Abstract 67: Deciphering the mechanisms underlying the pathophysiology and chemotherapy resistance in acute myeloid leukemia using intravital imaging and humanized NSGW41IL7 mouse
Bibliographic record
Abstract
Introduction: Acute Myeloid Leukemia (AML) is an aggressive & highly heterogeneous malignancy that originates within the bone marrow (BM). A major challenge in treating AML is its resistance to therapy & high relapse rates, driven in part by changes AML induces in the BM microenvironment, promoting immune suppression & creating hypoxic conditions. However, the mechanisms behind therapeutic resistance in AML patients in the presence of hypoxic BM niche, are poorly understood.Innovation & Clinical Impact: To address this knowledge gap, our study employs two pioneering methods: a humanized NSGW41IL7 mouse model engrafted with human AML cells & the femur window chamber (FWC) technique, enabling unprecedented real-time visualization of AML dynamics within BM. Using advanced intravital imaging, we are the first to observe the intricate, continuous interactions between AML cells & immune components, such as human gamma delta T cells, within the BM microenvironment. Furthermore, we are interested in using our new approach to investigate how standard chemotherapies impact AML cell survival & immune responses under hypoxic conditions over time. Ultimately, our findings will provide new insights into treatment resistance mechanisms & pave the way for innovative therapeutic strategies targeting the BM microenvironment, improving outcomes for AML patients. Citation Format: Raheleh Niavarani, Xi Lei, Michael Edson, Emily Chen, Shirley Wang, Ralph DaCosta. Deciphering the mechanisms underlying the pathophysiology and chemotherapy resistance in acute myeloid leukemia using intravital imaging and humanized NSGW41IL7 mouse [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 67.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".