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Molecular determinants of prostate specific antigen (PSA) kinetics and clinical response to apalutamide (APA) in patients (pts) with nonmetastatic castration-resistant prostate cancer (nmCRPC) in SPARTAN.

2020· article· en· W3032259164 on OpenAlexaff
Fred Saad, Julie N. Graff, Boris Hadaschik, Stéphane Oudard, Paul N. Mainwaring, Amitabha Bhaumik, Michael Gormley, Anil Londhe, Shibu Thomas, Angela Lopez‐Gitlitz, Suneel Mundle, Elai Davicioni, Eric J. Small, Matthew R. Smith, Felix Y. Feng

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersJanssen Research and Development
KeywordsMedicineProstate cancerProstate-specific antigenInternal medicineBasal (medicine)PopulationOncologyAndrogen deprivation therapyUrologyCancer

Abstract

fetched live from OpenAlex

5521 Background: In SPARTAN, APA + androgen deprivation therapy (ADT) prolonged metastasis-free survival (MFS) and improved PSA kinetics over placebo (PBO) + ADT in high-risk nmCRPC. All molecular subtypes derived benefit in MFS from APA (Feng FY, et al. ASCO GU 2019; abstract 42). We evaluated the association of PSA decline and efficacy outcomes in SPARTAN pts with different molecular subtypes. Methods: Gene expression from archival primary tumors (biomarker population) was assessed with the DECIPHER platform (Decipher Biosciences, Inc.) and stratified into genomic classifier (GC) high- and low-to-average risk using GC score > 0.6 and ≤ 0.6, respectively, and ADT-resistant or -sensitive basal or luminal A/B (PAM50 classifier) subtypes. PSA nadir and confirmed PSA decline (Table) were assessed in APA pts overall and at 3, 6, and 12 mo. Associations between molecular subtypes and outcomes were assessed. Results: Of 233 available samples, 154 were from APA pts; 49% of APA pts had high GC score and 66% had basal subtype. PSA levels at baseline were similar across all subtypes. Regardless of GC score or basal/luminal subtype, > 50% of patients achieved ≥ 90% reduction in PSA with APA. PSA declined faster and PSA reduction was deeper at 6 mo (Table) in GC low to average vs GC high risk and luminal vs basal subtypes. Overall, only luminal vs basal subtypes had a significantly higher % of pts with ≥ 90% PSA decline (Chi square p = 0.037). In luminal pts, deeper PSA decline with APA was consistent with improved MFS vs basal pts. In GC high pts, MFS benefit with APA was similar to that in GC low to average pts despite lower PSA decline. Although GC low to average and luminal pts had more rapid and deeper PSA responses than GC high or basal pts, respectively, all pts derived MFS benefit. Association of long-term outcomes with PSA decline in these molecular subtypes will be presented. Conclusions: In SPARTAN, all molecular subtypes of pts with nmCRPC treated with APA + ADT had MFS benefit and rapid and sustained PSA decline. PSA responses were deepest and most rapid in GC low to average and luminal subtypes. Clinical trial information: NCT01946204 . [Table: see text]

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.114
GPT teacher head0.460
Teacher spread0.347 · 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 designObservational
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".

Quick stats

Citations5
Published2020
Admission routes1
Has abstractyes

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