The Role of Extent of Resection in IDH1 Wild-Type or Mutant Low-Grade Gliomas

The Role of Extent of Resection in IDH1 Wild-Type or Mutant Low-Grade Gliomas Abstract BACKGROUND Maximizing extent of resection (EOR) improves outcomes in adults with World Health Organization (WHO) grade II low-grade gliomas (LGG). However, recent studies demonstrate that LGGs bearing a mutation in the isocitrate dehydrogenase 1 (IDH1) gene are a distinct molecular and clinical entity. It remains unclear whether maximizing EOR confers an equivalent clinical benefit in IDH mutated (mtIDH) and IDH wild-type (wtIDH) LGGs. OBJECTIVE To assess the impact of EOR on malignant progression-free survival (MPFS) and overall survival (OS) in mtIDH and wtIDH LGGs. METHODS We performed a retrospective review of 74 patients with WHO grade II gliomas and known IDH mutational status undergoing resection at a single institution. EOR was assessed with quantitative 3-dimensional volumetric analysis. The effect of predictor variables on MPFS and OS was analyzed with Cox regression models and the Kaplan–Meier method. RESULTS Fifty-two (70%) mtIDH patients and 22 (30%) wtIDH patients were included. Median preoperative tumor volume was 37.4 cm3; median EOR of 57.6% was achieved. Univariate Cox regression analysis confirmed EOR as a prognostic factor for the entire cohort. However, stratifying by IDH status demonstrates that greater EOR independently prolonged MPFS and OS for wtIDH patients (hazard ratio [HR] = 0.002 [95% confidence interval {CI} 0.000-0.074] and HR = 0.001 [95% CI 0.00-0.108], respectively), but not for mtIDH patients (HR = 0.84 [95% CI 0.17-4.13] and HR = 2.99 [95% CI 0.15-61.66], respectively). CONCLUSION Increasing EOR confers oncologic and survival benefits in IDH1 wtLGGs, but the impact on IDH1 mtLGGs requires further study. Extent of resection, Low-grade glioma, WHO grade II glioma, IDH1 mutation ABBREVIATIONS ABBREVIATIONS CI confidence interval EOR extent of resection IDH isocitrate dehydrogenase LGG low-grade glioma mtIDH isocitrate dehydrogenase mutated MPFS malignant progression-free survival MRI magnetic resonance imaging OS overall survival wtIDH isocitrate dehydrogenase wild-type WHO World Health Organization Accumulating evidence suggests that maximizing extent of resection (EOR) improves outcomes for patients with World Health Organization (WHO) grade II low-grade gliomas (LGGs).1 Historically, surgical resection of LGGs was typically reserved for clinical or radiographic progression. More recently, several groups have demonstrated that early surgery for LGGs extends both progression-free survival and overall survival (OS).2-5 This benefit is, however, contingent upon achieving a significant EOR (>90% in most cases). Over time, acceptance of this data has led to a significant shift in treatment recommendations. Currently, at many high-volume centers, patients with LGGs are offered upfront surgical resection.6 Furthermore, many groups demonstrate improved outcomes in patients whose tumors were amenable to higher EOR, often using awake or intraoperative electrophysiologic mapping adjuncts for lesions in eloquent locations, while others advocate supramaximal resections where possible.7-13 These aggressive surgical approaches often place the patient at greater risk of postoperative neurological deficits, which is thought to be justified by significant oncologic benefit.14 Historical classifications of gliomas emphasized the histological features of tumor tissue, but more recent studies have demonstrated recurrent mutations in these tumors that are predictive of clinical behavior and that are now incorporated into the most recent WHO classification.15 Chief amongst identified genetic aberrancies is a mutation in the isocitrate dehydrogenase 1 (IDH1) gene.16 To date, multiple studies have demonstrated that gliomas that harbor a mutation in the IDH gene have a better prognosis and an improved response to adjuvant nonsurgical therapies.17-20 What remains unknown, however, is whether maximizing EOR confers an equivalent clinical benefit in both IDH mutated (mtIDH) and IDH wild-type (wtIDH) LGGs. Modern advanced imaging modalities now offer the ability to investigate a tumor's IDH mutation status noninvasively, via magnetic resonance imaging (MRI) spectroscopy. These mutations are also typically clonal, suggesting that a simple needle biopsy would also reliably allow for minimally invasive IDH genotyping.21-23 Thus, for many patients, IDH mutation status can be determined preoperatively. As patients may be subject to greater risks following extensive resections of LGG, it is worth examining whether or not maximizing surgical resection provides the same clinical benefit in mtIDH and wtIDH LGGs. To answer this question, we evaluated a cohort of patients with surgically resected WHO grade II gliomas and known IDH1 mutation status to assess the impact of EOR on OS and malignant progression-free survival (MPFS). METHODS Study Design and Setting We performed an Institutional Review Board-approved retrospective review of the medical records of 266 consecutive patients with WHO grade II gliomas who underwent surgical resection at a single large academic medical center between 2001 and 2012. Patients were included in this study if they were at least 18 yr of age, had known IDH1 mutation status, and had immediate pre- and postoperative MRIs available for review. All patients provided informed consent for inclusion in a clinical database. Clinical data, including demographics, pathology results, date of last follow-up, and EOR was obtained from a review of the medical records. Seventy-four patients met the inclusion criteria and were included in this study; 178 patients were excluded due to lack of known IDH status or lack of available tissue for staining, and 14 patients were excluded due to lack of either pre- or postoperative MRI for volumetric analysis. Variables Two outcome measures were assessed: OS and MPFS. OS was defined as the time from initial surgery to death or last follow-up. MPFS was defined as the time from initial surgery to (1) the development of gadolinium enhancement, as noted by board-certified neuroradiologists, on surveillance MRI and/or WHO grade III or IV histopathology on subsequent biopsy; (2) death; or (3) last follow-up, whichever occurred first. Patients who did not die or demonstrate malignant progression were censored as of their last MRI date. Postoperative MRIs were obtained within 48 h of surgery. Surveillance MRIs were typically obtained at 3-mo intervals, or as deemed appropriate by patients’ neuro-oncology team. EOR was quantitatively assessed by performing manual segmentation and region-of-interest analysis on the pre- and postoperative axial fluid-attenuated inversion-recovery images, using the BrainLAB software (Brainlab AG, Feldkirchen, Germany) by an investigator blinded to IDH1 genotype. EOR was defined as: (preoperative tumor volume – postoperative tumor volume)/preoperative tumor volume. IDH mutation status was determined by immunostaining performed on a Discovery Ultra automated device (Ventana) with the Dianova IDH R132H antibody at a 1:30 dilution, as previously described.24 Statistical Methods Statistical analyses were performed using SAS, version 9.4 (Cary, North Carolina). Kaplan–Meier analysis was performed in R software, version 3.1.2. Descriptive statistics, including frequency distributions, medians, and ranges, were calculated for all variables. To compare variable distributions by IDH status, we used the Fisher's test or chi-squared test for categorical variables, where appropriate, and the Wilcoxon 2-sample test for continuous variables. A time-dependent Cox model accounting for time to treatment and death was used to compare chemotherapy and radiation regimens in the mutant and wild-type IDH groups. Two cohorts were examined: those with an IDH1 mutation and those without an IDH1 mutation. Within each cohort, univariate Cox regression models were used to evaluate the prognostic significance of predictor variables, including age, sex, EOR, and pre- and postoperative tumor volume. The Kaplan–Meier method was used to estimate OS and MPFS. An age-adjusted, stratified Cox regression model, based on IDH status, was used to evaluate the interaction between EOR, OS, and MPFS. For all analyses, P < .05 was considered significant. RESULTS Descriptive Data Between 2001 and 2012, 266 patients underwent surgical resection of a WHO grade II glioma at our institution. Of these, 74 patients met the inclusion criteria. Baseline clinical characteristics are summarized in Table 1. The study cohort included 36 men and 38 women with a median age of 43 years. Forty-three patients had astrocytomas, 19 patients had oligodendrogliomas, and 12 patients had mixed glial tumors (oligoastrocytomas). Median preoperative tumor volume was 37.4 cm3 (range: 0.9-190.2 cm3). Median EOR was 57.6% (range: 0.08%-99.3%). Eight patients only underwent biopsy in this cohort (5 in the mtIDH– group and 3 in the mtIDH+ group). At the conclusion of the study, 17 patients were deceased and 57 patients were alive. Malignant progression was identified in 31 patients. Malignant progression was based on both pathology and MRI for 23 patients and MRI alone for 8 patients. Median