B1153
Title: Spatial transcriptomics and spatial statistics as tools to study immune-mediated tumor killing in ovarian cancer
Authors: Celine M Laumont - Deeley Research Centre at BC Cancer Victoria (Canada) [presenting]
Shreena Nisha Kalaria - Deeley Research Centre at BC Cancer Victoria (Canada)
Abstract: High-grade serous ovarian cancer (HGSC) presents a significant clinical challenge, as only 15\% of patients survive more than 10 years following treatment. Interestingly, tumors of long-term survivors often contain immune cells, especially T and B cells. Thus, the collaboration between B and T cells is hypothesized to promote efficient killing (apoptosis) of tumor cells. To test the hypothesis, spatially-resolved gene expression is generated, as well as B cell receptor (BCR) and T cell receptor (TCR) data for 14 human primary, untreated ovarian tumors. First, an apoptotic gene signature is constructed to identify dying tumor cells potentially undergoing T and B cell-mediated killing. Then, the spatial-BCR and TCR data are leveraged to identify pairs of collaborating T and B cells. Specifically, (i) cross-K functions and simulation envelopes to evaluate spatial dependence, and (ii) the number of pixels shared to flag putative sites of direct contact between B and T cells are used. Finally, a hierarchical binomial-logistic model is used to relate the apoptosis rate in each pixel to the intensity of BCR and TCR clones. Hopefully, the results obtained using spatial statistics and regression models will inform the design of more effective immunotherapies, enhancing both B and T cell responses against ovarian cancer.
