Abstract 1485: Tumors with homologous recombination repair deficiency signatures associate with an RNAseq profile profile suggestive of T cell-inflammation
Bibliographic record
Abstract
Abstract Despite remarkable responses in several cancer types, immune checkpoint blockade (ICB) remains ineffective against a subset of tumors, including those presently recalcitrant to conventional treatment modalities. Given the ubiquitous nature of genomic aberrations in cancer and the potential roles they have in eliciting an immune-mediated antitumor response, genomic predictors of T cell-inflammation may help segregate patients responsive to ICB. Tumors with homologous recombination repair deficiencies (HRD) exhibit hypermutation and may therefore potentially harbour a genetically favourable milieu to elicit an antitumor immune response. However, the exact role of HRD in antitumor immunity remains unclear. Given that HRD accounts for up to 10% of pancreatic cancers and a higher proportion in ovarian cancers, the use of ICB to treat HRD tumors has clinical implications. Our group has previously shown a signature of four chemokines associates with a T cell-inflamed phenotype in pancreatic cancer, but it remains unknown whether this association holds in other tumors, and how HRD tumors fall on this immune spectrum. Using matched genomic and transcriptomic data from TCGA, we sought to investigate whether the four-chemokine signature predicted a T cell-inflamed phenotype in 158 treatment-naïve primary ovarian cancers, another tumor type with poor survival and low response rates to ICB attributed in part to a lack of T cell-inflammation. As in pancreatic cancer, 4-chemokinehi tumors displayed a phenotype suggesting proper mounting of the cancer-immunity cycle, including increased expression of innate immune signaling pathways implicated in mediating T cell-inflammation in tumors, antigen presenting and T cell activation and inhibition, MHC I presentation, cytolytic activity, PDL1 and CTLA4 expression, and signatures predictive of response to ICB. Interestingly, tumors with COSMIC signature 3, predictive of HRD had increased expression of gene signatures associated with T cell-inflammation, including increased expression of the 4-chemokine signature. Taken together, these results further validate the 4-chemokine signature at predicting T cell-inflammation and suggest similar mechanisms driving T cell-inflammation remain consistent across different tumor types. Moreover, these data further suggest that homologous recombination repair deficiency may predispose tumors to a favourable milieu amenable to immunotherapies requiring T cell-inflammation. Understanding mediators of T cell-inflammation, both at cellular and genomic resolutions, may lead to improved predictors of response to current and novel immunotherapies and in turn improved patient outcomes for patients suffering from these diseases. Citation Format: Joan Miguel Romero, Alain Pacis, Rob E. Denroche, Gun Ho Jang, Barbara Grünwald, Yifan Wang, Steven Gallinger, George Zogopoulos. Tumors with homologous recombination repair deficiency signatures associate with an RNAseq profile profile suggestive of T cell-inflammation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1485.
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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 | 0.000 | 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) | 0.002 | 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".