time to malignant progression was 45.8 mo; median overall follow-up was 44.4 mo. Median follow-up for survivors was 4 yr; 17 patients died and the remaining 57 were censored or lost to follow-up. TABLE 1. Baseline patient/Tumor Characteristics All patients (n = 74) Age (years)  Median 43  Range 20-71 Sex, n (%)  Male 36 (49)  Female 38 (51) IDH1 status, n (%)  Mutant 52 (70)  Wild-type 22 (30) Histopathology, n (%)  Astrocytoma 43 (58)  Oligodendroglioma 19 (26)  Oligoastrocytoma 12 (16) Preoperative tumor volume (cm3)  Median 37.4  Range 0.9-190.2 EOR (%)  Median 57.6  Range 0.08-99.3 Follow-up (months)  Median 44.4  Range 0.6-187.2 Malignant progression, n (%)  Yes 31 (42)  No 43 (58) Death, n (%)  Yes 17 (23)  No 57 (77) All patients (n = 74) Age (years)  Median 43  Range 20-71 Sex, n (%)  Male 36 (49)  Female 38 (51) IDH1 status, n (%)  Mutant 52 (70)  Wild-type 22 (30) Histopathology, n (%)  Astrocytoma 43 (58)  Oligodendroglioma 19 (26)  Oligoastrocytoma 12 (16) Preoperative tumor volume (cm3)  Median 37.4  Range 0.9-190.2 EOR (%)  Median 57.6  Range 0.08-99.3 Follow-up (months)  Median 44.4  Range 0.6-187.2 Malignant progression, n (%)  Yes 31 (42)  No 43 (58) Death, n (%)  Yes 17 (23)  No 57 (77) View Large TABLE 1. Baseline patient/Tumor Characteristics All patients (n = 74) Age (years)  Median 43  Range 20-71 Sex, n (%)  Male 36 (49)  Female 38 (51) IDH1 status, n (%)  Mutant 52 (70)  Wild-type 22 (30) Histopathology, n (%)  Astrocytoma 43 (58)  Oligodendroglioma 19 (26)  Oligoastrocytoma 12 (16) Preoperative tumor volume (cm3)  Median 37.4  Range 0.9-190.2 EOR (%)  Median 57.6  Range 0.08-99.3 Follow-up (months)  Median 44.4  Range 0.6-187.2 Malignant progression, n (%)  Yes 31 (42)  No 43 (58) Death, n (%)  Yes 17 (23)  No 57 (77) All patients (n = 74) Age (years)  Median 43  Range 20-71 Sex, n (%)  Male 36 (49)  Female 38 (51) IDH1 status, n (%)  Mutant 52 (70)  Wild-type 22 (30) Histopathology, n (%)  Astrocytoma 43 (58)  Oligodendroglioma 19 (26)  Oligoastrocytoma 12 (16) Preoperative tumor volume (cm3)  Median 37.4  Range 0.9-190.2 EOR (%)  Median 57.6  Range 0.08-99.3 Follow-up (months)  Median 44.4  Range 0.6-187.2 Malignant progression, n (%)  Yes 31 (42)  No 43 (58) Death, n (%)  Yes 17 (23)  No 57 (77) View Large Main Results The overall cohort was divided into 2 groups: those with an IDH1 mutation (n = 52) and those without an IDH1 mutation. Upon univariate analysis performed to assess baseline differences between the 2 groups, the IDH1 mutated subgroup (mtIDH) was younger than the wild-type cohort (wtIDH; P = .004) and contained a greater proportion of patients with oligodendrogliomas, as determined by histology (P = .03; Table 2). Sex and preoperative tumor volume did not vary significantly between the 2 groups. A majority of mtIDH tumors were located in the frontal lobe; wtIDH tumors were most commonly located in the temporal lobe (P = .003). However, no significant difference was found in the frequency of dominant hemisphere tumors (P = .36) between the 2 cohorts. While treatment with chemotherapy and radiation therapy did not differ between the groups (P = .90), a greater EOR was achieved in the mtIDH cohort (P < .001). Median follow-up was 49.6 mo for the mtIDH group and 36.5 mo for the wtIDH group. TABLE 2. Patient/Tumor Characteristics by IDH Status IDH1 mutant (n = 52) IDH1 wild-type (n = 22) Pa Age (years)  Median 40 51.5 .004  Range 20-68 23-71 Sex, n (%) .88  Male 25 (48) 11 (50)  Female 27 (52) 11 (50) Histopathology, n (%) .03  Astrocytoma 25 (48) 18 (82)  Oligodendroglioma 17 (33) 2 (9)  Oligoastrocytoma 10 (19) 2 (9) Preoperative tumor volume (cm3) .96  Median 38.2 36.2  Range 0.9-190.2 5.7-127.8 EOR (%) <.0001  Median 73.3 27.8  Range 0.1-99.3 0.08-73.9 Treatment, n (%) .90b  Chemo alone 8 (15) 3 (14)  RT alone 4 (8) 0 (0)  Chemo + RT 23 (44) 14 (63)  No chemo or RT 17 (33) 5 (22) Laterality, n (%) .30  Dominant 27 (52) 14 (64)  Nondominant 24 (46) 7 (32)  Bilateral 1 (2) 1 (4) Location, n (%) .005  Frontal 34 (65) 4 (18)  Temporal 11 (21) 12 (55)  Insular 4 (8) 2 (9)  Parietal 2 (4) 2 (9)  Other 1 (2) 2 (9) MPFS (years) .07  Median 6.5 3.2 OS (years) .01  Median 10.9 Not reached IDH1 mutant (n = 52) IDH1 wild-type (n = 22) Pa Age (years)  Median 40 51.5 .004  Range 20-68 23-71 Sex, n (%) .88  Male 25 (48) 11 (50)  Female 27 (52) 11 (50) Histopathology, n (%) .03  Astrocytoma 25 (48) 18 (82)  Oligodendroglioma 17 (33) 2 (9)  Oligoastrocytoma 10 (19) 2 (9) Preoperative tumor volume (cm3) .96  Median 38.2 36.2  Range 0.9-190.2 5.7-127.8 EOR (%) <.0001  Median 73.3 27.8  Range 0.1-99.3 0.08-73.9 Treatment, n (%) .90b  Chemo alone 8 (15) 3 (14)  RT alone 4 (8) 0 (0)  Chemo + RT 23 (44) 14 (63)  No chemo or RT 17 (33) 5 (22) Laterality, n (%) .30  Dominant 27 (52) 14 (64)  Nondominant 24 (46) 7 (32)  Bilateral 1 (2) 1 (4) Location, n (%) .005  Frontal 34 (65) 4 (18)  Temporal 11 (21) 12 (55)  Insular 4 (8) 2 (9)  Parietal 2 (4) 2 (9)  Other 1 (2) 2 (9) MPFS (years) .07  Median 6.5 3.2 OS (years) .01  Median 10.9 Not reached RT, radiation therapy. aP-values provided are from the Wilcoxon 2-sample test for continuous variables, Fisher's exact test, or chi-squared test for categorical variables, where appropriate, and from the Log-Rank test for MPFS and OS. bP-value calculated by Cox model for treatment accounting for time to treatment and death. View Large TABLE 2. Patient/Tumor Characteristics by IDH Status IDH1 mutant (n = 52) IDH1 wild-type (n = 22) Pa Age (years)  Median 40 51.5 .004  Range 20-68 23-71 Sex, n (%) .88  Male 25 (48) 11 (50)  Female 27 (52) 11 (50) Histopathology, n (%) .03  Astrocytoma 25 (48) 18 (82)  Oligodendroglioma 17 (33) 2 (9)  Oligoastrocytoma 10 (19) 2 (9) Preoperative tumor volume (cm3) .96  Median 38.2 36.2  Range 0.9-190.2 5.7-127.8 EOR (%) <.0001  Median 73.3 27.8  Range 0.1-99.3 0.08-73.9 Treatment, n (%) .90b  Chemo alone 8 (15) 3 (14)  RT alone 4 (8) 0 (0)  Chemo + RT 23 (44) 14 (63)  No chemo or RT 17 (33) 5 (22) Laterality, n (%) .30  Dominant 27 (52) 14 (64)  Nondominant 24 (46) 7 (32)  Bilateral 1 (2) 1 (4) Location, n (%) .005  Frontal 34 (65) 4 (18)  Temporal 11 (21) 12 (55)  Insular 4 (8) 2 (9)  Parietal 2 (4) 2 (9)  Other 1 (2) 2 (9) MPFS (years) .07  Median 6.5 3.2 OS (years) .01  Median 10.9 Not reached IDH1 mutant (n = 52) IDH1 wild-type (n = 22) Pa Age (years)  Median 40 51.5 .004  Range 20-68 23-71 Sex, n (%) .88  Male 25 (48) 11 (50)  Female 27 (52) 11 (50) Histopathology, n (%) .03  Astrocytoma 25 (48) 18 (82)  Oligodendroglioma 17 (33) 2 (9)  Oligoastrocytoma 10 (19) 2 (9) Preoperative tumor volume (cm3) .96  Median 38.2 36.2  Range 0.9-190.2 5.7-127.8 EOR (%) <.0001  Median 73.3 27.8  Range 0.1-99.3 0.08-73.9 Treatment, n (%) .90b  Chemo alone 8 (15) 3 (14)  RT alone 4 (8) 0 (0)  Chemo + RT 23 (44) 14 (63)  No chemo or RT 17 (33) 5 (22) Laterality, n (%) .30  Dominant 27 (52) 14 (64)  Nondominant 24 (46) 7 (32)  Bilateral 1 (2) 1 (4) Location, n (%) .005  Frontal 34 (65) 4 (18)  Temporal 11 (21) 12 (55)  Insular 4 (8) 2 (9)  Parietal 2 (4) 2 (9)  Other 1 (2) 2 (9) MPFS (years) .07  Median 6.5 3.2 OS (years) .01  Median 10.9 Not reached RT, radiation therapy. aP-values provided are from the Wilcoxon 2-sample test for continuous variables, Fisher's exact test, or chi-squared test for categorical variables, where appropriate, and from the Log-Rank test for MPFS and OS. bP-value calculated by Cox model for treatment accounting for time to treatment and death. View Large Survival rates were higher in mtIDH patients compared to wtIDH patients. The 3-yr MPFS rate for mtIDH patients was 88.8% (95% confidence interval [CI]: 79.6-98.1), compared to 55.0% (95% CI: 33.0-77.1) for wtIDH patients. Furthermore, the 3-yr OS rate was 95.2% (95% CI: 88.8-100) in mtIDH patients vs 64.2% (95% CI: 42.8-85.5) in the wtIDH group. mtIDH patients had correspondingly longer median MPFS (6.5 yr) and median OS (10.9 yr) vs wtIDH patients (median MPFS: 3.2 yr, median OS: not reached; Figures 1 and 2). FIGURE 1. View largeDownload slide Kaplan–Meier curve showing MPFS stratified by IDH mutation status. IDH, isocitrate dehydrogenase; MPFS, malignant progression-free survival. FIGURE 1. View largeDownload slide Kaplan–Meier curve showing MPFS stratified by IDH mutation status. IDH, isocitrate dehydrogenase; MPFS, malignant progression-free survival. FIGURE 2. View largeDownload slide Kaplan–Meier curve showing overall survival stratified by IDH mutation status. IDH, isocitrate dehydrogenase; OS, overall survival. FIGURE 2. View largeDownload slide Kaplan–Meier curve showing overall survival stratified by IDH mutation status. IDH, isocitrate dehydrogenase; OS, overall survival. Univariate Cox regression analysis was performed to assess the prognostic significance of a number of variables (Table 3). Evaluation of the entire cohort demonstrated that a greater EOR was the only variable assessed that prolonged MPFS (P = .009, hazard ratio [HR] = 0.21 [95% CI 0.06-0.67]) and OS (P = .03, HR = 0.14 [95% CI 0.03-0.78]). However, an age-adjusted Cox regression model, stratified by IDH mutation status (Table 4), revealed that a greater EOR was only associated with prolonged MPFS and OS in wtIDH patients (P < .001 HR = 0.002 [95% CI 0.000-0.074] and P = .003, HR = 0.001 [95% CI 0.00-0.108], respectively), but