Homologous recombination deficiency and host anti-tumor immunity in triple-negative breast cancer
Bibliographic record
Abstract
PURPOSE: Triple-negative breast cancer (TNBC) is associated with worse outcomes relative to other breast cancer subtypes. Chemotherapy remains the standard-of-care systemic therapy for patients with localized or metastatic disease, with few biomarkers to guide benefit. METHODS: We will discuss recent advances in our understanding of two key biological processes in TNBC, homologous recombination (HR) DNA repair deficiency and host anti-tumor immunity, and their intersection. RESULTS: Recent advances in our understanding of homologous recombination (HR) deficiency, including FDA approval of PARP inhibitor olaparib for BRCA1 or BRCA2 mutation carriers, and host anti-tumor immunity in TNBC offer potential for new and biomarker-driven approaches to treat TNBC. Assays interrogating HR DNA repair capacity may guide treatment with agents inducing or targeting DNA damage repair. Tumor infiltrating lymphocytes (TILs) are associated with improved prognosis in TNBC and recent efforts to characterize infiltrating immune cell subsets and activate host anti-tumor immunity offer promise, yet challenges remain particularly in tumors lacking pre-existing immune infiltrates. Advances in these fields provide potential biomarkers to stratify patients with TNBC and guide therapy: induction of DNA damage in HR-deficient tumors and activation of existing or recruitment of host anti-tumor immune cells. Importantly, these advances provide an opportunity to guide use of existing therapies and development of novel therapies for TNBC. Efforts to combine therapies that exploit HR deficiency to enhance the activity of immune-directed therapies offer promise. CONCLUSIONS: HR deficiency remains an important biomarker target and potentially effective adjunct to enhance immunogenicity of 'immune cold' TNBCs.
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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 | 0.001 |
| 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".