On May 10, 2023 Lunit (KRX:328130.KQ), a leading global provider of AI-powered cancer diagnostic solutions, reported the presentation of 16 abstracts featuring its AI-biomarker platform at the American Society of Clinical Oncology (ASCO) (Free ASCO Whitepaper) Annual Meeting, to be held in Chicago, Illinois, on June 2-6 (Press release, Lunit, MAY 10, 2023, View Source [SID1234631417]).
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Since 2019, the company has been releasing groundbreaking findings based on its AI-powered tissue analysis platform, Lunit SCOPE, at the ASCO (Free ASCO Whitepaper) annual meeting. At this year’s ASCO (Free ASCO Whitepaper), Lunit plans to showcase the largest number of studies in the company’s history, including 10 poster presentations and 6 online publications. The studies will cover a range of topics, from predicting clinical outcomes to analyzing tumor microenvironments in various cancer types using the Lunit SCOPE suite.
Some of Lunit’s highlighted abstracts at ASCO (Free ASCO Whitepaper) 2023 include:
A study conducted in collaboration with the National Cancer Center Hospital East (NCCE) found a correlation between tumor-infiltrating lymphocyte (TIL) density change during chemoradiotherapy (CRT) and pathologic complete response (pCR) rate in locally advanced rectal cancer (LARC), indicating the potential of Lunit SCOPE IO to predict favorable outcomes
Lunit SCOPE IO analyzed TILs in head and neck squamous cell carcinoma (HNSCC) patients treated with durvalumab +/- tremelimumab, showing improved outcomes due to increased immune inflammation
Lunit SCOPE IO validated the efficacy of neoadjuvant HPV vaccine and immunotherapy of HPV+ head and neck squamous cell carcinoma (HNSCC) patients
A collaborative study led by Mayo Clinic predicted prognosis in patients with colon cancer by applying AI-derived immune phenotypes
A study showing how Lunit SCOPE IO predicts the efficacy of immunotherapy based on the patient’s Transforming growth factor-beta (TGFβ) level
The use of Lunit SCOPE IO to predict MET pathogenic mutations in non-small cell lung cancer
The use of Lunit SCOPE UIHC (Universal Immunohistochemistry) for exploring target cancer types and predicting response to novel tumor-associated antigens (TAA) targeted agents
Using AI to analyze the spatial arrangement of macrophages within tumor environments and how it relates to the Interferon Gamma (IFNG) signature and immune phenotype across different types of cancer
"We are thrilled to showcase the largest number of studies in the company’s history at ASCO (Free ASCO Whitepaper) 2023, demonstrating Lunit SCOPE’s efficacy," said Brandon Suh, CEO of Lunit. "Through our novel academic research using Lunit SCOPE suite across various cancer types and treatment settings, we aim to bring cutting-edge technology to the forefront of optimized cancer treatment for all patients."
Visit team Lunit at Booth IH21. Reach out to schedule a meeting at ([email protected]).
Lunit’s Abstracts at ASCO (Free ASCO Whitepaper) 2023
No.
Abstract No. #
Title
Type
1
3608
Predictive value of tumor-infiltrating lymphocyte (TIL) dynamics in the tumor microenvironment (TME) during preoperative chemoradiotherapy (CRT) on pathologic complete response (pCR) in microsatellite-stable (MSS) locally advanced rectal cancer (LARC)
Poster
2
2578
Dynamic change of immune phenotype assessed by artificial intelligence (AI)-powered tumor-infiltrating lymphocytes (TILs) analysis during neoadjuvant durvalumab with or without tremelimumab (D+/-T) in head and neck squamous cell carcinoma (HNSCC)
Poster
3
6075
Neoadjuvant pembrolizumab, GX-188E, and GX-I7 in patients with human papilloma virus-16- and/or 18-positive head and neck squamous cell carcinoma: single-arm, phase 2 trial with single cell transcriptomic analysis and artificial intelligence-powered spatial analysis
Poster
4
3542
Artificial Intelligence-Derived Immune Phenotypes for Prediction of Prognosis in Patients with Stage III Colon Cancer (NCCTG N0147) [Alliance]
Poster
5
2585
Tumor microenvironment (TME)-based histomic TGFβ signature (TGFBs) reveals stromal fibroblast recruitment and exclusion of immune cells as immunotherapy resistance mechanisms
Poster
6
e13578
Deep learning-based ensemble model using hematoxylin and eosin (H&E) whole slide images (WSIs) for the prediction of MET mutations in non-small cell lung cancer (NSCLC)
Online Publication
7
3135
Exploring expression levels of HER2, HER3, MET, Claudin18.2, and MUC16 across 16 cancer types using an artificial intelligence-powered immunohistochemistry analyzer
Poster
8
2621
Artificial intelligence (AI) –powered spatial analysis of macrophages in tumor microenvironment and its association with interferon-gamma (IFNG) signature and immune phenotype (IP) in pan-cancer dataset
Poster
9
e20520
Artificial intelligence (AI) –powered H&E whole-slide image (WSI) analysis of tertiary lymphoid structure (TLS) correlates with immune phenotype and related molecular signatures in non–small-cell lung cancer
Online Publication
10
1049
Artificial intelligence–powered tumor-infiltrating lymphocytes analyzer to reveal distinct immune landscapes in breast cancer by molecular subtype and HER2 score
Poster
11
e21179
Immune phenotype-driven treatment outcome of IO-only versus chemo-IO in PD-L1-high, first-line, advanced non-small cell lung cancer (NSCLC)
Online Publication
12
4162
Artificial intelligence (AI) –powered spatial analysis of tumor-infiltrating lymphocytes (TILs) for prediction of prognosis in resectable pancreatic adenocarcinoma (PDAC)
Poster
13
6100
Artificial intelligence (AI) analysis of tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) slides to explore immune phenotypes in papillary thyroid cancer
Poster
14
e14657
Correlation of fragmented pattern of tumor mass captured by artificial intelligence (AI)-powered whole-slide image (WSI) analysis with biased fibroblast expansion over tumor growth and distinct mutational signatures
Online Publication
15
e13553
Performance validation of an artificial intelligence-powered PD-L1 combined positive score analyzer in six cancer types
Online Publication
16
e13546
Effect of an artificial intelligence–powered programmed death-ligand 1 combined positive score analyzer in urothelial cancer on inter-observer and inter-site variability
Online Publication