Abstract 488: A systems biology approach to defining tumor heterogeneity and prognostic and targetable master regulator protein signatures from bulk and single-cell RNAseq in osteosarcoma

Abstract

Abstract Despite many clinical trials, outcome for osteosarcoma (OS) patients remains poor, especially for those with metastatic or relapsed/refractory disease. We leveraged network-based systems biology approaches to discover Master Regulator (MR) proteins representing pharmacologically accessible, mechanistic determinants of OS cell state, and to dissect tumor transcriptional heterogeneity. MRs were identified by interrogating an OS regulatory network—generated from 87 RNAseq profiles in the TARGET OS cohort—with gene expression signatures from 149 diagnostic OS samples, using the VIPER algorithm. RNAseq profiles were generated in four sarcoma cell lines following perturbation with ~400 oncology drugs, and used to identify drugs capable of inverting MR activity profiles with the OncoTreat algorithm. Unsupervised, protein activity-based clustering of the samples identified two clusters, characterized by a significant overall survival difference (76% vs 38%, log-rank p-val 0.0041). Patients in the high-risk cluster presented aberrant activity of proteins involved in cell cycle (FOXM1, CENPF, TOP2A) and epigenetic remodeling (EZH2, KDM1A), while those in the low-risk cluster had aberrant activity of proteins involved in immune response (VAV1, CD86, CEBPE), interferon gama signaling (IRF5, MNDA), and senescence control (CREG1, TFEC). We further identified patients with metastatic disease and particularly poor outcome (<15% survival) showing high activity in stem cell-related proteins NANOG, ZSCAN4, and LIN28A. To address intra-tumor heterogeneity and immune infiltration (40-60% in the samples) in bulk RNAseq data, we inferred protein activity in previously published single-cell RNAseq profiles from 11 OS patients, using regulatory networks also produced by single-cell analysis. We identified three tumor subpopulations, co-existing in all the samples, with lineage similarity to osteoblasts, chondroblasts, and fibroblasts. The most active MR proteins (p-val < 10E-30) were highly distinct across these subpopulations: NANOG, REL, IGF2, TOP2A (osteoblasts), SSTR3, WNT1, KDM6B, EZH2 (chondroblasts), and NOD1, ERBB3, PRKDC, INSR (fibroblasts). Pharmacologically accessible MRs significantly active across the three subpopulations and in subsets of bulk RNAseq samples included EZH2, CDK2, TOP2A, HDAC5, and PRKDC. OncoTreat analysis predicted sensitivity to camptothecin, seliciclib, galunisertib, and rigosertib in both fibroblast- and chondroblast-like subpopulations. In conclusion, using network-based systems biology approaches in OS, we identified pharmacologically accessible MR subtypes differentiating survival at the bulk tissue level, and defining three distinct tumor subpopulations at the single-cell level with unique predicted drug sensitivities. Citation Format: Somnath Tagore, Jovana Pavisic, Aaron T. Griffin, Katherine Janeway, Andrew L. Kung, Filemon Dela Cruz, Alejandro Sweet-Cordero, Inge Behroozfard, Stanley Leung, Alex Lee, Darrell Yamashiro, Julia Glade Bender, Andrea Califano. A systems biology approach to defining tumor heterogeneity and prognostic and targetable master regulator protein signatures from bulk and single-cell RNAseq in osteosarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 488.