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Impact of androgen deprivation therapy (ADT) on circulating tumor DNA (ctDNA) detection in <i>de novo</i> metastatic castration-sensitive prostate cancer (mCSPC).

2024· article· en· W4391302587 on OpenAlexaff
Edmond M. Kwan, Andrew J. Murtha, Wilson Tu, Carlos Vasquez Rios, Cecily Q. Bernales, Shannen Keith Arviola, Gráinne Donnellan, Karan Parekh, Matti Annala, Corinne Maurice‐Dror, Gillian Vandekerkhove, Kim N., Alexander W. Wyatt

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineAndrogen deprivation therapyProstate cancerCastrationOncologyInternal medicineAndrogenCancer researchCancerHormone

Abstract

fetched live from OpenAlex

204 Background: Plasma ctDNA is detected in most patients with metastatic castration-resistant prostate cancer, enabling minimally invasive tumor genotyping that informs prognosis and therapy decisions. However, most patients with de novo mCSPC will commence ADT before referral for genomic testing, reducing ctDNA fraction (ctDNA%), potentially limiting its clinical utility. Here, we identify clinical predictors of ctDNA%, and assess its association with clinical outcomes in patients with de novo mCSPC. Methods: From a prospective British Columbia biobank, we identified patients with de novo mCSPC that provided blood prior to or within 50 days of ADT commencement. Plasma cell-free DNA and matched white blood cell DNA underwent deep targeted sequencing and ctDNA% was estimated (detection threshold ≥2%) using validated mutation- and copy number-based methods. Associations between clinical factors were related to ctDNA detection using 𝛘2 or Fisher's exact test. Time to castration-resistance (TTCR) and overall survival (OS) were stratified by ctDNA parameters (detected vs not detected, and by ctDNA% groups <2% vs 2-30% vs >30%), and compared using Kaplan-Meier and Cox regression analyses. Results: We examined 188 samples from 179 patients. Median age was 68 years, 87% received treatment intensification, and disease burden was consistent with typical de novo mCSPC (79% high-volume). CtDNA was detected in 66% (29/44) of ADT-naive samples compared to 26% (37/144) of ADT-exposed samples. The strongest predictor of ctDNA detection was duration of ADT exposure. CtDNA reduced over time, most evident >7 days after ADT commencement (1-7 days: 88% detected; 8-14: 31%, 15-50: 15%; p<0.001). Significant associations for ctDNA detection were also observed for abnormal hemoglobin (OR 3.5, p<0.001) and visceral (liver/lung) disease (OR 3.0; p=0.012), but not CHAARTED/LATITUDE criteria, PSA, or Gleason grade group. At a median follow-up of 48 months (mo), detected ctDNA and higher ctDNA% were associated with shorter TTCR irrespective of ADT exposure (ADT-naive: HR 3.6, 95% CI 1.2-11, p=0.02; ADT-exposed: HR 1.9, 95% CI 1.2-3, p=0.01; interaction p= 0.35), with outcomes poorest in the ADT-exposed group with high ctDNA% (median TTCR 32 vs 8.4 mo in <2% and >30% ctDNA groups, respectively). Only ADT-exposed ctDNA detection and ctDNA% were associated with OS (median 58 vs 29 mo in ctDNA– and ctDNA+ groups, respectively; HR 1.9, 95% CI 1.1-3.1, p=0.02). Conclusions: ADT exposure is the strongest predictor of ctDNA non-detection in de novo mCSPC. However, a quarter of patients will have detectable ctDNA despite receiving up to 50 days of ADT, indicating a window for complementing tumor tissue-based genotyping with ctDNA tests. Persistent ctDNA after ADT administration is prognostic and a potential biomarker to identify patients for treatment intensification.

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 score0.042
Threshold uncertainty score0.083

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 head0.057
GPT teacher head0.431
Teacher spread0.374 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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