Gene-by-gene analysis in the MAGNITUDE study of niraparib (NIRA) with abiraterone acetate and prednisone (AAP) in patients (pts) with metastatic castration-resistant prostate cancer (mCRPC) and homologous recombination repair (HRR) gene alterations.
Bibliographic record
Abstract
5020 Background: NIRA + AAP significantly improved outcomes in pts with mCRPC and HRR gene alterations in the Phase 3 MAGNTUDE study. There is a paucity of data supporting use of PARP inhibitors in pts with HRR gene alterations other than BRCA1/2. We report on the efficacy of NIRA + AAP in pts with mCRPC and a qualifying single gene HRR alteration other than BRCA1/2. Methods: A pre-specified analysis was undertaken of the primary endpoint (radiographic progression-free survival [rPFS] by BICR), secondary endpoints (time to cytotoxic chemotherapy [TCC], time to symptomatic progression [TSP], overall survival [OS]), as well as time to PSA progression (TPSA) and overall response rate (ORR) across 186 pts (91 randomized to NIRA + AAP, 95 to PBO + AAP) with an alteration in the ATM, BRIP1, CDK12, CHEK2, FANCA, HDAC2, or PALB2 gene (excluding cooccurring alterations) . This analysis of individual alterations was not powered for formal statistical inference. Given the rarity of some alterations, groups based on functional similarity are also presented. Results: (Table). Pts with PALB2 or CHEK2 alterations had consistent improvement across all endpoints. In pts with ATM alterations benefit was observed in TCC, TSP, TPSA and ORR. There was benefit only in TPSA and ORR for pts with CDK12 alterations. When combined into functional groups, pts with an alteration in the HRR-Fanconi pathway ( BRIP1, FANCA, and PALB2) as well as pts with a HRR associated alteration ( CHEK2 or HDAC2) showed improvement in all endpoints. Conclusions: These data support the overall conclusions of the MAGNITUDE primary analysis and support benefit of NIRA + AAP in pts with HRR mutations beyond BRCA1/2. Clinical trial information: NCT03748641. [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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".