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      Systematic Evaluation of the Prognostic Impact and Intratumour Heterogeneity of Clear Cell Renal Cell Carcinoma Biomarkers

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          Candidate biomarkers have been identified for clear cell renal cell carcinoma (ccRCC) patients, but most have not been validated.


          To validate published ccRCC prognostic biomarkers in an independent patient cohort and to assess intratumour heterogeneity (ITH) of the most promising markers to guide biomarker optimisation.

          Design, setting, and participants

          Cancer-specific survival (CSS) for each of 28 identified genetic or transcriptomic biomarkers was assessed in 350 ccRCC patients. ITH was interrogated in a multiregion biopsy data set of 10 ccRCCs.

          Outcome measurements and statistical analysis

          Biomarker association with CSS was analysed by univariate and multivariate analyses.

          Results and limitations

          A total of 17 of 28 biomarkers ( TP53 mutations; amplifications of chromosomes 8q, 12, 20q11.21q13.32, and 20 and deletions of 4p, 9p, 9p21.3p24.1, and 22q; low EDNRB and TSPAN7 expression and six gene expression signatures) were validated as predictors of poor CSS in univariate analysis. Tumour stage and the ccB expression signature were the only independent predictors in multivariate analysis. ITH of the ccB signature was identified in 8 of 10 tumours. Several genetic alterations that were significant in univariate analysis were enriched, and chromosomal instability indices were increased in samples expressing the ccB signature. The study may be underpowered to validate low-prevalence biomarkers.


          The ccB signature was the only independent prognostic biomarker. Enrichment of multiple poor prognosis genetic alterations in ccB samples indicated that several events may be required to establish this aggressive phenotype, catalysed in some tumours by chromosomal instability. Multiregion assessment may improve the precision of this biomarker.

          Patient summary

          We evaluated the ability of published biomarkers to predict the survival of patients with clear cell kidney cancer in an independent patient cohort. Only one molecular test adds prognostic information to routine clinical assessments. This marker showed good and poor prognosis results within most individual cancers. Future biomarkers need to consider variation within tumours to improve accuracy.

          Take Home Message

          A total of 17 of 28 published biomarkers for clear cell renal cell carcinoma have been validated as predictors of survival. The ccA/ccB signature outperforms all others and adds prognostic information. Intratumour heterogeneity was seen for this biomarker, and multiregion assessment of tumours may further improve its accuracy.

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          Most cited references 25

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          Prognostic factors for overall survival in patients with metastatic renal cell carcinoma treated with vascular endothelial growth factor-targeted agents: results from a large, multicenter study.

          There are no robust data on prognostic factors for overall survival (OS) in patients with metastatic renal cell carcinoma (RCC) treated with vascular endothelial growth factor (VEGF) -targeted therapy. Baseline characteristics and outcomes on 645 patients with anti-VEGF therapy-naïve metastatic RCC were collected from three US and four Canadian cancer centers. Cox proportional hazards regression, followed by bootstrap validation, was used to identify independent prognostic factors for OS. The median OS for the whole cohort was 22 months (95% CI, 20.2 to 26.5 months), and the median follow-up was 24.5 months. Overall, 396, 200, and 49 patients were treated with sunitinib, sorafenib, and bevacizumab, respectively. Four of the five adverse prognostic factors according to the Memorial Sloan-Kettering Cancer Center (MSKCC) were independent predictors of short survival: hemoglobin less than the lower limit of normal (P < .0001), corrected calcium greater than the upper limit of normal (ULN; P = .0006), Karnofsky performance status less than 80% (P < .0001), and time from diagnosis to treatment of less than 1 year (P = .01). In addition, neutrophils greater than the ULN (P < .0001) and platelets greater than the ULN (P = .01) were independent adverse prognostic factors. Patients were segregated into three risk categories: the favorable-risk group (no prognostic factors; n = 133), in which median OS (mOS) was not reached and 2-year OS (2y OS) was 75%; the intermediate-risk group (one or two prognostic factors; n = 301), in which mOS was 27 months and 2y OS was 53%; and the poor-risk group (three to six prognostic factors; n = 152), in which mOS was 8.8 months and 2y OS was 7% (log-rank P < .0001). The C-index was 0.73. This model validates components of the MSKCC model with the addition of platelet and neutrophil counts and can be incorporated into patient care and into clinical trials that use VEGF-targeted agents.
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            An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score.

