Subtyping Bladder Cancers: Biology vs Bioinformatics

Subtyping Bladder Cancers: Biology vs Bioinformatics Recent studies employing whole transcriptome expression data and unsupervised analytical methods concluded that bladder cancers can be grouped into basal and luminal molecular subtypes that have implications for prognostication and predicting response to therapy (1–7). Here Mo and colleagues used a supervised approach, based on their knowledge of biomarkers associated with normal differentiation, to assign bladder cancers from public data sets into two major molecular subtypes—basal and differentiated (8). Using this 18-gene biological classifier, they showed that the basal tumors were associated with shorter survival, consistent with their own previous observations (9) and those of the other groups (1–7). However, the relationship between basal subtype membership as defined by the 18-gene signature and poor clinical outcomes was stronger than was observed with some of the other classifiers (1–3), leading them to conclude that their classifier was a better tool for assigning bladder cancers to the basal subtype (“biology trumps pure bioinformatics”). While this may be true, it does not necessarily follow that the most accurate bladder cancer classifier will be the one that consistently shows that basal cancers are the most clinically aggressive. Recent studies concluded that patients with basal tumors obtained the most benefit from neoadjuvant chemotherapy (7,10), so the relative “aggressiveness” of basal tumors within a given cohort could be influenced by the relative proportion of chemosensitive basal tumors that are in it (and the MD Anderson “discovery” and The Cancer Genome Atlas (TCGA) cohorts contained some tumors from patients treated with neoadjuvant or adjuvant chemotherapy) (1,2). Immune checkpoint blockade will probably also complicate molecular subtype associations with survival (11). There is general consensus that, at the highest level, MIBCs can be segregated into basal and differentiated/luminal subtypes (12). However, there is also strong evidence that basal and luminal cancers can be subdivided further, and these subdivisions also appear to have clinical significance. For example, basal tumors can be segregated into “squamous” (TCGA cluster III) (1,6) and “claudin-low” (TCGA cluster IV) (3,7) subsets based on differential expression of biomarkers associated with epithelial-to-mesenchymal transition (EMT) (1,3,13), and the claudin-low tumors have unique T cell exhaustion signatures (13) and appear to be relatively resistant to neoadjuvant chemotherapy (7). Furthermore, the second TCGA article identifies another aggressive basal subset (“neuronal”) that may require unique therapeutic approaches (4). Similarly, the Lund group’s molecular taxonomy subdivides luminal tumors into “genomically unstable,” “infiltrated,” and “urothelial A” subsets (6,14), and the new TCGA marker article divides them into three clusters (4). A four-subtype classifier is now clinically available (7), so the relative value of separating basal and differentiated/luminal bladder cancers into additional subsets will be apparent soon. The current study (8) also prompts consideration of whether an even simpler approach could be developed to distinguish basal from differentiated cancers. In breast cancer, immunohistochemistry with only three biomarkers (ER, PR, and HER2) largely captures the intrinsic subtypes defined by gene expression profiling (15), and conventional immunohistochemistry is still much easier to integrate into routine clinical practice than RNA expression-based platforms. This group (9) and teams at Lund University (16) and MD Anderson (17) have developed candidate immunohistochemical approaches for subtyping bladder cancers, and additional efforts are underway elsewhere. It would be interesting to know how much concordance would have been observed between the calls made by the 18-gene RNA classifier as compared with immunohistochemistry with antibodies specific to one basal biomarker (ie, KRT14) and one differentiated (ie, UPKs, FOXA1, GATA3, or