title: Molecular Subtypes Improve Prognostic Value of International Metastatic Renal Cell Carcinoma Database Consortium Prognostic Model.
abstract: Gene-expression signatures for prognosis have been reported in localized renal cell carcinoma (RCC). The aim of this study was to test the predictive power of two different signatures, ClearCode34, a 34-gene signature model [Eur Urol 2014;66:77-84], and an 8-gene signature model [Eur Urol 2015;67:17-20], in the setting of systemic therapy for metastatic disease.
  
  
  
  Metastatic RCC (mRCC) patients from five institutions who were part of TCGA were identified and clinical data were retrieved. We trained and implemented each gene model as described by the original study. The latter was demonstrated by faithful regeneration of a figure and results from the original study. mRCC patients were dichotomized to good or poor prognostic risk groups using each gene model. Cox proportional hazard regression and concordance index (C-Index) analysis were used to investigate an association between each prognostic risk model and overall survival (OS) from first-line therapy.
  
  
  
  Overall, 54 patients were included in the final analysis. The primary endpoint was OS. Applying the ClearCode34 model, median survival for the low-risk-ccA (n= 17)-and the high-risk-ccB (n= 37)-subtypes were 27.6 and 22.3 months (hazard ratio (HR): 2.33;p= .039), respectively. ClearCode34 ccA/ccB and International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) classifications appear to represent distinct risk criteria in mRCC, and we observed no significant overlap in classification (p> .05, chi-square test). On multivariable analyses and adjusting for IMDC groups, ccB remained independently associated with a worse OS (p= .044); the joint model of ccA/ccB and IMDC was significantly more accurate in predicting OS than a model with IMDC alone (p= .045, F-test). This was also observed in C-Index analysis; a model with both ccA and ccB subtypes had higher accuracy (C-Index 0.63, 95% confidence interval [CI] = 0.51-0.75) and 95% CIs of the C-Index that did not include the null value of 0.5 in contrast to a model with IMDC alone (0.60, CI = 0.47-0.72). The 8-gene signature molecular subtype model was a weak but insignificant predictor of survival in this cohort (p= .13). A model that included both the 8-gene signature and IMDC (C-Index 0.62, CI = 0.49-0.76) was more prognostic than IMDC alone but did not reach significance, as the 95% CI included the null value of 0.5. These two genomic signatures share no genes in common and are enriched in different biological pathways. The ClearCode34 included genesARNTandEPAS1(also known as HIF2a), which are involved in regulation of gene expression by hypoxia-inducible factor.
  
  
  
  The ClearCode34 but not the 8-gene molecular model improved the prognostic predictive power of the IMDC model in this cohort of 54 patients with metastatic clear cell RCC.The Oncologist2017;22:286-292 IMPLICATIONS FOR PRACTICE: The clinical and laboratory factors included in the International Metastatic Renal Cell Carcinoma Database Consortium model provide prognostic information in metastatic renal cell carcinoma (mRCC). The present study shows that genomic signatures, originally validated in localized RCC, may add further complementary prognostic information in the metastatic setting. This study may provide new insights into the molecular basis of certain mRCC subgroups. The integration of clinical and molecular data has the potential to redefine mRCC classification, enhance the understanding of mRCC biology, and potentially predict response to treatment in the future.
authors.affiliation: Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.|Department of Medical Oncology, University Hospital 12 de Octubre, Madrid, Spain.
authors.forename: Guillermo
authors.initials: G
authors.lastname: de Velasco
authors.affiliation: Department of Biostatistics & Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.|Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
authors.forename: Aedín C
authors.initials: AC
authors.lastname: Culhane
authors.affiliation: Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.|Pontifical Catholic University of Rio Grande do Sul (PUCRS) School of Medicine, Porto Alegre, Brazil.
authors.forename: André P
authors.initials: AP
authors.lastname: Fay
authors.affiliation: Department of Surgery-Urology Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors.forename: A Ari
authors.initials: AA
authors.lastname: Hakimi
authors.affiliation: Department of Medical Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors.forename: Martin H
authors.initials: MH
authors.lastname: Voss
authors.affiliation: Department of Medical Oncology, MD Anderson Cancer Center, Houston, Texas, USA.
authors.forename: Nizar M
authors.initials: NM
authors.lastname: Tannir
authors.affiliation: Department of Pathology, MD Anderson Cancer Center, Houston, Texas, USA.
authors.forename: Pheroze
authors.initials: P
authors.lastname: Tamboli
authors.affiliation: Division of Hematology/Oncology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.
authors.forename: Leonard J
authors.initials: LJ
authors.lastname: Appleman
authors.affiliation: Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
authors.forename: Joaquim
authors.initials: J
authors.lastname: Bellmunt
authors.affiliation: Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
authors.forename: W
authors.initials: W
authors.lastname: Kimryn Rathmell
authors.affiliation: Department of Medical Oncology, Institut Gustave Roussy, Villejuif, France.
authors.forename: Laurence
authors.initials: L
authors.lastname: Albiges
authors.affiliation: Department of Medical Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
authors.forename: James J
authors.initials: JJ
authors.lastname: Hsieh
authors.affiliation: Department of Medical Oncology, Tom Baker Cancer Center and University of Calgary, Calgary, Canada.
authors.forename: Daniel Y C
authors.initials: DY
authors.lastname: Heng
authors.affiliation: Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.|Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
authors.forename: Sabina
authors.initials: S
authors.lastname: Signoretti
authors.affiliation: Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA Toni_Choueiri@dfci.harvard.edu.
authors.forename: Toni K
authors.initials: TK
authors.lastname: Choueiri
mesh_terms: D051784:Aryl Hydrocarbon Receptor Nuclear Translocator / Q000235:genetics; D051792:Basic Helix-Loop-Helix Proteins / Q000235:genetics; D002292:Carcinoma, Renal Cell / Q000145:classification / Q000235:genetics* / Q000473:pathology; D015331:Cohort Studies; D016208:Databases, Factual; D018572:Disease-Free Survival; D005260:Female; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D006801:Humans; D008297:Male; D058990:Molecular Targeted Therapy; D016609:Neoplasms, Second Primary / Q000235:genetics* / Q000473:pathology; D011379:Prognosis; D012307:Risk Factors; D000099162:Endothelial PAS Domain-Containing Protein 1
chemical_list: D051784:Aryl Hydrocarbon Receptor Nuclear Translocator; D051792:Basic Helix-Loop-Helix Proteins; D000099162:Endothelial PAS Domain-Containing Protein 1; C497396:ARNT protein, human
keywords: Genomics; Prognosis; Signature; TCGA; mRCC