alone (Spearman’s rho: −0.53) supporting the addition of GPRASP1. To validate clinically, we compared patients who received chemotherapy (with or without RT) to patients who did not (RT alone or no treatment). The Chemo-GS was associated with improved survival only in patients who received chemotherapy (P < 0.0001, HR = 0.66 [0.55–0.8]), but not in those who did not (P = 0.14, HR = 0.81 [0.62–1.07]; Fig. 2A, B). Higher Chemo-GS indicated an increased benefit from chemotherapy (Fig. 2C). MGMT promoter methylation was borderline associated with survival in patients who received chemotherapy (P = 0.065, HR = 0.86 [0.74–1.01]) and not associated in patients who did not receive chemotherapy (P = 0.96, HR = 1.01 [0.78–1.30]; Supplementary Fig. 5A, B). MGMT gene expression was borderline associated with survival in the chemotherapy treated patients (P = 0.085, HR = 1.1 [0.99–1.23]) and not associated in patients who did not receive chemotherapy (P = 0.48, HR = 1.07 [0.89–1.28]; Supplementary Fig. 5C, D). The MVA interactions were not significant for Chemo-GS (Table 1), MGMT promoter methylation (P = 0.64), or MGMT expression (P = 0.25). Table 1 Interaction MVA of each signature in the clinical validation cohort Chemo-GS1 RT-GS2 ChemoRT-GS3 P-value HR P-value HR P-value HR Chemo-GS:Chemo 0.8934 0.98 [0.72–1.33] Not included Not included Chemo-GS 0.0284 0.76 [0.6–0.97] RT-GS:RT Not included 0.0009 0.4 [0.23–0.69] Not included RT-GS 0.0019 2.26 [1.35–3.77] ChemoRT-GS:ChemoRT Not included Not included 0.0204 0.56 [0.34–0.91] ChemoRT-GS 0.5467 1.13 [0.76–1.68] Chemo 0.2085 0.68 [0.37–1.24] 0.0608 0.56 [0.3–1.03] 0.1876 0.67 [0.36–1.22] RT <0.0001 0.41 [0.29–0.57] <0.0001 0.36 [0.26–0.51] <0.0001 0.43 [0.3–0.6] ChemoRT 0.8923 0.96 [0.49–1.85] 0.7482 1.12 [0.57–2.18] 0.8708 0.95 [0.49–1.84] Prior treatment 0.1815 0.71 [0.42–1.18] 0.3877 0.8 [0.48–1.33] 0.2474 0.74 [0.45–1.23] Resection 0.2021 1.21 [0.9–1.63] 0.3255 1.16 [0.87–1.55] 0.2664 1.18 [0.88–1.58] Male vs Female 0.2974 1.12 [0.91–1.37] 0.2673 1.12 [0.91–1.38] 0.2486 1.13 [0.92–1.4] Age <0.0001 1.03 [1.02–1.04] <0.0001 1.03 [1.02–1.04] <0.0001 1.03 [1.02–1.04] Chemo-GS1 RT-GS2 ChemoRT-GS3 P-value HR P-value HR P-value HR Chemo-GS:Chemo 0.8934 0.98 [0.72–1.33] Not included Not included Chemo-GS 0.0284 0.76 [0.6–0.97] RT-GS:RT Not included 0.0009 0.4 [0.23–0.69] Not included RT-GS 0.0019 2.26 [1.35–3.77] ChemoRT-GS:ChemoRT Not included Not included 0.0204 0.56 [0.34–0.91] ChemoRT-GS 0.5467 1.13 [0.76–1.68] Chemo 0.2085 0.68 [0.37–1.24] 0.0608 0.56 [0.3–1.03] 0.1876 0.67 [0.36–1.22] RT <0.0001 0.41 [0.29–0.57] <0.0001 0.36 [0.26–0.51] <0.0001 0.43 [0.3–0.6] ChemoRT 0.8923 0.96 [0.49–1.85] 0.7482 1.12 [0.57–2.18] 0.8708 0.95 [0.49–1.84] Prior treatment 0.1815 0.71 [0.42–1.18] 0.3877 0.8 [0.48–1.33] 0.2474 0.74 [0.45–1.23] Resection 0.2021 1.21 [0.9–1.63] 0.3255 1.16 [0.87–1.55] 0.2664 1.18 [0.88–1.58] Male vs Female 0.2974 1.12 [0.91–1.37] 0.2673 1.12 [0.91–1.38] 0.2486 1.13 [0.92–1.4] Age <0.0001 1.03 [1.02–1.04] <0.0001 1.03 [1.02–1.04] <0.0001 1.03 [1.02–1.04] 1Left: interaction MVA for Chemo-GS (as a continuous variable) with alkylating chemotherapy. 