Impact of Bone Metastasis in Stage IV Non–Small Cell Lung Cancer Treated With Durvalumab and Tremelimumab With or Without Chemotherapy: A Retrospective Analysis of the CCTG BR.34 Trial
Bibliographic record
Abstract
PURPOSE This retrospective analysis examines the impact of bone metastasis on outcomes in patients with non–small cell lung cancer (NSCLC) from the CCTG BR.34 trial, which investigated the combined immune checkpoint blockade. MATERIALS AND METHODS The CCTG BR.34 trial was a randomized phase II study assessing durvalumab plus tremelimumab, with or without platinum-doublet chemotherapy, in 301 patients with metastatic NSCLC. Patients were categorized into two cohorts on the basis of bone metastasis: cohort A (n = 129) and cohort B (n = 172). The primary end point was overall survival (OS), analyzed using Cox regression and multivariable models. RESULTS Patients with bone metastasis had notably poorer outcomes. The median OS was 10.9 months for those with bone metastasis versus 18.7 months for those without (hazard ratio [HR] 1.68, P = .001). The median progression-free survival (PFS) was 3.4 months with bone metastasis compared with 7.2 months without (HR, 1.82, P < .0001). The overall response rate (ORR) was lower in patients with bone metastasis (29.5%) compared with those without (45.9%; odds ratio [OR], 0.52, P = .01). Adding chemotherapy to durvalumab plus tremelimumab did not significantly affect OS ( P = .23), PFS ( P = .84), or ORR ( P = .25) in relation to bone metastasis. Multivariable analysis reaffirmed that bone metastasis was linked to decreased OS (HR, 1.42, P = .036), PFS (HR, 1.69, P < .0001), and ORR (OR, 0.52, P = .01). CONCLUSION Bone metastasis was associated with worse outcomes in this dual immune checkpoint blockade trial, with or without chemotherapy. Future trials should consider bone metastasis as a stratification factor and explore combining immune checkpoint inhibitors with targeted therapies addressing bone microenvironment factors (eg, interleukin-8, PTHrP, and transforming growth factor-β).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".