Development and validation of a prognostic and predictive 32-gene signature for gastric cancer

Jae-Ho Cheong (), Sam C. Wang, Sunho Park, Matthew R. Porembka, Alana L. Christie, Hyunki Kim, Hyo Song Kim, Hong Zhu, Woo Jin Hyung, Sung Hoon Noh, Bo Hu, Changjin Hong, John D. Karalis, In-Ho Kim, Sung Hak Lee and Tae Hyun Hwang ()
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Jae-Ho Cheong: Yonsei University College of Medicine
Sam C. Wang: University of Texas Southwestern Medical Center
Sunho Park: Department of Artificial Intelligence and Informatics, Mayo Clinic
Matthew R. Porembka: University of Texas Southwestern Medical Center
Alana L. Christie: University of Texas Southwestern Medical Center
Hyunki Kim: Yonsei University College of Medicine
Hyo Song Kim: Yonsei University College of Medicine
Hong Zhu: University of Texas Southwestern Medical Center
Woo Jin Hyung: Yonsei University College of Medicine
Sung Hoon Noh: Yonsei University College of Medicine
Bo Hu: Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic
Changjin Hong: Department of Artificial Intelligence and Informatics, Mayo Clinic
John D. Karalis: University of Texas Southwestern Medical Center
In-Ho Kim: The Catholic University of Korea
Sung Hak Lee: The Catholic University of Korea
Tae Hyun Hwang: Department of Artificial Intelligence and Informatics, Mayo Clinic

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated using large patient cohorts in a prospective manner.

Date: 2022
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DOI: 10.1038/s41467-022-28437-y

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