Researchers may have uncovered a metabolic pathway that could lead to the development of predictive biomarkers for immune checkpoint inhibitors, according to new findings presented by Kulasinghe et al at the International Association for the Study of Lung Cancer (IASLC) 2024 World Conference on Lung Cancer (Abstract OA03.06).
Background
Although immune checkpoint inhibitors have proven effective in patients with non–small cell lung cancer (NSCLC), their efficacy is limited to a subset of patients. Identifying reliable predictive biomarkers may be crucial for optimizing treatment responses.
Study Methods and Results
In the new study, the researchers analyzed a retrospective cohort of NSCLC tissue samples from 45 patients who received treatment with immune checkpoint inhibitors. They used advanced multiplexed immunofluorescence staining combined with deep learning-based analysis, classified cells into 15 distinct types, and further categorized them with unsupervised clustering techniques.
“By mapping over 1,000 spatial features within [the] tumor and [tumor microenvironment] regions, we compared characteristics between responders and nonresponders to identify predictive patterns,” explained senior study author Ken O'Byrne, MBBS, FRACP, FRCPA, MD, of the Princess Alexandra Hospital.
The researchers identified 43 unique cell subsets that were primarily differentiated by their metabolic and activation states. Key proteins linked to oxidative phosphorylation and metabolic pathways—including CS, SDHA, ATPA5, HK1, GLUT1, and LDHA—were differentially expressed.
Further, they found an association between metabolically active lymphocytes—characterized by elevated PD-1, MHC class I and II, and CD44 levels—and their presence in tumor-infiltrating lymphocytes and tertiary lymphoid structures. The tumor cells were classified into three metabolic states: OXPHOS-positive, OXPHOS-negative, and PPP-positive, with PPP-positive cells showing increased proliferation, CD44 positivity, and a higher resistance to PD-1 blockade. The tumors with over 40% PPP-positive cells were associated with poorer response to PD-1 inhibitors and reduced overall survival.
Conclusions
The researchers noted that the intricate profiles of the tumor microenvironment may help predict which patients are likely to benefit from immune checkpoint inhibitors. The findings highlighted the potential for enhancing patient selection and treatment outcomes through detailed metabolic profiling of the tumor microenvironment.
“This research reveals complex relationships between metabolic states, immune cell functionality, and responses to immunotherapy and offers a promising pathway toward developing predictive biomarkers for [immune checkpoint inhibitors],” concluded lead study author Arutha Kulasinghe, PhD, of The University of Queensland.
Disclosure: For full disclosures of the study authors, visit cattendee.abstractsonline.com.
