EURURO-5579; No. of Pages 2 EUROPEAN UROLOGY XXX (2014) 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
The Molecular Biology of Renal Cancer: Another Piece of the Puzzle Grant D. Stewart a,b,*, David J. Harrison b,c, Daniel M. Berney d, Thomas Powles e a
Edinburgh Urological Cancer Group, University of Edinburgh, Edinburgh, UK; b Scottish Collaboration on Translational Research into Renal Cell Cancer, UK;
c
School of Medicine, University of St Andrews, St Andrews, UK;
d
Department of Molecular Oncology, Barts Cancer Institute, Queen Mary University of
London, London, UK; e Centre for Experimental Cancer Medicine, Barts Cancer Institute, Queen Mary University of London, London, UK
Over the last decade, the scientific methods available to unravel the process of oncogenesis have increased immeasurably. The days of testing for single-gene mutations or amplifications in an attempt to unravel the development of complex malignancies are behind us. Newer high-throughout methodologies have resulted in a plethora of data, and some may argue that bioinformatics and appropriate tissue collection are the biggest barriers inhibiting progress. We now know that the development of renal cell carcinoma (RCC) follows a process of clonal evolution, with multiple clones progressing simultaneously and developing unique features, maintaining the ability to grow and metastasise [1]. This process appears to be influenced and may even be accelerated by treatment [2,3]. Therefore we are not just dealing with a tumour sample for analysis, but the time, region, and cancer clone analysed will affect results. We are in the infancy of understanding the full extent of this tumour complexity, just as early astronomy was initially taken aback when the infinite nature of the universe was first revealed. In this month’s issue of European Urology, a study by Brooks et al. takes a small step towards clarifying some of the data available, by identifying and validating a 34-gene signature (ClearCode34) associated with prognosis [4]. ClearCode34 may not be markedly better than established prognostic factors, but work such as this is beginning to tell us that it is possible to identify genetic signatures in RCC associated with prognosis despite all the emerging problems. This study is an important piece of the jigsaw because it informs the community that we can find some molecular consistency in RCC tissue.
There is a great need to improve forecasting of the natural history of RCC for an individual patient. However, one might question the need for refined prognostication when there are no means of intervening, for example by the offer of proven adjuvant therapies. However, refinement of robust prognosis of disease recurrence is important on several levels: providing the patient with an understanding of likely disease course, tailoring optimal follow-up protocols, having the potential to guide patients with small renal masses (SRMs) following a biopsy towards active surveillance or surgical excision, and stratifying adjuvant trials of targeted therapy to provide the greatest chance of success. It is becoming increasingly clear that prognostic and predictive models based entirely on clinical and pathologic factors have plateaued, and for any further improvements to be made, biomarkers need to be added [5]. Although a raft of putative prognostic molecules have been published in the literature [6], none are ready for prime time. In addition to the spatial and temporal intratumoural heterogeneity alluded to earlier, failure to identify prognostic biomarkers in RCC is also due to the failure to conform to the Reporting Recommendations for Tumour Marker Prognostic Studies (REMARK) criteria for the publication of biomarker studies and specifically a lack of validation studies [6]. Gene expression profiles have been used to stratify clear cell renal cell carcinoma (ccRCC) into two distinct subtypes, ccA and ccB, with significantly superior cancer-specific survival for ccA patients confirmed in a validation cohort of 480 patients [7]. Following REMARK criteria, Brooks et al.
DOI of original article: http://dx.doi.org/10.1016/j.eururo.2014.02.035. * Corresponding author. Edinburgh Urological Cancer Group, Division of Pathology Laboratories, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, UK. Tel. +44 (0)131 537 1763; Fax: +44 (0)131 537 1019. E-mail address: [email protected] (G.D. Stewart). http://dx.doi.org/10.1016/j.eururo.2014.03.004 0302-2838/# 2014 European Association of Urology. Published by Elsevier B.V. All rights reserved.