2Middle: interaction MVA for RT-GS (as a continuous variable) with RT. 3Right: interaction MVA for ChemoRT-GS (as a continuous variable) with alkylating chemotherapy and RT. Open in new tab Table 1 Interaction MVA of each signature in the clinical validation cohort Chemo-GS1 RT-GS2 ChemoRT-GS3 P-value HR P-value HR P-value HR Chemo-GS:Chemo 0.8934 0.98 [0.72–1.33] Not included Not included Chemo-GS 0.0284 0.76 [0.6–0.97] RT-GS:RT Not included 0.0009 0.4 [0.23–0.69] Not included RT-GS 0.0019 2.26 [1.35–3.77] ChemoRT-GS:ChemoRT Not included Not included 0.0204 0.56 [0.34–0.91] ChemoRT-GS 0.5467 1.13 [0.76–1.68] Chemo 0.2085 0.68 [0.37–1.24] 0.0608 0.56 [0.3–1.03] 0.1876 0.67 [0.36–1.22] RT <0.0001 0.41 [0.29–0.57] <0.0001 0.36 [0.26–0.51] <0.0001 0.43 [0.3–0.6] ChemoRT 0.8923 0.96 [0.49–1.85] 0.7482 1.12 [0.57–2.18] 0.8708 0.95 [0.49–1.84] Prior treatment 0.1815 0.71 [0.42–1.18] 0.3877 0.8 [0.48–1.33] 0.2474 0.74 [0.45–1.23] Resection 0.2021 1.21 [0.9–1.63] 0.3255 1.16 [0.87–1.55] 0.2664 1.18 [0.88–1.58] Male vs Female 0.2974 1.12 [0.91–1.37] 0.2673 1.12 [0.91–1.38] 0.2486 1.13 [0.92–1.4] Age <0.0001 1.03 [1.02–1.04] <0.0001 1.03 [1.02–1.04] <0.0001 1.03 [1.02–1.04] Chemo-GS1 RT-GS2 ChemoRT-GS3 P-value HR P-value HR P-value HR Chemo-GS:Chemo 0.8934 0.98 [0.72–1.33] Not included Not included Chemo-GS 0.0284 0.76 [0.6–0.97] RT-GS:RT Not included 0.0009 0.4 [0.23–0.69] Not included RT-GS 0.0019 2.26 [1.35–3.77] ChemoRT-GS:ChemoRT Not included Not included 0.0204 0.56 [0.34–0.91] ChemoRT-GS 0.5467 1.13 [0.76–1.68] Chemo 0.2085 0.68 [0.37–1.24] 0.0608 0.56 [0.3–1.03] 0.1876 0.67 [0.36–1.22] RT <0.0001 0.41 [0.29–0.57] <0.0001 0.36 [0.26–0.51] <0.0001 0.43 [0.3–0.6] ChemoRT 0.8923 0.96 [0.49–1.85] 0.7482 1.12 [0.57–2.18] 0.8708 0.95 [0.49–1.84] Prior treatment 0.1815 0.71 [0.42–1.18] 0.3877 0.8 [0.48–1.33] 0.2474 0.74 [0.45–1.23] Resection 0.2021 1.21 [0.9–1.63] 0.3255 1.16 [0.87–1.55] 0.2664 1.18 [0.88–1.58] Male vs Female 0.2974 1.12 [0.91–1.37] 0.2673 1.12 [0.91–1.38] 0.2486 1.13 [0.92–1.4] Age <0.0001 1.03 [1.02–1.04] <0.0001 1.03 [1.02–1.04] <0.0001 1.03 [1.02–1.04] 1Left: interaction MVA for Chemo-GS (as a continuous variable) with alkylating chemotherapy. 2Middle: interaction MVA for RT-GS (as a continuous variable) with RT. 3Right: interaction MVA for ChemoRT-GS (as a continuous variable) with alkylating chemotherapy and RT. Open in new tab Fig. 2 Open in new tabDownload slide (A-C) Assessment of performance of Chemo-GS in independent clinical validation. Chemotherapy is defined as alkylating chemotherapy with or without RT. No chemotherapy is defined as RT alone or no treatment. For visualization purposes, continuous scores are divided into tertiles (low, intermediate, high). (A) Kaplan–Meier curves showing that Chemo-GS is not prognostic in patients who did not receive chemotherapy. (B) Kaplan–Meier curves showing that Chemo-GS is prognostic in patients who received chemotherapy. (C) Interaction plot showing logistic regression models fitting Chemo-GS to 2-year overall survival. Patients with a higher Chemo-GS score derived more benefit from chemotherapy. Fig. 2 Open in new tabDownload slide (A-C) Assessment of performance of Chemo-GS in independent clinical validation. Chemotherapy is defined as alkylating chemotherapy with or without RT. No chemotherapy is defined as RT alone or no treatment. For visualization purposes, continuous scores are divided into tertiles (low, intermediate, high). (A) Kaplan–Meier curves showing that Chemo-GS is not prognostic in patients who did not receive chemotherapy. (B) Kaplan–Meier curves showing that Chemo-GS is prognostic in patients who received chemotherapy. (C) Interaction plot showing logistic regression models fitting Chemo-GS to 2-year overall survival. Patients with a higher Chemo-GS score derived more benefit from chemotherapy. Radiation Response Signature We next examined RT response, for which there are no clinically utilized predictive markers in GBM. Applying the exact same methodology used to generate Chemo-GS, we integrated the top 2 most correlated genes from the PDXs into RT-GS (average of CHGA, MAPK8, Spearman’s rho = 0.47, 0.41, respectively). In clinical validation, we