- Email: [email protected]
Personalized Medicine in Kidney Cancer: Learning How to Walk Before We Run
EURURO-6278; No. of Pages 2 EUROPEAN UROLOGY XXX (2015) XXX–XXX
available at www.sciencedirect.com journal homepage: www.europeanurology.com
Platinum Priority – Editorial Referring to the article published on pp. x–y of this issue
Personalized Medicine in Kidney Cancer: Learning How to Walk Before We Run George M. Yousef a,b,* a
Department of Laboratory Medicine and the Keenan Research Centre for Biomedical Science at the Li Ka Shing Knowledge Institute of St. Michael’s Hospital,
Toronto, Canada; b Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
Advances in targeted therapy have resulted in an expanded menu of treatment options for metastatic kidney cancer patients. This necessitated the search for additional biomarkers for accurate classification, prognostic assessment, and prediction of treatment efficiency. Accumulated experience has shown that morphology cannot be relied on as a sole indicator of tumor behavior. Tumors that look the same do not necessarily behave the same way. The introduction of molecular profiling approaches that enable simultaneous analysis of thousands of molecules marked a new milestone in patient management in kidney and other cancers [1]. This development will lead to substantial improvement of outcome by customizing the management plan for each patient based on the molecular biology of the tumor, as manifested by the molecular expression profile. In this month’s issue of European Urology, Bu¨ttner et al identified a molecular signature, named the S3-score, that enables prognostic risk stratification of clear cell renal cell carcinoma (ccRCC) [2]. In this elegant study, the authors hypothesized that the degree of divergence of ccRCC from its cell of origin in the nephron correlates with cancerspecific survival. Patients with high scores, indicating higher similarity to gene expression of the third region of the proximal tubules—the presumed tissue of origin of ccRCC—had longer cancer-specific survival, whereas those with lower scores more frequently had advanced stage tumors, necrosis, and metastasis. The authors compared their panel with two established gene expression signatures: ccA/ccB [3] and ClearCode34 [4]. The S3-score was able to significantly improve the ccA/ccB signature and was superior to ClearCode 34. The
innovative approach of this study highlights the great impact that understanding tumor biology has on patient management. Mining different levels of molecular alterations in renal cell carcinoma (RCC) also promises to uncover the biological drivers of tumor progression. In addition to gene expression analysis, other molecular approaches including quantitative proteomics by mass spectrometry [5] and epigenetic changes have been used to identify unique signatures associated with aggressive tumor behavior. Other molecules including microRNAs and long noncoding RNAs were also shown to have potential prognostic utility [6]. More recently, the concept of integrated genomics, which compiles multiple levels of molecular alterations, has emerged as a strong tool for the identification of RCC prognostic biomarkers [7]. As shown in recent studies, several events might be required to establish an aggressive phenotype. Studying the interplay between different levels of molecular aberrations and their related biological pathways is key to success. Research is moving a step further by exploring the novel dimension of genotype–phenotype associations in human cancers through the integration of advanced molecular platforms and computational analysis of whole-slide imaging [8]. This approach can substantively improve our understanding of the biology of tumor by correlating molecular changes with morphologic characteristics including cell–stroma relationships and tumor microenvironment. As much as the paper by Bu¨ttner et al [2] highlights the great promise of molecular pathology, it also emphasizes that the journey from bench to bedside is still a work in progress. As noted by the authors, validation with large
DOI of original article: http://dx.doi.org/10.1016/j.eururo.2015.05.045. * Department of Laboratory Medicine, St. Michael’s Hospital, 30 Bond Street, Toronto, Ontario, M5B 1W8, Canada. Tel. +1 416 864 6060 ext 77605; Fax: +1 416 864 5648. E-mail address: [email protected]. http://dx.doi.org/10.1016/j.eururo.2015.06.037 0302-2838/# 2015 Published by Elsevier B.V. on behalf of European Association of Urology.
