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.