Key Takeaways & Executive Findings
- •• • The integrated model achieved a C-index of 0.78 for OS and 0.75 for PFS, outperforming clinical-only models by +0.12, enabling more accurate risk stratification in NSCLC. • • High-risk patients (top quartile by risk score) had a median OS of 18.2 months vs. 42.6 months for low-risk (p < 0.001), demonstrating strong discriminative power for treatment decisions. • • A 15-gene immune-related signature predicted immune checkpoint inhibitor response with AUC = 0.82, facilitating patient selection for immunotherapy. • • Tumor mutation burden (TMB) and PD-L1 expression were confirmed as independent prognostic factors, with hazard ratios of 1.45 (95% CI: 1.20-1.75) and 1.32 (95% CI: 1.10-1.58), respectively, guiding combination therapy strategies.
Abstract
Non-small cell lung cancer (NSCLC) exhibits heterogeneous clinical trajectories, necessitating robust predictive models for personalized therapy. We integrated transcriptomic, genomic, and clinical data from 1,024 NSCLC patients (cohort A: 612, cohort B: 412) to develop a machine learning framework for overall survival (OS) and progression-free survival (PFS) prediction. Using a random survival forest algorithm with 500 trees and 10-fold cross-validation, we achieved a concordance index (C-index) of 0.78 (95% CI: 0.74-0.82) for OS and 0.75 (95% CI: 0.71-0.79) for PFS in the validation cohort. Key predictive biomarkers included tumor mutation burden (TMB), PD-L1 expression, and specific immune cell infiltration signatures. The model stratified patients into high- and low-risk groups with median OS of 18.2 vs. 42.6 months (p < 0.001). Furthermore, we identified a 15-gene immune-related signature that independently predicted response to immune checkpoint inhibitors (AUC = 0.82). Our findings demonstrate that integrating multi-omics data with clinical parameters significantly improves prognostic accuracy over clinical variables alone (C-index improvement: +0.12). This framework offers a clinically actionable tool for treatment stratification and trial design.
1. Introduction
Non-small cell lung cancer (NSCLC) remains a leading cause of cancer mortality, with five-year survival rates below 20% for advanced stages. Despite advances in targeted therapies and immunotherapies, patient outcomes vary widely due to tumor heterogeneity and complex tumor-microenvironment interactions. Conventional prognostic tools, such as TNM staging and single biomarkers, fail to capture this complexity, leading to suboptimal treatment stratification and missed therapeutic opportunities.
Recent multi-omics studies have revealed that integrating genomic, transcriptomic, and clinical data can uncover novel prognostic signatures and therapeutic targets. However, translating these findings into clinically robust predictive models remains challenging due to data dimensionality, batch effects, and model overfitting. Our study addresses this bottleneck by employing a rigorous machine learning framework with feature selection and cross-validation on a large, multi-center cohort. We demonstrate that a random survival forest model, trained on a curated set of multi-omics features, significantly improves prognostic accuracy over clinical variables alone. This approach provides a practical tool for personalized risk assessment and treatment planning in NSCLC.
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ZHANG Wei, LI Ming, WANG Fang, et al. (2025). Predictive Modeling of Clinical Outcomes in Non-Small Cell Lung Cancer Using Multi-Omics Data and Machine Learning. Chinese Journal of New Drugs. https://doi.org/pub_80__articleID_231
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Frequently Asked Questions
How does the model perform under external validation, and what are the potential failure modes when applied to different patient populations?
The model was validated on an independent cohort (n=412) with a C-index of 0.78 for OS. However, performance may degrade in populations with different ethnic backgrounds or treatment protocols due to variations in genetic background and clinical practices. We recommend recalibration using local data before clinical deployment.
What is the cost and time overhead for implementing this multi-omics model in a routine clinical setting?
The model requires whole-exome sequencing and RNA-seq data, which typically cost $1,500-$3,000 per patient and take 2-3 weeks for processing. This may be prohibitive for some healthcare systems. However, we have developed a reduced panel of 15 genes that can be assayed via targeted sequencing at lower cost (~$500) with minimal loss in predictive accuracy (AUC = 0.82).
How does the model handle missing data, and what is the impact on prediction accuracy?
We used multiple imputation by chained equations to handle missing clinical variables. In sensitivity analyses, up to 20% missingness in key variables (e.g., TMB) resulted in a C-index decrease of less than 0.02, indicating robustness. For complete-case analysis, the C-index was 0.79.
What are the key biological pathways driving the prognostic signature, and how can they inform targeted therapy?
The 15-gene signature is enriched in immune-related pathways, including T-cell activation and cytokine signaling. High expression of these genes correlates with improved response to PD-1/PD-L1 inhibitors, suggesting that the model can identify patients likely to benefit from immunotherapy. Additionally, we observed upregulation of DNA repair genes in high-risk patients, indicating potential sensitivity to PARP inhibitors.
How does this model compare to existing prognostic tools like TNM staging or established gene signatures (e.g., Oncotype DX)?
In our cohort, the model achieved a C-index of 0.78, significantly outperforming TNM staging alone (C-index = 0.65) and a published 12-gene signature (C-index = 0.70). The improvement is clinically meaningful, as it reclassifies approximately 25% of patients into different risk groups, potentially altering treatment decisions.
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