Please cite this article in press as: Yousef GM. Personalized Medicine in Kidney Cancer: Learning How to Walk Before We Run. Eur Urol (2015), http://dx.doi.org/10.1016/j.eururo.2015.06.037
EURURO-6278; No. of Pages 2 2
EUROPEAN UROLOGY XXX (2015) XXX–XXX
independent data sets is key for confirming the clinical utility of molecular profiling. The study also highlights the great value of public databases. The continual development of high-quality, freely accessible databases will have a profoundly positive effect on the progress of genomic medicine. Discrepancy between studies is an obstacle that Bu¨ttner et al clearly note. They observed 20% discordance between their predictive score and the previously published ccA/ccB signature [3]. Multiple factors can lead to this inconsistency, such as technical issues and differences in the study population and the material used for analysis [1]. A number of limitations and challenges should be carefully addressed. An important issue is that relying on morphology as the gold standard for RCC classification is not always accurate. This was emphasized by Bu¨ttner et al [2], as 16 ccRCC cases were reclassified into chromophobe RCC based on gene expression and copy number variations. Subclassification of RCC is evolving, and new entities with overlapping morphologic features are now being recognized. Another important limitation is intratumor heterogeneity. As emphasized in recent publications [9], this requires the use of multiregion biopsies from the same tumor. A caveat of handling large data sets generated by high-throughput technologies is the risk of false-positive results and incidental findings. It is clear that a molecular revolution is gradually evolving. This can challenge classic morphologic tumor typing. New cancer subtypes will emerge based on molecular rather than morphologic characteristics. It is also possible that some histologic subtypes will be merged into single entities based on similarities in biological performance. Molecular information can also lead to reevaluation of cancer terminology, especially for small renal masses. Should small RCC tumors with benign behavior be relabeled as ‘‘renal tumors with low malignant potential’’ to avoid unnecessary invasive therapeutic interventions? As is the case in prostate cancer, there is a trend toward more conservative therapy (including active surveillance and local ablation) for small renal masses with anticipated indolent behavior. The spectrum of applications for molecular analysis goes beyond identification of prognostic markers to have a significant impact on therapy by stratifying RCC patients into molecular subtypes associated with response to therapy [10]. Biomarkers for early detection of recurrence and relapse are also urgently needed. There is a continuous search for noninvasive serum and urine RCC diagnostic biomarkers that can replace invasive biopsy that has limited
success and potential complications. Finally, achieving the promise of new targeted therapy requires new paradigms of public–private partnership. The area of molecular diagnostics continues to grow, and although not expected to replace traditional clinicopathologic parameters, it is anticipated to provide a significant addition to them. It must be noted, however, that several steps are included on the journey from research discovery to clinic. Conflicts of interest: The author has nothing to disclose. Funding support: This work was supported by grants from the Canadian Institute of Health Research (MOP 119606), Kidney Foundation of Canada (KFOC130030), Kidney Cancer Research Network of Canada, and Prostate Cancer Canada Movember Discovery Grants (D2013-39).
References [1] Pasic MD, Samaan S, Yousef GM. Genomic medicine: new frontiers and new challenges. Clin Chem 2013;59:158–67. [2] Bu¨ttner F, Winter S, Rausch S, et al. Survival prediction of clear cell renal cell carcinoma based on gene expression similarity to the proximal tubule of the nephron. Eur Urol. In press. http://dx.doi. org/10.1016/j.eururo.2015.05.045 [3] Brannon AR, Reddy A, Seiler M, et al. Molecular stratification of clear cell renal cell carcinoma by consensus clustering reveals distinct subtypes and survival patterns. Genes Cancer 2010;1:152–63. [4] Brooks SA, Brannon AR, Parker JS, et al. ClearCode34: a prognostic risk predictor for localized clear cell renal cell carcinoma. Eur Urol 2014;66:77–84. [5] Masui O, White NM, DeSouza LV, et al. Quantitative proteomic analysis in metastatic renal cell carcinoma reveals a unique set of proteins with potential prognostic significance. Mol Cell Proteomics 2013;12:132–44. [6] White NM, Khella HW, Grigull J, et al. miRNA profiling in metastatic renal cell carcinoma reveals a tumour-suppressor effect for miR215. Br J Cancer 2011;105:1741–9. [7] Butz H, Szabo PM, Nofech-Mozes R, et al. Integrative bioinformatics analysis reveals new prognostic biomarkers of clear cell renal cell carcinoma. Clin Chem 2014;60:1314–26. [8] Cooper LA, Kong J, Gutman DA, Dunn WD, Nalisnik M, Brat DJ. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images. Lab Invest 2015;95:366–76. [9] Gulati S, Martinez P, Joshi T, et al. Systematic evaluation of the prognostic impact and intratumour heterogeneity of clear cell renal cell carcinoma biomarkers. Eur Urol 2014;66:936–48. [10] Beuselinck B, Job S, Becht E, et al. Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin Cancer Res 2015;21:1329–39.
