A single institution study evaluating outcomes of PD-L1 high KRAS-mutant advanced non-small cell lung cancer (NSCLC) patients treated with first line immune checkpoint inhibitors
Bibliographic record
Abstract
AIM: This study aimed to evaluate the impact of KRAS status on the efficacy of first-line immune checkpoint inhibitors (ICI) in patients with advanced non-small cell lung cancer (NSCLC). PATIENTS AND METHODS: Patients with advanced incurable or metastatic NSCLC with PD-L1 ≥50% treated with palliative-intent, single-agent PD-1/PD-L1 inhibitors at the Cancer Centre of Southeastern Ontario were included. KRAS mutation status was determined via massively parallel sequencing. Primary study outcome was median overall survival (mOS). RESULTS: Seventy-eight patients (59 non-squamous, 19 squamous) were identified; only non-squamous patients were included in KRAS mutation analyses. Thirty patients (51%) were KRAS-MT (mutant), with G12C (19%), G12V (15%), and G12D (13%) accounting for the most common KRAS mutation subtypes. There was no difference in mOS between KRAS-MT and KRAS-WT (wild-type) patients (12.9 vs. 19.3 months, p = 0.879). There was a non-significant trend towards worse mOS in KRAS G12C patients compared to non-G12C and KRAS-WT patients (11.4 vs. 44.9 vs. 19.3 months, p = 0.772). On multivariable analysis, KRAS-MT status was not associated with mOS (HR 0.901, 95%CI 0.417-1.946, p = 0.791). ECOG≥2 was an independent prognostic factor for worse mOS (HR 2.853, 95%CI 1.237-6.583, p = 0.014). Immune-related adverse events did not differ between KRAS-MT and KRAS-WT groups (48% vs. 52%, p = 1.000). CONCLUSIONS: KRAS mutation status did not have a significant impact on ICI efficacy or safety. However, a non-significant trend towards worse survival was noted in patients treated with ICI whose tumours harboured the KRAS G12C variant. This study provides valuable information for comparative analysis in the future.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".