compared patients who received RT (with or without chemotherapy) with those who did not (chemotherapy alone or no treatment). The 2-gene RT-GS was associated with improved survival only in the patients who received RT (P = 0.0031, HR = 0.78 [0.66–0.92]) and not in patients who did not receive RT (P = 0.28, HR = 1.28 [0.82–2.0]; Fig. 3A, B). Higher RT-GS scores indicated more of a benefit from RT (Fig. 3C). On interaction MVA, the RT-GS:RT treatment interaction term was highly significant (P = 0.0009; Table 1), indicating that RT-GS is a predictive biomarker for response to radiation. Fig. 3 Open in new tabDownload slide (A–C) Assessment of performance of RT-GS in independent clinical validation. RT is defined as RT with or without chemotherapy. No RT is defined as chemotherapy alone or no treatment. For visualization purposes, continuous scores are divided into tertiles (low, intermediate, high). (A) Kaplan–Meier curves showing that RT-GS is not prognostic in patients who did not receive RT. (B) Kaplan–Meier curves showing that RT-GS is prognostic in patients who received RT. (C) Interaction plot showing logistic regression models fitting RT-GS to 2-year overall survival. Patients with a higher RT-GS score derived more benefit from RT. Fig. 3 Open in new tabDownload slide (A–C) Assessment of performance of RT-GS in independent clinical validation. RT is defined as RT with or without chemotherapy. No RT is defined as chemotherapy alone or no treatment. For visualization purposes, continuous scores are divided into tertiles (low, intermediate, high). (A) Kaplan–Meier curves showing that RT-GS is not prognostic in patients who did not receive RT. (B) Kaplan–Meier curves showing that RT-GS is prognostic in patients who received RT. (C) Interaction plot showing logistic regression models fitting RT-GS to 2-year overall survival. Patients with a higher RT-GS score derived more benefit from RT. Chemotherapy and Radiation Response Signature We next examined response to combined modality therapy, for which there is also no clinically utilized predictive marker. Since chemotherapy and RT response may independently contribute to ChemoRT response, we incorporated the individual chemotherapy and RT response signatures from above into the combined response score. To account for interactions between RT and chemotherapy (such as the generation of complex DNA damage), we also used the top 2 genes specifically correlated with RT+TMZ treatment in the PDXs (ATP6V0A2, FGF7, Spearman’s rho = −0.7, −0.69, respectively) to develop a 6-gene ChemoRT-GS. We then compared patients who received ChemoRT with those who had received single modality treatment or no treatment. As with the other 2 signatures, ChemoRT-GS was associated with improved survival only in patients treated with ChemoRT (P = 0.0001, HR = 0.54 [0.4–0.74]) and not in those not treated with ChemoRT (P = 0.26, HR = 0.8 [0.54–1.18]; Fig. 4A, B). Higher ChemoRT-GS scores indicated more of a benefit from ChemoRT (Fig. 4C). The multivariate interaction term was significant (P = 0.02; Table 1), indicating that ChemoRT-GS is a predictive biomarker for response to dual therapy with ChemoRT. MGMT promoter methylation was associated with survival in patients who received ChemoRT (P = 0.033, HR = 0.84 [0.71–0.99]) and not associated in patients who did not receive ChemoRT (P = 0.79, HR = 1.03 [0.82–1.31]; Supplementary Fig. 6A, B), with a non-significant MVA interaction (P = 0.55). Similarly, MGMT gene expression was borderline associated with survival in patients who received ChemoRT (P = 0.057, HR = 1.11 [1.00-0.25]) and not associated in patients who did not receive ChemoRT (P = 