Lunit Presents Studies at SITC 2021, Highlighting the Effectiveness of AI in Predicting Response to Immunotherapy in a Clinical Trial Setting

- Lunit to present three abstracts about its AI biomarker platform Lunit SCOPE IO,also to be demonstrated during the event in booth #423

- One immunotherapy combination study adds significant evidence for the potential value of using Lunit SCOPE IO in clinical practice to predict patient response to immunotherapy across multiple cancer types

SEOUL,South Korea,Nov. 11,2021 -- Lunit today announced that it will present three abstracts at the upcoming Society for Immunotherapy of Cancer's (SITC) 36th Annual Meeting being held in Washington D.C. and virtually November 10-14,2021. The abstracts feature the predictive power of the company's AI biomarker platform 'Lunit SCOPE IO',which is also on demonstration during the event in its booth #423,Exhibition Hall E.

As a leading medical AI provider,Lunit focuses on developing the AI biomarker platform Lunit SCOPE IO,which analyzes cancer patients' tissue slide images and predicts response to immunotherapy. According to the company,Lunit SCOPE IO analyzes a patient's cancer tissue slide image by observing the distribution of tumor-infiltrating lymphocytes (TIL),one of the representative immune cells,and accurately classifying the result into three immune phenotypes (3-IP; inflamed,excluded,desert).

Lunit's AI biomarker platform,Lunit SCOPE IO

In the main study presented at SITC 2021,Lunit SCOPE IO was applied in a phase I/II clinical trial of a TGF-β inhibitor,MedPacto's vactosertib,in combination with pembrolizumab to treat metastatic colorectal cancer patients. This clinical trial is significant because the novel treatment regimen targets a subpopulation of colorectal cancer patients known to have a very low overall response rate. The regular lack of response to immune checkpoint inhibitors such as pembrolizumab is expected to be overcome via combination with the TGF-β inhibitor vactosertib,changing the biology of the cancer to be responsive to the combination.

According to the study,among the group of patients categorized as high "immune-excluded" by Lunit SCOPE IO,25% responded to treatment,compared to no response among the group of patients categorized as low "immune-excluded" by Lunit SCOPE IO.

"These results demonstrate that the AI-powered analysis of the immune cells surrounding the cancer is highly predictive of response to immunotherapy,and adds significant evidence to the usefulness of applying this technology in clinical practice to identify the right target patient population," explained Chan-Young Ock,Chief Medical Officer of Lunit.

"This study is also significant because the usefulness of Lunit SCOPE has been successfully demonstrated for combination immunotherapy,in addition to its usefulness for predicting response to monotherapy of immune checkpoint inhibitors,as previously demonstrated in ASCO 2019,2020,2021 and ESMO 2021," added Ock.

In the other two studies,the molecular mechanisms that are associated with Lunit SCOPE IO's prediction of response to immunotherapy have been demonstrated. According to the studies,the "immune excluded" subpopulation categorized by Lunit SCOPE IO is linked to the APOBEC signature and subclonal tumor heterogeneity,as well as oncogenic PI3K/Akt/mTOR pathway,adding biological plausibility to why Lunit SCOPE IO is highly predictive of response to immunotherapy.

"Over the years we have been validating the predictive power of our AI biomarker through multiple studies and we are excited to present meaningful results at SITC showing its further potential," said Brandon Suh,CEO of Lunit. "With the official launch of Lunit SCOPE IO for research use approaching within a few months,we hope our AI will contribute to offering optimized treatment to solid tumor cancer patients."

Lunit at SITC 2021

Exhibition booth: #423,Exhibition Hall E (floor plan)

Poster Title: Spatial analysis of tumor-infiltrating lymphocytes correlates with the response of metastatic colorectal cancer patients treated with vactosertibin combination with pembrolizumab

Abstract number: 823

Session Date/Time: Nov. 12,7:00 a.m. – 8:30 p.m. ET (on-site) and Nov. 12,7:00 a.m. ET (ePoster)

Poster Title: Artificial intelligence powered spatial analysis of tumor infiltrating lymphocytesreveals Immune excluded phenotype related to APOBEC signature and clonal evolution of cancer

Abstract number: 830

Session Date/Time: Nov. 13,7:00 a.m. ET (ePoster)

Poster Title: Comparison of PI3K/AKT/mTOR Pathway Profiles Amongst Three Immune Phenotypes Classified by Artificial Intelligence-Powered H&E Analyzer in Non-Small Cell Lung Cancer

Abstract number: 921

Session Date/Time: Nov. 12,7:00 a.m. ET (ePoster)

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