Abstract 5768: Identifying signaling changes that give rise to drug resistance in acute myeloid leukemia through multiomic modeling across diverse model systems
Authors: Juan Camilo Posso, Setareh Sharzehi, Marina A. Gritsenko, McKenna Finley, Chelsea M. Hutchinson-Bunch, Keith Beadle, Karl K. Weitz, Melissa Stewart, Samantha A. Obermiller, Belinda B. Garana, Anupriya Agarwal, Cristina Tognon, Jeffrey Tyner, Paul D. Piehowski, Elie Traer, Sara J. Gosline
Journal: Cancer Research (2025)

Abstract Acute myeloid leukemia (AML) is a blood cancer that affects ∼20, 000 people annually, with an overall 5-year survival rate of ∼30%. Leukemia cells originate in the bone marrow where normal cell differentiation goes awry, leading to the expansion of immature myeloid cells that retain partial differentiation, or cell state (monocytic, granulocytic, primitive, etc. [Van Galen et al, Cell 2019]). However, the mechanism by which cell state shifts cooperate with protein signaling changes to bring about secondary mutations is still unknown. In this work we generated AML cell lines resistant to venetoclax (BCL2 inhibitor), decitabine (hypomethylating agent) and gilteritinib (FLT3 inhibitor) as single agents and in combination and found changes in cell state, protein signaling, and mutational burden. To interrogate the mechanisms behind these changes, we treated four primary AML cells with these drugs (alone and in combination) and captured transcriptomic and proteomic measurements of persistent AML patient cells after 1 month of treatment to compare drug-induced shifts in cell state. We collected single-cell transcriptomic and global proteomic measurements across two models of venetoclax, gilteritinib, and decitabine resistance: (1) MOLM14 cells treated for up to 6 months with each drug/drug combination, and (2) 4 ex vivo patient primary AML cells treated with monotherapy and drug combination for 1 month. In each model system, we measured shifts in cell populations using single cell-derived signatures of cell state based on the transcriptomic data, then calculated differential expression and pathway enrichment statistics of drug resistance models compared to their untreated control using the proteomic data. In our model of short-term drug resistance, transcriptomic data indicated that leukemia cells acquired a more granulocytic phenotype upon treatment with gilteritinib, became more monocytic with venetoclax treatment, and remained unchanged with decitabine treatment. These shifts occurred with little change in protein abundance (&amp;lt;40 proteins differentially expressed) according to our proteomics measurements of the same samples. Venetoclax combined with decitabine also caused cells to become more monocytic, while combining venetoclax with gilteritinib caused a decrease in monocytic cells as well a noticeable shift (100-500 proteins) in protein expression. Long term drug exposure caused further changes in MOLM14 cells, with many drug-resistant cells becoming more monocytic and exhibiting dramatic changes to protein signaling with up to ∼2000 proteins differentially expressed. We are now using these omics measurements to identify putative drug targets that prevent the development of drug resistance. In conclusion, we found that drug-induced protein changes can alter leukemia cell phenotypes that influence mutations and resistance in AML. Citation Format: Juan Camilo Posso, Setareh Sharzehi, Marina A. Gritsenko, McKenna Finley, Chelsea M. Hutchinson-Bunch, Keith Beadle, Karl K. Weitz, Melissa Stewart, Samantha A. Obermiller, Belinda B. Garana, Anupriya Agarwal, Cristina Tognon, Jeffrey Tyner, Paul D. Piehowski, Elie Traer, Sara J. Gosline. Identifying signaling changes that give rise to drug resistance in acute myeloid leukemia through multiomic modeling across diverse model systems [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 5768.