Use of chemotherapy (CT) in <i>BRCA1/2</i>-deficient ovarian cancer (BDOC) patients (pts) with poly-ADP-ribose polymerase inhibitor (PARPi) resistance: A multi-institutional study.
Bibliographic record
Abstract
5022 Background: Emerging clinical data point to the clinical utility of PARPi in BDOC. However, the impact of PARPi exposure on the prospects of response to further CT remains unclear. In our previous single centre study, we provided the first clinical data relating to the use of post-PARPi CT (JCO 2010: 28(15S), A5041). Our aim here was to re-examine this issue in a larger multi-institutional population. Methods: We included pts with advanced BDOC who received CT, having progressed on ≥200 mg bd olaparib. Pt, tumor and treatment characteristics and clinical outcomes were documented. Relationships between post-PARPi CT overall survival (OS) and other variables were explored using Cox regression. Results: We collected data on 75 pts (median age 51 y [range 31-77], BRCA1:BRCA2 54:21, mean 3 previous lines of CT [95% CI 2.5-3.4], pre-PARPi platinum (Plt) resistance rate 49% and olaparib RECIST-response rate [RR] 39% [95% CI 28-50]). Following olaparib, most pts received Plt alone or in combination with taxane (Tx) or liposomal doxorubicin (PLD). Weekly Plt was used in 36% of all Plt-treated pts, mainly in combination with weekly Tx. Overall RECIST and CA125 (GCIG) RRs were 38% and 48%, respectively; these responses occurred independently of PARPi response or pre-PARPi Plt sensitivity (all p>.1). The median progression free survival and OS of RECIST responders were 7.9 m (95% CI 5.8-10.9) and 10.5 m (95% CI 1.4-19.6), respectively. In all pts, the median OS from the start of post-PARPi CT was 7.9 m (95% CI 5.7-10.1) while that from diagnosis was 64.9 m (95% CI 52.9-76.9). Factors associated with improved OS on post-PARPi CT in the MVA included best olaparib response of non-disease progression (p=.003, HR 0.28), optimal initial debulking (p=.01, HR 0.36) and pre-PARPi Plt sensitivity (p=.05, HR 0.46). Conclusions: These data indicate potential for meaningful responses to CT in BDOC pts with PARPi resistance. Analysis to identify molecular predictors of response is ongoing. [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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".