Please cite this article in press as: Stewart GD, et al. The Molecular Biology of Renal Cancer: Another Piece of the Puzzle. Eur Urol (2014), http://dx.doi.org/10.1016/j.eururo.2014.03.004
EURURO-5579; No. of Pages 2 2
EUROPEAN UROLOGY XXX (2014) XXX–XXX
honed down these prognostic subtypes from 121 genes, selected in a biased fashion by the researchers, to 34 genes whilst maintaining the discriminatory power to differentiate the two prognostic groups in 94% of the discovery cohort patients. Cancer Genome Atlas data from 380 ccRCC patients was used for testing, and then samples from 157 further ccRCC patients were used to validate results and also develop an assay using formalin-fixed paraffin-embedded (FFPE) tissues. Although extremes of tumour grade and stage remained the most significant discriminators of patient outcome, the optimal prognostic model included grade and stage as well as the ClearCell34 subtype. The ClearCode34 prognostic model outperformed current established prognostic nomograms such as the Mayo Clinic SSiGN (tumour stage, size, grade and necrosis) and UCLA Integrated Staging System, which is important for this approach to advance the field. Characteristics such as necrosis and performance status, which are currently key to commonly used scoring systems, were excluded from the ClearCode34 prognostic model. This may be because the authors sought to exclude any subjective factors, but nevertheless it opens up a criticism that not all the important prognostic factors were assessed in the multivariate model. Brooks et al. validated ClearCode34 using RNA from FFPE specimens with the NanoString platform; conversion of such approaches for use with routinely processed tissue samples from any patients with a reproducible assay is important. However, before ClearCode34 can be relied on clinically, preanalytical, analytical, and postanalytical questions remain. Firstly, although some biologic replicates were mentioned by the authors, exactly how will intratumoural heterogeneity affect the results [1]? Is multiregion sampling required? Secondly, although briefly mentioned in the discussion, how successfully can ClearCode34 be performed on renal tumour biopsy specimens, in the way that the Cell Cycle Progression score can be on approximately 20-mm biopsy FFPE sections in prostate cancer [8]? The use of biopsies has the potential to allow better stratification of treatment of SRM patients. Thirdly, how is ClearCell34 stratification affected by biosampling conditions? It is well established that mRNA levels are significantly altered by warm ischaemia times occurring during RCC surgery [9]. Finally, it will be interesting to observe in future studies with metastatic patients if ClearCode34 is helpful in the predictive setting, just as other prognostic markers appear to be [10]. Prognostic studies such as this are important because RCC lags behind other malignancies in patient stratification
of all disease stages as well as development of personalised therapies. Yes, we have improved surgical techniques, yes we have fantastic drugs, but we are not going to shift the survival curves without a greater understanding of the biology of individual tumours. The work by Brooks et al. separates patients into clinically relevant groups based on gene expression [4]. We need to do the same when selecting patients for treatments. Such an ambition requires randomised trials and prospective tissue collection. It is a major undertaking in both time and effort. However, the work presented here suggests this enormous and complex task might just be worth doing. Conflicts of interest: The authors have nothing to disclose.
References [1] Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012;366:883–92. [2] Stewart G, Laird A, O’Mahony F, et al. The effect of sunitinib on biomarkers and tumor heterogeneity in metastatic clear cell renal cancer. J Clin Oncol 2014:32. [3] Sharpe K, Stewart GD, Mackay A, et al. The effect of VEGF-targeted therapy on biomarker expression in sequential tissue from patients with metastatic clear cell renal cancer. Clin Cancer Res 2013;19: 6924–34. [4] Brooks SA, Brannon AR, Parker JS, et al. ClearCode34: a prognostic risk predictor for localized clear cell renal cell carcinoma. Eur Urol. In press. http://dx.doi.org/10.1016/j.eururo.2014.02.035. [5] Galsky MD. A prognostic model for metastatic renal-cell carcinoma. Lancet Oncol 2013;14:102–3. [6] Stewart GD, O’Mahony FC, Powles T, Riddick ACP, Harrison DJ, Faratian D. What can molecular pathology contribute to the management of renal cell carcinoma? Nat Rev Urol 2011;8:255–65. [7] Brannon AR, Haake SM, Hacker KE, et al. Meta-analysis of clear cell renal cell carcinoma gene expression defines a variant subgroup and identifies gender influences on tumor biology. Eur Urol 2012;61: 258–68. [8] Cuzick J, Swanson GP, Fisher G, et al. Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study. Lancet Oncol 2011;12:245–55. [9] Liu NW, Sanford T, Srinivasan R, et al. Impact of ischemia and procurement conditions on gene expression in renal cell carcinoma. Clin Cancer Res 2013;19:42–9. [10] Tran HT, Liu Y, Zurita AJ, et al. Prognostic or predictive plasma cytokines and angiogenic factors for patients treated with pazopanib for metastatic renal-cell cancer: a retrospective analysis of phase 2 and phase 3 trials. Lancet Oncol 2012;13:827–37.
