Proteogenomic and clinical implications of unique recurrent splice variants in clear cell renal cell carcinoma.
Authors: Andrew Chang, Paul Stewart, Nicholas Haig Chakiryan, Alex C. Soupir, Yijun Tian, Dongliang Du, Jamie K. Teer, Youngchul Kim, Philippe E. Spiess, Jad Chahoud, Yonghong Zhang, John M Koomen, Anders E. Berglund, Liang Wang, Timothy J. Robinson, Brandon J. Manley
Journal: Journal of Clinical Oncology (2022)

380 Background: Alternative mRNA splicing is recognized as a key driver of proteomic diversity. In cancer, this splicing process can be altered resulting in generation of aberrant splice variants (SvPs) that can contribute to tumor pathogenesis. However, our understanding of the significance of splice variants in clear cell renal cell carcinoma (ccRCC) is currently limited. Given the lack of actionable genomic mutations in ccRCC, aberrant SpVs may be the avenue to new pathogenic mechanisms and biomarkers. Methods: We implemented a novel pipeline to screen for and select SpVs frequent in and relatively specific to ccRCC. We started with RNA-seq data from the Cancer Cell Line Encyclopedia to identify SpVs specific to ccRCC cell lines. These were then screened across normal tissue in the Genotype-Tissue Expression Project (GTEx) and excluded if expressed. We analyzed bulk RNA-seq data of ccRCC primary tumors obtained from our institutional Total Cancer Care cohort (TCC; n = 111), The Cancer Genome Atlas (TCGA; n = 484) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC; n = 110) to analyze SpV expression in these samples. Using raw proteomics files from the CPTAC portal, proteins were identified and quantified using MaxQuant. Associations of SvP with protein expression were filtered by a Spearman correlation cutoff of +/-0.3. The Enrichr R library was used for pathway enrichment. Finally, we correlated splice variant expression with overall (OS) and cancer-specific survival (CSS). Using LASSO Cox regression analysis, we derived a SpV-based risk score trained on OS from the TCGA cohort and validated on the TCC and CPTAC cohorts. Results: Our pipeline selected 16 previously uncharacterized SpVs, including variants of suspected oncogenes and tumor suppressors. Proteogenomic analysis identified interesting biological associations. Among patients with high levels of EGFR SpV, we found significantly higher expression of the protein regulatory T cell marker CD70 (p adj = 0.03). MVK SvP was highly correlated with 25 proteins enriched for the mTOR pathway (p adj = 0.002). We derived a survival risk score based on expression of 5 SpVs ( PDZD2, COBLL1, PTPN14, RNASET2, FGD1) in the TCGA cohort. This risk score remained significant on multivariate analysis (HR 1.4, p = 0.002) adjusting for covariates including AJCC stage. This was validated on multivariate analysis in the TCC (HR 3.56, p &lt; 0.001) and CPTAC (HR 3.18, p = 0.019) cohorts. Conclusions: Our novel pipeline selected 16 unique SpVs frequent in and relatively specific for ccRCC. Some are associated with proteins expressed in oncogenic pathways, suggesting a potential role in disease pathogenesis. Additionally, our SpV-based risk score is strongly associated with OS and CSS across multiple cohorts. This study provides a template for identifying and characterizing disease-specific aberrant SpVs to aid discovery of new mechanisms and biomarkers.