Quantifying immune-based counterselection of somatic mutations
Bibliographic record
Abstract
Somatic mutations in protein-coding regions can generate 'neoantigens' causing developing cancers to be eliminated by the immune system.Quantitative estimates of the strength of this counterselection phenomenon have been lacking.We quantified the extent to which somatic mutations are depleted in peptides that are predicted to be displayed by major histocompatibility complex (MHC) class I proteins.The extent of this depletion depended on expression level of the neoantigenic gene, and on whether the patient had one or two MHCencoding alleles that can display the peptide, suggesting MHC-encoding alleles are incompletely dominant.This study provides an initial quantitative understanding of counter-selection of identifiable subclasses of neoantigenic somatic variation. Author summaryCancer immunotherapy and personalized cancer vaccines depend on clearance of cancer and pre-cancer cells by the immune system.However, little is known about the strength of this phenomenon as it acts on the cell populations which give rise to tumors.Here we provide an initial quantitative estimate of the fraction of neo-antigen-containing cells in this population that are cleared by the MHC class I-dependent immune system.The impacts of both neo-antigenic gene expression and the number of neo-antigen-displaying MHC alleles on this clearance phenomenon were examined.A more complete understanding of immune clearance of neoantigenic cells and how this phenomenon varies between patients and cancers, has the potential to guide immunotherapy and cancer vaccines.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".