            Currently outcome prediction in renal cell carcinoma is largely based on pathological stage and tumor grade. We developed an outcome prediction model for patients treated with radical nephrectomy for clear cell renal cell carcinoma, which was based on all available clinical and pathological features significantly associated with death from renal cell carcinoma. We identified 1,801 adult patients with unilateral clear cell renal cell carcinoma treated with radical nephrectomy between 1970 and 1998. Clinical features examined included age, sex, smoking history, and signs and symptoms at presentation. Pathological features examined included 1997 TNM stage, tumor size, nuclear grade, histological tumor necrosis, sarcomatoid component, cystic architecture, multifocality and surgical margin status. Cancer specific survival was estimated using the Kaplan-Meier method. Cox proportional hazards regression models were used to test associations between features studied and outcome. The selection of features included in the multivariate model was validated using bootstrap methodology. Mean followup was 9.7 years (range 0.1 to 31). Estimated cancer specific survival rates at 1, 3, 5, 7 and 10 years were 86.6%, 74.0%, 68.7%, 63.8% and 60.0%, respectively. Several features were multivariately associated with death from clear cell renal cell carcinoma, including 1997 TNM stage (p <0.001), tumor size 5 cm. or greater (p <0.001), nuclear grade (p <0.001) and histological tumor necrosis (p <0.001). In patients with clear cell renal cell carcinoma 1997 TNM stage, tumor size, nuclear grade and histological tumor necrosis were significantly associated with cancer specific survival. We present a scoring system based on these features that can be used to predict outcome.
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              Molecular Stratification of Clear Cell Renal Cell Carcinoma by Consensus Clustering Reveals Distinct Subtypes and Survival Patterns.

              Clear cell renal cell carcinoma (ccRCC) is the predominant RCC subtype, but even within this classification, the natural history is heterogeneous and difficult to predict. A sophisticated understanding of the molecular features most discriminatory for the underlying tumor heterogeneity should be predicated on identifiable and biologically meaningful patterns of gene expression. Gene expression microarray data were analyzed using software that implements iterative unsupervised consensus clustering algorithms to identify the optimal molecular subclasses, without clinical or other classifying information. ConsensusCluster analysis identified two distinct subtypes of ccRCC within the training set, designated clear cell type A (ccA) and B (ccB). Based on the core tumors, or most well-defined arrays, in each subtype, logical analysis of data (LAD) defined a small, highly predictive gene set that could then be used to classify additional tumors individually. The subclasses were corroborated in a validation data set of 177 tumors and analyzed for clinical outcome. Based on individual tumor assignment, tumors designated ccA have markedly improved disease-specific survival compared to ccB (median survival of 8.6 vs 2.0 years, P = 0.002). Analyzed by both univariate and multivariate analysis, the classification schema was independently associated with survival. Using patterns of gene expression based on a defined gene set, ccRCC was classified into two robust subclasses based on inherent molecular features that ultimately correspond to marked differences in clinical outcome. This classification schema thus provides a molecular stratification applicable to individual tumors that has implications to influence treatment decisions, define biological mechanisms involved in ccRCC tumor progression, and direct future drug discovery.

                Author and article information

                Eur Urol
                Eur. Urol
                European Urology
                Elsevier Science
                1 November 2014
                November 2014
                : 66
                : 5
                : 936-948
                [a ]Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, UK
                [b ]Translational Cancer Therapeutics Laboratory, Cancer Research UK London Research Institute, London, UK
                [c ]Centre for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark
                [d ]The Royal Marsden Hospital, London, UK
                [e ]Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
                [f ]UCL Cancer Institute, London, UK
                [g ]Present address: Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK
                Author notes
                [* ]Corresponding authors. Charles Swanton, Cancer Research UK, London Research Institute, 44 Lincoln's Inn Fields, London WC2A 3LY, UK. Tel. +44 (0)20 7269 3515; Fax: +44 (0)20 7269 3094. paul.batest@ 123456cancer.org.uk charles.swanton@ 123456cancer.org.uk
                © 2014 Elsevier B.V. on behalf of European Association of Urology. All rights reserved.
                Kidney Cancer


                personalised medicine, prognostic marker, kidney cancer, intratumour heterogeneity, biomarker


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