MICA/B antibody induces macrophage-mediated immunity against acute myeloid leukemia
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is a clonal hematopoietic stem and progenitor cell malignancy characterized by poor clinical outcomes. Major histocompatibility complex class I polypeptide-related sequence A and B (MICA/B) are stress proteins expressed by cancer cells, and antibody-mediated inhibition of MICA/B shedding represents a novel approach to stimulate immunity against cancers. We found that the MICA/B antibody 7C6 potently inhibits the outgrowth of AML in 2 models in immunocompetent mice. Macrophages were essential for therapeutic efficacy, and 7C6 triggered antibody-dependent phagocytosis of AML cells. Furthermore, we found that romidepsin, a selective histone deacetylase inhibitor, increased MICB messenger RNA in AML cells and enabled subsequent stabilization of the translated protein by 7C6. This drug combination substantially increased surface MICA/B expression in a human AML line, pluripotent stem cell-derived AML blasts and leukemia stem cells, as well as primary cells from 3 untreated patients with AML. Human macrophages phagocytosed AML cells following treatment with 7C6 and romidepsin, and the combination therapy lowered leukemia burden in a humanized model of AML. Therefore, inhibition of MICA/B shedding promotes macrophage-driven immunity against AML via Fc receptor signaling and synergizes with an epigenetic regulator. These results provide the rationale for the clinical testing of this innovative immunotherapeutic approach for the treatment of AML.
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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) | 0.001 | 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".