Related Experiment Video
Updated: Aug 13, 2026
Ultra-Fast Amplicon-Based Next-Generation Sequencing in Non-Squamous Non-Small Cell Lung Cancer
Published on: September 8, 2023
Evolving non-invasive biomarkers in NSCLC immunotherapy: integrating liquid biopsy and multi-omics profiling for
1Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Abstract:
The therapeutic landscape for advanced non-small cell lung cancer (NSCLC) has been transformed by immune checkpoint inhibitors (ICIs), yet significant response heterogeneity necessitates robust, dynamic predictive biomarkers. Conventional tissue-based markers, such as PD-L1 expression and tumor mutational burden (TMB), are hindered by their invasive nature and inability to capture the dynamic tumor-host immune interplay. This review synthesizes the paradigm shift toward a dynamic, multi-parametric framework for precision immuno-oncology. We highlight the clinical utility of the liquid biopsy toolbox-including circulating tumor DNA (ctDNA) for molecular residual disease (MRD) monitoring, circulating tumor cells (CTCs), and extracellular vesicles (EVs) in reflecting systemic immune status. Furthermore, we explore biological insights from multi-omics profiling, covering genomic drivers of resistance (e.g., STK11/KEAP1), immunometabolic crosstalk, and systemic inflammatory indicators like the NLR/PLR ratio. The potential of radiomics and pathomics to extract spatial signatures via artificial intelligence (AI) is discussed to address whole-tumor heterogeneity. Finally, we emphasize the integration of these disparate data streams through multimodal AI and Explainable AI (XAI) to construct high-fidelity predictive models. This integrated approach aims to overcome standardization hurdles and enable personalized, adaptive management in NSCLC immunotherapy.
Insights
Predicting response to advanced non-small cell lung cancer immunotherapy requires dynamic biomarkers beyond tissue tests. Liquid biopsies and AI integration offer a multi-parametric approach for personalized treatment strategies.
Area of Science:
- Oncology
- Immunology
- Biomarker Discovery
Background:
- Immune checkpoint inhibitors (ICIs) have revolutionized advanced non-small cell lung cancer (NSCLC) treatment, but patient response varies significantly.
- Current tissue-based biomarkers (PD-L1, TMB) are limited by invasiveness and inability to capture dynamic immune interactions.
- There is a critical need for dynamic, multi-parametric biomarkers to guide personalized NSCLC immunotherapy.
Purpose of the Study:
- To review the shift towards dynamic, multi-parametric frameworks for precision immuno-oncology in NSCLC.
- To highlight the utility of liquid biopsy tools and multi-omics profiling for predicting immunotherapy response.
- To discuss the role of AI in integrating diverse data streams for enhanced predictive modeling.
Main Methods:
- Review of current literature on predictive biomarkers for NSCLC immunotherapy.
- Exploration of liquid biopsy components: circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs).
- Analysis of multi-omics data, including genomic drivers, immunometabolism, and inflammatory markers (NLR/PLR).
- Discussion of radiomics, pathomics, and artificial intelligence (AI) for heterogeneity assessment.
- Emphasis on multimodal AI and Explainable AI (XAI) for predictive model construction.
Main Results:
- Liquid biopsies offer dynamic insights into tumor-host immune interplay and molecular residual disease (MRD).
- Multi-omics profiling reveals resistance mechanisms (e.g., STK11/KEAP1 mutations) and systemic inflammatory status.
- Radiomics, pathomics, and AI can address tumor heterogeneity by extracting spatial signatures.
- Integration of diverse data streams via multimodal AI and XAI is crucial for robust predictive models.
Conclusions:
- A dynamic, multi-parametric approach integrating liquid biopsies, multi-omics, and AI is essential for precision NSCLC immunotherapy.
- This framework moves beyond static tissue markers to capture the complex tumor immune microenvironment.
- Personalized and adaptive treatment strategies can be enabled by high-fidelity predictive models derived from multimodal data.
