Early Cancer Detection from Multianalyte Blood Test Results

Ka-Chun Wong; Junyi Chen; Jiao Zhang; Jiecong Lin; Shankai Yan; Shxiong Zhang; Xiangtao Li; Cheng Liang; Chengbin Peng; Qiuzhen Lin; Sam Kwong; Jun Yu

doi:10.1016/j.isci.2019.04.035

Early Cancer Detection from Multianalyte Blood Test Results

iScience. 2019 May 31:15:332-341. doi: 10.1016/j.isci.2019.04.035. Epub 2019 May 4.

Authors

Ka-Chun Wong¹, Junyi Chen², Jiao Zhang², Jiecong Lin², Shankai Yan², Shxiong Zhang², Xiangtao Li³, Cheng Liang⁴, Chengbin Peng⁵, Qiuzhen Lin⁶, Sam Kwong², Jun Yu⁷

Affiliations

¹ Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR. Electronic address: kc.w@cityu.edu.hk.
² Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.
³ School of Information Science and Technology, Northeast Normal University, Jilin, China.
⁴ School of Information Science and Engineering, Shandong Normal University, Shandong, China.
⁵ Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo, China.
⁶ College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
⁷ Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR.

Abstract

The early detection of cancers has the potential to save many lives. A recent attempt has been demonstrated successful. However, we note several critical limitations. Given the central importance and broad impact of early cancer detection, we aspire to address those limitations. We explore different supervised learning approaches for multiple cancer type detection and observe significant improvements; for instance, one of our approaches (i.e., CancerA1DE) can double the existing sensitivity from 38% to 77% for the earliest cancer detection (i.e., Stage I) at the 99% specificity level. For Stage II, it can even reach up to about 90% across multiple cancer types. In addition, CancerA1DE can also double the existing sensitivity from 30% to 70% for detecting breast cancers at the 99% specificity level. Data and model analysis are conducted to reveal the underlying reasons. A website is built at http://cancer.cs.cityu.edu.hk/.

Keywords: Algorithms; Bioinformatics; Biological Sciences; Cancer; Cancer Systems Biology.