Subclass-specific Prognosis and Treatment Efficacy Inference in Head and Neck Squamous Carcinoma

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

View graph of relations

Detail(s)

Original languageEnglish
Pages (from-to)4303-4313
Journal / PublicationIEEE Journal of Biomedical and Health Informatics
Volume26
Issue number8
Online published19 Apr 2022
Publication statusPublished - Aug 2022

Abstract

Exploring the prognostic classification and biomarkers in Head and Neck Squamous Carcinoma (HNSC) is of great clinical significance. We hybridized three prominent strategies to comprehensively characterize the molecular features of HNSC. We constructed a 15-gene signature to predict patients death risk with an average AUC of 0.744 for 1-, 3-, and 5-year on TCGA-HNSC training set, and average AUCs of 0.636, 0.584, 0.755 in GSE65858, GSE-112026, CPTAC-HNSCC datasets, respectively. By combined with NMF clustering and consensus clustering of fraction of tumor immune cell infiltration (ICI) in the tumor microenvironment (TME), we captured a more refined biological characteristics of HNSC, and observed a prognosis heterogeneity in high tumor immunity patients. By matching tumor subset-specific expression signatures to drug-induced cell line expression profiles from large-scale pharmacogenomic databases in the OCTAD workspace, we identified a group of HNSC patients featured with poor prognosis and demonstrated that the individuals in this group are likely to receive increased drug sensitivity to reverse differentially expressed disease signature genes. This trend is especially highlighted among those with higher death risk and tumour immunity.

Research Area(s)

  • Cancer, Drugs, Gene expression, Immune system, Prognostics and health management, Tumors, Urban areas, Cancer treatment, computational biology

Citation Format(s)

Subclass-specific Prognosis and Treatment Efficacy Inference in Head and Neck Squamous Carcinoma. / Zheng, Zetian; Xie, Weidun; Chen, Xingjian; Wang, Fuzhou; Huang, Lei; Li, Xiangtao; Lin, Qiuzhen; Wong, Ka-Chun.

In: IEEE Journal of Biomedical and Health Informatics, Vol. 26, No. 8, 08.2022, p. 4303-4313.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review