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Neoadjuvant nivolumab (NIVO) + platinum-doublet chemotherapy (chemo) versus chemo for resectable (IB–IIIA) non-small cell lung cancer (NSCLC): Association of pathological regression with event-free survival (EFS) in CheckMate 816.

2022· article· en· W4289820683 on OpenAlexaff
Mariano Provencio-Pulla, Jonathan Spicer, Janis M. Taube, Claudio Martín, David R. Spigel, Changli Wang, Nicolas Girard, Shun Lü, Tetsuya Mitsudomi, Mark M. Awad, Enriqueta Felip, Julie R. Brahmer, Stephen Broderick, Steven Swanson, Keith M. Kerr, Li Li, Junliang Cai, Judith Bushong, Phuong Tran, Patrick M. Forde

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicinePathologicalClinical endpointInternal medicineOncologyNivolumabLung cancerNeoadjuvant therapyReceiver operating characteristicPost-hoc analysisStage (stratigraphy)Chemotherapynon-small cell lung cancer (NSCLC)Surrogate endpointCancerClinical trialImmunotherapyBreast cancer

Abstract

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LBA8511 Background: Several studies have shown an association of pathological response, a common efficacy endpoint in neoadjuvant therapy trials, with survival for chemo in various cancers including resectable NSCLC. However, the association between pathological complete response (pCR) and survival as well as the degree of pathological regression that may be predictive of EFS for neoadjuvant immunotherapy has not been rigorously studied. CheckMate 816 (NCT02998528), a randomized phase 3 study of neoadjuvant NIVO + chemo vs chemo in resectable NSCLC, met both of its primary endpoints with a statistically significant and clinically meaningful improvement in EFS and pCR. Here, we report a post hoc analysis from CheckMate 816, characterizing the association between pathological regression and EFS. Methods: Adults with resectable NSCLC were randomized to NIVO 360 mg + platinum-doublet chemo Q3W or chemo alone Q3W for 3 cycles. Primary endpoints were EFS and pCR (0% residual viable tumor [RVT] in the primary tumor [PT] and lymph nodes [LN] based on immune-related pathological response criteria), both assessed by blinded independent review. Major pathological response (MPR; ≤10% RVT in the PT and LN) was a secondary endpoint. In this post hoc analysis, EFS was assessed based on depth of pathological regression (measured by %RVT) in the PT only. Also, a time-dependent receiver operating characteristic curve analysis assessed the predictive ability of %RVT (PT only) for EFS outcome at 2 years, using area under the curve (AUC) to summarize the overall diagnostic accuracy (0.5 = random chance; 1 = perfect accuracy). Results: Baseline characteristics in patients (pts) with pathologically evaluable samples were well balanced between the NIVO + chemo and chemo arms, similar to the overall population. In both treatment arms, EFS (minimum follow-up, 21 months) was improved in pts with vs without pCR or MPR (Table). %RVT appeared to be predictive of EFS at 2 years for NIVO + chemo (AUC = 0.74) but an association was not clear for chemo (AUC = 0.54). 2-year EFS rates for NIVO + chemo were 90%, 60%, 57%, and 39% for pts with 0–5%, >5–30%, >30–80%, and >80% RVT, respectively. Conclusions: In CheckMate 816, pathological response (pCR and MPR) in the PT was associated with improved EFS with neoadjuvant NIVO + chemo. Additionally, depth of pathological regression appeared to be predictive of improved EFS. Clinical trial information: NCT02998528. [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)0.002
metaresearch head score (Gemma)0.002
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: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.069
GPT teacher head0.415
Teacher spread0.346 · 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".

Quick stats

Citations19
Published2022
Admission routes1
Has abstractyes

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