BRCA1 and BRCA2 deficient tumour models generate distinct ovarian tumour microenvironments and differential responses to therapy
Bibliographic record
Abstract
Clinical trials are currently exploring combinations of PARP inhibitors and immunotherapies for the treatment of ovarian cancer, but their effects on the ovarian tumour microenvironment (TME) remain unclear. Here, we investigate how olaparib, PD-L1 monoclonal antibodies, and their combination can influence TME composition and survival of tumour-bearing mice. We further explored how BRCA deficiencies can influence the response to therapy. Olaparib and combination therapies similarly improved the median survival of Brca1- and Brca2-deficient tumour-bearing mice. Anti-PD-L1 monotherapy improved the survival of mice with Brca1-null tumours, but not Brca2-null tumours. A detailed analysis of the TME revealed that olaparib monotherapy resulted in a large number of immunosuppressive and immunomodulatory effects in the more inflamed Brca1-deficient TME but not Brca2-deficient tumours. Anti-PD-L1 treatment was mostly immunosuppressive, resulting in a systemic reduction of cytokines and a compensatory increase in PD-L1 expression. The results of the combination therapy generally resembled the effects of one or both of the monotherapies, along with unique changes observed in certain immune populations. In-silico analysis of RNA-seq data also revealed numerous differences between Brca-deficient tumour models, such as the expression of genes involved in inflammation, angiogenesis and PD-L1 expression. In summary, these findings shed light on the influence of novel therapeutics and BRCA mutations on the ovarian TME.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".