Convergent genomic and molecular features predict risk of metachronous metastasis in clear cell renal cell carcinoma

The molecular features determining the risk of metachronous metastases in clear cell renal cell carcinoma (ccRCC) are poorly defined. This study aimed to identify molecular factors associated with the risk of metachronous metastasis.

Using a systematic tumor transcriptome deconvolution approach, we investigated the genomic and transcriptomic profiles of 192 ccRCC primary tumors with extended clinical follow-up to identify cancer- and stromal cell-specific molecular features associated with metastatic risk. Based on these features, we applied multivariate Cox regression to develop a compact 5-gene predictive model for metachronous metastasis.

At the genomic level, we identify a significantly higher frequency of copy number loss at 1p31-36 in primary tumors that later progress with metastases. Tumor transcriptome deconvolution identifies significant down-regulation of epithelial cell polarity, including PATJ (1p31), and fatty acid metabolism, including CYP4A11 (1p33), in cancer cells of tumors that develop metastatic progression. We develop and benchmark a compact 5-gene predictive model (5G) that demonstrates improved accuracy over existing ccRCC gene signatures in the prediction of metachronous metastasis risk.

Overall, our study highlights convergent genomic and transcriptomic alterations in chromosome 1p, driving dysregulation of epithelial cell polarity and fatty acid metabolism, as putative risk factors of metachronous metastasis in ccRCC.