Please cite this article in press as: Yousef GM. Personalized Medicine in Kidney Cancer: Learning How to Walk Before We Run. Eur Urol (2015), http://dx.doi.org/10.1016/j.eururo.2015.06.037
available at www.sciencedirect.com journal homepage: www.europeanurology.com
Platinum Priority – Editorial Referring to the article published on pp. x–y of this issue
Personalized Medicine in Kidney Cancer: Learning How to Walk Before We Run George M. Yousef a,b,* a
Department of Laboratory Medicine and the Keenan Research Centre for Biomedical Science at the Li Ka Shing Knowledge Institute of St. Michael’s Hospital,
Toronto, Canada; b Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
Advances in targeted therapy have resulted in an expanded menu of treatment options for metastatic kidney cancer patients. This necessitated the search for additional biomarkers for accurate classification, prognostic assessment, and prediction of treatment efficiency. Accumulated experience has shown that morphology cannot be relied on as a sole indicator of tumor behavior. Tumors that look the same do not necessarily behave the same way. The introduction of molecular profiling approaches that enable simultaneous analysis of thousands of molecules marked a new milestone in patient management in kidney and other cancers [1]. This development will lead to substantial improvement of outcome by customizing the management plan for each patient based on the molecular biology of the tumor, as manifested by the molecular expression profile. In this month’s issue of European Urology, Bu¨ttner et al identified a molecular signature, named the S3-score, that enables prognostic risk stratification of clear cell renal cell carcinoma (ccRCC) [2]. In this elegant study, the authors hypothesized that the degree of divergence of ccRCC from its cell of origin in the nephron correlates with cancerspecific survival. Patients with high scores, indicating higher similarity to gene expression of the third region of the proximal tubules—the presumed tissue of origin of ccRCC—had longer cancer-specific survival, whereas those with lower scores more frequently had advanced stage tumors, necrosis, and metastasis. The authors compared their panel with two established gene expression signatures: ccA/ccB [3] and ClearCode34 [4]. The S3-score was able to significantly improve the ccA/ccB signature and was superior to ClearCode 34. The
innovative approach of this study highlights the great impact that understanding tumor biology has on patient management. Mining different levels of molecular alterations in renal cell carcinoma (RCC) also promises to uncover the biological drivers of tumor progression. In addition to gene expression analysis, other molecular approaches including quantitative proteomics by mass spectrometry [5] and epigenetic changes have been used to identify unique signatures associated with aggressive tumor behavior. Other molecules including microRNAs and long noncoding RNAs were also shown to have potential prognostic utility [6]. More recently, the concept of integrated genomics, which compiles multiple levels of molecular alterations, has emerged as a strong tool for the identification of RCC prognostic biomarkers [7]. As shown in recent studies, several events might be required to establish an aggressive phenotype. Studying the interplay between different levels of molecular aberrations and their related biological pathways is key to success. Research is moving a step further by exploring the novel dimension of genotype–phenotype associations in human cancers through the integration of advanced molecular platforms and computational analysis of whole-slide imaging [8]. This approach can substantively improve our understanding of the biology of tumor by correlating molecular changes with morphologic characteristics including cell–stroma relationships and tumor microenvironment. As much as the paper by Bu¨ttner et al [2] highlights the great promise of molecular pathology, it also emphasizes that the journey from bench to bedside is still a work in progress. As noted by the authors, validation with large
DOI of original article: http://dx.doi.org/10.1016/j.eururo.2015.05.045. * Department of Laboratory Medicine, St. Michael’s Hospital, 30 Bond Street, Toronto, Ontario, M5B 1W8, Canada. Tel. +1 416 864 6060 ext 77605; Fax: +1 416 864 5648. E-mail address: [email protected]. http://dx.doi.org/10.1016/j.eururo.2015.06.037 0302-2838/# 2015 Published by Elsevier B.V. on behalf of European Association of Urology.
