Surveillance of the Tumor Mutanome by T Cells during Progression from Primary to Recurrent Ovarian Cancer
Bibliographic record
Abstract
PURPOSE: Cancers accumulate mutations over time, each of which brings the potential for recognition by the immune system. We evaluated T-cell recognition of the tumor mutanome in patients with ovarian cancer undergoing standard treatment. EXPERIMENTAL DESIGN: Tumor-associated T cells from 3 patients with ovarian cancer were assessed by ELISPOT for recognition of nonsynonymous mutations identified by whole exome sequencing of autologous tumor. The relative levels of mutations and responding T cells were monitored in serial tumor samples collected at primary surgery and first and second recurrence. RESULTS: The vast majority of mutations (78/79) were not recognized by tumor-associated T cells; however, a highly specific CD8(+) T-cell response to the mutation hydroxysteroid dehydrogenase-like protein 1 (HSDL1)(L25V) was detected in one patient. In the primary tumor, the HSDL1(L25V) mutation had low prevalence and expression, and a corresponding T-cell response was undetectable. At first recurrence, there was a striking increase in the abundance of the mutation and corresponding MHC class I epitope, and this was accompanied by the emergence of the HSDL1(L25V)-specific CD8(+) T-cell response. At second recurrence, the HSDL1(L25V) mutation and epitope continued to be expressed; however, the corresponding T-cell response was no longer detectable. CONCLUSION: The immune system can respond to the evolving ovarian cancer genome. However, the T-cell response detected here was rare, was transient, and ultimately failed to prevent disease progression. These findings reveal the limitations of spontaneous tumor immunity in the setting of standard treatments and suggest a high degree of ignorance of tumor mutations that could potentially be reversed by 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 | 0.001 |
| 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".