A 4-chemokine signature to predict intermediate immunogenicity in homologous recombination repair deficient tumors.
Bibliographic record
Abstract
2546 Background: Treatment responses to immune checkpoint blockade (ICB) associate with T cell tumor inflammation. Tumors with mismatch repair deficiency display an inflammatory tumor phenotype and respond to ICB. Similarly, tumors with homologous recombination repair deficiency (HR-d) may be primed for ICB treatment. We have previously shown that a panel of 4 chemokines identifies a subclass of pancreatic cancer with markers of T cell-inflammation. Here, we evaluated this 4-chemokine signature in cancer types with HR-d molecular subclasses. Methods: We combined paired transcriptomes and genomic data of breast (n = 699), ovarian (n = 174) and prostate (n = 457) cancers from the Cancer Genome Atlas and tumor-enriched pancreas cancers (n = 121) to evaluate the 4-chemokine signature in HR-d vs. HR-proficient tumors across these 4 cancers. Metrics of antitumor immunity were also compared. Results: Across tumor types, elevated expression of the 4-chemokine signature (chemokine-hi) associated with transcriptional hallmarks of a T cell-mediated antitumor response, including antigen presenting cell stimulation, antigen presentation, and T cell activity. In tumors with a predominant COSMIC signature 3, which associates with HR-d, the 4-chemokine signature predicted intermediate levels of T cell-inflammation. Conclusions: These data suggest that 1) the 4-chemokine signature may be a clinically relevant biomarker in identifying subclasses of tumours responsive to immunotherapies, and that 2) HR-d tumors harbor intermediate immunogenicity. Correlation of treatment responses to immunotherapies with the 4-chemokine signature is needed validate its predictive value as a biomarker for treatment stratification with immunotherapies.[Table: see text]
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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".