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Unraveling the molecular profile underpinning pancreatic tropisms in metastatic clear cell renal cell carcinoma

Background:
The tropism of cancer metastases is poorly understood yet holds prognostic value. Clear cell renal cell carcinoma (ccRCC) exhibits a broad pattern of metastases, making it an optimal model to study organotropism. Notably, when ccRCC metastasizes to the pancreas (PM) independently of other sites, it is associated with favorable outcomes in patients for unclear reasons. Here, we comprehensively analyzed the clinical and molecular profile of patients with PM.

Methods:
RCC patients with PM from UTSW and Cleveland Clinic were identified. Clinicopathologic data and oncologic outcomes were analyzed. Whole exome sequencing (WES), RNAseq, and histologic assessment of primary and metastatic tumors from PM patients were conducted.

Results:
31 RCC patients with PM were identified. We observed remarkably favorable outcomes in our PM cohort, with a median overall survival (OS) of 10.7 years from metastatic diagnosis and a long latency between initial diagnosis and development of metastasis (median 69 months in patients who were non-metastatic at diagnosis). OS was independent of both metastatic tumor burden and known IMDC prognostic factors. We discovered that tumors from PM patients were markedly uniform and clustered together by gene expression analysis. WES and DNA copy number analyses revealed a high frequency of VHL and PBRM1 mutations, 3p loss, and 5q amplification, along with a lower frequency of 9p, 14q and 4q losses and BAP1 mutations, characteristic of indolent ccRCC. Furthermore, the genomic and histologic features of tumors from patients with PM can be recapitulated in patient-derived xenograft models.

Conclusions:
To our knowledge, this is the first report to unravel molecular determinants of organotropism, and we highlight that organotropism can be an independent prognostic factor. Understanding tumor heterogeneity may help refine prognostic models for metastatic RCC and hold implications for improved personalization of therapy.