From Variant to Biomarker in NSCLC Immunotherapy Resistance: Multiomics Evidence Chains and Accountable AI...

Area of Science:

• Oncology
• Immunology
• Genomics

Background:

• Immune checkpoint inhibitors (ICIs) are crucial in non-small cell lung cancer (NSCLC) therapy.
• Primary and acquired resistance to ICIs are significant clinical challenges.
• Current biomarkers like PD-L1 and TMB incompletely predict treatment response.

Purpose of the Study:

• To explore the multifaceted nature of resistance to immune checkpoint inhibitors in NSCLC.
• To highlight the potential of multiomics profiling for deciphering complex resistance mechanisms.
• To identify novel biomarker strategies for predicting and overcoming ICI resistance.

Main Methods:

• Review of current understanding of ICI resistance mechanisms in NSCLC.
• Discussion of multiomics approaches including genomics, transcriptomics, epigenomics, proteomics, and metabolomics.
• Integration of spatial assays, digital pathology, and imaging surrogates.

Main Results:

• Resistance to ICIs is a complex phenotype influenced by antigenicity, immune cell function, tumor microenvironment, and metabolism.
• Multiomics profiling provides a framework for integrating diverse data types to understand resistance.
• This approach allows for the development of more comprehensive and mechanistically interpretable biomarkers.

Conclusions:

• Understanding ICI resistance requires a systems-level approach beyond traditional biomarkers.
• Multiomics profiling offers a powerful strategy to unravel the complexities of NSCLC ICI resistance.
• This integrated approach can lead to improved patient stratification and therapeutic strategies.