Tumor antigens preferentially derive from unmutated genomic sequences in melanoma and non-small cell lung cancer
Bibliographic record
Abstract
Melanoma and non-small cell lung cancer (NSCLC) display exceptionally high mutational burdens. Hence, immune targeting in these cancers has primarily focused on tumor antigens (TAs) predicted to derive from nonsynonymous mutations. Using comprehensive proteogenomic analyses, we identified 589 TAs in cutaneous melanoma (n = 505) and NSCLC (n = 90). Of these, only 1% were derived from mutated sequences, which was explained by a low RNA expression of most nonsynonymous mutations and their localization outside genomic regions proficient for major histocompatibility complex (MHC) class I-associated peptide generation. By contrast, 99% of TAs originated from unmutated genomic sequences specific to cancer (aberrantly expressed tumor-specific antigens (aeTSAs), n = 220), overexpressed in cancer (tumor-associated antigens (TAAs), n = 165) or specific to the cell lineage of origin (lineage-specific antigens (LSAs), n = 198). Expression of aeTSAs was epigenetically regulated, and most were encoded by noncanonical genomic sequences. aeTSAs were shared among tumor samples, were immunogenic and could contribute to the response to immune checkpoint blockade observed in previous studies, supporting their immune targeting across cancers.
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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 | 0.001 | 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 | 0.000 |
| 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".