Response to neoadjuvant immune checkpoint inhibitor (ICI)-based therapy in oncogene-driven resectable non-small cell lung cancer (NSCLC).
Bibliographic record
Abstract
8523 Background: Neoadjuvant (NEO) chemotherapy (chemo) plus nivolumab (nivo) has received FDA approval for treatment of resectable NSCLC, demonstrating superior event-free survival compared to chemo alone. However, outcomes of NEO ICI-based therapies in patients (pts) with oncogene-driven (OD)-NSCLC remains an unanswered question. Methods: We conducted a retrospective secondary analysis of pts enrolled in the clinical trial NCT02259621 with clinical stage I-IIIA NSCLC, treated with NEO ICI-based therapies: nivo alone, nivo+ipilimumab (ipi), or nivo+chemo followed by surgery. Pts underwent baseline genomic profiling using NGS. Pts with KRAS, EGFR, MET, BRAF, ALK, HER2, ROS1, RET or NTRK alterations were identified as OD-NSCLC. STK11 and KEAP1 mutations (muts) were also recorded. For pts who underwent definitive resection (DR) and baseline NGS testing, association of OD-status and clinical outcomes, including major pathologic response (MPR) and recurrence-free survival (RFS) are reported. Proportions are reported with exact 95% binomial confidence intervals (CI). Binomial probabilities are compared with Chi-square or Fisher’s exact tests. RFS and median follow-up (f/u) are reported using the Kaplan-Meier (KM) and reverse KM methods, respectively. Results: 61 pts received NEO ICI-based therapy: 60% treated with nivo alone, 15% with ipi+nvo and 25% with chemo+nivo. 92% of pts underwent DR. Pathologic complete response (pCR) and MPR rates were, 12.5% and 37.5%, respectively. Baseline NGS testing was available for 47 pts, of whom, 49% had OD-NSCLC, with the majority (78%) harboring a KRAS mut. Additional OD alterations included non-classical EGFR (9%), METex14 skipping (9%) and ROS1 fusion (4%), of whom, 3/5 pts had a MPR after NEO-ICI. STK11 muts were noted in 16% pts, and co-altered with KRAS in 9%. Median f/u for OD-NSCLC was 42.58 months. Pts with OD-NSCLC had comparable RFS after NEO-ICI compared to those with non-OD-NSCLC (HR 0.64, CI 0.19-2.13, p=0.5), including those with KRAS+ disease (HR 0.73, CI 0.2-2.73, p=0.6). Pts with co-mut KRAS+ STK11+ trended toward shorter RFS compared to KRAS+ STK11- (HR 6.36, CI 0.56-72.82, p=0.1). STK11+ also trended toward shorter RFS without reaching statistical significance (HR 2.51, CI 0.67-9.35, p=0.17). No significant associations between OD-status and MPR rates were seen, however KRAS+ (4/18) vs. Kras- (13/29) trended toward lower MPR rate (p=0.1). Four of 5 pts with disease progression preventing DR harbored an STK11 or KEAP1 mut. Conclusions: Findings from this cohort treated with various NEO ICI-based therapies, suggests comparable RFS, regardless of OD status- with the majority harboring KRAS muts. Lower MPR rates were observed for pts with KRAS+ tumors. STK11 mut and KRAS+ STK11+ co-mut status both trended toward shorter RFS, though definitive conclusions are limited by cohort size and treatment heterogeneity.
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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 | 0.001 |
| 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".