HLA polymorphism impacts immune response to neoepitopes and survival in APOBEC-mutated cancers
Bibliographic record
Abstract
Summary APOBEC3A and APOBEC3B genome mutators drive tumor evolution and drug resistance but may also generate neoepitopes for cytotoxic T cells (CTL). Given the extensive polymorphism of Class I HLA, the CTL immunopeptidome, comprised of all 8-11mer peptides presented by an individual’s six HLA class I alleles, varies person-to-person. We predicted the genome-wide impact of APOBEC3A/B-driven mutations on the immunogenicity of the immunopeptidomes of several thousand class I HLA alleles. Analysis of several billion APOBEC3-mediated mutations revealed that HLA class I alleles vary markedly in the susceptibility of their immunopeptidome to mutations. A subset of alleles of A1-A3 and B44 supertype supported increased neoepitopes. Notably, the immunogenicity changes supported by an individual’s HLA class I alleles in response to APOBEC3 mutations predict survival in APOBEC3-mutated tumors and correlate with CTL activation. Thus, immunogenicity changes mediated by APOBEC3s impact survival, making HLA class I genotype a prognostic marker in APOBEC3-mutated tumors.
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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) | 0.002 | 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".