0.45, HR = 1.07 [0.9–1.27]; Supplementary Fig. 6C, D), with a non-significant MVA interaction (P = 0.77). Fig. 4 Open in new tabDownload slide (A–C) Assessment of performance of ChemoRT-GS in independent clinical validation. ChemoRT is defined as alkylating chemotherapy with RT. No ChemoRT is defined as chemo alone, RT alone, or no treatment. For visualization purposes, continuous scores are divided into tertiles (low, intermediate, high). (A) Kaplan–Meier curves showing that ChemoRT-GS is not prognostic in patients who did not receive ChemoRT. (B) Kaplan–Meier curves showing that ChemoRT-GS is prognostic in patients who received ChemoRT. (C) Interaction plot showing logistic regression models fitting ChemoRT-GS to 2-year overall survival. Patients with a higher ChemoRT-GS score derived more benefit from ChemoRT. Fig. 4 Open in new tabDownload slide (A–C) Assessment of performance of ChemoRT-GS in independent clinical validation. ChemoRT is defined as alkylating chemotherapy with RT. No ChemoRT is defined as chemo alone, RT alone, or no treatment. For visualization purposes, continuous scores are divided into tertiles (low, intermediate, high). (A) Kaplan–Meier curves showing that ChemoRT-GS is not prognostic in patients who did not receive ChemoRT. (B) Kaplan–Meier curves showing that ChemoRT-GS is prognostic in patients who received ChemoRT. (C) Interaction plot showing logistic regression models fitting ChemoRT-GS to 2-year overall survival. Patients with a higher ChemoRT-GS score derived more benefit from ChemoRT. Clinical and Molecular Associations Associations between the three signatures and clinical and molecular variables are presented in Supplementary Tables 4–6. Of note, Chemo-GS was associated with MGMT promoter methylation as expected, since MGMT gene expression is part of the signature. Chemo-GS was also associated with age at diagnosis. Higher RT-GS scores were also associated with younger age at diagnosis, consistent with the observation that younger patients may benefit more from RT.27 Similarly, ChemoRT-GS was associated with both MGMT promoter methylation and age, which is expected, as both Chemo-GS and RT-GS are components of ChemoRT-GS. Scores of all 3 signatures were higher in IDH1-mutant tumors, suggesting that patients whose tumors harbor the IDH1 mutation may derive increased benefit from multiple therapies. When we include IDH1 mutation as a covariate in the MVA interaction, the signature:treatment interactions remain significant for both RT-GS (P = 0.025) and ChemoRT-GS (P = 0.042), suggesting that the IDH1 mutation is not exclusively responsible for the predictive nature of these signatures. Discussion In the first study of its kind, we have utilized a PDX-based approach to develop 3 different gene signatures to predict GBM responsiveness to chemotherapy, radiation, and the combination. We independently validated these signatures in a clinical cohort of GBM patients. Each signature was prognostic only in patients receiving the signature-associated treatment. RT-GS and ChemoRT-GS represent the first molecular predictors of RT and ChemoRT response in GBM. The significant interaction between signatures and treatments indicate that they predict response to therapy rather than simply being prognostic. The pathways associated with treatment resistance are consistent with known biology. MGMT, which predicts for TMZ resistance in patients and laboratory models of GBM,26 was the second most highly correlated gene with TMZ resistance across our PDX cohort. GSEA also revealed biologically relevant pathways associated with therapy resistance. Pathways involved with epithelial-to-mesenchymal transition