Abstract B013: Deep learning-based prediction of synthetic essentialities in CTNNB1-mutated hepatocellular carcinoma

Abstract

Abstract Background: Synthetic essentiality represents a promising therapeutic approach by identifying genes that are necessary for the proliferation and survival of tumors harboring hard-to-target gene alterations. Understanding and accurately predicting synthetic essential genes, through genetic dependencies, may reveal therapeutically effective drug targets in a specific molecular context. Deep learning, as exemplified by our published DeepDEP model, has the potential to capture intricate multi-omic profiles for such prediction tasks. However, the validity of such tools in specific biological contexts remains to be fully examined and presents a major obstacle to adoption by researchers. Materials and Methods: To address this gap, we conducted a case study in which we screened for synthetic essential genes for one of the most frequently mutated and yet undruggable genes, CTNNB1, in hepatocellular carcinoma (HCC). Specifically, we predicted the genetic dependencies of each HCC patient in The Cancer Genome Atlas (TCGA; n=346) by DeepDEP and identified potential dependencies that were intensified with the presence of CTNNB1 mutations. The top 10 genes, ranked by p-value of differential gene-effect scores for CTNNB1-mutated (n=92) versus CTNNB1-WT HCC (n=254), were reviewed in the literature to validate their essentiality in CTNNB1-mutated HCC as well as their potential for pharmacologic inhibition. Survival analysis was performed using published data from the IMBrave150 trial to validate one of the findings. Results: Experimental evidence in the literature supported the essentiality of many of the top 10 predicted genes for CTNNB1-mutated HCC, including one gene with mechanistic evidence of being a transcriptional co-activator of β-catenin target genes. Furthermore, several of these genes have known pharmacologic inhibitors which are either natural compounds or FDA-approved drugs. One example was PDGFB, which encodes a ligand activating the PDGF signaling pathway. PDGF signaling is targeted by sorafenib, an FDA-approved first line drug for HCC. Survival analysis of the sorafenib-treated arm of the IMBrave150 trial showed that patients with mutated CTNNB1 had improved progression-free survival compared to those with wild-type CTNNB1 (p = 0.044). Conclusions: Our study illustrates a potential application of deep learning to identify synthetic essential genes, including genes with readily available pharmacologic inhibitors, for targeting challenging gene alterations. Remarkably, our tool demonstrates the ability to predict cancer dependencies with molecular subtype specificity, suggesting a potential for in silico screening of gene dependencies to facilitate drug discovery and personalized medicine approaches. Our current efforts are focused on optimizing this computational pipeline and making it publicly available for cancer researchers. Citation Format: Tyler M. Yasaka, Michael Kasper, Li-Ju Wang, Michael Ning, Yufei Huang, Satdarshan P Monga, Yu-Chiao Chiu. Deep learning-based prediction of synthetic essentialities in CTNNB1-mutated hepatocellular carcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B013.