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Distinct gene expression patterns identify patients who relapse after neoadjuvant pembrolizumab and radical cystectomy in the PURE-01 study.

2023· article· en· W4324136749 on OpenAlexaff
Moritz J. Reike, Daniele Raggi, Laura Marandino, Damiano Alfio Patanè, Emanuele Crupi, Tiago Costa de Pádua, Peter C. Black, Ewan A. Gibb, Andrea Necchi

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCystectomyMedicineBladder cancerPembrolizumabOncologyInternal medicineNeoadjuvant therapyGene expression profilingGene expressionImmunotherapyCancerGeneBiologyBreast cancer

Abstract

fetched live from OpenAlex

549 Background: The PURE-01 clinical trial reported the use of neoadjuvant treatment with pembrolizumab prior to radical cystectomy (RC) in patients with muscle-invasive bladder cancer (MIBC; cT2–3N0M0; PMID 30343614). We have previously reported that specific molecular subtypes (i.e. basal or claudin-low) and immune signatures are associated with more favorable survival. However, reports on the detailed tumor biology of patients (pts) who relapsed after neoadjuvant pembrolizumab are lacking. Herein, we provide a detailed characterization of relapsing pts and identify distinct gene expression patterns with potential utility as novel biomarkers. Methods: Microarray data from transurethral resection of the bladder tumor (TURBT; N=102) and matched RC (N=25) tissue from the PURE-01 trial were analyzed. Gene expression signatures and molecular subtypes were assigned as previously described (PMID 32165065). The Kaplan-Meier method was used to estimate differences in patient outcomes. Immune-signatures were split by median for survival analysis. All significance testing used a two-sided t-test at a threshold of 0.05. Results: Differential gene expression analysis identified KRT20 and H19 to be enriched in relapsing patients (both p<0.001). Expression below the median was predictive of favourable 3-year overall (OS) and recurrence-free (RFS) survival (OS, 37.3% vs. 21.6%, p=0.118; RFS, 35.3% vs. 21.6%, p<0.01 and OS, 41.2% vs. 17.6%, p=0.03; RFS, 39.2% vs. 17.6%, p=0.012, respectively). Distribution of molecular subtypes showed tumors with neuronal character (NE-like) were enriched in pts who relapsed. Low immune-signatures, including Interferon-gamma and -alpha, were predictive of relapse (3-year RFS, 31.4% vs. 25.5%, p<0.001 and 31.4% vs. 25.5%, p<0.01, respectively), whereas Immunophenoscore for CD4 was not. None of these markers were predictive for relapse in matched RC-samples. Conclusions: Relapsing patients demonstrated distinct biological patterns in pre-therapy tumor samples that show prognostic value, potentially making an earlier identification and therefore adapted treatment possible. Low expression of immune-related signatures was associated with relapse.

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 communication0.0010.000
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 head0.067
GPT teacher head0.435
Teacher spread0.368 · 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
Published2023
Admission routes1
Has abstractyes

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