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Molecular determinants of outcome for metastatic castration-sensitive prostate cancer (mCSPC) with addition of apalutamide (APA) or placebo (PBO) to androgen deprivation therapy (ADT) in TITAN.

2020· article· en· W3031849367 on OpenAlexaff
Felix Y. Feng, Shibu Thomas, Clemente Aguilar-Bonavides, Michael Gormley, Neeraj Agarwal, Gerhardt Attard, Alexander W. Wyatt, Elai Davicioni, Deborah Ricci, Angela Lopez‐Gitlitz, Julie S. Larsen, Simon Chowdhury, Kim N.

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of British Columbia
FundersJanssen Research and Development
KeywordsProstate cancerMedicinePopulationAndrogen deprivation therapyInternal medicineOncologyPlaceboAndrogen receptorProportional hazards modelCancerPathology

Abstract

fetched live from OpenAlex

5535 Background: In TITAN, addition of APA to ADT improved radiographic progression-free survival (rPFS) and overall survival (OS) versus PBO plus ADT in patients (pts) with mCSPC. In this post hoc analysis, we performed transcriptome-wide profiling of tumor samples and assessed association of molecular subtypes with rPFS. Methods: The DECIPHER platform (Decipher Biosciences, Inc.) was used to assess gene expression in archival primary prostate tumors from TITAN. Samples were classified into high versus low to average risk of metastases (DECIPHER genomic classifier [GC] > 0.6 and ≤ 0.6, respectively), basal and luminal A/B (PAM50 classifier), and androgen receptor activity (AR-A) signature high and low. Associations between subtypes with rPFS were assessed with Cox proportional hazards model. Results: The biomarker population included 222 pts (APA, 110; PBO, 112). Benefit in rPFS from APA in the biomarker population (HR [95% CI]; p value; 0.49 [0.31-0.78]; 0.002) resembled that in the overall study population (0.49 [0.40-0.61]; < 0.0001). The majority of TITAN pts had GC high scores (n = 166, 75%). GC high risk subtype in the PBO group had poorer prognosis for rPFS than GC low to average risk subtype (median rPFS 18.2 mos for GC high vs not reached [NR] for GC low to average, 0.28 [0.11-0.69]; 0.006), but there was no difference in prognosis between high and low to average GC risk subtypes in the APA group (GC high NR vs GC low to average NR; 0.81 [0.35-1.89]; 0.625). Pts were further stratified based on basal/luminal and AR-A signatures. Basal (n = 112, 50%) and AR-A low (n = 96, 43%) subtypes, known to be nonresponsive to ADT, both showed significant benefit from APA vs PBO (0.30 [0.16-0.57]; < 0.001 and 0.25 [0.12-0.52]; < 0.001, respectively). The majority of AR-A low subtype (74%, 71/96) overlapped with basal subtype. Further conclusions for risk of rPFS in GC low, luminal, and AR-A high subtypes and OS across all subtypes will be assessed as more events occur. Conclusions: In TITAN, addition of APA to ADT improved rPFS for all subtypes of pts with mCSPC. APA overcame the poor prognosis of GC high risk subtype and prolonged rPFS in ADT-resistant AR-A low and basal molecular subtypes, suggesting APA is beneficial especially for the highest risk molecular subtypes. Clinical trial information: NCT02489318 .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.232
GPT teacher head0.513
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

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Citations17
Published2020
Admission routes1
Has abstractyes

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