By leveraging tumorgraft (patient-derived xenograft) RNA-sequencing data, we developed an empirical approach, DisHet, to dissect the tumor microenvironment (eTME). We found that 65% of previously defined immune signature genes are not abundantly expressed in renal cell carcinoma (RCC) and identified 610 novel immune/stromal transcripts. Using eTME, genomics, pathology, and medical record data involving >1,000 patients, we established an inflamed pan-RCC subtype (IS) enriched for regulatory T cells, natural killer cells, T1 cells, neutrophils, macrophages, B cells, and CD8 T cells. IS is enriched for aggressive RCCs, including -deficient clear-cell and type 2 papillary tumors. The IS subtype correlated with systemic manifestations of inflammation such as thrombocytosis and anemia, which are enigmatic predictors of poor prognosis. Furthermore, IS was a strong predictor of poor survival. Our analyses suggest that tumor cells drive the stromal immune response. These data provide a missing link between tumor cells, the TME, and systemic factors. We undertook a novel empirical approach to dissect the renal cell carcinoma TME by leveraging tumorgrafts. The dissection and downstream analyses uncovered missing links between tumor cells, the TME, systemic manifestations of inflammation, and poor prognosis. .

The adaptive immune system recognizes tumor antigens at an early stage to eradicate cancer cells. This process is accompanied by systemic proliferation of the tumor antigen–specific T lymphocytes. While detection of asymptomatic early-stage cancers is challenging due to small tumor size and limited somatic alterations, tracking peripheral T cell repertoire changes may provide an attractive solution to cancer diagnosis. Here, we developed a deep learning method called DeepCAT to enable de novo prediction of cancer-associated T cell receptors (TCRs). We validated DeepCAT using cancer-specific or non-cancer TCRs obtained from multiple major histocompatibility complex I (MHC-I) multimer-sorting experiments and demonstrated its prediction power for TCRs specific to cancer antigens. We blindly applied DeepCAT to distinguish over 250 patients with cancer from over 600 healthy individuals using blood TCR sequences and observed high prediction accuracy, with area under the curve (AUC) ≥ 0.95 for multiple early-stage cancers. This work sets the stage for using the peripheral blood TCR repertoire for noninvasive cancer detection.

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Metastasis is the principal cause of cancer related deaths. Tumor invasion is essential for metastatic spread. However, determinants of invasion are poorly understood. We addressed this knowledge gap by leveraging a unique attribute of kidney cancer. Renal tumors invade into large vessels forming tumor thrombi (TT) that migrate extending sometimes into the heart. Over a decade, we prospectively enrolled 83 ethnically-diverse patients undergoing surgical resection for grossly invasive tumors at UT Southwestern Kidney Cancer Program. In this study, we perform comprehensive histological analyses, integrate multi-region genomic studies, generate in vivo models, and execute functional studies to define tumor invasion and metastatic competence. We find that invasion is not always associated with the most aggressive clone. Driven by immediate early genes, invasion appears to be an opportunistic trait attained by subclones with diverse oncogenomic status in geospatial proximity to vasculature. We show that not all invasive tumors metastasize and identify determinants of metastatic competency. TT associated with metastases are characterized by higher grade, mTOR activation and a particular immune contexture. Moreover, TT grade is a better predictor of metastasis than overall tumor grade, which may have implications for clinical practice.

Purpose: HIF2α is a key driver of kidney cancer. Using a belzutifan analogue (PT2399), we previously showed in tumorgrafts (TGs) that ~50% of clear cell renal cell carcinomas (ccRCCs) are HIF2α dependent. However, prolonged treatment induced resistance mutations, which we also identified in humans. Here, we evaluated a tumor-directed, systemically-delivered, siRNA drug (siHIF2) active against wild-type and resistant mutant HIF2α. Experimental Design: Using our credentialed TG platform, we performed pharmacokinetic and pharmacodynamic analyses evaluating uptake, HIF2α silencing, target gene inactivation and anti-tumor activity. Orthogonal RNA-seq studies of siHIF2 and PT2399 were pursued to define the HIF2 transcriptome. Analyses were extended to a TG line generated from a study biopsy of a siHIF2 phase I clinical trial (NCT04169711) participant and the corresponding patient, an extensively pretreated individual with rapidly progressive ccRCC and paraneoplastic polycythemia likely evidencing a HIF2 dependency. Results: siHIF2 was taken up by ccRCC TGs, effectively depleted HIF2α, deactivated orthogonally-defined effector pathways (including Myc and novel E2F pathways), downregulated cell cycle genes and inhibited tumor growth. Effects on the study subject TG paralleled those in the patient, where HIF2α was silenced in tumor biopsies, circulating erythropoietin was downregulated, polycythemia was suppressed obviating the need for phlebotomies, and a partial response was induced. Conclusions: To our knowledge, this is the first example of functional inactivation of an oncoprotein and tumor suppression with a systemic, tumor-directed, RNA-silencing drug. These studies provide a proof-of-principle of HIF2α inhibition by RNA-targeting drugs in ccRCC and establish a paradigm for tumor-directed RNA-based therapeutics in cancer.