Novel deep learning-based survival prediction for oral cancer by analyzing tumor-infiltrating lymphocyte profiles through CIBERSORT

• Published in: Oncoimmunology Vol. 10; no. 1; p. 1904573
• Main Authors: Kim, Yeongjoo, Kang, Ji Wan, Kang, Junho, Kwon, Eun Jung, Ha, Mihyang, Kim, Yoon Kyeong, Lee, Hansong, Rhee, Je-Keun, Kim, Yun Hak
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 01-01-2021; Taylor & Francis Group
• Subjects: cibersort; deep learning; Head and neck cancer; international cancer genome consortium; oral cancer; Original Research; the cancer genome atlas; tumor microenvironment; tumor-infiltrating lymphocytes; the cancer genome atlas; deep learning; Head and neck cancer; tumor-infiltrating lymphocytes; international cancer genome consortium; tumor microenvironment; cibersort; oral cancer
• ISSN: 2162-402X; 2162-4011; 2162-402X
• DOI: 10.1080/2162402X.2021.1904573

The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered from the publicly accessible data of patients with head and neck cancer in The Cancer Genome Atlas (TCGA) using hierarchical clustering where patients were regrouped into binary risk groups based on the clustering-measuring scores and survival patterns associated with individual groups. Based on this analysis, clinically reasonable differences were identified in 16 out of 22 TIL fractions between groups. A deep neural network classifier was trained using the TIL fraction patterns. This internally validated classifier was used on another individual ORCA dataset from the International Cancer Genome Consortium data portal, and patient survival patterns were precisely predicted. Seven common differentially expressed genes between the two risk groups were obtained. This new approach confirms the importance of TILs in the TME and provides a direction for the use of a novel deep-learning approach for cancer prognosis.