not for mtIDH patients (P = .83, HR = 0.84 [95% CI 0.17-4.13] and P = .48, HR = 2.99 [95% CI 0.15-61.66], respectively). Furthermore, the heterogeneity P values for the overall interaction between IDH status, EOR, and MPFS (P < .001) and IDH status, EOR, and OS (P = .0016) were statistically significant. Age and gender did not demonstrate a significant prognostic value in univariate Cox regression models for MPFS or OS in the entire cohort or in the IDH subgroup analysis (Tables 3 and 5). Preoperative tumor volume, while not prognostic for MPFS or OS in the whole cohort, did have a small statistically significant prognostic value for MPFS in the mtIDH cohort (P = .01 HR = 1.01 [95% CI 1.00-1.02]) but not the wtIDH cohort (P = .23 HR = 0.99 [95% CI 0.96-1.01]). Multivariate analysis was not performed due to event size. TABLE 3. Univariate Cox Regression Model of OS and PFS in Full Cohort OS MPFSa Variable HR 95% CI P HR 95% CI P Age 1.02 0.99-1.06 .23 1.02 0.99-1.05 .20 EOR 0.14 0.03-0.78 .03 0.21 0.06-0.67 .009 Preop tumor volume 1.000 0.99-1.01 .98 1.005 0.997-1.014 .21 Gender: male ref ref Gender: female 0.63 0.24-1.66 .35 0.99 0.50-1.96 .97 OS MPFSa Variable HR 95% CI P HR 95% CI P Age 1.02 0.99-1.06 .23 1.02 0.99-1.05 .20 EOR 0.14 0.03-0.78 .03 0.21 0.06-0.67 .009 Preop tumor volume 1.000 0.99-1.01 .98 1.005 0.997-1.014 .21 Gender: male ref ref Gender: female 0.63 0.24-1.66 .35 0.99 0.50-1.96 .97 aEvent defined as either death or high-grade progression. View Large TABLE 3. Univariate Cox Regression Model of OS and PFS in Full Cohort OS MPFSa Variable HR 95% CI P HR 95% CI P Age 1.02 0.99-1.06 .23 1.02 0.99-1.05 .20 EOR 0.14 0.03-0.78 .03 0.21 0.06-0.67 .009 Preop tumor volume 1.000 0.99-1.01 .98 1.005 0.997-1.014 .21 Gender: male ref ref Gender: female 0.63 0.24-1.66 .35 0.99 0.50-1.96 .97 OS MPFSa Variable HR 95% CI P HR 95% CI P Age 1.02 0.99-1.06 .23 1.02 0.99-1.05 .20 EOR 0.14 0.03-0.78 .03 0.21 0.06-0.67 .009 Preop tumor volume 1.000 0.99-1.01 .98 1.005 0.997-1.014 .21 Gender: male ref ref Gender: female 0.63 0.24-1.66 .35 0.99 0.50-1.96 .97 aEvent defined as either death or high-grade progression. View Large TABLE 4. Cox Regression Model of EOR, Stratified by IDH Statusa IDH1 mutant IDH1 wild-type HR 95% CI P HRa 95% CI P Heterogeneity P-value MPFS  EOR 0.84 0.17-4.13 .83 0.002 0.0-0.074 .0007 .0009 OS  EOR 2.99 0.15-61.66 .48 0.001 0.0-0.108 .003 .0016 IDH1 mutant IDH1 wild-type HR 95% CI P HRa 95% CI P Heterogeneity P-value MPFS  EOR 0.84 0.17-4.13 .83 0.002 0.0-0.074 .0007 .0009 OS  EOR 2.99 0.15-61.66 .48 0.001 0.0-0.108 .003 .0016 aAge-adjusted. View Large TABLE 4. Cox Regression Model of EOR, Stratified by IDH Statusa IDH1 mutant IDH1 wild-type HR 95% CI P HRa 95% CI P Heterogeneity P-value MPFS  EOR 0.84 0.17-4.13 .83 0.002 0.0-0.074 .0007 .0009 OS  EOR 2.99 0.15-61.66 .48 0.001 0.0-0.108 .003 .0016 IDH1 mutant IDH1 wild-type HR 95% CI P HRa 95% CI P Heterogeneity P-value MPFS  EOR 0.84 0.17-4.13 .83 0.002 0.0-0.074 .0007 .0009 OS  EOR 2.99 0.15-61.66 .48 0.001 0.0-0.108 .003 .0016 aAge-adjusted. View Large TABLE 5. Univariate Cox Regression Model, Stratified by IDH Statusa IDH1 mutant (mtIDH) IDH1 wild-type (wtIDH) HR 95% CI P HR 95% CI P OS  Age 0.97 0.91-1.03 .27 1.04 0.99-1.09 .17  Preop tumor volume 1.01 0.997-1.03 .10 0.98 0.95-1.01 .20  Gender: male ref ref  Gender: female 0.74 0.17-3.12 .68 0.41 0.11-1.55 .19 MPFS  Age 1.00 0.96-1.04 .86 1.02 0.99-1.07 .23  Preop tumor volume 1.01 1.00-1.02 .01 0.99 0.96-1.01 .23  Gender: male ref ref  Gender: female 1.06 0.44-2.55 .90 0.81 0.27-2.42 .70 IDH1 mutant (mtIDH) IDH1 wild-type (wtIDH) HR 95% CI P HR 95% CI P OS  Age 0.97 0.91-1.03 .27 1.04 0.99-1.09 .17  Preop tumor volume 1.01 0.997-1.03 .10 0.98 0.95-1.01 .20  Gender: male ref ref  Gender: female 0.74 0.17-3.12 .68 0.41 0.11-1.55 .19 MPFS  Age 1.00 0.96-1.04 .86 1.02 0.99-1.07 .23  Preop tumor volume 1.01 1.00-1.02 .01 0.99 0.96-1.01 .23  Gender: male ref ref  Gender: female 1.06 0.44-2.55 .90 0.81 0.27-2.42 .70 aEvent defined as either death or high-grade progression. View Large TABLE 5. Univariate Cox Regression Model, Stratified by IDH Statusa IDH1 mutant (mtIDH) IDH1 wild-type (wtIDH) HR 95% CI P HR 95% CI P OS  Age 0.97 0.91-1.03 .27 1.04 0.99-1.09 .17  Preop tumor volume 1.01 0.997-1.03 .10 0.98 0.95-1.01 .20  Gender: male ref ref  Gender: female 0.74 0.17-3.12 .68 0.41 0.11-1.55 .19 MPFS  Age 1.00 0.96-1.04 .86 1.02 0.99-1.07 .23  Preop tumor volume 1.01 1.00-1.02 .01 0.99 0.96-1.01 .23  Gender: male ref ref  Gender: female 1.06 0.44-2.55 .90 0.81 0.27-2.42 .70 IDH1 mutant (mtIDH) IDH1 wild-type (wtIDH) HR 95% CI P HR 95% CI P OS  Age 0.97 0.91-1.03 .27 1.04 0.99-1.09 .17  Preop tumor volume 1.01 0.997-1.03 .10 0.98 0.95-1.01 .20  Gender: male ref ref  Gender: female 0.74 0.17-3.12 .68 0.41 0.11-1.55 .19 MPFS  Age 1.00 0.96-1.04 .86 1.02 0.99-1.07 .23  Preop tumor volume 1.01 1.00-1.02 .01 0.99 0.96-1.01 .23  Gender: male ref ref  Gender: female 1.06 0.44-2.55 .90 0.81 0.27-2.42 .70 aEvent defined as either death or high-grade progression. View Large DISCUSSION Aggressive surgical intervention for LGGs has become routine practice at high-volume centers over the past decade.6 This strategy is based on several retrospective series that have correlated an increasing EOR to improvements in overall and progression-free survival. Nonetheless, the potential for causing neurological morbidity, especially when removing tumors from eloquent regions, remains a concern. Additionally, while one well-designed population-based parallel cohort study suggests survival and oncologic advantage to early resection, this treatment paradigm is largely supported by observational reports hindered by selection bias.25,26 Thus, studies that help identify particularly high-risk populations that could preferentially benefit from an aggressive surgical approach are necessary. Key Results Here, we present the first report that specifically examines the utility of maximizing surgical resection in patients with WHO grade II gliomas informed by IDH1 mutation status. It is important to emphasize that we have focused this study exclusively on grade II patients. In evaluating the entire cohort of patients, our data confirm that greater EOR results in an improved OS and MPFS. Interestingly, however, on subgroup analysis, a greater EOR did not confer an equivalent clinical benefit in wtIDH and mtIDH patients. Specifically, in our study, greater EOR was associated with improved survival in only wtIDH patients. This is the first time that such a relationship has been described in the literature and is contrary to prior studies in high-grade gliomas.27 Although the biological mechanisms underlying our findings are as yet unclear, it has been known for several years that mtIDH patients have a better prognosis than their wild-type counterparts, independent of tumor grade.28 As our ability to noninvasively assess IDH mutation status improves, for example, via MRI spectroscopy, the data presented in this manuscript may provide justification to delay or to perform more conservative surgery for mtIDH LGGs located in particularly eloquent locations. Instead, neoadjuvant chemotherapy and/or radiation approaches may be preferable in a select group of patients, with surgery reserved for radiographic progression or mass effect. Seizure control provides a separate surgical justification.29-31 Interpretation Our manuscript confirms two recently reported findings from other surgical glioma series. First, in our cohort, EOR was significantly higher in mtIDH LGGs, as compared to wild-type lesions. This has been previously reported, although the reasons underlying this difference have not yet been elucidated.27 Anecdotally, mtIDH LGGs tend to have a more favorable texture than wtIDH LGGs, which may facilitate surgical resection and, speculatively, may reflect a less aggressive biology. Recently, another series examined the impact of surgical resection on the malignant transformation of pure oligodendrogliomas.32 Although IDH status was not assessed in this cohort, it is reasonable to assume, based on prior studies, that the majority of these pure oligodendrogliomas harbored an IDH mutation. The authors determined that while greater EOR increased OS, it did not prolong MPFS. These findings are similar to the results reported in this series and suggest that maximizing EOR may be more important in nonoligodendroglial LGGs, which are often wild type for IDH mutations. Furthermore, wtIDH LGGs are known to be associated with a clinical behavior closer to that of higher grade gliomas,15 and our data suggest that with higher 3-yr malignant progression rates, wtIDH LGGs more quickly transform into high-grade tumors. Thus, our data may further support treating wtIDH LGGs more like high-grade gliomas, with upfront maximal surgical resection. An improved understanding of the fundamental biological differences between mtIDH and wtIDH LGGs will be critical to future patient care.18 Indeed, an even more sophisticated molecular subclassification incorporating DNA methylation and epigenetic profiling may help further dissect the impact of surgical therapies on specific subgroups of LGG. As an example, in a recent analysis of a large number of patients, demethylated mtIDH gliomas are