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

• Published in: OncoImmunology
• Main Authors: Yeongjoo Kim, Ji Wan Kang, Junho Kang, Eun Jung Kwon, Mihyang Ha, Yoon Kyeong Kim, Hansong Lee, Je-Keun Rhee, Yun Hak Kim
• Format: Article in Journal/Newspaper
• Language: English
• Published: Taylor & Francis Group 2021
• Subjects: head and neck cancer; oral cancer; cibersort; tumor-infiltrating lymphocytes; tumor microenvironment; the cancer genome atlas; international cancer genome consortium; deep learning; Immunologic diseases. Allergy; RC581-607; Neoplasms. Tumors. Oncology. Including cancer and carcinogens; RC254-282; Orca
• Online Access: https://doi.org/10.1080/2162402X.2021.1904573; https://doaj.org/article/0d9aca22058342808a6d41bb8e980a3e

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.