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pmid:33854823
Novel deep learning-based survival prediction for oral cancer by analyzing tumor-infiltrating lymphocyte profiles through CIBERSORT.
Yeongjoo Kim · Ji Wan Kang · Junho Kang · Eun Jung Kwon · Mihyang Ha · Yoon Kyeong Kim · Hansong Lee · Je-Keun Rhee · Yun Hak Kim
Oncoimmunology · 2021 doi:10.1080/2162402X.2021.1904573
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Complete file
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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. 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