associated with poor outcome, and wtIDH cases molecularly similar to pilocytic astrocytomas are associated with favorable survival, suggesting that a more detailed stratification may soon become necessary to optimize treatment strategies for glioma patients.33 Another important question that cannot be readily addressed in retrospective studies, but that would greatly impact therapeutic strategies, is whether early aggressive surgical intervention could delay malignant progression. Limitations This study is a single-institution, retrospective analysis, and thus is subject to the inherent limitations of this design. The baseline differences in histology (ie, higher percentage of astrocytomas in the wtIDH cohort) and higher baseline EOR for mtIDH patients may confound comparison of the 2 groups. However, these baseline differences exemplify that LGGs should not be treated as a single group in future studies and require stratification based on IDH status and, preferably, additional genetic mutations. Furthermore, our wtIDH survival data, with its subset of long-term survivors, is consistent with previous reports that wtIDH LGGs are heterogeneous populations within themselves.33,34 The wtIDH cohort likely includes patients with pilocytic astrocytoma-like LGGs and patients with V600E BRAF mutations. However, due to our cohort size and lack of data on other mutations (eg, BRAF, H3.3, ATRX, TP53, 1p/19q, etc.), we are unable to determine how these other genetic variables further define the subpopulations in our study. Over the past several years, it has become more commonplace to assess IDH mutation status and other genetic mutations in tumors. Thus, in the coming years, there will be an increasing volume of clinical data that will allow for further validation and refinement of our findings. Additionally, the cohort described in this paper will continue to be monitored and treated at our institution; a follow-up report in several years would be of significant interest. Lastly, due to the retrospective nature and size of the study, the difference in median EOR achieved between the 2 groups cannot be ruled out as a confounding variable in our results. However, these EOR results were achieved by experienced glioma surgeons, at a tertiary referral center. Thus, we believe that the surgical results presented in this series represent realistic clinical outcomes. CONCLUSION The treatment paradigm for LGGs continues to evolve. Historical observation approaches to this disease, which reserved surgery for radiographic or clinical progression, have recently evolved towards protocols advocating maximal upfront surgical resection, as recently outlined in the updated national management guidelines. This manuscript suggests that WHO grade II gliomas without an IDH mutation may benefit significantly from maximizing the extent of surgical resection, while the data for those with an IDH mutation remains unclear. Further investigation of these findings will be critical to our long-term surgical management of this disease. Disclosures Funding was received from the National Institutes of Health (P30 CA008748). The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article. REFERENCES 1. Sanai N , Chang S , Berger MS . Low-grade gliomas in adults . J Neurosurg . 2011 ; 115 ( 5 ): 948 - 965 . Google Scholar CrossRef Search ADS PubMed 2. 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Where are we now? And where are we going? A report from the accelerate brain cancer cure (ABC2) low-grade glioma research workshop . Neuro Oncol . 2014 : 16 ( 2 ): 173 - 178 . Google Scholar CrossRef Search ADS PubMed 7. Chang EF , Clark A , Smith JS et al. Functional mapping-guided resection of low-grade gliomas in eloquent areas of the brain: improvement of long-term survival. Clinical article . J Neurosurg . 2011 ; 114 ( 3 ): 566 - 573 . Google Scholar CrossRef Search ADS PubMed 8. Duffau H , Capelle L , Denvil D et al. Usefulness of intraoperative electrical subcortical mapping during surgery for low-grade gliomas located within eloquent brain regions: functional results in a consecutive series of 103 patients . J Neurosurg . 2003 ; 98 ( 4 ): 764 - 778 . Google Scholar CrossRef Search ADS PubMed 9. Hervey-Jumper SL , Li J , Lau D et al. Awake craniotomy to maximize glioma resection: methods and technical nuances over a 27-year period . J Neurosurg . 2015 ; 123 ( 2 ): 325 - 339 . Google Scholar CrossRef Search ADS PubMed 10. Sanai N , Mirzadeh Z , Berger MS . Functional outcome after language mapping for glioma resection . N Engl J Med . 2008 ; 358 ( 1 ): 18 - 27 . Google Scholar CrossRef Search ADS PubMed 11. Szelényi A , Bello L , Duffau H et al. Intraoperative electrical stimulation in awake craniotomy: methodological aspects of current practice . Neurosurg Focus . 2010 ; 28 ( 2 ): E7 . Google Scholar CrossRef Search ADS PubMed 12. Yordanova YN , Moritz-Gasser S , Duffau H . Awake surgery for WHO grade II gliomas within “noneloquent” areas in the left dominant hemisphere: toward a “supratotal” resection. Clinical article . J Neurosurg . 2011 ; 115 ( 2 ): 232 - 239 . Google Scholar CrossRef Search ADS PubMed 13. De Witt Hamer PC , Robles SG , Zwinderman AH , Duffau H , Berger MS . Impact of intraoperative stimulation brain mapping on glioma surgery outcome: a meta-analysis . J Clin Oncol . 2012 ; 30 ( 20 ): 2559 - 2565 . Google Scholar CrossRef Search ADS PubMed 14. Sanai N , Martino J , Berger MS . Morbidity profile following aggressive resection of parietal lobe gliomas . J Neurosurg . 2012 ; 116 ( 6 ): 1182 - 1186 . Google Scholar CrossRef Search ADS PubMed 15. Louis DN , Perry A , Reifenberger G et al. The 2016 world health organization classification of tumors of the central nervous system: a summary . Acta Neuropathol . 2016 ; 131 ( 6 ): 803 - 820 . Google Scholar CrossRef Search ADS PubMed 16. Yan H , Parsons DW , Jin G et al. IDH1 and IDH2 mutations in gliomas . N Engl J Med . 2009 ; 360 ( 8 ): 765 - 773 . Google Scholar CrossRef Search ADS PubMed 17. Claus EB , Walsh KM , Wiencke JK et al. Survival and low-grade glioma: the emergence of genetic information . Neurosurg Focus . 2015 ; 38 ( 1 ): E6 . Google Scholar CrossRef Search ADS PubMed 18. Johnson BE , Mazor T , Hong C et al. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma . Science . 2014 ; 343 ( 6167 ): 189 - 193 . Google Scholar CrossRef Search ADS PubMed 19. Leu S , von Felten S , Frank S et al. IDH/MGMT-driven molecular classification of low-grade glioma is a strong predictor for long-term survival . Neuro Oncol . 2013 ; 15 ( 4 ): 469 - 479 . Google Scholar CrossRef Search ADS PubMed 20. Zhang C , Moore LM , Li X , Yung WK , Zhang W . IDH1/2 mutations target a key hallmark of cancer by deregulating cellular metabolism in glioma . Neuro Oncol . 2013 ; 15 ( 9 ): 1114 - 1126 . Google Scholar CrossRef Search ADS PubMed 21. de la Fuente MI , Young RJ , Rubel J et al. Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-mutant glioma . Neuro Oncol . 2016 ; 18 ( 2 ): 283 - 290 . Google Scholar CrossRef Search ADS PubMed 22. Choi C , Ganji SK , DeBerardinis RJ et al. 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas . Nat Med . 2012 ; 18 ( 4 ): 624 - 629 . Google Scholar CrossRef Search ADS PubMed 23. Pope WB , Prins RM , Albert Thomas M et al. Non-invasive detection of 2-hydroxyglutarate and other metabolites in IDH1 mutant glioma patients using magnetic resonance spectroscopy . J Neurooncol . 2012 ; 107 ( 1 ): 197 - 205 . Google Scholar CrossRef Search ADS PubMed 24. Gorovets D , Kannan K , Shen R et al. IDH mutation and neuroglial developmental features define clinically distinct subclasses of lower grade diffuse astrocytic glioma . Clin Cancer Res . 2012 ; 18 ( 9 ): 2490 - 2501 . Google Scholar CrossRef Search ADS PubMed 25. Jakola AS , Myrmel KS , Kloster R et al. Comparison of a strategy favoring early surgical resection vs a strategy favoring watchful waiting in low-grade gliomas . JAMA . 2012 ; 308 ( 18 ): 1881 - 1888 . Google Scholar CrossRef Search ADS PubMed 26. Riva M , Bello L . Low-grade glioma management: a contemporary surgical approach . Curr Opin Oncol . 2014 ; 26 ( 6 ): 615 - 621 . Google Scholar CrossRef Search ADS PubMed 27. Beiko J , Suki D , Hess KR et al. IDH1 mutant malignant astrocytomas are more amenable to surgical resection and have a survival benefit associated with maximal surgical resection . Neuro Oncol . 2014 ; 16 ( 1 ): 81 - 91 . Google Scholar CrossRef Search ADS PubMed 28. Eckel-Passow JE , Lachance DH , Molinaro AM et al. Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors . N Engl J Med . 2015 ; 372 ( 26 ): 2499 - 2508 . Google Scholar CrossRef Search ADS PubMed 29. Chang EF , Potts MB , Keles GE et al. Seizure characteristics and control following resection in 332 patients with low-grade gliomas . J Neurosurg . 2008 ; 108 ( 2 ): 227 - 235 . Google Scholar CrossRef Search ADS PubMed 30. Englot DJ , Han SJ , Berger MS , Barbaro NM , Chang EF . Extent of surgical resection predicts seizure freedom in low-grade temporal lobe brain tumors . Neurosurgery . 2012 ; 70 ( 4 ): 921 - 928 ; discussion 928 . Google Scholar CrossRef Search ADS PubMed 31. Pallud J , Audureau E , Blonski M et al. Epileptic seizures in diffuse low-grade gliomas in adults . Brain J Neurol . 