Lung cancer, close-up
Credit: Science Photo Library-NCI/Getty Images

A new AI-guided tool from South Korea-based Lunit has shown promise in predicting clinical outcomes of immunotherapy in patients with advanced non-small cell lung cancer (NSCLC). 

Lunit has published a study in the Journal of Clinical Oncology (JCO) supporting the effectiveness of its AI biomarker, Lunit SCOPE IO.  

The team used slides from Samsung Medical Center, Seoul, Korea, and Seoul National University Bundang Hospital, Korea, as well as TCGA images during the early step of development.

See-Hong Lee, senior author, told Inside Precision Oncology that the team “Used AI to calculate and analyze spatial distribution of tumor infiltrating lymphocytes [TILs]. From that we determined our method can predict outcome, in addition to PD-L1, a previously well-established predictive marker. We used two, independent clinical cohorts from two Korean hospitals.”

Immune checkpoint inhibitors (ICI) are a standard therapy method for advanced NSCLC with programmed death ligand-1 (PD-L1) expression. However, outcomes vary depending on the patient’s tumor microenvironment.

“Immune phenotyping of tumor microenvironment is a logical biomarker for immunotherapy, but objective measurement of such would be extremely challenging,” said Professor Tony Mok from the Chinese University of Hong Kong, co-senior author of the publication.

He added that, “This is the first study that adopted AI technology to define the tumor immune phenotype, and to demonstrate its ability in predicting treatment outcomes of anti-PD-L1 therapy in two large cohorts of patients with advanced non-small cell lung cancer.”

Assessing the PD-L1 tumor proportion score (TPS) can bring predictive benefit for patients with high expression (over 50%), who show superior response to ICI therapy over standard chemotherapy. However, ICIs lose their potency in patients with PD-L1 TPS between 1% and 49%, showing outcomes similar to chemotherapy. Therefore, the development of an accuracy-enhanced biomarker to predict ICI response in NSCLC patients with low PD-L1 expression is highly warranted.

While TILs are promising biomarkers for predicting ICI treatment outcomes apart from PD-L1, clinical application remains challenging as TIL quantification involves a manual evaluation process bound to practical limitations of interobserver bias and intensive labor. Employing AI’s superhuman computational capabilities should open new possibilities for the objective quantification of TIL.

To validate immune phenotyping as a complementary biomarker in NSCLC, researchers divided 518 NSCLC patients into three groups based on their tumor microenvironment: inflamed, immune-excluded, and immune-desert. As a result, clinical characteristics based on each immune phenotype group showed statistically significant differences in progression-free survival (PFS) and overall survival (OS).

Furthermore, analysis of NSCLC patients with PD-L1 TPS between 1% and 49% based on their immune phenotype found that the inflamed group showed significantly higher results in objective response rate (ORR) and progression-free survival (PFS), compared to the non-inflamed groups. This shows Lunit SCOPE IO’s ability to supplement PD-L1 TPS as a biomarker by accurately predicting immunotherapy response for patients with low PD-L1 TPS.

“Lunit has demonstrated through several abstracts the credibility of Lunit SCOPE IO as a companion diagnostic tool to predict immunotherapy treatment outcomes,” said Chan-Young Ock, Chief Medical Officer at Lunit. “This study is a proof-of-concept that compiles all of our past research that elucidates Lunit AI’s ability to optimize cancer treatment selection.”

Last year, Lunit announced a strategic investment of USD 26 million from Guardant Health, Inc., Lunit continues to refine its global position by validating the effectiveness of its AI technology through various studies.

 

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