Abstract PO-037: Machine learning-enabled transomics identifies three therapeutic targets for MYC-driven diffuse large B cell lymphoma

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：Blood Cancer Discovery · 年份：2024 · DOI：10.1158/2643-3249.lymphoma24-po-037 · 研究领域：Monoclonal and Polyclonal Antibodies Research、Lymphoma Diagnosis and Treatment、HER2/EGFR in Cancer Research

Abstract MYC activation and dysregulation is a powerful oncogenic driver in multiple cancers, including diffuse large B cell lymphoma (DLBCL). There is a known correlation between MYC expression and poor prognosis. While the 5-year overall survival for DLBCL patients on first-line therapy, R-CHOP, is approximately 75%, the 5-year overall survival drops to a staggering 30% for patients co-expressing MYC and BCL2 (double expressor lymphoma). However, it is challenging to target MYC directly. Therefore, we identified a novel approach to circumvent targeting MYC directly by leveraging Pepper’s proprietary machine learning (ML) transomic analysis platform, COMPASS, to identify novel targets associated with high MYC activity that, when inhibited, are predicted to recapitulate the effect of MYC inactivation. Specifically, COMPASS unlocks functional drivers of disease to identify novel drug targets. To study the role of MYC in lymphoma biology, we utilize a MYC-conditional T cell acute lymphoblastic leukemia/lymphoma (T-ALL) cell line (4188) with tunable MYC expression. We collect genomic, transcriptomic, proteomic, and phosphoproteomic data from samples with MYC transgene expression “on” and “off” and compare these biological cell states via COMPASS to identify novel targets that reproduce MYC inactivation. This provides new hope for patients suffering from MYC driven DLBCL. The MYC-conditional cell line allows the regulation of MYC expression via the tetracycline regulatory (Tet-Of...