AI predicts effectiveness of lung cancer immunotherapy


Researchers from the Chinese University of Hong Kong (CUHK), Seoul National University, Sungkyunkwan University, Ajou University, and AI start-up Lunit have developed an AI-powered spatial analytical tool for predicting the effectiveness of immune checkpoint inhibitors (ICIs) in advanced non-small-cell lung cancer (NSCLC).

“ICIs are standard of care for advanced NSCLC with positive programmed death ligand-1 [PD-L1] expression,” “Treatment outcome with PD-L1 checkpoint inhibitors may vary depending on the patient’s tumour microenvironment, but there is currently no standard biomarker addressing this.” 

“Tumour infiltrating lymphocytes [TILs], in theory, are the main activator of antitumour immunity, which could be a promising biomarker for predicting treatment outcomes with ICIs,” “However, the current quantification method is labour-intensive and relies on spatial distribution in whole-slide images [WSIs], limiting utility and objectivity.”

Professor Tony Shu-Kam Mok of the Department of Clinical Oncology, CUHK

Multiple histologic components from WSIs, such as TILs, cancer epithelium (CE), and cancer stroma, can be segmented and quantified using the AI-powered spatial TIL analyzer (CS). Through deep learning–based AI model training, three immune phenotypes (IPs) are defined: immunological-excluded (TIL density in CE; below threshold of 106/mm2; TIL density in CS, above threshold of 357mm2), inflamed, and immune-desert.

At the WSI level, there was a substantial positive association between the AI model’s tumour proportion score (TPS; current technique of PD-L1 assessment) and pathologists’ control TPS (p0.001). “We used an original image with independent annotated markings of CE, CS, and TILs as assessed by a panel of qualified pathologists in a supervised learning model… The researchers said that “this rigorous monitoring permitted pixel-based spatial analysis of TILs in reference to CE and CS, which is crucial for objective quantification of IP and eventual classification of IP.”

To validate IPs as a complementary biomarker, the researchers analyzed patients with advanced NSCLC treated with ICI monotherapy in a retrospective proof-of-concept cohort study (n=518; mean age, 65.0 years; male, 75.9 percent).

Results of the study showed that the tumour response rate to ICIs in patients with inflamed IP was higher than that in patients with immune-excluded and immune-desert IPs (26.8 percent vs 11.5 percent vs 11.2 percent). Similarly, their median progression-free survival (PFS) with ICIs was significantly longer than that in patients with immune-excluded and immune-desert IPs (4.1 months vs 2.2 months vs 2.4 months; both p<0.001), as was overall survival (24.8 months vs 14.0 months vs 10.6 months; both p<0.05), indicating that IP may be a promising biomarker for predicting treatment outcomes.

According to the researchers, 42.5 percent of patients with PD-L1 TPS 1–49 percent were classified as having inflamed IP, which appeared to be higher than the corresponding reported rate of 14.8 percent from the KEYNOTE 001 study, suggesting that more potential ICI responders could be found with the help of the AI-powered spatial TIL analyzer.

“To our knowledge, this is the first study on AI-powered automated TIL analysis in advanced NSCLC,” concluded the researchers. “In this study, we have proved that the AI-powered spatial TIL analyzer is capable of predicting clinical outcome with ICIs in patients with advanced NSCLC. This may serve as a complementary biomarker to TPS, especially for the subgroup of patients with PD-L1 TPS 1–49 percent.”

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