Abstract 5088: Single cell-based elucidation and pharmacologic targeting of master regulator dependencies in small cell lung cancer (SCLC) subpopulations

Abstract Small Cell Lung Cancer (SCLC) is a highly aggressive and deadly neuroendocrine lung cancer with no major effective treatments. SCLCs are usually detected after wide metastasis, which leads to extremely low five-year survival rate (around 7%). To address this challenge, we leveraged network-based computational approach to first dissect the heterogeneity of SCLC tumors and to discover Master Regulator (MR) proteins representing pharmacologically accessible, mechanistic determinants of molecularly distinct SCLC cell populations. Specifically, we first profiled transcriptomes of 182, 189 cells from 41 fresh SCLC human tissue samples, acquired from 14 patients. Samples are collected from both primary site (lung) and metastases (Mediastinum, liver and adrenal gland). Built upon this dataset, we reverse-engineered single-cell based SCLC-specific regulatory networks through ARACNe algorithm. Using these networks, we then measured protein activity of over 6, 000 regulatory and signaling proteins for all the cells via VIPER. To dissect intra-tumor heterogeneity of SCLC, protein activity-based analysis of tumor cells identified cell subpopulations representing distinct differentiation and proliferation stages. Our analysis helped identify cell populations presenting highly aberrant activity of proliferative MRs, which are termed as “Tumor Checkpoint” regulatory module. We then identified SCLC cell lines showing high protein activity similarity with the target tumor cell populations using rank-based statistics. Following perturbation of the selected cancer cell lines with hundreds of oncology drugs, we identified potent drugs that could invert the activities of the MRs of target tumor subpopulations using OncoTreat algorithm. The identified drugs are subsequently validated using in vivo mouse model. The outcome of this project is four-fold: First, the patient derived SCLC single-cell RNA-Seq dataset generated in this project represents the largest cohort of this kind up to date; Second, our analysis helps study SCLC intra-tumor heterogeneity from the protein activity angle, which is complementary to gene expression measurement alone; Third, our drug perturbation dataset is the first whole-genome based drug perturbation gene expression profile in SCLC cell lines. Fourth, our comprehensive analysis identifies drugs effectively induce tumor population demise in SCLC. Currently, we are working on the drug perturbation experiment on SCLC cell lines to finalize potent drug predictions. Citation Format: Lucas ZhongMing Hu, Anish Thomas, Andrea Califano. Single cell-based elucidation and pharmacologic targeting of master regulator dependencies in small cell lung cancer (SCLC) subpopulations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5088.