Please cite this article in press as: Stewart GD, et al. The Molecular Biology of Renal Cancer: Another Piece of the Puzzle. Eur Urol (2014), http://dx.doi.org/10.1016/j.eururo.2014.03.004
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
The Molecular Biology of Renal Cancer: Another Piece of the Puzzle Grant D. Stewart a,b,*, David J. Harrison b,c, Daniel M. Berney d, Thomas Powles e a
Edinburgh Urological Cancer Group, University of Edinburgh, Edinburgh, UK; b Scottish Collaboration on Translational Research into Renal Cell Cancer, UK;
c
School of Medicine, University of St Andrews, St Andrews, UK;
d
Department of Molecular Oncology, Barts Cancer Institute, Queen Mary University of
London, London, UK; e Centre for Experimental Cancer Medicine, Barts Cancer Institute, Queen Mary University of London, London, UK
Over the last decade, the scientific methods available to unravel the process of oncogenesis have increased immeasurably. The days of testing for single-gene mutations or amplifications in an attempt to unravel the development of complex malignancies are behind us. Newer high-throughout methodologies have resulted in a plethora of data, and some may argue that bioinformatics and appropriate tissue collection are the biggest barriers inhibiting progress. We now know that the development of renal cell carcinoma (RCC) follows a process of clonal evolution, with multiple clones progressing simultaneously and developing unique features, maintaining the ability to grow and metastasise [1]. This process appears to be influenced and may even be accelerated by treatment [2,3]. Therefore we are not just dealing with a tumour sample for analysis, but the time, region, and cancer clone analysed will affect results. We are in the infancy of understanding the full extent of this tumour complexity, just as early astronomy was initially taken aback when the infinite nature of the universe was first revealed. In this month’s issue of European Urology, a study by Brooks et al. takes a small step towards clarifying some of the data available, by identifying and validating a 34-gene signature (ClearCode34) associated with prognosis [4]. ClearCode34 may not be markedly better than established prognostic factors, but work such as this is beginning to tell us that it is possible to identify genetic signatures in RCC associated with prognosis despite all the emerging problems. This study is an important piece of the jigsaw because it informs the community that we can find some molecular consistency in RCC tissue.
There is a great need to improve forecasting of the natural history of RCC for an individual patient. However, one might question the need for refined prognostication when there are no means of intervening, for example by the offer of proven adjuvant therapies. However, refinement of robust prognosis of disease recurrence is important on several levels: providing the patient with an understanding of likely disease course, tailoring optimal follow-up protocols, having the potential to guide patients with small renal masses (SRMs) following a biopsy towards active surveillance or surgical excision, and stratifying adjuvant trials of targeted therapy to provide the greatest chance of success. It is becoming increasingly clear that prognostic and predictive models based entirely on clinical and pathologic factors have plateaued, and for any further improvements to be made, biomarkers need to be added [5]. Although a raft of putative prognostic molecules have been published in the literature [6], none are ready for prime time. In addition to the spatial and temporal intratumoural heterogeneity alluded to earlier, failure to identify prognostic biomarkers in RCC is also due to the failure to conform to the Reporting Recommendations for Tumour Marker Prognostic Studies (REMARK) criteria for the publication of biomarker studies and specifically a lack of validation studies [6]. Gene expression profiles have been used to stratify clear cell renal cell carcinoma (ccRCC) into two distinct subtypes, ccA and ccB, with significantly superior cancer-specific survival for ccA patients confirmed in a validation cohort of 480 patients [7]. Following REMARK criteria, Brooks et al.
DOI of original article: http://dx.doi.org/10.1016/j.eururo.2014.02.035. * Corresponding author. Edinburgh Urological Cancer Group, Division of Pathology Laboratories, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XU, UK. Tel. +44 (0)131 537 1763; Fax: +44 (0)131 537 1019. E-mail address: [email protected] (G.D. Stewart). http://dx.doi.org/10.1016/j.eururo.2014.03.004 0302-2838/# 2014 European Association of Urology. Published by Elsevier B.V. All rights reserved.