were associated with GBM PDX radioresistance. This finding is in agreement with literature reports in GBM and other cancers and suggests that therapeutic approaches targeting this phenotype should be explored in combination with radiotherapy in GBM.28–30 Increased expression of RAS signaling pathways was associated with TMZ resistance, which could be due to the role of RAS/mitogen-activated protein kinase signaling in cell survival.31 Numerous pathways related to DNA elongation and replication were associated with resistance to combined TMZ and radiation treatment, perhaps indicating that this machinery allows GBMs to detoxify the complex DNA damage that forms when radiation is combined with alkylating chemotherapy.32 This xenograft-driven methodology may be especially beneficial for rare tumors. Conventional approaches to predictive biomarker development require molecular profiling of large numbers of patient samples from patients treated with control or the treatment of interest in order to account for population-level genomic heterogeneity. This has been most successful in cancers with high incidences such as breast and prostate but is less feasible in rarer cancers.5,7 In our PDX-driven approach, genomically identical PDXs are treated with placebo or the treatment of interest, which allows for the quantification of gene-level effects on treatment benefit without large numbers of profiled samples. While the signatures developed from PDXs must still be validated on samples from patients, this methodology greatly increases the feasibility of biomarker development in cancers where patient numbers or clinical trial samples are limiting. This methodology is also versatile. A single, simple approach led to predictive biomarkers of 3 standard-of-care treatments for GBM. Because PDX cohorts are also amenable to high-throughput testing of novel therapies,13 we anticipate that this methodology could be used during drug development to identify the patients most likely to benefit from a given therapy and potentially guide initial clinical trial design. The versatility of this methodology is also enhanced by its platform independence. Because these biomarkers of response can be applied to tumors whose transcript levels have been quantified by quantitative PCR, microarray, or RNAseq, they do not require that a specific commercial test be performed. This platform independence should facilitate future validation of these signatures using clinical trial specimens. These gene signatures have potential clinical utility. Outside of a clinical trial, we expect that most patients will continue to receive combined chemoradiation regardless of their gene score. However, in patients able to tolerate only a single treatment modality, the RT-GS and Chemo-GS scores could be used to select RT or TMZ with more precision than the currently used MGMT promoter methylation assay.3,4 In the setting of a clinical trial, patients with a high ChemoRT-GS score should strongly consider treatment regimens involving both radiation and chemotherapy, whereas patients with a low score could be offered trials with novel therapy strategies. Patients with high RT-GS scores but low RT-Chemo and ChemoRT-GS scores may be excellent candidates for trials involving standard radiation but novel systemic therapy and/or novel radiosensitizing strategies. Our work has limitations. PDX RNAseq data were obtained at a single timepoint, while treatment response experiments were performed numerous times over several years, during which mouse-specific evolution could have occurred.33 Experimental number also varied between PDXs, which could bias our results toward PDXs