DEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection

Andreas Halner; Luke Hankey; Zhu Liang; Francesco Pozzetti; Daniel Szulc; Ella Mi; Geoffrey Liu; Benedikt M Kessler; Junetha Syed; Peter Jianrui Liu

doi:10.1016/j.isci.2023.106610

DEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection

iScience. 2023 Apr 11;26(5):106610. doi: 10.1016/j.isci.2023.106610. eCollection 2023 May 19.

Authors

Affiliations

¹ Oxford Cancer Analytics Ltd, 696, BioEscalator, