Proteogenomics Uncovers a Vast Repertoire of Shared Tumor-Specific Antigens in Ovarian Cancer
Bibliographic record
Abstract
Abstract High-grade serous ovarian cancer (HGSC), the principal cause of death from gynecologic malignancies in the world, has not significantly benefited from advances in cancer immunotherapy. Although HGSC infiltration by lymphocytes correlates with superior survival, the nature of antigens that can elicit anti-HGSC immune responses is unknown. The goal of this study was to establish the global landscape of HGSC tumor-specific antigens (TSA) using a mass spectrometry pipeline that interrogated all reading frames of all genomic regions. In 23 HGSC tumors, we identified 103 TSAs. Classic TSA discovery approaches focusing only on mutated exonic sequences would have uncovered only three of these TSAs. Other mutated TSAs resulted from out-of-frame exonic translation (n = 2) or from noncoding sequences (n = 7). One group of TSAs (n = 91) derived from aberrantly expressed unmutated genomic sequences, which were not expressed in normal tissues. These aberrantly expressed TSAs (aeTSA) originated primarily from nonexonic sequences, in particular intronic (29%) and intergenic (22%) sequences. Their expression was regulated at the transcriptional level by variations in gene copy number and DNA methylation. Although mutated TSAs were unique to individual tumors, aeTSAs were shared by a large proportion of HGSCs. Taking into account the frequency of aeTSA expression and HLA allele frequencies, we calculated that, in Caucasians, the median number of aeTSAs per tumor would be five. We conclude that, in view of their number and the fact that they are shared by many tumors, aeTSAs may be the most attractive targets for HGSC immunotherapy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".