with more replicates. Despite these limitations, MGMT was the second most correlated gene with TMZ response, which underscores the validity of this model. While we believe this approach may be generalizable to develop predictive biomarkers for other therapeutics, care should be taken regarding immune-oncology agents, as PDXs are typically grown in immunocompromised mice. The validation dataset is not a randomized control trial and therefore has treatment selection bias, unaccounted confounders, and imbalances in the numbers of patients receiving different treatment modalities. We attempted to adjust for this by including potential confounders in our MVA,24 but such corrections are likely imperfect and additional validation is needed prior to using these tools in the clinic. Clinical validation would ideally be in prospective trials, with arms containing RT or TMZ monotherapy and/or observation. We also noted that TMZ monotherapy outperformed RT monotherapy in PDX models, but RT monotherapy was more efficacious in TCGA. We believe that this finding is due to the lower absolute dose of RT (20 Gy in 10 fractions) typically used in the PDX experiments. As oncology moves toward the molecular classification of tumors, there is a strong need for molecular signatures that not only risk-stratify patients (prognostic biomarkers) but can also guide treatment decisions (predictive biomarkers). These PDX-derived signatures may be useful to personalize GBM treatment in the clinic. Furthermore, this PDX approach may be a generalizable method to identify predictive biomarkers, which is especially useful in rarer cancers. Funding DRW is funded by a University of Michigan Cancer Center Core grant (P30CA046592), as well as grants from the American Cancer Society, the Forbes Institute for Cancer Discovery, and the Jones Family Foundation Fund within the Chad Carr Pediatric Brain Tumor Center. JNS and the Mayo GBM Xenograft National Resource are supported by grants from the NCI including R24NS092940 and P50CA108961. MY is funded by a Patient-Centered Outcomes Research Institute (PCORI) award (ME-1409–21219). The views in this publication are solely the responsibility of the authors and do not necessarily represent the views of the PCORI, its Board of Governors or Methodology Committee. Conflict of interest statement. A provisional patent application on this work has been filed. Unrelated to the content of this manuscript, SGZ has received travel expenses from GenomeDx Biosciences Inc, a genitourinary cancer molecular diagnostics company, and has patent applications on gene signatures not related to this work. SLC is an employee of and FYF and CWS have an ownership interest in PFS Genomics Inc, a breast cancer molecular diagnostics company. Authorship statement: Conception and design: SGZ, JNS, DRW. Acquisition of data: BLC, ACM, TLS, JNS, DRW. Analysis and interpretation of data: SGZ, MY, DES, SLC, FYF, MMK, CWS. Drafting the article or revising it critically for important intellectual content: All authors. Final approval of the version to be published: All authors. Acknowledgments We would like to acknowledge the graphical artist assistance of Steven Kronenberg. References 1. Hegi ME , Diserens AC , Gorlia T , et al. MGMT gene silencing and benefit from temozolomide in glioblastoma . N Engl J Med. 2005 ; 352 ( 10 ): 997 – 1003 . 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Ben-David U , Ha G , Tseng YY , et al. Patient-derived xenografts undergo mouse-specific tumor evolution . Nat Genet. 2017 ; 49 ( 11 ): 1567 – 1575 . Google Scholar Crossref Search ADS WorldCat © The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)