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Effect of bone metastasis on outcomes in the CCTG BR.34 phase II randomized trial of dual immune checkpoint inhibitor (ICI) treatment with or without chemotherapy in high-risk, stage IVA/B NSCLC.

2022· article· en· W4281868030 on OpenAlexaff
Kim Leitzel, Suhail M. Ali, Keyue Ding, Natasha B. Leighl, Francisco Emilio Vera Badillo, Pierre-Olivier Gaudreau, Penelope Ann Bradbury, Glenwood Goss, Lois E. Shepherd, Luís Costa, Larry J. Suva, Joseph J. Drabick, C. Patrick, Monika Joshi, Hyma Polimera, Allan Lipton

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsOttawa HospitalCentre Hospitalier de l’Université de MontréalPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineInternal medicineDurvalumabNivolumabOncologyChemotherapyLung cancerPopulationBone metastasisGastroenterologyMetastasisCancerImmunotherapy

Abstract

fetched live from OpenAlex

9067 Background: Bone metastasis (BM) occurs in about 40% of patients with metastatic lung cancer. Recently, BM was associated with decreased OS to nivolumab in previously-treated NSCLC (Landi L et al, P1.01.53, 19th WCLC, 2018). CCTG BR.34 (NCT03057106) was an open-label, randomized phase II clinical trial that randomized 301 patients with treatment-naïve, high-risk, stage IVA/B NSCLC without sensitizing EGFR or ALK alterations (1:1) to durvalumab plus tremelimumab with or without platinum doublet chemotherapy. First, 109 patients accrued with stage IVB, or selected IVA disease. Then 192 patients accrued with any stage IVA/B disease. In CCTG BR.34, median OS was not significantly different: 16.6 mo in the chemotherapy plus immunotherapy (C+IO) arm, vs 14.1 mo in the IO alone arm (HR 0.88, p = 0.46) (Leighl NB et al, J Thor Oncol, 2021). However, in BR.34 PFS was significantly longer in the C+IO arm (7.7 mo) compared to the IO alone arm (3.2 mo) (HR 0.67, 95% CI, 0.52 – 0.88). Here we analyzed the effect of BM on outcomes in BR.34. Methods: The 301 patients in the trial were characterized by the presence of BM at study entry (129-yes, 172-no). BM effect was evaluated on trial outcomes (OS, PFS, and ORR) using Cox/logistic regression analysis. Multivariable analysis was performed adjusting for the clinical and molecular covariates available. Results: In univariate analysis of the entire study population, median OS was significantly shorter for patients with BM vs those without BM (10. 9 vs 18.7 mos, HR 1.68, p = 0.001), as was median PFS (3.4 vs 7.2 mos, HR 1.82, p < 0.0001), and lower ORR (29.5% vs 45.9%, OR 0.52, p = 0.003), respectively. There was no evidence of differential association of BM with treatment arms for OS (p = 0.23), PFS (p = 0.84), and ORR (p = 0.25, Breslow-Day test). In multivariate analysis (MVA), BM remained significantly associated with worse OS (HR 1.44, p = 0.026), PFS (HR 1.69, p < 0.0001), and ORR (OR 0.52, p = 0.01). In MVA for OS: TMB, histology type, race, and ECOG were also significant; but age, smoking history, and PD-L1 IHC status were not significant. Conclusions: In CCTG BR.34 the presence of BM at trial entry was associated with significantly shorter OS, PFS, and lower ORR. BM is therefore a significant adverse prognostic factor in high-risk, stage IVA/B NSCLC treated with durvalumab and tremilimumab (with or without platinum doublet chemotherapy). If confirmed in a larger phase III trial, BM should be considered as an important new stratification factor in all clinical trials of immune checkpoint inhibitor (ICI) therapy. We and others have reported that molecules arising in the bone microenvironment (e.g: IL-8, PTHrP, TGF-b, sclerostin, and activin A) cause immunosuppression in cancer, and future trials should evaluate the addition of targeted therapies against these factors in combination with the ICIs in patients with BM.

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)0.003
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 score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.061
GPT teacher head0.443
Teacher spread0.382 · 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".

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Citations2
Published2022
Admission routes1
Has abstractyes

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