Abstract LB005: Identification of novel targets for MYC-driven hepatocellular carcinoma via transomics

作者：Simon P. Fricker, Christopher Nicholson, Samuel J. Roth, Arudhir Singh, Caitlin S. Brown, Jon Hu, Petronela Buiga, Vishnu P Kanakaveti, Anja Deutzmann, Dean W. Felsher, Samantha Dale Strasser · 发表于：Cancer Research · 年份：2024 · DOI：10.1158/1538-7445.am2024-lb005 · 研究领域：Monoclonal and Polyclonal Antibodies Research、Hepatocellular Carcinoma Treatment and Prognosis、Cancer Mechanisms and Therapy

Abstract Hepatocellular carcinoma (HCC) is an aggressive tumor, and treatment options for patients with unresectable HCC are limited to targeted therapies and immunotherapy. The activation and dysregulation of MYC have been implicated in multiple cancers, including HCC, where approximately 30% of human HCC samples show MYC gene amplification. However, it is challenging to target MYC directly. Therefore, we applied a novel approach to circumvent targeting MYC directly by leveraging Pepper’s proprietary transomic analysis platform, COMPASS, to identify novel targets that are predicted to mimic the effect of turning MYC off. Specifically, COMPASS unlocks functional drivers of disease to identify novel drug targets. To study the role of MYC in tumor biology, we utilize an HCC cell line with tunable MYC expression. We generate genomic, transcriptomic, proteomic, and phosphoproteomic data from samples with MYC-on and MYC-off and compare via COMPASS to identify novel targets that mimic turning MYC off. The MYC-conditional HCC cell line (EC4) allows the regulation of MYC expression via the tetracycline regulatory (Tet-Off) system. Four omic datasets were collected from each sample: genomics (next-generation sequencing, NGS), transcriptomics (NGS), proteomics (mass spectrometry), and phosphoproteomics (mass spectrometry). We employed the COMPASS target prioritization algorithm to identify and rank novel targets that mimic turning MYC off. Targets were then filtered to select those wit...