Efficacy and clinicogenomic biomarkers of response to dual versus single agent checkpoint inhibitor therapy in untreated metastatic non-small cell lung cancer.
Bibliographic record
Abstract
8549 Background: Biomarkers to guide the application of CTLA-4 inhibitors with anti-PD-(L)1 in untreated EGFR/ALK-negative metastatic non-small cell lung cancer (mNSCLC) are underexplored. Methods: Patients with ECOG performance status 0-1 and untreated EGFR/ALK-negative mNSCLC who received dual immune checkpoint inhibitor (ICI) therapy, or single agent PD-(L)1 inhibitor alone (ICI-mono) or with chemotherapy (ICI-chemo) at three institutions were analyzed. Clinical progression-free survival (PFS), and overall survival (OS) were the primary outcomes. Patients with progressive disease (PD) or stable disease (SD) as per RECIST 1.1 were considered non-responders, whereas those with partial or complete response (PR/CR) were considered responders. Inverse propensity weighting (IPW) for clinical and sociodemographic characteristics was used to control for differences in treatment selection. To identify subgroups of patients who benefit from dual ICI therapy, we conducted IPW multivariate analysis, including clinico-genomic covariates alone and in interaction with treatment assignment. Results: A total of 2347 patients (48% female, median age 67; dual ICI 255, ICI-mono 789, ICI-chemo 1303) were included. In unadjusted analysis, as compared to ICI-mono or ICI-chemo, dual IC was associated with an increase in OS (ICI-mono: HR = 0.8, p = 0.011; ICI-chemo: HR = 0.7, p = 4.7e-05) and a relatively smaller increase in PFS (ICI-mono: HR = 0.8, p = 0.041; ICI-chemo: HR = 0.9, p = 0.076). Among responders who received dual vs ICI+/-chemo there was no difference in OS (dual ICI vs ICI-mono: HR = 1.0, p = 0.89; dual ICI vs ICI-chemo: HR = 0.7, p = 0.11), whereas there was an increase in OS among non-responders (dual-ICI vs ICI-mono: PD: HR = 0.7, p = 0.033; SD: HR = 0.8, p = 0.1; dual-ICI vs ICI-chemo: PD: HR = 0.5, p = 1.7e-06; SD: HR = 0.7, p = 0.033. On IPW-adjusted analysis among patients with complete clinical annotation (n=478), OS (ICI-mono: HR = 0.70, p = 0.03; ICI-chemo: HR = 0.60, p = 0.0051) but not PFS (ICI-chemo: HR = 1.0, p = 0.70; ICI-mono: HR = 0.80, p = 0.11) was increased in patients treated with dual vs ICI+/-chemo. In multivariate IPW analysis among patients with complete clinical and genomic annotation (n = 369), PFS was increased in dual ICI vs ICI-chemo treated patients with bone mets (HR = 0.34, p = 0.0029) and squamous histology (HR = 0.35, p = 0.0076). PFS was increased in dual ICI vs ICI-mono treated patients with bone mets (HR = 0.45, p = 0.041). Conclusions: In this retrospective cohort of patients with untreated mNSCLC, dual ICI therapy improved overall survival as compared to ICI+/-chemo among patients without, but not with, response to treatment. Dual ICI therapy increased time to progression in patients with bone mets and squamous histology. Further studies are warranted to develop an optimal treatment-selection strategy for untreated mNSCLC.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".