Systems analysis of mechanisms driving response to immunotherapy in clear cell cancers

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Clear cell ovarian cancer (ccOC) is a rare and lethal cancer with few treatment options. Based on molecular analysis ccOC appears intrinsically immunogenic but with an immunosuppressive tumor microenvironment, similar to other ovarian cancer types. However, ccOC is very distinct from high grade serous ovarian carcinoma. Strikingly, it is similar in gene expression profiles to more frequent clear cell renal cell carcinomas (ccRCC), suggesting that clear cell cancers share intrinsic mechanistic or microenvironment properties, not just morphological appearance. Around 25% of ccRCC respond well to immune checkpoint inhibitors (ICIs), but markers for predicting response are lacking. The objective response rate for monotherapy pembrolizumab in one study was 33.3% for ccOC patients; but, in general, it is unknown which clear cell cancer patients could benefit from ICI treatment. Recent work has shown that tumor behavior is driven not just by cellular composition, but also by the spatial organization of different cell types including immune and stromal cells, as well as malignant cells themselves. Knowledge of clear cell cancer tumor microenvironments and their spatial architecture is lacking. Addressing this gap will improve our understanding of mechanisms of response to ICIs in clear cell cancers, including rare ones like ccOC, and improve selection of patients for immunotherapy. This study will use systems biology approaches to (i) elucidate and compare the cell types and their transcriptional states present in ccOC and ccRCC; (ii) characterize the spatial architecture of these cells within tumors using the CODEX (CODetection by indEXing) single cell proteomic imaging platform; and (iii) model and validate cell-cell interactions in the spatial tumor microenvironment that drive clear cell cancer response to immunotherapy through extensions of causal signaling inference algorithms to incorporate spatial context, and to optimize experimental validations in mouse models that maximize the information gain about interaction networks. Similar intrinsic and tumor microenvironmental features shared by ccOC and ccRCC, will nominate common mechanisms of immunotherapy response, and identify the subset of both who might benefit from treatment with ICIs. Successful development and application of these methods to clear cell cancers will establish a framework that can be applied to other cancer types, notably to rare ones. The expected outcome of this proposal is a comprehensive definition and dissection of the tumor microenvironment of ccRCC and ccOC. It will identify common features and mechanisms between these clear cell cancers, providing a basis to extend the approach to other classes of cancer, opening new avenues for treatment, particularly in rare cancer types.