Abstract P02: Uncovering Tailored Therapies for Acute Myeloid Leukemia in Children with Down Syndrome
Bibliographic record
Abstract
Abstract Acute megakaryoblastic leukemia (AMKL), a subtype of acute myeloid leukemia (AML), is a rare hematological malignancy affecting children with Down syndrome (T21-AML) or without (non-DS-AMKL). T21-AML and non-DS AMKL are characterized by malignant megakaryoblasts that harbour distinct genetic profiles and prognoses. Prognoses for T21-AML are favourable; however, refractory/relapsed cases are met with dismal cure rates that are in part driven by drug resistance. Moreover, the paucity of patient samples introduces a challenge for studying this rare cancer. Thus, there is an urgency to develop a well-characterized model for uncovering novel therapies. Our group generated T21-AML patient xenograft models (PDX) by using primary DS patient samples, classified either as high- or low-risk according to co-occurring genetic alterations. Primary cells were transplanted into immunodeficient SGM3 mice. Through this method, we observed latencies between 2.9 to 3.0 weeks and 11.9 to 16.7 weeks for high-risk and low-risk primary mice, respectively. Engraftment and phenotypic characterization were done through flow cytometry and cytology. Phenotypic and molecular characterization of blasts from the bone marrow and spleen confirmed that the models recapitulate the disease. This was evident by the expression of previously characterized megakaryopoietic markers such as CD41 and CD61 and gene expression signatures correlating with leukemic patient profiles. Additionally, the models were sustained through serial transplantation for up to three generations. To monitor disease progression non-invasively, a luciferase-based bioluminescent model was engineered. Following optimization of cell culture conditions, high-throughput chemical screens against a library of 2,000 FDA-approved compounds were performed using a CellTiterGlo cell viability assay (n=1 high- and n=1 low-risk T21-AML PDX, along with normal CD34+ hematopoietic stem and progenitor cells/HSPC from cord blood). Drugs were identified as hits if they reduced the viability of leukemic cells by >75% with or without sparing normal CD34+ HSPC. Hits were validated using dose-response curve analyses and IC50 values were determined using a CellTiterGlo assay. We identified navitoclax, an apoptotic inducer with broad affinity to BCL-2, BCL-XL and BCL-W, BCL-XL-specific inhibitors, and other compound classes as potential therapeutic vulnerabilities. T21-AML PDX were resistant to venetoclax (e.g., IC50 >10µM), a selective inhibitor of BCL-2 currently used in pediatric relapsed/refractory malignancies. Overall, children affected by DS-AMKL generally fare favourable outcomes but endure long-term toxicities resulting from exposure to conventional genotoxic agents. Therefore, we generated a model of study to understand specific genotypes that would aid in the development of tailored therapies. Additionally, drug studies demonstrate BCL-XL as a therapeutic vulnerability as opposed to BCL-2. Our approach allows us to leverage the molecular understanding of this leukemia and discover a new realm of targeted therapeutics. Citation Format: Billy Ta, Emma Rose Cheetham, Sophie Cardin, Verena Gress, Anne-Cécile Soufflet, Nehme Hachem, Melanie Bilodeau, Sonia Cellot, Carolina Marmolejo, Louise Laramée. Uncovering Tailored Therapies for Acute Myeloid Leukemia in Children with Down Syndrome [abstract]. In: Proceedings of the Blood Cancer Discovery Symposium; 2024 Mar 4-6; Boston, MA. Philadelphia (PA): AACR; Blood Cancer Discov 2024;5(2_Suppl):Abstract nr P02.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 0.000 |
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".