Abstract CT018: TMPRSS2-ERG and RB1 as candidate predictive biomarkers for efficacy in TALAPRO-2: Phase 3 study of talazoparib (TALA) + enzalutamide (ENZA) vs placebo (PBO) + ENZA as first-line (1L) treatment in patients (pts) with metastatic castration-resistant prostate cancer (mCRPC)
Bibliographic record
Abstract
Abstract Background: In TALAPRO-2 (NCT03395197), pts unselected for homologous recombination repair (HRR) gene mutations (muts) received TALA + ENZA or PBO + ENZA in 1L mCRPC. TALA + ENZA significantly improved radiographic progression-free survival (rPFS). Previous analyses focused on the association of tumor HRR gene muts with outcome; here, we examined TALA + ENZA efficacy in pts with ≥1 non-HRR gene mut with/without HRR gene muts. Methods: This is a post-hoc exploratory agnostic analysis of a TALAPRO-2 dataset of prospectively collected/retrospectively analyzed plasma ctDNA (FoundationOne®Liquid CDx). Response was assessed per RECIST v1.1. Results: Overall, 616 pts in the intent-to-treat population had non-HRR gene muts with/without HRR gene muts; 98 (16%) pts had TMPRSS2-ERG fusions. In these 616 pts, TALA + ENZA improved rPFS vs PBO + ENZA (Table). TALA + ENZA improved rPFS in pts with TMPRSS2-ERG, RB1, or MLL2 muts, and objective response rates (ORR) in pts with TMPRSS2-ERG or RB1 muts and measurable disease at baseline (the association of MLL2 with enhanced rPFS was lost without TMPRSS2-ERG, not shown). In pts with non-HRR gene muts and no HRR gene muts (n=427), TALA + ENZA showed improvement in rPFS vs PBO + ENZA. Enhanced rPFS benefit with TALA + ENZA vs PBO + ENZA was noted in pts with TMPRSS2-ERG, RB1, or MLL2 muts, but TMPRSS2-ERG ORRs were similar across treatment arms. Conclusions: TALA + ENZA improved outcomes compared with PBO + ENZA in patients with muts in specific non-HRR genes (regardless of HRR gene muts). TMPRSS2-ERG and RB1 emerged as candidate predictive biomarkers for differential efficacy favoring TALA + ENZA vs PBO + ENZA. PARP inhibitors may induce a synthetically lethal interaction with TMPRSS2-ERG-mediated inhibition of non-homologous end joining and help overcome RB1-mediated ENZA resistance. ≥1 non-HRR gene muts ≥1 non-HRR gene muts with no HRR gene muts Median rPFS, mo HR (95% CI) Median rPFS, mo HR (95% CI) Gene mutation TALA + ENZA (N=310) PBO + ENZA (N=306) TALA + ENZA (N=218) PBO + ENZA (N=209) Any non-HRR NR 19.3 0.63 (0.50–0.80) NR 22.5 0.66 (0.49–0.89) TMPRSS2-ERG, [n] 25.9 [n=43] 11.0 [n=55] 0.36 (0.20–0.64) 19.4 [n=32] 11.0 [n=44] 0.40 (0.20–0.76) MLL2, [n] 18.2 [n=20] 13.8 [n=20] 0.43 (0.19–0.98) NR [n=8] 17.2 [n=11] 0.50 (0.14–1.74) RB1, [n] 9.8 [n=14] 1.9 [n=8] 0.22 (0.07–0.73) 10.8 [n=6] 1.8 [n=3] 0.40 (0.05–2.92) ORR, % (n/N)a OR (95% CI) ORR, % (n/N)a OR (95% CI) TALA + ENZA (N=96) PBO + ENZA (N=105) TALA + ENZA (N=62) PBO + ENZA (N=75) TMPRSS2-ERG 56 (13/23) 41 (7/17) 0.54 (0.12–2.27) 53 (9/17) 47 (7/15) 0.78 (0.16–3.85) MLL2 64 (7/11) 70 (7/10) 1.33 (0.15–12.60) ND ND ND RB1 62 (5/8) 0 (0/6) 0.00 (0.00–0.75) ND ND ND CI, confidence interval; HR, hazard ratio; mo, months; ND, not displayed (combined prevalence <10 pts); NR, not reached; OR, odds ratio. aMeasurable disease at baseline Citation Format: Josep M. Piulats, Arun A. Azad, A. Douglas Laird, Nobuaki Matsubara, Karim Fizazi, Neal D. Shore, Lawrence Karsh, Glenn Liu, Andre P. Fay, Joan Carles, Robert J. Jones, Eric Voog, Stefanie Zschäbitz, Ugo De Giorgi, Steven M. Yip, Xinmeng Jasmine Mu, Xun Lin, Arne Engelsberg, Neeraj Agarwal. TMPRSS2-ERG and RB1 as candidate predictive biomarkers for efficacy in TALAPRO-2: Phase 3 study of talazoparib (TALA) + enzalutamide (ENZA) vs placebo (PBO) + ENZA as first-line (1L) treatment in patients (pts) with metastatic castration-resistant prostate cancer (mCRPC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT018.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".