Expressed HNSCC variants by HPV-status in a well-characterized Michigan cohort
Bibliographic record
Abstract
While whole-exome DNA sequencing is the most common technology to study genetic variants in tumors in known exonic regions, RNA-seq is cheaper, covers most of the same exonic regions, and is often more readily available. In this study, we show the utility of mRNA-seq-based variant analysis combined with targeted gene sequencing performed on both tumor and matched blood as an alternative when exome data is unavailable. We use the approach to study expressed variant profiles in the well-characterized University of Michigan (UM) head and neck squamous carcinoma (HNSCC) cohort (n = 36). We found that 441 out of 455 (~97%) identified cancer genes with an expressed variant in the UM cohort also harbor a somatic mutation in TCGA. Fourteen (39%) patients had a germline variant in a cancer-related Fanconi Anemia (FA) pathway gene. HPV-positive patients had more nonsynonymous, rare, and damaging (NRD) variants in those genes than HPV-negative patients. Moreover, the known mutational signatures for DNA mismatch repair and APOBEC activation were attributive to the UM expressed NRD variants, and the APOBEC signature contribution differed by HPV status. Our results provide additional support to certain TCGA findings and suggest an association of expressed variants in FA/DNA repair pathways with HPV-associated HNSCC tumorigenesis. These results will benefit future studies on this and other cohorts by providing the genetic variants of key cancer-related genes.
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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 | 0.001 |
| 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 | 0.000 | 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".