2014 ; 137 ( pt 2 ): 449 - 462 . Google Scholar CrossRef Search ADS 32. Snyder LA , Wolf AB , Oppenlander ME et al. The impact of extent of resection on malignant transformation of pure oligodendrogliomas . J Neurosurg . 2014 ; 120 ( 2 ): 309 - 314 . Google Scholar CrossRef Search ADS PubMed 33. Ceccarelli M , Barthel FP , Malta TM et al. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma . Cell . 2016 ; 164 ( 3 ): 550 - 563 . Google Scholar CrossRef Search ADS PubMed 34. Chi AS , Batchelor TT , Yang D et al. BRAF V600E Mutation identifies a subset of low-grade diffusely infiltrating gliomas in adults . J Clin Oncol . 2013 ; 31 ( 14 ): e233 - e236 . Google Scholar CrossRef Search ADS PubMed Neurosurgery Speaks! Audio abstracts available for this article at www.neurosurgery-online.com. Acknowledgments We would like to acknowledge Shahiba Ogilvie, Natalie DiStefano, and Lilly McLaughlin for their expert assistance in data acquisition and management. Neurosurgery Speaks (Audio Abstracts) Listen to audio translations of this paper's abstract into select languages by choosing from one of the selections below. Chinese: Kai Wang, MD Department of Neurosurgery, Weihai Central Hospital Weihai, Shandong, China Chinese: Kai Wang, MD Department of Neurosurgery, Weihai Central Hospital Weihai, Shandong, China Close English: Roberto Jose Diaz, MD, PhD Department of Neurological Surgery, University of Miami Miller School of Medicine Miami, Florida English: Roberto Jose Diaz, MD, PhD Department of Neurological Surgery, University of Miami Miller School of Medicine Miami, Florida Close French: Georges Abi Lahoud, MD, MSc, MS Department of Neurosurgery, Sainte-Anne University Hospital, Paris Descartes University Paris, France French: Georges Abi Lahoud, MD, MSc, MS Department of Neurosurgery, Sainte-Anne University Hospital, Paris Descartes University Paris, France Close Italian: Alfredo Conti, MD, PhD Department of Neurosurgery, University of Messina Messina, Italy Italian: Alfredo Conti, MD, PhD Department of Neurosurgery, University of Messina Messina, Italy Close Japanese: Toshiki (Kimura) Endo, MD, PhD Department of Neurosurgery, Tohoku University Sendai, Miyagi, Japan Japanese: Toshiki (Kimura) Endo, MD, PhD Department of Neurosurgery, Tohoku University Sendai, Miyagi, Japan Close Korean: Tae Gon Kim, MD Division of Vascular Section, Department of Neurosurgery, Bundang CHA Hospital Seongnam, Republic of Korea Korean: Tae Gon Kim, MD Division of Vascular Section, Department of Neurosurgery, Bundang CHA Hospital Seongnam, Republic of Korea Close Portuguese: Marcos Dellaretti, MD Department of Neurosurgery, Santa Casa de Belo Horizonte Belo Horizonte, Brazil Portuguese: Marcos Dellaretti, MD Department of Neurosurgery, Santa Casa de Belo Horizonte Belo Horizonte, Brazil Close Russian: Juri Kivelev, MD, PhD Neurosurgical Department No 2, Federal Northwest Research Medical Institute Saint Petersburg, Russia Russian: Juri Kivelev, MD, PhD Neurosurgical Department No 2, Federal Northwest Research Medical Institute Saint Petersburg, Russia Close Spanish: Rodrigo Carrasco, MD Department of Neurosurgery, Hospital Universitario Ramon y Cajal Madrid, Spain Spanish: Rodrigo Carrasco, MD Department of Neurosurgery, Hospital Universitario Ramon y Cajal Madrid, Spain Close Greek: Alexiou A Georgios, MD Department of Neurosurgery, University Hospital of Ioannina Ioannina, Greece Greek: Alexiou A Georgios, MD Department of Neurosurgery, University Hospital of Ioannina Ioannina, Greece Close Copyright © 2017 by the Congress of Neurological Surgeons This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neurosurgery Oxford University Press

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Copyright © 2017 by the Congress of Neurological Surgeons
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Abstract

Abstract BACKGROUND Maximizing extent of resection (EOR) improves outcomes in adults with World Health Organization (WHO) grade II low-grade gliomas (LGG). However, recent studies demonstrate that LGGs bearing a mutation in the isocitrate dehydrogenase 1 (IDH1) gene are a distinct molecular and clinical entity. It remains unclear whether maximizing EOR confers an equivalent clinical benefit in IDH mutated (mtIDH) and IDH wild-type (wtIDH) LGGs. OBJECTIVE To assess the impact of EOR on malignant progression-free survival (MPFS) and overall survival (OS) in mtIDH and wtIDH LGGs. METHODS We performed a retrospective review of 74 patients with WHO grade II gliomas and known IDH mutational status undergoing resection at a single institution. EOR was assessed with quantitative 3-dimensional volumetric analysis. The effect of predictor variables on MPFS and OS was analyzed with Cox regression models and the Kaplan–Meier method. RESULTS Fifty-two (70%) mtIDH patients and 22 (30%) wtIDH patients were included. Median preoperative tumor volume was 37.4 cm3; median EOR of 57.6% was achieved. Univariate Cox regression analysis confirmed EOR as a prognostic factor for the entire cohort. However, stratifying by IDH status demonstrates that greater EOR independently prolonged MPFS and OS for wtIDH patients (hazard ratio [HR] = 0.002 [95% confidence interval {CI} 0.000-0.074] and HR = 0.001 [95% CI 0.00-0.108], respectively), but not for mtIDH patients (HR = 0.84 [95% CI 0.17-4.13] and HR = 2.99 [95% CI 0.15-61.66], respectively). CONCLUSION Increasing EOR confers oncologic and survival benefits in IDH1 wtLGGs, but the impact on IDH1 mtLGGs requires further study. Extent of resection, Low-grade glioma, WHO grade II glioma, IDH1 mutation ABBREVIATIONS ABBREVIATIONS CI confidence interval EOR extent of resection IDH isocitrate dehydrogenase LGG low-grade glioma mtIDH isocitrate dehydrogenase mutated MPFS malignant progression-free survival MRI magnetic resonance imaging OS overall survival wtIDH isocitrate dehydrogenase wild-type WHO World Health Organization Accumulating evidence suggests that maximizing extent of resection (EOR) improves outcomes for patients with World Health Organization (WHO) grade II low-grade gliomas (LGGs).1 Historically, surgical resection of LGGs was typically reserved for clinical or radiographic progression. More recently, several groups have demonstrated that early surgery for LGGs extends both progression-free survival and overall survival (OS).2-5 This benefit is, however, contingent upon achieving a significant EOR (>90% in most cases). Over time, acceptance of this data has led to a significant shift in treatment recommendations. Currently, at many high-volume centers, patients with LGGs are offered upfront surgical resection.6 Furthermore, many groups demonstrate improved outcomes in patients whose tumors were amenable to higher EOR, often using awake or intraoperative electrophysiologic mapping adjuncts for lesions in eloquent locations, while others advocate supramaximal resections where possible.7-13 These aggressive surgical approaches often place the patient at greater risk of postoperative neurological deficits, which is thought to be justified by significant oncologic benefit.14 Historical classifications of gliomas emphasized the histological features of tumor tissue, but more recent studies have demonstrated recurrent mutations in these tumors that are predictive of clinical behavior and that are now incorporated into the most recent WHO classification.15 Chief amongst identified genetic aberrancies is a mutation in the isocitrate dehydrogenase 1 (IDH1) gene.16 To date, multiple studies have demonstrated that gliomas that harbor a mutation in the IDH gene have a better prognosis and an improved response to adjuvant nonsurgical therapies.17-20 What remains unknown, however, is whether maximizing EOR confers an equivalent clinical benefit in both IDH mutated (mtIDH) and IDH wild-type (wtIDH) LGGs. Modern advanced imaging modalities now offer the ability to investigate a tumor's IDH mutation status noninvasively, via magnetic resonance imaging (MRI) spectroscopy. These mutations are also typically clonal, suggesting that a simple needle biopsy would also reliably allow for minimally invasive IDH genotyping.21-23 Thus, for many patients, IDH mutation status can be determined preoperatively. As patients may be subject to greater risks following extensive resections of LGG, it is worth examining whether or not maximizing surgical resection provides the same clinical benefit in mtIDH and wtIDH LGGs. To answer this question, we evaluated a cohort of patients with surgically resected WHO grade II gliomas and known IDH1 mutation status to assess the impact of EOR on OS and malignant progression-free survival (MPFS). METHODS Study Design and Setting We performed an Institutional Review Board-approved retrospective review of the medical records of 266 consecutive patients with WHO grade II gliomas who underwent surgical resection at a single large academic medical center between 2001 and 2012. Patients were included in this study if they were at least 18 yr of age, had known IDH1 mutation status, and had immediate pre- and postoperative MRIs available for review. All patients provided informed consent for inclusion in a clinical database. Clinical data, including demographics, pathology results, date of last follow-up, and EOR was obtained from a review of the medical