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American Society of Clinical Oncology, Journal of Clinical Oncology, 7_suppl(33), p. 406-406, 2015

DOI: 10.1200/jco.2015.33.7_suppl.406

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Proteomic stratification of clear cell renal cell carcinoma utilizing The Cancer Genome Atlas (TCGA) with external validation

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Data provided by SHERPA/RoMEO

Abstract

406 Background: Proteomics represents the ultimate convergence of DNA and expression alterations. We therefore sought to leverage TCGA reverse phase protein array (RPPA) data with an independent proteomic platform to identify druggable targets and pathways associated with prognosis in clear cell renal cell carcinoma (ccRCC). Methods: Unsupervised hierarchical consensus clustering was performed and differentially expressed proteins were identified for pathway analysis. Associations with clinicogenomic factors were assessed and Cox proportional hazards models were performed for disease-specific survival (DSS). Results: RPPA clustering of 324 patients from the ccRCC TCGA revealed 5 robust clusters characterized by alterations in specific pathways and divergent prognoses. Cluster 1 was characterized by poor DSS, decreased expression of receptor tyrosine kinases (RTK) and upregulation of the mTOR pathway. It was also associated with mTOR pathway genomic alterations, sarcomatoid histology and the ccb prognostic mRNA signature (all p<0.001). Cluster 2 was characterized by increased expression of RTKs and interestingly, had upregulation of the mTOR pathway with excellent DSS. After accounting for stage and grade, cluster designation remained independently associated with DSS (HR 0.23 for cluster 2, 95% CI 0.08-0.68; p=0.008). External validation was performed on a separate cohort of 189 patients with a different quantitative proteomics platform. A panel of phosphoproteins (pHER1, pHER2, pHER3, pSHC, pMEK, pAKT), highly discriminant between the most divergent RPPA clusters (1 and 2) was evaluated. Those at the highest quartile of activation in > 3 proteins were associated with improved DSS (HR 0.19, 95% CI 0.05-0.082; p=0.03). Patients with mTOR pathway activation segregated to those with coincident RTK activation (n=83) and those without (n=13). Conclusions: We have identified and validated proteomic signatures which cluster ccRCC patients into 5 prognostic groups. Furthermore, two distinct mTOR-activated clusters—one with high RTK activity and one with increased mTOR pathway genomic alterations were revealed, which may have prognostic and therapeutic implications.