Please cite this article in press as: Stewart GD, et al. The Molecular Biology of Renal Cancer: Another Piece of the Puzzle. Eur Urol (2014), http://dx.doi.org/10.1016/j.eururo.2014.03.004
EURURO-5579; No. of Pages 2 2
EUROPEAN UROLOGY XXX (2014) XXX–XXX
honed down these prognostic subtypes from 121 genes, selected in a biased fashion by the researchers, to 34 genes whilst maintaining the discriminatory power to differentiate the two prognostic groups in 94% of the discovery cohort patients. Cancer Genome Atlas data from 380 ccRCC patients was used for testing, and then samples from 157 further ccRCC patients were used to validate results and also develop an assay using formalin-fixed paraffin-embedded (FFPE) tissues. Although extremes of tumour grade and stage remained the most significant discriminators of patient outcome, the optimal prognostic model included grade and stage as well as the ClearCell34 subtype. The ClearCode34 prognostic model outperformed current established prognostic nomograms such as the Mayo Clinic SSiGN (tumour stage, size, grade and necrosis) and UCLA Integrated Staging System, which is important for this approach to advance the field. Characteristics such as necrosis and performance status, which are currently key to commonly used scoring systems, were excluded from the ClearCode34 prognostic model. This may be because the authors sought to exclude any subjective factors, but nevertheless it opens up a criticism that not all the important prognostic factors were assessed in the multivariate model. Brooks et al. validated ClearCode34 using RNA from FFPE specimens with the NanoString platform; conversion of such approaches for use with routinely processed tissue samples from any patients with a reproducible assay is important. However, before ClearCode34 can be relied on clinically, preanalytical, analytical, and postanalytical questions remain. Firstly, although some biologic replicates were mentioned by the authors, exactly how will intratumoural heterogeneity affect the results [1]? Is multiregion sampling required? Secondly, although briefly mentioned in the discussion, how successfully can ClearCode34 be performed on renal tumour biopsy specimens, in the way that the Cell Cycle Progression score can be on approximately 20-mm biopsy FFPE sections in prostate cancer [8]? The use of biopsies has the potential to allow better stratification of treatment of SRM patients. Thirdly, how is ClearCell34 stratification affected by biosampling conditions? It is well established that mRNA levels are significantly altered by warm ischaemia times occurring during RCC surgery [9]. Finally, it will be interesting to observe in future studies with metastatic patients if ClearCode34 is helpful in the predictive setting, just as other prognostic markers appear to be [10]. Prognostic studies such as this are important because RCC lags behind other malignancies in patient stratification
of all disease stages as well as development of personalised therapies. Yes, we have improved surgical techniques, yes we have fantastic drugs, but we are not going to shift the survival curves without a greater understanding of the biology of individual tumours. The work by Brooks et al. separates patients into clinically relevant groups based on gene expression [4]. We need to do the same when selecting patients for treatments. Such an ambition requires randomised trials and prospective tissue collection. It is a major undertaking in both time and effort. However, the work presented here suggests this enormous and complex task might just be worth doing. Conflicts of interest: The authors have nothing to disclose.
References [1] Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012;366:883–92. [2] Stewart G, Laird A, O’Mahony F, et al. The effect of sunitinib on biomarkers and tumor heterogeneity in metastatic clear cell renal cancer. J Clin Oncol 2014:32. [3] Sharpe K, Stewart GD, Mackay A, et al. The effect of VEGF-targeted therapy on biomarker expression in sequential tissue from patients with metastatic clear cell renal cancer. Clin Cancer Res 2013;19: 6924–34. [4] Brooks SA, Brannon AR, Parker JS, et al. ClearCode34: a prognostic risk predictor for localized clear cell renal cell carcinoma. Eur Urol. In press. http://dx.doi.org/10.1016/j.eururo.2014.02.035. [5] Galsky MD. A prognostic model for metastatic renal-cell carcinoma. Lancet Oncol 2013;14:102–3. [6] Stewart GD, O’Mahony FC, Powles T, Riddick ACP, Harrison DJ, Faratian D. What can molecular pathology contribute to the management of renal cell carcinoma? Nat Rev Urol 2011;8:255–65. [7] Brannon AR, Haake SM, Hacker KE, et al. Meta-analysis of clear cell renal cell carcinoma gene expression defines a variant subgroup and identifies gender influences on tumor biology. Eur Urol 2012;61: 258–68. [8] Cuzick J, Swanson GP, Fisher G, et al. Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study. Lancet Oncol 2011;12:245–55. [9] Liu NW, Sanford T, Srinivasan R, et al. Impact of ischemia and procurement conditions on gene expression in renal cell carcinoma. Clin Cancer Res 2013;19:42–9. [10] Tran HT, Liu Y, Zurita AJ, et al. Prognostic or predictive plasma cytokines and angiogenic factors for patients treated with pazopanib for metastatic renal-cell cancer: a retrospective analysis of phase 2 and phase 3 trials. Lancet Oncol 2012;13:827–37.
Please cite this article in press as: Stewart GD, et al. The Molecular Biology of Renal Cancer: Another Piece of the Puzzle. Eur Urol (2014), http://dx.doi.org/10.1016/j.eururo.2014.03.004