records. Seventy-four patients met the inclusion criteria and were included in this study; 178 patients were excluded due to lack of known IDH status or lack of available tissue for staining, and 14 patients were excluded due to lack of either pre- or postoperative MRI for volumetric analysis. Variables Two outcome measures were assessed: OS and MPFS. OS was defined as the time from initial surgery to death or last follow-up. MPFS was defined as the time from initial surgery to (1) the development of gadolinium enhancement, as noted by board-certified neuroradiologists, on surveillance MRI and/or WHO grade III or IV histopathology on subsequent biopsy; (2) death; or (3) last follow-up, whichever occurred first. Patients who did not die or demonstrate malignant progression were censored as of their last MRI date. Postoperative MRIs were obtained within 48 h of surgery. Surveillance MRIs were typically obtained at 3-mo intervals, or as deemed appropriate by patients’ neuro-oncology team. EOR was quantitatively assessed by performing manual segmentation and region-of-interest analysis on the pre- and postoperative axial fluid-attenuated inversion-recovery images, using the BrainLAB software (Brainlab AG, Feldkirchen, Germany) by an investigator blinded to IDH1 genotype. EOR was defined as: (preoperative tumor volume – postoperative tumor volume)/preoperative tumor volume. IDH mutation status was determined by immunostaining performed on a Discovery Ultra automated device (Ventana) with the Dianova IDH R132H antibody at a 1:30 dilution, as previously described.24 Statistical Methods Statistical analyses were performed using SAS, version 9.4 (Cary, North Carolina). Kaplan–Meier analysis was performed in R software, version 3.1.2. Descriptive statistics, including frequency distributions, medians, and ranges, were calculated for all variables. To compare variable distributions by IDH status, we used the Fisher's test or chi-squared test for categorical variables, where appropriate, and the Wilcoxon 2-sample test for continuous variables. A time-dependent Cox model accounting for time to treatment and death was used to compare chemotherapy and radiation regimens in the mutant and wild-type IDH groups. Two cohorts were examined: those with an IDH1 mutation and those without an IDH1 mutation. Within each cohort, univariate Cox regression models were used to evaluate the prognostic significance of predictor variables, including age, sex, EOR, and pre- and postoperative tumor volume. The Kaplan–Meier method was used to estimate OS and MPFS. An age-adjusted, stratified Cox regression model, based on IDH status, was used to evaluate the interaction between EOR, OS, and MPFS. For all analyses, P < .05 was considered significant. RESULTS Descriptive Data Between 2001 and 2012, 266 patients underwent surgical resection of a WHO grade II glioma at our institution. Of these, 74 patients met the inclusion criteria. Baseline clinical characteristics are summarized in Table 1. The study cohort included 36 men and 38 women with a median age of 43 years. Forty-three patients had astrocytomas, 19 patients had oligodendrogliomas, and 12 patients had mixed glial tumors (oligoastrocytomas). Median preoperative tumor volume was 37.4 cm3 (range: 0.9-190.2 cm3). Median EOR was 57.6% (range: 0.08%-99.3%). Eight patients only underwent biopsy in this cohort (5 in the mtIDH– group and 3 in the mtIDH+ group). At the conclusion of the study, 17 patients were deceased and 57 patients were alive. Malignant progression was identified in 31 patients. Malignant progression was based on both pathology and MRI for 23 patients and MRI alone for 8 patients. Median time to malignant progression was 45.8 mo; median overall follow-up was 44.4 mo. Median follow-up for survivors was 4 yr; 17 patients died and the remaining 57 were censored or lost to follow-up. TABLE 1. Baseline patient/Tumor Characteristics All patients (n = 74) Age (years)  Median 43  Range 20-71 Sex, n (%)  Male 36 (49)  Female 38 (51) IDH1 status, n (%)  Mutant 52 (70)  Wild-type 22 (30) Histopathology, n (%)  Astrocytoma 43 (58)  Oligodendroglioma 19 (26)  Oligoastrocytoma 12 (16) Preoperative tumor volume (cm3)  Median 37.4  Range 0.9-190.2 EOR (%)  Median 57.6  Range 0.08-99.3 Follow-up (months)  Median 44.4  Range 0.6-187.2 Malignant progression, n (%)  Yes 31 (42)  No 43 (58) Death, n (%)  Yes 17 (23)  No 57 (77) All patients (n = 74) Age (years)  Median 43  Range 20-71 Sex, n (%)  Male 36 (49)  Female 38 (51) IDH1 status, n (%)  Mutant 52 (70)  Wild-type 22 (30) Histopathology, n (%)  Astrocytoma 43 (58)  Oligodendroglioma 19 (26)  Oligoastrocytoma 12 (16) Preoperative tumor volume (cm3)  Median 37.4  Range 0.9-190.2 EOR (%)  Median 57.6  Range 0.08-99.3 Follow-up (months)  Median 44.4  Range 0.6-187.2 Malignant progression, n (%)  Yes 31 (42)  No 43 (58) Death, n (%)  Yes 17 (23)  No 57 (77) View Large TABLE 1. Baseline patient/Tumor Characteristics All patients (n = 74) Age (years)  Median 43  Range 20-71 Sex, n (%)  Male 36 (49)  Female 38 (51) IDH1 status, n (%)  Mutant 52 (70)  Wild-type 22 (30) Histopathology, n (%)  Astrocytoma 43 (58)  Oligodendroglioma 19 (26)  Oligoastrocytoma 12 (16) Preoperative tumor volume (cm3)  Median 37.4  Range 0.9-190.2 EOR (%)  Median 57.6  Range 0.08-99.3 Follow-up (months)  Median 44.4  Range 0.6-187.2 Malignant progression, n (%)  Yes 31 (42)  No 43 (58) Death, n (%)  Yes 17 (23)  No 57 (77) All patients (n = 74) Age (years)  Median 43  Range 20-71 Sex, n (%)  Male 36 (49)  Female 38 (51) IDH1 status, n (%)  Mutant 52 (70)  Wild-type 22 (30) Histopathology, n (%)  Astrocytoma 43 (58)  Oligodendroglioma 19 (26)  Oligoastrocytoma 12 (16) Preoperative tumor volume (cm3)  Median 37.4  Range 0.9-190.2 EOR (%)  Median 57.6  Range 0.08-99.3 Follow-up (months)  Median 44.4  Range 0.6-187.2 Malignant progression, n (%)  Yes 31 (42)  No 43 (58) Death, n (%)  Yes 17 (23)  No 57 (77) View Large Main Results The overall cohort was divided into 2 groups: those with an IDH1 mutation (n = 52) and those without an IDH1 mutation. Upon univariate analysis performed to assess baseline differences between the 2 groups, the IDH1 mutated subgroup (mtIDH) was younger than the wild-type cohort (wtIDH; P = .004) and contained a greater proportion of patients with oligodendrogliomas, as determined by histology (P = .03; Table 2). Sex and preoperative tumor volume did not vary significantly between the 2 groups. A majority of mtIDH tumors were located in the frontal lobe; wtIDH tumors were most commonly located in the temporal lobe (P = .003). However, no significant difference was found in the frequency of dominant hemisphere tumors (P = .36) between the 2 cohorts. While treatment with chemotherapy and radiation therapy did not differ between the groups (P = .90), a greater EOR was achieved in the mtIDH cohort (P < .001). Median follow-up was 49.6 mo for the mtIDH group and 36.5 mo for the wtIDH group. TABLE 2. Patient/Tumor Characteristics by IDH Status IDH1 mutant (n = 52) IDH1 wild-type (n = 22) Pa Age (years)  Median 40 51.5 .004  Range 20-68 23-71 Sex, n (%) .88  Male 25 (48) 11 (50)  Female 27 (52) 11 (50) Histopathology, n (%) .03  Astrocytoma 25 (48) 18 (82)  Oligodendroglioma 17 (33) 2 (9)  Oligoastrocytoma 10 (19) 2 (9) Preoperative tumor volume (cm3) .96  Median 38.2 36.2  Range 0.9-190.2 5.7-127.8 EOR (%) <.0001  Median 73.3 27.8  Range 0.1-99.3 0.08-73.9 Treatment, n (%) .90b  Chemo alone 8 (15) 3 (14)  RT alone 4 (8) 0 (0)  Chemo + RT 23 (44) 14 (63)  No chemo or RT 17 (33) 5 (22) Laterality, n (%) .30  Dominant 27 (52) 14 (64)  Nondominant 24 (46) 7 (32)  Bilateral 1 (2) 1 (4) Location, n (%) .005  Frontal 34 (65) 4 (18)  Temporal 11 (21) 12 (55)  Insular 4 (8) 2 (9)  Parietal 2 (4) 2 (9)  Other 1 (2) 2 (9) MPFS (years) .07  Median 6.5 3.2 OS (years) .01  Median 10.9 Not reached IDH1 mutant (n = 52) IDH1 wild-type (n = 22) Pa Age (years)  Median 40 51.5 .004  Range 20-68 23-71 Sex, n (%) .88  Male 25 (48) 11 (50)  Female 27 (52) 11 (50) Histopathology, n (%) .03  Astrocytoma 25 (48) 18 (82)  Oligodendroglioma 17 (33) 2 (9)  Oligoastrocytoma 10 (19) 2 (9) Preoperative tumor volume (cm3) .96  Median 38.2 36.2  Range 0.9-190.2 5.7-127.8 EOR (%) <.0001  Median 73.3 27.8  Range 0.1-99.3 0.08-73.9 Treatment, n (%) .90b  Chemo alone 8 (15) 3 (14)  RT alone 4 (8) 0 (0)  Chemo + RT 23 (44) 14 (63)  No chemo or RT 17 (33) 5 (22) Laterality, n (%) .30  Dominant 27 (52) 14 (64)  Nondominant 24 (46) 7 (32)  Bilateral 1 (2) 1 (4) Location, n (%) .005  Frontal 34 (65) 4 (18)  Temporal 11 (21) 12 (55)  Insular 4 (8) 2 (9)  Parietal 2 (4) 2 (9)  Other 1 (2) 2 (9) MPFS (years) .07  Median 6.5 3.2 OS (years) .01  Median 10.9 Not reached RT, radiation therapy. aP-values provided are from the Wilcoxon 2-sample test for continuous variables, Fisher's exact test, or chi-squared test for categorical variables, where appropriate, and from the Log-Rank test for MPFS and OS. bP-value calculated by Cox model for treatment accounting for time to treatment and death. View Large TABLE 2. Patient/Tumor Characteristics by IDH Status IDH1 mutant (n = 52) IDH1 wild-type (n = 22) Pa Age (years)  Median 40 51.5 .004  Range 20-68 23-71 Sex, n (%) .88  Male 25 (48) 11 (50)  Female 27 (52) 11 (50) Histopathology, n (%) .03  Astrocytoma 25 (48) 18 (82)  Oligodendroglioma 17 (33) 2 (9)  Oligoastrocytoma 10 (19) 2 (9) Preoperative tumor volume (cm3) .96  Median 38.2 36.2  Range 0.9-190.2 5.7-127.8 EOR (%) <.0001  Median 73.3 27.8  Range 0.1-99.3 0.08-73.9 Treatment, n (%) .90b  Chemo alone 8 (15) 3 (14)  RT alone 4 (8) 0 (0)  Chemo + RT 23 (44) 14 (63)  No chemo or RT 17 (33) 5 (22) Laterality, n (%) .30  Dominant 27 (52) 14 (64)  Nondominant 24 (46) 7 (32)  Bilateral 1 (2) 1 (4) Location, n (%) .005  Frontal 34 (65) 4 (18)  Temporal 11 (21) 12 (55)  Insular 4 (8) 2 (9)  Parietal 2 (4) 2 (9)  Other 1 (2) 2 (9) MPFS (years) .07  Median 6.5 3.2 OS (years) .01  Median 10.9 Not reached IDH1 mutant (n = 52) IDH1 wild-type (n = 22) Pa Age (years)  Median 40 51.5 .004  Range 20-68 23-71 Sex, n (%) .88  Male 25 (48) 11 (50)  Female 27 (52) 11 (50) Histopathology, n (%) .03  Astrocytoma 25 (48) 18 (82)  Oligodendroglioma 17 (33) 2 (9)  Oligoastrocytoma 10 (19) 2 (9) Preoperative tumor volume (cm3) .96  Median 38.2 36.2  Range 0.9-190.2 5.7-127.8 EOR (%) <.0001  Median 73.3 27.8  Range 0.1-99.3 0.08-73.9 Treatment, n (%) .90b  Chemo alone 8 (15) 3 (14)  RT alone 4 (8) 0 (0)  Chemo + RT 23 (44) 14 (63)  No chemo or RT 17 (33) 5 (22) Laterality, n (%) .30  Dominant 27 (52) 14 (64)  Nondominant 24 (46) 7 (32)  Bilateral 1 (2) 1 (4) Location, n (%) .005  Frontal 34 (65) 4 (18)  Temporal 11 (21) 12 (55)  Insular 4 (8) 2 (9)  Parietal 2 (4) 2 (9)  Other 1 (2) 2 (9) MPFS (years) .07  Median 6.5 3.2 OS (years) .01  Median 10.9 Not reached RT, radiation therapy. aP-values provided are from the Wilcoxon 2-sample test for continuous variables, Fisher's exact test, or chi-squared test for categorical variables, where appropriate, and from the Log-Rank test for MPFS and OS. bP-value calculated by Cox model for treatment accounting for time to treatment and death. View Large Survival rates were higher in mtIDH patients compared to wtIDH patients. The 3-yr MPFS rate for mtIDH patients was 88.8% (95% confidence interval [CI]: 79.6-98.1), compared to 55.0% (95% CI: 33.0-77.1) for wtIDH patients. Furthermore, the 3-yr OS rate was 95.2% (95% CI: 88.8-100) in mtIDH patients vs 64.2% (95% CI: 42.8-85.5) in the wtIDH group. mtIDH patients had correspondingly longer median MPFS (6.5 yr) and median OS (10.9 yr) vs wtIDH patients (median MPFS: 3.2 yr, median OS: not reached; Figures 1 and 2). FIGURE 1. View largeDownload slide Kaplan–Meier curve showing MPFS stratified by IDH mutation status. IDH, isocitrate dehydrogenase; MPFS, malignant progression-free survival. FIGURE 1. View largeDownload slide Kaplan–Meier curve showing MPFS stratified by IDH mutation status. IDH, isocitrate dehydrogenase; MPFS, malignant progression-free survival. FIGURE 2. View largeDownload slide Kaplan–Meier curve showing overall survival stratified by IDH mutation status. IDH, isocitrate dehydrogenase; OS, overall survival. FIGURE 2. View largeDownload slide Kaplan–Meier curve showing overall survival stratified by IDH mutation status. IDH, isocitrate dehydrogenase; OS, overall survival. Univariate Cox regression analysis was performed to assess the prognostic significance of a number of variables (Table 3). Evaluation of the entire cohort demonstrated that a greater EOR was the only variable assessed that prolonged MPFS (P = .009, hazard ratio [HR] = 0.21 [95% CI 0.06-0.67]) and OS (P = .03, HR = 0.14 [95% CI 0.03-0.78]). However, an age-adjusted Cox regression model, stratified by IDH mutation status (Table 4), revealed that a greater EOR was only associated with prolonged MPFS and OS in wtIDH patients (P < .001 HR = 0.002 [95% CI 0.000-0.074] and P = .003, HR = 0.001 [95% CI 0.00-0.108], respectively), but not for mtIDH patients (P = .83, HR = 0.84 [95% CI 0.17-4.13] and P = .48, HR = 2.99 [95% CI 0.15-61.66], respectively). Furthermore, the heterogeneity P values for the overall interaction between IDH status, EOR, and MPFS (P < .001) and IDH status, EOR, and OS (P = .0016) were statistically significant. Age and gender did not demonstrate a significant prognostic value in univariate Cox regression models for MPFS or OS in the entire cohort or in the IDH subgroup analysis (Tables 3 and 5). Preoperative tumor volume, while not prognostic for MPFS or OS in the whole cohort, did have a small statistically significant prognostic value for MPFS in the mtIDH cohort (P = .01 HR = 1.01 [95% CI 1.00-1.02]) but not the wtIDH cohort (P = .23 HR = 0.99 [95% CI 0.96-1.01]). Multivariate analysis was not performed due to event size. TABLE 3. Univariate Cox Regression Model of OS and PFS in Full Cohort OS MPFSa Variable HR 95% CI P HR 95% CI P Age 1.02 0.99-1.06 .23 1.02 0.99-1.05 .20 EOR 0.14 0.03-0.78 .03 0.21 0.06-0.67 .009 Preop tumor volume 1.000 0.99-1.01 .98 1.005 0.997-1.014 .21 Gender: male ref ref Gender: female 0.63 0.24-1.66 .35 0.99 0.50-1.96 .97 OS MPFSa Variable HR 95% CI P HR 95% CI P Age 1.02 0.99-1.06 .23 1.02 0.99-1.05 .20 EOR 0.14 0.03-0.78 .03 0.21 0.06-0.67 .009 Preop tumor volume 1.000 0.99-1.01 .98 1.005 0.997-1.014 .21 Gender: male ref ref Gender: female 0.63 0.24-1.66 .35 0.99 0.50-1.96 .97 aEvent defined as either death or high-grade progression. View Large TABLE 3. Univariate Cox Regression Model of OS and PFS in Full Cohort OS MPFSa Variable HR 95% CI P HR 95% CI P Age 1.02 0.99-1.06 .23 1.02 0.99-1.05 .20 EOR 0.14 0.03-0.78 .03 0.21 0.06-0.67 .009 Preop tumor volume 1.000 0.99-1.01 .98 1.005 0.997-1.014 .21 Gender: male ref ref Gender: female 0.63 0.24-1.66 .35 0.99 0.50-1.96 .97 OS MPFSa Variable HR 95% CI P HR 95% CI P Age 1.02 0.99-1.06 .23 1.02 0.99-1.05 .20 EOR 0.14 0.03-0.78 .03 0.21 0.06-0.67 .009 Preop tumor volume 1.000 0.99-1.01 .98 1.005 0.997-1.014 .21 Gender: male ref ref Gender: female 0.63 0.24-1.66 .35 0.99 0.50-1.96 .97 aEvent defined as either death or high-grade progression. View Large TABLE 4. Cox Regression Model of EOR, Stratified by IDH Statusa IDH1 mutant IDH1 wild-type HR 95% CI P HRa 95% CI P Heterogeneity P-value MPFS  EOR 0.84 0.17-4.13 .83 0.002 0.0-0.074 .0007 .0009 OS  EOR 2.99 0.15-61.66 .48 0.001 0.0-0.108 .003 .0016 IDH1 mutant IDH1 wild-type HR 95% CI P HRa 95% CI P Heterogeneity P-value MPFS  EOR 0.84 0.17-4.13 .83 0.002 0.0-0.074 .0007 .0009 OS  EOR 2.99 0.15-61.66 .48 0.001 0.0-0.108 .003 .0016 aAge-adjusted. View Large TABLE 4. Cox Regression Model of EOR, Stratified by IDH Statusa IDH1 mutant IDH1 wild-type HR 95% CI P HRa 95% CI P Heterogeneity P-value MPFS  EOR 0.84 0.17-4.13 .83 0.002 0.0-0.074 .0007 .0009 OS  EOR 2.99 0.15-61.66 .48 0.001 0.0-0.108 .003 .0016 IDH1 mutant IDH1 wild-type HR 95% CI P HRa 95% CI P Heterogeneity P-value MPFS  EOR 0.84 0.17-4.13 .83 0.002 0.0-0.074 .0007 .0009 OS  EOR 2.99 0.15-61.66 .48 0.001 0.0-0.108 .003 .0016 aAge-adjusted. View Large TABLE 5. Univariate Cox Regression Model, Stratified by IDH Statusa IDH1 mutant (mtIDH) IDH1 wild-type (wtIDH) HR 95% CI P HR 95% CI P OS  Age 0.97 0.91-1.03 .27 1.04 0.99-1.09 .17  Preop tumor volume 1.01 0.997-1.03 .10 0.98 0.95-1.01 .20  Gender: male ref ref  Gender: female 0.74 0.17-3.12 .68 0.41 0.11-1.55 .19 MPFS  Age 1.00 0.96-1.04 .86 1.02 0.99-1.07 .23  Preop tumor volume 1.01 1.00-1.02 .01 0.99 0.96-1.01 .23  Gender: male ref ref  Gender: female 1.06 0.44-2.55 .90 0.81 0.27-2.42 .70 IDH1 mutant (mtIDH) IDH1 wild-type (wtIDH) HR 95% CI P HR 95% CI P OS  Age 0.97 0.91-1.03 .27 1.04 0.99-1.09 .17  Preop tumor volume 1.01 0.997-1.03 .10 0.98 0.95-1.01 .20  Gender: male ref ref  Gender: female 0.74 0.17-3.12 .68 0.41 0.11-1.55 .19 MPFS  Age 1.00 0.96-1.04 .86 1.02 0.99-1.07 .23  Preop tumor volume 1.01 1.00-1.02 .01 0.99 0.96-1.01 .23  Gender: male ref ref  Gender: female 1.06 0.44-2.55 .90 0.81 0.27-2.42 .70 aEvent defined as either death or high-grade progression. View Large TABLE 5. Univariate Cox Regression Model, Stratified by IDH Statusa IDH1 mutant (mtIDH) IDH1 wild-type (wtIDH) HR 95% CI P HR 95% CI P OS  Age 0.97 0.91-1.03 .27 1.04 0.99-1.09 .17  Preop tumor volume 1.01 0.997-1.03 .10 0.98 0.95-1.01 .20  Gender: male ref ref  Gender: female 0.74 0.17-3.12 .68 0.41 0.11-1.55 .19 MPFS  Age 1.00 0.96-1.04 .86 1.02 0.99-1.07 .23  Preop tumor volume 1.01 1.00-1.02 .01 0.99 0.96-1.01 .23  Gender: male ref ref  Gender: female 1.06 0.44-2.55 .90 0.81 0.27-2.42 .70 IDH1 mutant (mtIDH) IDH1 wild-type (wtIDH) HR 95% CI P HR 95% CI P OS  Age 0.97 0.91-1.03 .27 1.04 0.99-1.09 .17  Preop tumor volume 1.01 0.997-1.03 .10 0.98 0.95-1.01 .20  Gender: male ref ref  Gender: female 0.74 0.17-3.12 .68 0.41 0.11-1.55 .19 MPFS  Age 1.00 0.96-1.04 .86 1.02 0.99-1.07 .23  Preop tumor volume 1.01 1.00-1.02 .01 0.99 0.96-1.01 .23  Gender: male ref ref  Gender: female 1.06 0.44-2.55 .90 0.81 0.27-2.42 .70 aEvent defined as either death or high-grade progression. View Large DISCUSSION Aggressive surgical intervention for LGGs has become routine practice at high-volume centers over the past decade.6 This strategy is based on several retrospective series that have correlated an increasing EOR to improvements in overall and progression-free survival. Nonetheless, the potential for causing neurological morbidity, especially when removing tumors from eloquent regions, remains a concern. Additionally, while one well-designed population-based parallel cohort study suggests survival and oncologic advantage to early resection, this treatment