Sandoval, Leticia Assad Maia

e17535 Background: High-grade serous ovarian carcinoma (HGSOC) is the most frequent and deadliest type of ovarian cancer (OC). With the recent advances in transcriptomic and epigenomic profiling of cancer at single cell resolution, it has become clear that intra-tumor heterogeneity driven by genetic and epigenetic factors may be the basis of drug resistance. Current treatments are not homogeneously effective against all the cancer cell subpopulations, thus enabling resistance. With single cell sequencing technology, characterization of cell signatures and regulatory mechanisms may translate to prognostic markers and novel drug targets. Methods: The transcriptomic and epigenomic landscape of HGSOC was mapped for five tumor samples from debulking surgeries and one tissue patient-derived xenograft (PDX) using the 10X genomics sequencing platform. This strategy permits the combination of single cell ATAC-seq and single cell RNA-seq within the same nucleus (Multiome). Isolation of nuclei, library preparation and sequencing were performed following 10X genomics protocols. Sample quality and library quality were assessed by nuclear morphology and fragment size analysis, respectively. Sequenced reads were mapped to the human genome and downstream analysis including copy number variant prediction, pathway enrichment, motif enrichment and gene regulatory network analysis were performed. The sampled patients continue to be followed for clinical outcomes, such as response to therapy. Clinical data including patient age, grade, and stage of cancer, debulking status, CA-125 levels, and neoadjuvant status, were used in conjunction with the genomics data to characterize patient specific molecular regulatory signatures. Results: Cells from the six samples (total N= 26,421) were projected via UMAP. Immune and stromal cells were shown to cluster by cell-type while cancer cells clustered by patient. Cancer cells were identified as cycling cells, ciliated cells, cells with enrichment of the JAK-STAT signaling pathway, and cells exhibiting cancer stemness signatures. Transcription factor motifs and binding-site enrichment in open chromatin regions reveal transcription regulation-based subpopulations. Using the combined gene expression and open chromatin information from these cells, there is potential to uncover the genetic regulatory network(s) that drives treatment resistance. Conclusions: With single cell technology, specific clusters of cancer and tumor microenvironment cells can be classified. Beyond characterizing patient specific signatures, multiome enables the discovery of genomic, transcriptomic and epigenomic signatures that provide insight in tumor progression and treatment resistance.

Abstract Ovarian cancer is one of the most common gynecologic malignancies, with high-grade serous carcinoma (HGSC) as the most frequent and deadliest type of ovarian cancer (OC). With the recent advances in cancer epigenomic profiling, it has become clear that intra-tumor heterogeneity driven by genetic and epigenetic factors may be the basis of resistance, where current treatments could efficiently target sub-populations of cells while a sub-group may be resistant, or prone to develop resistance. With epigenomic profiling and single cell sequencing technology, we aim to characterize cell signatures that translate as prognostic markers and novel drug targets. We have mapped the epigenomic landscape of HGSC in 12 patient-derived xenografts (PDX), and their metastatic counterparts (circulating tumor cells and ascites present in the PDX models) using an assay for transposase-accessible chromatin (or ATAC-seq) and compared it with normal fallopian tube organoids to identify tumor and metastatic-specific regulatory elements. We also performed RNA-seq to validate potential transcriptional dependencies associated with resistance to chemotherapy. In order to identify potentially chemotherapy-resistant subpopulations and further validate the activity of some of the transcription factors that can be driving resistance, we performed single cell multiomic profiling using the 10x Multiome sequencing platform (scRNA+scATAC from the same nuclei) in HGSC patient samples and PDX. Finally, we performed immunohistochemistry of patient tissue slides for target markers and correlated it with overall survival in OC patients. As previously suggested, a subset of cancer clonal cells can drive treatment resistance and metastasis and result in poor patient outcomes. Data derived from PDX ATAC-seq showed a cancer stem cell signature driven by SOX2 in metastatic samples compared to the paired PDX primary tumor, suggesting a role for SOX2 in disease progression. Interestingly, SOX2 expression in epithelial cells in the Multiome analysis is more patient-specific than other stem cell markers, like CD133. We also observed that SOX2 expression negatively correlated with prognosis. These data, taken together, point out the promising role of SOX2 as a prognostic and targetable marker for HGSC. Citation Format: Leticia Assad Maia Sandoval, Wazim Mohammed Ismail, Amelia Mazzone, Amik Munankarmy, Jagneet Kaur, Tsering Tashi, Xiaonan Hou, Nagarajan Kannan, Ann Oberg, Scott H. Kaufmann, Saravut John Weroha, Alexandre Gaspar-Maia. Single cell multiomic profiling of high-grade serous carcinoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4724.