Biomarkers for immunotherapy resistance in non-small cell lung cancer

Abstract

Immunotherapy has revolutionised the treatment landscape of non-small cell lung cancer (NSCLC), significantly improving survival outcomes and offering renewed hope to patients with advanced disease. However, the majority of patients experience limited long-term benefits from immune checkpoint inhibition (ICI) due to the development of primary or acquired immunotherapy resistance. Accurate predictive biomarkers for immunotherapy resistance are essential for individualising treatment strategies, improving survival outcomes, and minimising potential treatment-related harm. This review discusses the mechanisms underlying resistance to immunotherapy, addressing both cancer cell-intrinsic and cancer cell-extrinsic resistance processes. We summarise the current utility and limitations of two clinically established biomarkers: programmed death ligand 1 (PD-L1) expression and tumour mutational burden (TMB). Following this, we present a comprehensive review of emerging immunotherapy biomarkers in NSCLC, including tumour neoantigens, epigenetic signatures, markers of the tumour microenvironment (TME), genomic alterations, host-microbiome composition, and circulating biomarkers. The potential clinical applications of these biomarkers, along with novel approaches to their biomarker identification and targeting, are discussed. Additionally, we explore current strategies to overcome immunotherapy resistance and propose incorporating predictive biomarkers into an adaptive clinical trial design, where specific immune signatures guide subsequent treatment selection.

Keywords: biomarkers; circulating biomarkers; immunotherapy resistance; non-small cell lung cancer (NSCLC); precision oncology strategies.

Figure 1

A proposed schema for biomarker-adapted immunotherapy selection. Comprehensive multi-omic biomarker analyses of various biospecimens (including tumoural tissue, blood, sputum, urine, etc.) in conjunction with functional imaging. Evaluation of matched samples prior to and following neoadjuvant therapy can characterise changes within a tumour, which can then be harnessed to guide the selection of subsequent adjuvant treatment. A pathological complete response could potentially lead to treatment de-escalation, whilst pathologic partial responders or non-responders could undergo treatment escalation guided by the primary drivers of immune resistance. The use of identified immune signatures could help identify combination therapeutics with an increased likelihood of response for each participating individual. Whilst the allocated treatments shown here are definitive, this simply serves to illustrate a potential framework whereby therapy selection can be stratified leveraging existing biomarkers. Anti-PD1, anti-programmed death-1 monoclonal antibody; ICI, immune checkpoint inhibitor; PD-L1 programmed death-ligand 1 expression; pCR, pathologic complete response; pPR, pathologic partial response; pNR, pathologic non-responder; TILs, tumour infiltrating lymphocytes; TME, tumour microenvironment.