PPARG) biomarker. The Baylor group’s own results suggest that a similar relationship to prognosis would be obtained (9). One of the most important unmet needs is to identify potentially lethal nonmuscle invasive bladder cancers (NMIBCs) so that they can be managed aggressively before they become a threat to the life of the patient. Various groups have identified biomarkers that may address this need, including a 12-gene panel that was recently validated in a large, prospective, multicenter study (18). One of the most interesting findings in the Mo et al. study was that the basal NMIBCs identified by the 18-gene classifier in a recently generated public NMIBC RNAseq data set (19) were associated with a higher risk for progression. Using an unsupervised approach, the authors of the original study (19) concluded that the tumors could actually be grouped into three clusters, one of which (“class 3”) was enriched with basal (ie, BASE47) (3) biomarkers (19). However, patients with class 3/basal-like tumors had better clinical outcomes than did patients with class 2 (“genomically unstable”/luminal) tumors (19). Mo and colleagues performed a comparison of their basal/differentiated calls with the three classes reported in the paper by Hedgaard et al. (19), and the level of concordance is not very impressive (Figure 5D) (8). In addition, the generally mutually exclusive patterns of basal and differentiated biomarker expression observed in MIBCs does not seem to be as clear (Figure 5A) (8). Clearly, reaching consensus about the nature of the molecular subtypes that exist in NMIBCs is another top priority for further research and discussion. Finally, the clinical value of any prognostic or predictive biomarker will be linked to the stability of the phenotype being measured—tumor “plasticity” and heterogeneity can undermine predictions about tumor biology based on static measurements. The Lund “infiltrated” (6), MD Anderson “p53-like”(2), and TCGA “luminal infiltrated” (4) tumors are defined by stromal cell infiltration, and as a consequence, substantial plasticity in subtype membership may be encountered (2). Although segregating bladder cancers at the very highest level (12) seems much safer, a cautionary note comes from a recent whole-organ mapping study, which concluded that tumors from different lesions (or even different regions of the same lesion) were assigned to different basal vs luminal subtypes in patients with multifocal bladder cancers (20). As molecular subtype classifiers become integrated into clinical practice (7), it will be critical for the research community to determine whether molecular subtype membership is truly an “intrinsic” feature of a given bladder cancer. Tumor heterogeneity and plasticity may undermine our attempts at precise molecular classification. Note The authors have no conflicts of interest to disclose. References 1 Cancer Genome Atlas Research Network . Comprehensive molecular characterization of urothelial bladder carcinoma . Nature . 2014 ; 507 7492 : 315 – 322 . http://dx.doi.org/10.1038/nature12965 CrossRef Search ADS PubMed 2 Choi W , Porten S , Kim S et al. , Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy . Cancer Cell. 2014 ; 25 2 : 152 – 165 . http://dx.doi.org/10.1016/j.ccr.2014.01.009 Google Scholar CrossRef Search ADS PubMed 3 Damrauer JS , Hoadley KA , Chism DD et al. , Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology . Proc Natl Acad Sci U S A . 2014 ; 111 8 : 3110 – 3115 . http://dx.doi.org/10.1073/pnas.1318376111 Google Scholar CrossRef Search ADS PubMed 4 Robertson AG , Kim J , Al-Ahmadie H et al. , Comprehensive molecular characterization of muscle-invasive bladder cancer . Cell . 