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Association of RNA-sequencing signatures with clinical outcomes of pembrolizumab + chemotherapy in patients with metastatic nonsquamous non–small cell lung cancer (NSCLC) enrolled in the phase 2 KEYNOTE-782 trial.

2024· article· en· W4400036601 on OpenAlexaff
Enriqueta Felip, Santiago Ponce Aix, Jair Bar, Emilio Esteban, Delvys Rodríguez‐Abreu, Tibor Csőszi, Maya Gottfried, Zsuzsanna Szalai, Mariano Provencio, Andrea Fülöp, S. Rao, David Ross Camidge, Giovanna Speranza, Scott K. Pruitt, Steven M. Townson, Julie Kobie, Elisha J. Dettman, Andrey Loboda, Michael Nebozhyn, Andrew Robinson

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicinePembrolizumabOncologyLung cancerInternal medicineChemotherapyClinical trialCancerImmunotherapy

Abstract

fetched live from OpenAlex

8578 Background: KEYNOTE-782 (NCT03664024) was a single-arm phase 2 study designed to assess possible biomarkers of response (objective response rate [ORR]) to first-line pembrolizumab + chemotherapy (pemetrexed + carboplatin or cisplatin) in patients with previously untreated metastatic nonsquamous NSCLC. Herein, we examined relationships between the T-cell–inflamed gene expression profile (TcellinfGEP) and other tumor microenvironment consensus signatures and efficacy of pembrolizumab + chemotherapy in an exploratory analysis of KEYNOTE-782. Methods: All patients were to receive pembrolizumab 200 mg Q3W, pemetrexed 500 mg/m2 Q3W, and 4 Q3W cycles of carboplatin AUC 5 mg/mL/min or cisplatin 75 mg/m2. Using tumor samples, RNA sequencing was used to measure expression of the TcellinfGEP and 10 non-TcellinfGEP signatures (angiogenesis, gMDSC, glycolysis, hypoxia, mMDSC, MYC, proliferation, RAS, stromal/EMT/TGFβ, and WNT). The association between each signature and clinical outcomes was analyzed using logistic regression (ORR) and Cox proportional hazards regression (progression-free survival [PFS] and overall survival [OS]); the prespecified significance level was α = 0.05 for TcellinfGEP (hypothesized positive association) and α = 0.10 for non-TcellinfGEP signatures (hypothesized negative associations). The clinical utility of TcellinfGEP was descriptively assessed using a prespecified cutoff of the first tertile. The clinical database cutoff was November 5, 2021. Results: Of 117 patients enrolled, 69 (59.0%) had evaluable RNA-sequencing data. TcellinfGEP was not associated with ORR ( P = 0.183), PFS ( P = 0.071), or OS ( P = 0.142); the area under the receiver operating characteristic curve for discriminating response was 0.56 (95% CI, 0.42-0.71). None of the 10 non-TcellinfGEP consensus signatures showed a statistically significant association with clinical outcomes after adjusting for TcellinfGEP (multiplicity adjusted P > 0.10). ORR and median PFS were comparable in the TcellinfGEP low and nonlow subgroups; there was a trend towards longer OS in the nonlow subgroup (Table). Conclusions: In this exploratory analysis of patients with metastatic nonsquamous NSCLC, none of the RNA-sequencing signatures evaluated were associated with clinical outcomes of pembrolizumab + chemotherapy. There was little evidence of clinical utility of TcellinfGEP when evaluated as a dichotomous variable. Data support the use of pembrolizumab + chemotherapy as first-line therapy for patients with nonsquamous NSCLC regardless of consensus signature status. Clinical trial information: NCT03664024 . [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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.053
GPT teacher head0.439
Teacher spread0.386 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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