paradigm is largely supported by observational reports hindered by selection bias.25,26 Thus, studies that help identify particularly high-risk populations that could preferentially benefit from an aggressive surgical approach are necessary. Key Results Here, we present the first report that specifically examines the utility of maximizing surgical resection in patients with WHO grade II gliomas informed by IDH1 mutation status. It is important to emphasize that we have focused this study exclusively on grade II patients. In evaluating the entire cohort of patients, our data confirm that greater EOR results in an improved OS and MPFS. Interestingly, however, on subgroup analysis, a greater EOR did not confer an equivalent clinical benefit in wtIDH and mtIDH patients. Specifically, in our study, greater EOR was associated with improved survival in only wtIDH patients. This is the first time that such a relationship has been described in the literature and is contrary to prior studies in high-grade gliomas.27 Although the biological mechanisms underlying our findings are as yet unclear, it has been known for several years that mtIDH patients have a better prognosis than their wild-type counterparts, independent of tumor grade.28 As our ability to noninvasively assess IDH mutation status improves, for example, via MRI spectroscopy, the data presented in this manuscript may provide justification to delay or to perform more conservative surgery for mtIDH LGGs located in particularly eloquent locations. Instead, neoadjuvant chemotherapy and/or radiation approaches may be preferable in a select group of patients, with surgery reserved for radiographic progression or mass effect. Seizure control provides a separate surgical justification.29-31 Interpretation Our manuscript confirms two recently reported findings from other surgical glioma series. First, in our cohort, EOR was significantly higher in mtIDH LGGs, as compared to wild-type lesions. This has been previously reported, although the reasons underlying this difference have not yet been elucidated.27 Anecdotally, mtIDH LGGs tend to have a more favorable texture than wtIDH LGGs, which may facilitate surgical resection and, speculatively, may reflect a less aggressive biology. Recently, another series examined the impact of surgical resection on the malignant transformation of pure oligodendrogliomas.32 Although IDH status was not assessed in this cohort, it is reasonable to assume, based on prior studies, that the majority of these pure oligodendrogliomas harbored an IDH mutation. The authors determined that while greater EOR increased OS, it did not prolong MPFS. These findings are similar to the results reported in this series and suggest that maximizing EOR may be more important in nonoligodendroglial LGGs, which are often wild type for IDH mutations. Furthermore, wtIDH LGGs are known to be associated with a clinical behavior closer to that of higher grade gliomas,15 and our data suggest that with higher 3-yr malignant progression rates, wtIDH LGGs more quickly transform into high-grade tumors. Thus, our data may further support treating wtIDH LGGs more like high-grade gliomas, with upfront maximal surgical resection. An improved understanding of the fundamental biological differences between mtIDH and wtIDH LGGs will be critical to future patient care.18 Indeed, an even more sophisticated molecular subclassification incorporating DNA methylation and epigenetic profiling may help further dissect the impact of surgical therapies on specific subgroups of LGG. As an example, in a recent analysis of a large number of patients, demethylated mtIDH gliomas are associated with poor outcome, and wtIDH cases molecularly similar to pilocytic astrocytomas are associated with favorable survival, suggesting that a more detailed stratification may soon become necessary to optimize treatment strategies for glioma patients.33 Another important question that cannot be readily addressed in retrospective studies, but that would greatly impact therapeutic strategies, is whether early aggressive surgical intervention could delay malignant progression. Limitations This study is a single-institution, retrospective analysis, and thus is subject to the inherent limitations of this design. The baseline differences in histology (ie, higher percentage of astrocytomas in the wtIDH cohort) and higher baseline EOR for mtIDH patients may confound comparison of the 2 groups. However, these baseline differences exemplify that LGGs should not be treated as a single group in future studies and require stratification based on IDH status and, preferably, additional genetic mutations. Furthermore, our wtIDH survival data, with its subset of long-term survivors, is consistent with previous reports that wtIDH LGGs are heterogeneous populations within themselves.33,34 The wtIDH cohort likely includes patients with pilocytic astrocytoma-like LGGs and patients with V600E BRAF mutations. However, due to our cohort size and lack of data on other mutations (eg, BRAF, H3.3, ATRX, TP53, 1p/19q, etc.), we are unable to determine how these other genetic variables further define the subpopulations in our study. Over the past several years, it has become more commonplace to assess IDH mutation status and other genetic mutations in tumors. Thus, in the coming years, there will be an increasing volume of clinical data that will allow for further validation and refinement of our findings. Additionally, the cohort described in this paper will continue to be monitored and treated at our institution; a follow-up report in several years would be of significant interest. Lastly, due to the retrospective nature and size of the study, the difference in median EOR achieved between the 2 groups cannot be ruled out as a confounding variable in our results. However, these EOR results were achieved by experienced glioma surgeons, at a tertiary referral center. Thus, we believe that the surgical results presented in this series represent realistic clinical outcomes. CONCLUSION The treatment paradigm for LGGs continues to evolve. 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J Clin Oncol . 2013 ; 31 ( 14 ): e233 - e236 . Google Scholar CrossRef Search ADS PubMed Neurosurgery Speaks! Audio abstracts available for this article at www.neurosurgery-online.com. Acknowledgments We would like to acknowledge Shahiba Ogilvie, Natalie DiStefano, and Lilly McLaughlin for their expert assistance in data acquisition and management. Neurosurgery Speaks (Audio Abstracts) Listen to audio translations of this paper's abstract into select languages by choosing from one of the selections below. Chinese: Kai Wang, MD Department of Neurosurgery, Weihai Central Hospital Weihai, Shandong, China Chinese: Kai Wang, MD Department of Neurosurgery, Weihai Central Hospital Weihai, Shandong, China Close English: Roberto Jose Diaz, MD, PhD Department of Neurological Surgery, University of Miami Miller School of Medicine Miami, Florida English: Roberto Jose Diaz, MD, PhD Department of Neurological Surgery, University of Miami Miller School of Medicine Miami, Florida Close French: Georges Abi Lahoud, MD, MSc, MS Department of Neurosurgery, Sainte-Anne University Hospital, Paris Descartes University Paris, France French: Georges Abi Lahoud, MD, MSc, MS Department of Neurosurgery, Sainte-Anne University Hospital, Paris Descartes University Paris, France Close Italian: Alfredo Conti, MD, PhD Department of Neurosurgery, University of Messina Messina, Italy Italian: Alfredo Conti, MD, PhD Department of Neurosurgery, University of Messina Messina, Italy Close Japanese: Toshiki (Kimura) Endo, MD, PhD Department of Neurosurgery, Tohoku University Sendai, Miyagi, Japan Japanese: Toshiki (Kimura) Endo, MD, PhD Department of Neurosurgery, Tohoku University Sendai, Miyagi, Japan Close Korean: Tae Gon Kim, MD Division of Vascular Section, Department of Neurosurgery, Bundang CHA Hospital Seongnam, Republic of Korea Korean: Tae Gon Kim, MD Division of Vascular Section, Department of Neurosurgery, Bundang CHA Hospital Seongnam, Republic of Korea Close Portuguese: Marcos Dellaretti, MD Department of Neurosurgery, Santa Casa de Belo Horizonte Belo Horizonte, Brazil Portuguese: Marcos Dellaretti, MD Department of Neurosurgery, Santa Casa de Belo Horizonte Belo Horizonte, Brazil Close Russian: Juri Kivelev, MD, PhD Neurosurgical Department No 2, Federal Northwest Research Medical Institute Saint Petersburg, Russia Russian: Juri Kivelev, MD, PhD Neurosurgical Department No 2, Federal Northwest Research Medical Institute Saint Petersburg, Russia Close Spanish: Rodrigo Carrasco, MD Department of Neurosurgery, Hospital Universitario Ramon y Cajal Madrid, Spain Spanish: Rodrigo Carrasco, MD Department of Neurosurgery, Hospital Universitario Ramon y Cajal Madrid, Spain Close Greek: Alexiou A Georgios, MD Department of Neurosurgery, University Hospital of Ioannina Ioannina, Greece Greek: Alexiou A Georgios, MD Department of Neurosurgery, University Hospital of Ioannina Ioannina, Greece Close Copyright © 2017 by the Congress of Neurological Surgeons This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

NeurosurgeryOxford University Press

Published: Jul 27, 2017

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