Quantifying Immune-Based Counterselection of Somatic Mutations
Bibliographic record
Abstract
ABSTRACT It is now well established that somatic mutations in protein-coding regions can generate ‘neoantigens’, and that these can be recognized by the immune system and contribute to clearance of developing cancers. However, there is currently no model that can quantitatively predict the neoantigenic effect of any given somatic mutation. Here, we examined signatures of immune selection pressure on the distribution of somatic mutations. We quantified the extent to which somatic mutations are significantly depleted in peptides that are predicted to be displayed by major histocompatibility complex (MHC) class I proteins. We characterized the dependence of this depletion on expression level. We then examined whether immune selection pressure on somatic mutations changes depending on whether the patient had either one or two MHC-encoding alleles that can display the peptide. Our results indicate that MHC-encoding alleles are, in general, incompletely dominant, i.e., that having two copies of the display-enabling allele is more effective in displaying that peptide than having just one copy. More generally, a quantitative understanding of counter-selection of identifiable subclasses of neoantigenic somatic variation could guide immunotherapy or aid in developing personalized cancer vaccines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".