2017 ; 171 3 : 540 – 556.e25 . Google Scholar CrossRef Search ADS PubMed 5 Rebouissou S , Bernard-Pierrot I , de Reynies A et al. , EGFR as a potential therapeutic target for a subset of muscle-invasive bladder cancers presenting a basal-like phenotype . Sci Transl Med. 2014 ; 6 244 : 244ra91 . Google Scholar CrossRef Search ADS PubMed 6 Sjodahl G , Lauss M , Lovgren K et al. , A molecular taxonomy for urothelial carcinoma . Clin Cancer Res. 2012 ; 18 12 : 3377 – 3386 . http://dx.doi.org/10.1158/1078-0432.CCR-12-0077-T Google Scholar CrossRef Search ADS PubMed 7 Seiler R , Ashab HAD , Erho N et al. , Impact of molecular subtypes in muscle-invasive bladder cancer on predicting response and survival after neoadjuvant chemotherapy . Eur Urol. 2017 ; 72 4 : 544 – 554 . http://dx.doi.org/10.1016/j.eururo.2017.03.030 Google Scholar CrossRef Search ADS PubMed 8 Mo Q , Nikolos F , Chen F et al. , Prognostic power of a tumor differentiation gene signature for bladder urothelial carcinomas . J Natl Cancer Inst. 2018 ; 110 5 : 448 – 459 . 9 Volkmer JP , Sahoo D , Chin RK et al. , Three differentiation states risk-stratify bladder cancer into distinct subtypes . Proc Natl Acad Sci U S A . 2012 ; 109 6 : 2078 – 2083 . http://dx.doi.org/10.1073/pnas.1120605109 Google Scholar CrossRef Search ADS PubMed 10 McConkey DJ , Choi W , Shen Y et al. , A prognostic gene expression signature in the molecular classification of chemotherapy-naive urothelial cancer is predictive of clinical outcomes from neoadjuvant chemotherapy: A phase 2 trial of dose-dense methotrexate, vinblastine, doxorubicin, and cisplatin with bevacizumab in urothelial cancer . Eur Urol. 2016 ; 69 5 : 855 – 862 . http://dx.doi.org/10.1016/j.eururo.2015.08.034 Google Scholar CrossRef Search ADS PubMed 11 Rosenberg JE , Hoffman-Censits J , Powles T et al. , Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: A single-arm, multicentre, phase 2 trial . Lancet. 2016 ; 387 10031 : 1909 – 1920 . http://dx.doi.org/10.1016/S0140-6736(16)00561-4 Google Scholar CrossRef Search ADS PubMed 12 Lerner SP , McConkey DJ , Hoadley KA et al. , Bladder cancer molecular taxonomy: Summary from a consensus meeting . Bladder Cancer . 2016 ; 2 1 : 37 – 47 . http://dx.doi.org/10.3233/BLC-150037 Google Scholar CrossRef Search ADS PubMed 13 Kardos J , Chai S , Mose LE et al. , Claudin-low bladder tumors are immune infiltrated and actively immune suppressed . JCI Insight . 2016 ; 1 3 : e85902 . Google Scholar CrossRef Search ADS PubMed 14 Choi W , Ochoa A , McConkey DJ et al. , Genetic alterations in the molecular subtypes of bladder cancer: Illustration in The Cancer Genome Atlas dataset . Eur Urol. 2017 ; 72 3 : 354 – 365 . http://dx.doi.org/10.1016/j.eururo.2017.03.010 Google Scholar CrossRef Search ADS PubMed 15 Prat A , Ellis MJ , Perou CM. Practical implications of gene-expression-based assays for breast oncologists . Nat Rev Clin Oncol. 2012 ; 9 1 : 48 – 57 . Google Scholar CrossRef Search ADS 16 Sjodahl G , Lovgren K , Lauss M et al. , Toward a molecular pathologic classification of urothelial carcinoma . Am J Pathol. 2013 ; 183 3 : 681 – 691 . http://dx.doi.org/10.1016/j.ajpath.2013.05.013 Google Scholar CrossRef Search ADS PubMed 17 Dadhania V , Zhang M , Zhang L et al. , Meta-analysis of the luminal and basal subtypes of bladder cancer and the identification of signature immunohistochemical markers for clinical use . EBioMedicine. 2016 ; 12 : 105 – 117 . http://dx.doi.org/10.1016/j.ebiom.2016.08.036 Google Scholar CrossRef Search ADS PubMed 18 Dyrskjot L , Reinert T , Algaba F et al. , Prognostic impact of a 12-gene progression score in non-muscle-invasive bladder cancer: A prospective multicentre validation study . Eur Urol. 2017 ; 72 3 : 461 – 469 . http://dx.doi.org/10.1016/j.eururo.2017.05.040 Google Scholar CrossRef Search ADS PubMed 19 Hedegaard J , Lamy P , Nordentoft I et al. , Comprehensive transcriptional analysis of early-stage urothelial carcinoma . Cancer Cell. 2016 ; 30 1 : 27 – 42 . http://dx.doi.org/10.1016/j.ccell.2016.05.004 Google Scholar CrossRef Search ADS PubMed 20 Thomsen MBH , Nordentoft I , Lamy P et al. , Comprehensive multiregional analysis of molecular heterogeneity in bladder cancer . Sci Rep. 2017 ; 7 1 : 11702 . http://dx.doi.org/10.1038/s41598-017-11291-0 Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. 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 JNCI: Journal of the National Cancer Institute Oxford University Press

Subtyping Bladder Cancers: Biology vs Bioinformatics

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
