Multi-omics analysis reveals differential benefits of immunotherapy±chemotherapy based on detailed smoking history in advanced non-small cell lung cancer
Bibliographic record
Abstract
Background Despite immunotherapy±chemotherapy has transformed the therapeutic landscape for patients with non-small cell lung cancer (NSCLC), critical questions remain regarding how detailed smoking history affects the evolving treatment options and the underlying molecular mechanisms driving these effects. Methods We analyzed 4157 patients with advanced NSCLC who were treated with immunotherapy monotherapy (IO alone) (n=2768) or chemoimmunotherapy (chemo-IO) (n=1389) at the Dana-Farber Cancer Institute and Memorial Sloan Kettering Cancer Center (2010–2023). Associations between detailed smoking history (status and cumulative pack-years) and clinical outcomes were assessed using multivariable analyses. First-line chemo-IO versus IO alone was compared in programmed death receptor ligand 1 (PD-L1) Tumor Proportion Score (TPS) of ≥50% EGFR/ALK wild-type patients. Tobacco smoking-related mutational signature (TSMS) was inferred from the targeted next generation sequencing (NGS) panel. We investigated relationships between detailed smoking history and tumor genomics/transcriptomics, PD-L1 expression, tumor-infiltrating lymphocytes, and circulating plasma proteomics. Results In patients receiving IO alone, both smoking status and intensity showed dose-dependent associations with improved response and survival outcomes. In contrast, among patients receiving chemo-IO, smoking history did not affect initial response but patients with active (HR=0.73, 95% CI 0.57 to 0.94, p=0.01) and heavy tobacco use (HR=0.76, 95% CI 0.62 to 0.93, p=0.001) showed improved progression-free survival (PFS), and a trend toward improved overall survival (OS). Importantly, these associations remained independent of STK11 , KEAP1 , and KRAS co-mutation status. In patients with PD-L1 TPS of ≥50% lacking EGFR/ALK alterations, patients who do not smoke derived significant benefit from first-line chemo-IO versus IO alone with higher response rates (70.0% vs 23.9%, p=0.001), prolonged PFS (median PFS 9.5 vs 3.7 months, HR=0.51, 95% CI 0.27 to 0.95, p=0.04), and a trend toward prolonged OS, while patients who smoke showed comparable outcomes with either strategy. TSMS independently predicted improved outcomes of IO alone, even after tumor mutational burden (TMB) adjustment. Molecular analyses revealed associations between tobacco use and higher TMB, increased tumor-infiltrating lymphocytes (CD8 + , PD-1 + , CD8 + PD-1 + , FOXP3 + ) and distinct plasma protein profiles involved in immune signaling pathways (CCL7, CXCL17, CDCP1, TNFRSF6B). Conclusions Detailed smoking history provides crucial insights for optimizing IO selection in advanced NSCLC through mechanistic alterations in both the tumor microenvironment and systemic plasma protein profiles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".