Abstract 6317: Molecular subtyping in prostate cancer associate with outcomes to abiraterone and ipatasertib treatment from the phase III IPATential150 trial
Bibliographic record
Abstract
Abstract Prostate cancer is a heterogeneous disease and genomic subtyping offers an opportunity to better understand underlying disease biology. Here, we performed transcriptional profiling by RNA-Seq (n=582) and targeted genomic sequencing by FoundationONE CDx (n=743) from prostate cancer tumor specimens in the phase III IPATential150 trial of first-line ipatasertib (Ipat) plus abiraterone (Abi) in metastatic castration-resistant prostate cancer (N=1101). Unsupervised transcriptomic analysis of the 582 samples with RNA-Seq with Nonnegative Matrix Factorization (NMF) reveals four consensus subtypes. This includes an immune/cell cycle-high, an AR signature/cell cycle-high, a stroma program-enriched, and an ERG fusion-enriched subtype. Subgroups of patients with immune/cell cycle-high and AR signature/cell cycle-high tumors had the shortest radiographic progression-free survival (rPFS) and were characterized by high MYC and cell cycle-related gene signatures. Patients with AR signature/cell cycle-high tumors showed the greatest increase in rPFS with Ipat + Abi vs. Placebo (Pbo) + Abi (HR = 0.58). Cluster n Enriched processes Median rPFS (Pbo + Abi) (months, 95% CI) Median rPFS (Ipat + Abi) (months, 95% CI) HR (95% CI) NMF1 84 Immune processes, metabolism, cell cycle 10.3 (8.3 - 12.7) 13.9 (10.9 - 16.4) 0.77 (0.47 - 1.28) NMF2 165 Androgen response signature, cell cycle, MYC signature 11.9 (8.8 - 16.5) 20.9 (16.4 - NA) 0.58 (0.38 - 0.90) NMF3 156 Fibroblast, Wnt, Notch, Hedgehog, TGFb 20.0 (16.4 - NA) 22.3 (15.6 - 24.9) 0.96 (0.59 - 1.56) NMF4 177 ERG fusion 18.4 (13.8 - 23.8) 24.7 (16.2 - NA) 0.78 (0.50 - 1.20) Citation Format: Zhen Shi, Malgorzata Nowicka, Johann de Bono, Kim N. Chi, Christopher Sweeney, Cora N. Sternberg, David Olmos, Sergio Bracarda, Christophe Massard, Nobuaki Matsubara, Josep Garcia, Geng Chen, Matthew Wongchenko, Shahneen K. Sandhu. Molecular subtyping in prostate cancer associate with outcomes to abiraterone and ipatasertib treatment from the phase III IPATential150 trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6317.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".