Autophagy degrades immunogenic endogenous retroelements induced by 5-azacytidine in acute myeloid leukemia
Bibliographic record
Abstract
ABSTRACT The hypomethylating agent 5-azacytidine (AZA) is the first-line therapy for acute myeloid leukemia (AML) patients unfit for intensive chemotherapy. Evidence suggests that the anti-tumor effect of AZA results partly from T-cell cytotoxic responses against MHC-I-associated peptides (MAPs) whose expression is induced by hypomethylation. Through a proteogenomic approach, we analyzed the impact of AZA on the transcriptome and MAP repertoire of four AML cell lines and validated salient findings in the transcriptome of 437 primary AML samples. We demonstrate that AZA caused pleiotropic changes in AML cells via perturbation of transcription, translation, and protein degradation. Overall, 1,364 MAPs were upregulated in AZA-treated cells, including several cancer-testis antigens. Increased MAP abundance was due to the upregulation of corresponding transcripts in a minority of cases and post-translational events in most cases. Furthermore, AZA-induced hypomethylation increased the abundance of numerous transcripts, of which 38% were endogenous retroelements (EREs). Upregulated ERE transcripts triggered innate immune responses but were degraded by autophagy and not processed into MAPs. Autophagy resulted from the formation of protein aggregates caused by AZA-dependent inhibition of DNMT2, a tRNA-methyl transferase enzyme. We found that autophagy inhibition had a synergistic effect with AZA on AML cell proliferation and survival, increased ERE levels and triggered pro-inflammatory responses. Finally, autophagy gene signatures were associated with a lower abundance of CD8 + T-cell markers in AML patients expressing high levels of EREs. Altogether, this work demonstrates that the impact of AZA is regulated at several levels and suggests that inhibiting autophagy could improve the immune recognition of AML blasts in patients.
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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".