Comprehensive Genomic Profiling Identifies Novel Genetic Predictors of Response to Anti–PD-(L)1 Therapies in Non–Small Cell Lung Cancer
Bibliographic record
Abstract
Abstract Purpose: Immune checkpoint inhibitors (ICI) have revolutionized cancer management. However, molecular determinants of response to ICIs remain incompletely understood. Experimental Design: We performed genomic profiling of 78 patients with non–small cell lung cancer (NSCLC) who underwent anti–PD-(L)1 therapies by both whole-exome and targeted next-generation sequencing (a 422-cancer-gene panel) to explore the predictive biomarkers of ICI response. Tumor mutation burden (TMB), and specific somatic mutations and copy-number alterations (CNA) were evaluated for their associations with immunotherapy response. Results: We confirmed that high TMB was associated with improved clinical outcomes, and TMB quantified by gene panel strongly correlated with WES results (Spearman's ρ = 0.81). Compared with wild-type, patients with FAT1 mutations had higher durable clinical benefit (DCB, 71.4% vs. 22.7%, P = 0.01) and objective response rates (ORR, 57.1% vs. 15.2%, P = 0.02). On the other hand, patients with activating mutations in EGFR/ERBB2 had reduced median progression-free survival (mPFS) compared with others [51.0 vs. 70.5 days, P = 0.0037, HR, 2.47; 95% confidence interval (CI), 1.32–4.62]. In addition, copy-number loss in specific chromosome 3p segments containing the tumor-suppressor ITGA9 and several chemokine receptor pathway genes, were highly predictive of poor clinical outcome (survival rates at 6 months, 0% vs. 31%, P = 0.012, HR, 2.08; 95% CI, 1.09–4.00). Our findings were further validated in two independently published datasets comprising multiple cancer types. Conclusions: We identified novel genomic biomarkers that were predictive of response to anti–PD-(L)1 therapies. Our findings suggest that comprehensive profiling of TMB and the aforementioned molecular markers could result in greater predictive power of response to ICI therapies in NSCLC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".