Please cite this article in press as: Yousef GM. Personalized Medicine in Kidney Cancer: Learning How to Walk Before We Run. Eur Urol (2015), http://dx.doi.org/10.1016/j.eururo.2015.06.037
EURURO-6278; No. of Pages 2 2
EUROPEAN UROLOGY XXX (2015) XXX–XXX
independent data sets is key for confirming the clinical utility of molecular profiling. The study also highlights the great value of public databases. The continual development of high-quality, freely accessible databases will have a profoundly positive effect on the progress of genomic medicine. Discrepancy between studies is an obstacle that Bu¨ttner et al clearly note. They observed 20% discordance between their predictive score and the previously published ccA/ccB signature [3]. Multiple factors can lead to this inconsistency, such as technical issues and differences in the study population and the material used for analysis [1]. A number of limitations and challenges should be carefully addressed. An important issue is that relying on morphology as the gold standard for RCC classification is not always accurate. This was emphasized by Bu¨ttner et al [2], as 16 ccRCC cases were reclassified into chromophobe RCC based on gene expression and copy number variations. Subclassification of RCC is evolving, and new entities with overlapping morphologic features are now being recognized. Another important limitation is intratumor heterogeneity. As emphasized in recent publications [9], this requires the use of multiregion biopsies from the same tumor. A caveat of handling large data sets generated by high-throughput technologies is the risk of false-positive results and incidental findings. It is clear that a molecular revolution is gradually evolving. This can challenge classic morphologic tumor typing. New cancer subtypes will emerge based on molecular rather than morphologic characteristics. It is also possible that some histologic subtypes will be merged into single entities based on similarities in biological performance. Molecular information can also lead to reevaluation of cancer terminology, especially for small renal masses. Should small RCC tumors with benign behavior be relabeled as ‘‘renal tumors with low malignant potential’’ to avoid unnecessary invasive therapeutic interventions? As is the case in prostate cancer, there is a trend toward more conservative therapy (including active surveillance and local ablation) for small renal masses with anticipated indolent behavior. The spectrum of applications for molecular analysis goes beyond identification of prognostic markers to have a significant impact on therapy by stratifying RCC patients into molecular subtypes associated with response to therapy [10]. Biomarkers for early detection of recurrence and relapse are also urgently needed. There is a continuous search for noninvasive serum and urine RCC diagnostic biomarkers that can replace invasive biopsy that has limited
success and potential complications. Finally, achieving the promise of new targeted therapy requires new paradigms of public–private partnership. The area of molecular diagnostics continues to grow, and although not expected to replace traditional clinicopathologic parameters, it is anticipated to provide a significant addition to them. It must be noted, however, that several steps are included on the journey from research discovery to clinic. Conflicts of interest: The author has nothing to disclose. Funding support: This work was supported by grants from the Canadian Institute of Health Research (MOP 119606), Kidney Foundation of Canada (KFOC130030), Kidney Cancer Research Network of Canada, and Prostate Cancer Canada Movember Discovery Grants (D2013-39).
References [1] Pasic MD, Samaan S, Yousef GM. Genomic medicine: new frontiers and new challenges. Clin Chem 2013;59:158–67. [2] Bu¨ttner F, Winter S, Rausch S, et al. Survival prediction of clear cell renal cell carcinoma based on gene expression similarity to the proximal tubule of the nephron. Eur Urol. In press. http://dx.doi. org/10.1016/j.eururo.2015.05.045 [3] Brannon AR, Reddy A, Seiler M, et al. Molecular stratification of clear cell renal cell carcinoma by consensus clustering reveals distinct subtypes and survival patterns. Genes Cancer 2010;1:152–63. [4] Brooks SA, Brannon AR, Parker JS, et al. ClearCode34: a prognostic risk predictor for localized clear cell renal cell carcinoma. Eur Urol 2014;66:77–84. [5] Masui O, White NM, DeSouza LV, et al. Quantitative proteomic analysis in metastatic renal cell carcinoma reveals a unique set of proteins with potential prognostic significance. Mol Cell Proteomics 2013;12:132–44. [6] White NM, Khella HW, Grigull J, et al. miRNA profiling in metastatic renal cell carcinoma reveals a tumour-suppressor effect for miR215. Br J Cancer 2011;105:1741–9. [7] Butz H, Szabo PM, Nofech-Mozes R, et al. Integrative bioinformatics analysis reveals new prognostic biomarkers of clear cell renal cell carcinoma. Clin Chem 2014;60:1314–26. [8] Cooper LA, Kong J, Gutman DA, Dunn WD, Nalisnik M, Brat DJ. Novel genotype-phenotype associations in human cancers enabled by advanced molecular platforms and computational analysis of whole slide images. Lab Invest 2015;95:366–76. [9] Gulati S, Martinez P, Joshi T, et al. Systematic evaluation of the prognostic impact and intratumour heterogeneity of clear cell renal cell carcinoma biomarkers. Eur Urol 2014;66:936–48. [10] Beuselinck B, Job S, Becht E, et al. Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin Cancer Res 2015;21:1329–39.
Please cite this article in press as: Yousef GM. Personalized Medicine in Kidney Cancer: Learning How to Walk Before We Run. Eur Urol (2015), http://dx.doi.org/10.1016/j.eururo.2015.06.037