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0027-8874
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Abstract

Recent studies employing whole transcriptome expression data and unsupervised analytical methods concluded that bladder cancers can be grouped into basal and luminal molecular subtypes that have implications for prognostication and predicting response to therapy (1–7). Here Mo and colleagues used a supervised approach, based on their knowledge of biomarkers associated with normal differentiation, to assign bladder cancers from public data sets into two major molecular subtypes—basal and differentiated (8). Using this 18-gene biological classifier, they showed that the basal tumors were associated with shorter survival, consistent with their own previous observations (9) and those of the other groups (1–7). However, the relationship between basal subtype membership as defined by the 18-gene signature and poor clinical outcomes was stronger than was observed with some of the other classifiers (1–3), leading them to conclude that their classifier was a better tool for assigning bladder cancers to the basal subtype (“biology trumps pure bioinformatics”). While this may be true, it does not necessarily follow that the most accurate bladder cancer classifier will be the one that consistently shows that basal cancers are the most clinically aggressive. Recent studies concluded that patients with basal tumors obtained the most benefit from neoadjuvant chemotherapy (7,10), so the relative “aggressiveness” of basal tumors within a given cohort could be influenced by the relative proportion of chemosensitive basal tumors that are in it (and the MD Anderson “discovery” and The Cancer Genome Atlas (TCGA) cohorts contained some tumors from patients treated with neoadjuvant or adjuvant chemotherapy) (1,2). Immune checkpoint blockade will probably also complicate molecular subtype associations with survival (11). There is general consensus that, at the highest level, MIBCs can be segregated into basal and differentiated/luminal subtypes (12). However, there is also strong evidence that basal and luminal cancers can be subdivided further, and these subdivisions also appear to have clinical significance. For example, basal tumors can be segregated into “squamous” (TCGA cluster III) (1,6) and “claudin-low” (TCGA cluster IV) (3,7) subsets based on differential expression of biomarkers associated with epithelial-to-mesenchymal transition (EMT) (1,3,13), and the claudin-low tumors have unique T cell exhaustion signatures (13) and appear to be relatively resistant to neoadjuvant chemotherapy (7). Furthermore, the second TCGA article identifies another aggressive basal subset (“neuronal”) that may require unique therapeutic approaches (4). Similarly, the Lund group’s molecular taxonomy subdivides luminal tumors into “genomically unstable,” “infiltrated,” and “urothelial A” subsets (6,14), and the new TCGA marker article divides them into three clusters (4). A four-subtype classifier is now clinically available (7), so the relative value of separating basal and differentiated/luminal bladder cancers into additional subsets will be apparent soon. The current study (8) also prompts consideration of whether an even simpler approach could be developed to distinguish basal from differentiated cancers. In breast cancer, immunohistochemistry with only three biomarkers (ER, PR, and HER2) largely captures the intrinsic subtypes defined by gene expression profiling (15), and conventional immunohistochemistry is still much easier to integrate into routine clinical practice than RNA expression-based platforms. This group (9) and teams at Lund University (16) and MD Anderson (17) have developed candidate immunohistochemical approaches for subtyping bladder cancers, and additional efforts are underway elsewhere. It would be interesting to know how much concordance would have been observed between the calls made by the 18-gene RNA classifier as compared with immunohistochemistry with antibodies specific to one basal biomarker (ie, KRT14) and one differentiated (ie, UPKs, FOXA1, GATA3, or PPARG) biomarker. The Baylor group’s own results suggest that a similar relationship to prognosis would be obtained (9). One of the most important unmet needs is to identify potentially lethal nonmuscle invasive bladder cancers (NMIBCs) so that they can be managed aggressively before they become a threat to the life of the patient. Various groups have identified biomarkers that may address this need, including a 12-gene panel that was recently validated in a large, prospective, multicenter study (18). One of the most interesting findings in the Mo et al. study was that the basal NMIBCs identified by the 18-gene classifier in a recently generated public NMIBC RNAseq data set (19) were associated with a higher risk for progression. Using an unsupervised approach, the authors of the original study (19) concluded that the tumors could actually be grouped into three clusters, one of which (“class 3”) was enriched with basal (ie, BASE47) (3) biomarkers (19). However, patients with class 3/basal-like tumors had better clinical outcomes than did patients with class 2 (“genomically unstable”/luminal) tumors (19). Mo and colleagues performed a comparison of their basal/differentiated calls with the three classes reported in the paper by Hedgaard et al. (19), and the level of concordance is not very impressive (Figure 5D) (8). In addition, the generally mutually exclusive patterns of basal and differentiated biomarker expression observed in MIBCs does not seem to be as clear (Figure 5A) (8). Clearly, reaching consensus about the nature of the molecular subtypes that exist in NMIBCs is another top priority for further research and discussion. Finally, the clinical value of any prognostic or predictive biomarker will be linked to the stability of the phenotype being measured—tumor “plasticity” and heterogeneity can undermine predictions about tumor biology based on static measurements. The Lund “infiltrated” (6), MD Anderson “p53-like”(2), and TCGA “luminal infiltrated” (4) tumors are defined by stromal cell infiltration, and as a consequence, substantial plasticity in subtype membership may be encountered (2). Although segregating bladder cancers at the very highest level (12) seems much safer, a cautionary note comes from a recent whole-organ mapping study, which concluded that tumors from different lesions (or even different regions of the same lesion) were assigned to different basal vs luminal subtypes in patients with multifocal bladder cancers (20). As molecular subtype classifiers become integrated into clinical practice (7), it will be critical for the research community to determine whether molecular subtype membership is truly an “intrinsic” feature of a given bladder cancer. Tumor heterogeneity and plasticity may undermine our attempts at precise molecular classification. Note The authors have no conflicts of interest to disclose. References 1 Cancer Genome Atlas Research Network . Comprehensive molecular characterization of urothelial bladder carcinoma . Nature . 2014 ; 507 7492 : 315 – 322 . http://dx.doi.org/10.1038/nature12965 CrossRef Search ADS PubMed 2 Choi W , Porten S , Kim S et al. , Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy . Cancer Cell. 2014 ; 25 2 : 152 – 165 . http://dx.doi.org/10.1016/j.ccr.2014.01.009 Google Scholar CrossRef Search ADS PubMed 3 Damrauer JS , Hoadley KA , Chism DD et al. , Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology . Proc Natl Acad Sci U S A . 2014 ; 111 8 : 3110 – 3115 . http://dx.doi.org/10.1073/pnas.1318376111 Google Scholar CrossRef Search ADS PubMed 4 Robertson AG , Kim J , Al-Ahmadie H et al. , Comprehensive molecular characterization of muscle-invasive bladder cancer . Cell . 