Immunotherapeutic targeting of surfaceome heterogeneity in AML
Bibliographic record
Abstract
Immunotherapy remains underexploited in acute myeloid leukemia (AML) compared to other hematological malignancies. Currently, gemtuzumab ozogamicin is the only therapeutic antibody approved for this disease. Here, to identify potential targets for immunotherapeutic intervention, we analyze the surface proteome of 100 genetically diverse primary human AML specimens for the identification of cell surface proteins and conduct single-cell transcriptome analyses on a subset of these specimens to assess antigen expression at the sub-population level. Through this comprehensive effort, we successfully identify numerous antigens and markers preferentially expressed by primitive AML cells. Many identified antigens are targeted by therapeutic antibodies currently under clinical evaluation for various cancer types, highlighting the potential therapeutic value of the approach. Importantly, this initiative uncovers AML heterogeneity at the surfaceome level, identifies several antigens and potential primitive cell markers characterizing AML subgroups, and positions immunotherapy as a promising approach to target AML subgroup specificities.
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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 | 0.000 |
| 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".