Abstract A161: A differentiation based therapeutic approach for KMT2A rearranged leukemia in infants and children
Bibliographic record
Abstract
Abstract Introduction: Translocations in the KMT2A gene (KMT2A-r) in leukemia is an aggressive oncodriver. Currently, few therapeutic options are available for these patients and novel therapeutic strategies are urgently needed to improve outcomes. Characteristically, leukemic cells undergo a differentiation block at an early stage of development and agents that restore terminal differentiation represent an effective therapeutic strategy. Based on the initial reports that the inhibition of the Menin-KMT2A fusion is a potent inducer of leukemic cell differentiation, we sought to study a novel treatment approach to enhance the current treatment outcomes in KMT2A-r leukemia. Methods: Pediatric leukemia cells with and without KMT2A-r were studied. Differentiation was induced by the Menin inhibitor MI-463 for 12 days, followed by assessment of differentiation on day 6 and day 12 by Giemsa-Wright staining and visualization using the Infinity analyzer software. In the next set of experiments, the differentiated and undifferentiated cells were treated with common anti-leukemic chemotherapeutic agents and the induction of apoptosis and target modulation analyses were carried out by Annexin/PI staining and immunoblotting. The differentiated cells were also treated with a panel of 220 FDA approved agents, targeting diverse oncogenic pathways to generate a sensitivity signature specific to the differentiated cells. Gene expression array studies were used to identify differentially expressed genes in the two cell populations. Results: The KMT2A-r leukemia cells showed increased dose dependent sensitivity to MI-463. The highest drug sensitivity was exhibited by the infant leukemia cell line MV4-11. At 1µM, no significant loss of cell viability was noted but the rate of proliferation was significantly decreased compared to DMSO control, providing optimal conditions for differentiation. Leukemic cells cultured with MI-463 showed characteristic morphological changes of differentiation including vacuolated cytoplasm, kidney shaped nuclei and decreased nuclei to cytoplasmic ratio. Importantly, we found that the differentiated cells showed a similar chemosensitivity pattern but the sensitivity to AraC was greatly reduced. For example, in MV4-11 cells, IC50 was 170 nM compared to 8 nM in undifferentiated cells. Directed drug screen and differential gene expression studies identified specific growth and survival pathways that can be positively targeted in differentiated cells. Discussion: Data presented demonstrate the activity of MI-463 to induce cell death as well as to initiate differentiation in KMT2A-r cells. We further characterize the sensitivity profile of current chemotherapeutic agents for multi-agent treatment regimens and identify novel agents to optimize future treatment protocols. Differential gene expression analyses were used to understand the potential mechanisms of therapeutic vulnerabilities in these cells. Our results provide the first preclinical evidence to select agents and combinations to improve the outcomes in KMT2A-r leukemia in children. Citation Format: Ritul Sharma, Aru Narendran. A differentiation based therapeutic approach for KMT2A rearranged leukemia in infants and children [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A161.
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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".