2017 ; 171 3 : 540 – 556.e25 . Google Scholar CrossRef Search ADS PubMed 5 Rebouissou S , Bernard-Pierrot I , de Reynies A et al. , EGFR as a potential therapeutic target for a subset of muscle-invasive bladder cancers presenting a basal-like phenotype . Sci Transl Med. 2014 ; 6 244 : 244ra91 . Google Scholar CrossRef Search ADS PubMed 6 Sjodahl G , Lauss M , Lovgren K et al. , A molecular taxonomy for urothelial carcinoma . Clin Cancer Res. 2012 ; 18 12 : 3377 – 3386 . http://dx.doi.org/10.1158/1078-0432.CCR-12-0077-T Google Scholar CrossRef Search ADS PubMed 7 Seiler R , Ashab HAD , Erho N et al. , Impact of molecular subtypes in muscle-invasive bladder cancer on predicting response and survival after neoadjuvant chemotherapy . Eur Urol. 2017 ; 72 4 : 544 – 554 . http://dx.doi.org/10.1016/j.eururo.2017.03.030 Google Scholar CrossRef Search ADS PubMed 8 Mo Q , Nikolos F , Chen F et al. , Prognostic power of a tumor differentiation gene signature for bladder urothelial carcinomas . J Natl Cancer Inst. 2018 ; 110 5 : 448 – 459 . 9 Volkmer JP , Sahoo D , Chin RK et al. , Three differentiation states risk-stratify bladder cancer into distinct subtypes . Proc Natl Acad Sci U S A . 2012 ; 109 6 : 2078 – 2083 . http://dx.doi.org/10.1073/pnas.1120605109 Google Scholar CrossRef Search ADS PubMed 10 McConkey DJ , Choi W , Shen Y et al. , A prognostic gene expression signature in the molecular classification of chemotherapy-naive urothelial cancer is predictive of clinical outcomes from neoadjuvant chemotherapy: A phase 2 trial of dose-dense methotrexate, vinblastine, doxorubicin, and cisplatin with bevacizumab in urothelial cancer . Eur Urol. 2016 ; 69 5 : 855 – 862 . http://dx.doi.org/10.1016/j.eururo.2015.08.034 Google Scholar CrossRef Search ADS PubMed 11 Rosenberg JE , Hoffman-Censits J , Powles T et al. , Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: A single-arm, multicentre, phase 2 trial . Lancet. 2016 ; 387 10031 : 1909 – 1920 . http://dx.doi.org/10.1016/S0140-6736(16)00561-4 Google Scholar CrossRef Search ADS PubMed 12 Lerner SP , McConkey DJ , Hoadley KA et al. , Bladder cancer molecular taxonomy: Summary from a consensus meeting . Bladder Cancer . 2016 ; 2 1 : 37 – 47 . http://dx.doi.org/10.3233/BLC-150037 Google Scholar CrossRef Search ADS PubMed 13 Kardos J , Chai S , Mose LE et al. , Claudin-low bladder tumors are immune infiltrated and actively immune suppressed . JCI Insight . 2016 ; 1 3 : e85902 . Google Scholar CrossRef Search ADS PubMed 14 Choi W , Ochoa A , McConkey DJ et al. , Genetic alterations in the molecular subtypes of bladder cancer: Illustration in The Cancer Genome Atlas dataset . Eur Urol. 2017 ; 72 3 : 354 – 365 . http://dx.doi.org/10.1016/j.eururo.2017.03.010 Google Scholar CrossRef Search ADS PubMed 15 Prat A , Ellis MJ , Perou CM. Practical implications of gene-expression-based assays for breast oncologists . Nat Rev Clin Oncol. 2012 ; 9 1 : 48 – 57 . Google Scholar CrossRef Search ADS 16 Sjodahl G , Lovgren K , Lauss M et al. , Toward a molecular pathologic classification of urothelial carcinoma . Am J Pathol. 2013 ; 183 3 : 681 – 691 . http://dx.doi.org/10.1016/j.ajpath.2013.05.013 Google Scholar CrossRef Search ADS PubMed 17 Dadhania V , Zhang M , Zhang L et al. , Meta-analysis of the luminal and basal subtypes of bladder cancer and the identification of signature immunohistochemical markers for clinical use . EBioMedicine. 2016 ; 12 : 105 – 117 . http://dx.doi.org/10.1016/j.ebiom.2016.08.036 Google Scholar CrossRef Search ADS PubMed 18 Dyrskjot L , Reinert T , Algaba F et al. , Prognostic impact of a 12-gene progression score in non-muscle-invasive bladder cancer: A prospective multicentre validation study . Eur Urol. 2017 ; 72 3 : 461 – 469 . http://dx.doi.org/10.1016/j.eururo.2017.05.040 Google Scholar CrossRef Search ADS PubMed 19 Hedegaard J , Lamy P , Nordentoft I et al. , Comprehensive transcriptional analysis of early-stage urothelial carcinoma . Cancer Cell. 2016 ; 30 1 : 27 – 42 . http://dx.doi.org/10.1016/j.ccell.2016.05.004 Google Scholar CrossRef Search ADS PubMed 20 Thomsen MBH , Nordentoft I , Lamy P et al. , Comprehensive multiregional analysis of molecular heterogeneity in bladder cancer . Sci Rep. 2017 ; 7 1 : 11702 . http://dx.doi.org/10.1038/s41598-017-11291-0 Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. 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

JNCI: Journal of the National Cancer InstituteOxford University Press

Published: Jan 12, 2018

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