Proteomic approach to discover human cancer viruses from formalin-fixed tissues
Bibliographic record
Abstract
The challenge of discovering a completely new human tumor virus of unknown phylogeny or sequence depends on detecting viral molecules and differentiating them from host molecules in the virus-associated neoplasm. We developed differential peptide subtraction (DPS) using differential mass spectrometry (dMS) followed by targeted analysis to facilitate this discovery. We validated this approach by analyzing Merkel cell carcinoma (MCC), an aggressive human neoplasm, in which ~80% of cases are caused by the human Merkel cell polyomavirus (MCV). Approximately 20% of MCC have a high mutational burden and are negative for MCV, but are microscopically indistinguishable from virus positive cases. Using 23 (12 MCV+, 11 MCV-) formalin-fixed MCC, DPS identified both viral and human biomarkers (MCV large T antigen, CDKN2AIP, SERPINB5, and TRIM29) that discriminate MCV+ and MCV- MCC. Statistical analysis of 498,131 dMS features not matching the human proteome by DPS revealed 562 (0.11%) to be upregulated in virus-infected samples. Remarkably, 4 (20%) of the top 20 candidate MS spectra originated from MCV T oncoprotein peptides and confirmed by reverse translation degenerate oligonucleotide sequencing. DPS is a robust proteomic approach to identify potentially novel viral sequences in infectious tumors when nucleic acid-based methods are not feasible.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".