Abstract B035: Zebrafish modeling predicts clinical outcomes in human acute lymphoblastic leukemia

Abstract The complex heterogeneity of Acute lymphoblastic leukemia (ALL) often predicts poor prognosis, high morbidity, and drug resistance. Yet, the molecular drivers that initiate aggressive ALL have yet to be fully elucidated, especially in the context of the >30 subtypes of human T- and B-ALL. Here, we used a large-scale F0 transgenic screen in zebrafish to identify synergistic combinations of 65 putative oncogenes at inducing both T- and B-cell leukemia. This approach identified a new proto-oncogene SET that collaborates with both notch1a icn and mutationally activated Interleukin 7 Receptor (IL7R) to initiate aggressive leukemia in vivo. SET is a multifunctional protein that can act as a transcriptional regulator, a histone chaperone, and an inhibitor of protein phosphatase 2A (PP2A). The high expression of SET along with mutational activation of NOTCH1 and IL7R was also found in human B- and T-ALL, supporting their oncogenic roles in leukemogenesis. Our discovery that SET is capable of independently synergizing with known drivers to initiate ALL presents a unique opportunity to explore the nature of oncogene driven ALL and define the mechanisms by which it drives transformation. We next used Non-negative Matrix Factorization to unbiasedly identify six gene programs shared across the zebrafish ALL subtypes and assessed if each was predictive of outcome in human disease. One program comprised a MYC-driven transcription signature that unexpectedly predicted good outcomes in human T-ALL patients. Additional conserved programs independently stratified patients into poor outcomes and when combined with the MYC signature profile were able to better discern disease outcomes in patients, including the aggressive ETP-like and TAL1 DP-like T-ALL. Taken together, we have uncovered new roles for SET in driving ALL initiation, uncovered unexpected high MYC expression as predictive of good outcome, and defined a new combination biomarker gene signature that predicts poor overall survival in aggressive subtypes of human T-ALL. Our zebrafish-based screening approach is a powerful tool for comparative genomic studies to identify new oncogenic drivers, vulnerability pathways, and new biomarkers of aggression in ALL. Citation Format: James R Allen, Luis Antonio Corchete Sanchez, Mohamed N Bakr, Miriam Fernández-Lajarín, Alexandra Hazelwood, Nathan Ford, Anna M Lucianò, Alexandra Veloso, Alexander D Weissman, Olivia A Strom, Esther Rheinbay, David M Langenau. Zebrafish modeling predicts clinical outcomes in human acute lymphoblastic leukemia [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Discovery and Innovation in Pediatric Cancer— From Biology to Breakthrough Therapies; 2025 Sep 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_2):Abstract nr B035.