Adaptive Responses to PARP Inhibition Predict Response to Olaparib and Durvalumab: Multi-omic Analysis of Serial Biopsies in the AMTEC Trial
Bibliographic record
Abstract
ABSTRACT In syngeneic murine breast cancer models, poly(ADP-ribose) polymerase inhibitor (PARPi) and anti-PD-L1 combinations induce deep, sustained responses independent of BRCA1 or BRCA2 mutation (BRCAm) status. We therefore investigated this combination in the AMTEC clinical trial, in which a one-month olaparib run-in was followed by combined olaparib and durvalumab in participants with non-BRCAm metastatic triple negative breast cancer. To characterize adaptive responses to olaparib monotherapy, paired biopsies taken before and during the PARPi lead-in were deeply characterized by DNA, RNA, and protein multi-omic analyses, including spatially resolved single-cell proteomics for tumor and immune contexture. We identified multiple potential tumor-intrinsic and microenvironmental biomarkers from pre-treatment and on-olaparib biopsies that robustly predicted participant response to combined olaparib and durvalumab. Notably, the on-olaparib biopsy provided the greatest information content, suggesting that early adaptations of malignant and immune cells to PARPi can serve as a predictor of potential benefit from combined PARPi and anti-PD-L1 therapy.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 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".