KRAS Mutations as Predictive Biomarkers for First-Line Immune Checkpoint Inhibitor Monotherapy in Advanced NSCLC: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Recent research suggests a link between KRAS mutations and the effectiveness of ICIs, as KRAS-driven tumors may possess unique immunogenic features that influence the tumor microenvironment. These mutations can increase tumor mutation burden (TMB) and neoantigen load, potentially leading to improved responses to ICIs. This meta-analysis aims to consolidate existing evidence on the impact of KRAS mutations as a predictive factor for survival and treatment outcomes in patients with advanced NSCLC treated with ICIs. A comprehensive search strategy was designed by a biostatistician and pulmonologist, targeting PubMed, Web of Science, and Scopus databases up to May 2022. The outcomes assessed included overall survival (OS) and progression-free survival (PFS), reported as log hazard ratios (HRs) with corresponding standard errors (SEs). A pooled estimate of the HR effect size was calculated using Review Manager (RevMan, Cochrane Collaboration, London, UK). Heterogeneity among studies was evaluated using the Cochran Q test and the I2 statistic. Ultimately, 10 articles were deemed suitable for inclusion in the systematic review from a total of 8722 screened titles and abstracts. The presence of KRAS+ mutations had a significant prognostic factor for better OS in NSCLC patients treated with checkpoint inhibitors (HR = 0.89, 95% CI: 0.79–0.99) and for better PFS in NSCLC patients treated with checkpoint inhibitors (HR = 0.72, 95% CI: 0.59–0.87). In conclusion, our study indicates that KRAS mutations may serve as a potential positive predictive biomarker in patients with advanced non-small-cell lung cancer treated with immune checkpoint inhibitor monotherapy.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".