Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes as a predictive biomarker for axitinib in adenoid cystic carcinoma

Dong Hyun Kim; Yoojoo Lim; Chan-Young Ock; Gahee Park; Seonwook Park; Heon Song; Minuk Ma; Mohammad Mostafavi; Eun Joo Kang; Myung-Ju Ahn; Keun-Wook Lee; Jung Hye Kwon; Yaewon Yang; Yoon Hee Choi; Min Kyoung Kim; Jun Ho Ji; Tak Yun; Sung-Bae Kim; Bhumsuk Keam

doi:10.1002/hed.27537

Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes as a predictive biomarker for axitinib in adenoid cystic carcinoma

Head Neck. 2023 Dec;45(12):3086-3095. doi: 10.1002/hed.27537. Epub 2023 Oct 13.

Authors

Dong Hyun Kim¹, Yoojoo Lim², Chan-Young Ock², Gahee Park², Seonwook Park², Heon Song², Minuk Ma², Mohammad Mostafavi², Eun Joo Kang³, Myung-Ju Ahn⁴, Keun-Wook Lee⁵, Jung Hye Kwon⁶, Yaewon Yang⁷, Yoon Hee Choi⁸, Min Kyoung Kim⁹, Jun Ho Ji¹⁰, Tak Yun¹¹, Sung-Bae Kim¹², Bhumsuk Keam^{1

13}

Affiliations

¹ Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea.
² Lunit, Seoul, South Korea.
³ Department of Internal Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea.
⁴ Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
⁵ Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, South Korea.
⁶ Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, South Korea.
⁷ Department of Internal Medicine, Chungbuk National University Hospital, Cheongju, South Korea.
⁸ Department of Internal Medicine, Dongnam Institute of Radiological and Medical Sciences, Busan, South Korea.
⁹ Division of Hematology-Oncology, Department of Internal Medicine, Yeungnam University Hospital, Yeungnam University College of Medicine, Daegu, South Korea.
¹⁰ Department of Internal Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, South Korea.
¹¹ Rare Cancers Clinic, Center for Specific Organs Cancer, National Cancer Center, Goyang, South Korea.
¹² Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
¹³ Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea.

PMID: 37828867
DOI: 10.1002/hed.27537

Abstract

Background: This study analyzed the predictive value of artificial intelligence (AI)-powered tumor-infiltrating lymphocyte (TIL) analysis in recurrent or metastatic (R/M) adenoid cystic carcinoma (ACC) treated with axitinib.

Methods: Patients from a multicenter, prospective phase II trial evaluating axitinib efficacy in R/M ACC were included in this study. H&E whole-side images of archival tumor tissues were analyzed by Lunit SCOPE IO, an AI-powered spatial TIL analyzer.

Results: Twenty-seven patients were included in the analysis. The best response was stable disease, and the median progression-free survival (PFS) was 11.1 months (95% CI, 9.2-13.7 months). Median TIL densities in the cancer and surrounding stroma were 25.8/mm² (IQR, 8.3-73.0) and 180.4/mm² (IQR, 69.6-342.8), respectively. Patients with stromal TIL density >342.5/mm² exhibited longer PFS (p = 0.012).

Conclusions: Cancer and stromal area TIL infiltration were generally low in R/M ACC. Higher stromal TIL infiltration was associated with a longer PFS with axitinib treatment.

Keywords: adenoid cystic carcinoma; artificial intelligence-analysis; axitinib; predictive biomarker; tumor-infiltrating lymphocyte.

Publication types

Clinical Trial, Phase II
Multicenter Study
Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence
Axitinib / therapeutic use
Biomarkers
Carcinoma, Adenoid Cystic* / drug therapy
Carcinoma, Adenoid Cystic* / pathology
Humans
Lymphocytes, Tumor-Infiltrating
Neoplasm Recurrence, Local / pathology
Prospective Studies

Substances

Axitinib
Biomarkers