Kapur, Payal

Abstract Background The mechanisms underlying metastatic latency in renal cell carcinoma (RCC) remain poorly understood. Methods This study evaluated 2 large independent cohorts for differences in tumor biology between patients who developed metastases early (≤1 year after nephrectomy) and those with late onset (>3 years). Results In the discovery cohort (n = 161), late metastatic RCC was associated with clear cell histology (88.9% vs 78.7%), lower pathological stage (pT1-2; 40.3% vs 18.0%), and favorable histopathological features including low grade (40.0% vs 2.3%), less sarcomatoid (5.6% vs 21.8%), and reduced necrosis (37.7% vs 78.3%; all P < .02). Late metastatic RCC tumors exhibited increased angiogenesis (63.5% vs 19.4%) and reduced inflammation (78.8% vs 50.0%; all P < .02) profiles. Genomic driver analyses revealed comparable rates of PBRM1 and SETD2 loss in late and early metastatic RCC, while BAP1 loss was significantly less common in late metastatic RCC (7.5% vs 27.1%; P < .02). In multivariable models, BAP1/PBRM1/SETD2 status and tumor necrosis emerged as key discriminators of late metastatic RCCs. These findings were confirmed in the second cohort (n = 307). Late metastatic RCC was enriched for fatty acid oxidation and angiogenesis pathways, supporting a less aggressive phenotype. This was further evidenced by a lower engraftment rate in murine models (0% vs 36.5%; P < .001) and significantly longer overall survival from the time of metastasis (median survival doubled, P < .001). Interestingly, late metastatic RCC shared genomic and phenotypic features with RCC that metastasizes to the pancreas, suggesting a common underlying biology influencing both metastatic latency and pancreatic tropism. Conclusions Overall, these findings advocate for recognition of late metastatic RCC because of its distinct biology and improved prognosis.

Abstract Intratumoral heterogeneity arising from tumor evolution poses significant challenges biologically and clinically. Dissecting this complexity may benefit from deep learning (DL) algorithms, which can infer molecular features from ubiquitous hematoxylin and eosin (H&E)–stained tissue sections. Although DL algorithms have been developed to predict some driver mutations from H&E images, the ability of these DL algorithms to resolve intratumoral mutation heterogeneity at subclonal spatial resolution is unexplored. Here, we apply DL to a paradigm of intratumoral heterogeneity, clear cell renal cell carcinoma (ccRCC), the most common type of kidney cancer. Matched IHC and H&E images were leveraged to develop DL models for predicting intratumoral genetic heterogeneity of the three most frequently mutated ccRCC genes, BAP1, PBRM1, and SETD2. DL models were generated on a large cohort (N = 1,282) and tested on several independent cohorts, including a TCGA cohort (N = 363 patients) and two tissue microarray (TMA) cohorts (N = 118 and 365 patients). These models were also expanded to a patient-derived xenograft (PDX) TMA, affording analysis of homotopic and heterotopic interactions of tumor and stroma. The status of all three genes could be inferred by DL, with BAP1 showing the highest sensitivity and performance within and across tissue samples (AUC = 0.87–0.89 on holdout). BAP1 results were validated on independent human (AUC = 0.77–0.84) and PDX (AUC = 0.80) cohorts. Finally, BAP1 predictions correlated with clinical outputs such as disease-specific survival. Overall, these data show that DL models can resolve intratumoral heterogeneity in cancer with potential diagnostic, prognostic, and biological implications. Significance: This work demonstrates the potential for deep learning analysis of histopathologic images to serve as a fast, low-cost method to assess genetic intratumoral heterogeneity. See related commentary by Song et al., p. 2672