Abstract A002 A transgenic zebrafish screen identifies new collaborating oncogenic drivers in acute lymphoblastic leukemia
Bibliographic record
Abstract
Abstract Acute lymphoblastic leukemia (ALL) is an aggressive hematological malignancy that affects thousands of pediatric and adult patients each year. A major hurdle to developing more effective treatments is a limited understanding of the genes and pathways that drive ALL malignancy, and which are required for continued tumor growth. ALL is often diagnosed with the onset of advanced symptoms, making it difficult to study the early drivers of oncogenic initiation. Identifying novel genetic interactions and pathways capable of driving ALL will help to develop new diagnostic criteria and identify actionable drug targets for clinical treatments. In addition, genetic interactions between co-expressed oncogenes and their impact on initiation, progression, and therapy resistance is difficult to study and requires the costly use of genetically engineered animal models, usually mice. Here, we report a novel screening method using transgenic zebrafish to unbiasedly identify collaborating oncogenic drivers that when co-expressed lead to T- and B-ALL. We screened a transgene pool of 68 putative oncogenes identified from human ALL for synergies in driving the initiation of leukemia using a large-scale F0 transgenic gain-of-function screen in zebrafish. We recovered zebrafish with tumors that were predominantly T- and B-ALL, but also identified several fish with thymic T cell lymphomas, muscle sarcomas, and liposarcomas, demonstrating the robust capability of our approach to discover novel oncogenic collaborations. Comparative transcriptome analysis using bulk RNA sequencing between zebrafish tumors (>150 tumors) revealed candidate transgene combinations that likely act as drivers of tumor formation in T- and B-ALL. When microinjected for validation studies, two transgene combinations: 1) rag2:SET + rag2:intracellular notch1a and 2) rag2:SET + rag2:mutationally-activated IL7R lead to robust ALL formation in zebrafish, validating the oncogenic role of these factors in leukemia initiation. SET is a multifunctional protein involved in transcriptional regulation, histone binding, and cell death. While SET has been well characterized in other cancers, we found that SET is capable of collaborating with known drivers to initiate T-ALL in vivo and highly expressed in a vast majority of human T-ALL. Our SET models also revealed an enrichment of key pathways that drive proliferation in both zebrafish and human disease. We are currently performing molecular characterization of SET in human ALL cell lines to investigate SET driven ALL growth and maintenance. In addition, our panel of unique zebrafish tumors will allow help establish new models of hematologic malignancies, launch future investigations into leukemia pathology, and identify novel targets of therapy resistance. Citation Format: James R. Allen, Luis Antonio Corchete Sanchez, Mohamed N. Bakr, Miriam Fernandez-Lajarin, Alexandra Hazelwood, Nathan Ford, Alexandra Veloso, Victoriano Mulero, Maria L. Cayuela, Esther Rheinbay, David M. Langenau. A transgenic zebrafish screen identifies new collaborating oncogenic drivers in acute lymphoblastic leukemia [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A002.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".