Dissecting transcriptome signals of anti-PD-1 response in lung adenocarcinoma.

Immune checkpoint blockades are actively adopted in diverse cancer types including metastatic melanoma and lung cancer. Despite of durable response in 20-30% of patients, we still lack molecular markers that could predict the patient responses reliably before treatment. Here we present a composite model for predicting anti-PD-1 response based on tumor mutation burden (TMB) and transcriptome sequencing data of 85 lung adenocarcinoma (LUAD) patients who received anti-PD-(L)1 treatment. We found that TMB was a good predictor (AUC = 0.81) for PD-L1 negative patients (n = 20). For PD-L1 positive patients (n = 65), we built an ensemble model of 100 XGBoost learning machines where gene expression, gene set activities and cell type composition were used as input features. The transcriptome-based models showed excellent accuracy (AUC > 0.9) and highlighted the contribution of T cell activities. Importantly, nonresponder patients with high prediction score turned out to have high CTLA4 expression, which suggested that neoadjuvant CTLA4 combination therapy might be effective for these patients. Our data and analysis results provide valuable insights into developing biomarkers and strategies for treating LUAD patients using immune checkpoint inhibitors.

Programmed Death-Ligand 1 Copy Number Alteration as an Adjunct Biomarker of Response to Immunotherapy in Advanced NSCLC.

INTRODUCTION: This study aimed to evaluate the value of programmed death-ligand 1 (PD-L1) copy number (CN) alteration as an additional biomarker to standard immunohistochemistry (IHC) in predicting response to immune checkpoint inhibitor (ICI) therapy in advanced NSCLC. METHODS: Before ICI monotherapy, tumor PD-L1 CN alteration (gain, neutral, or loss) was called using whole-exome sequencing data and compared with IHC results (tumor proportion score ≥50, 1-49, or 0). Progression-free survival (PFS) and overall survival were correlated with both biomarkers. In addition, the impact of CN alteration was further evaluated in two independent cohorts using next-generation sequencing panel. RESULTS: A total of 291 patients with advanced-stage NSCLC met the study inclusion criteria. Although the IHC classification distinguished the best responsive group (tumor proportion score ≥ 50), the CN-based classification distinguished the worst responsive group (CN loss) from the others (PFS, p = 0.020; overall survival, p = 0.004). After adjusting for IHC results, CN loss was an independent risk factor for progression (adjusted hazard ratio = 1.32, 95% confidence interval: 1.00-1.73, p = 0.049) and death (adjusted hazard ratio = 1.39, 95% confidence interval: 1.05-1.85, p = 0.022). A risk classification system was developed on the basis of IHC and CN profiles, which outperformed the conventional IHC system. In the validation cohorts, CN loss determined by next-generation sequencing panel was independently associated with worse PFS after ICI treatment, revealing its practical value. CONCLUSIONS: This is the first study to directly compare CN alterations with IHC results and survival outcomes after anti-PD-(L)1 therapy. Tumor PD-L1 CN loss can serve as an adjunct biomarker to predict the lack of response. Prospective studies are required to further validate this biomarker.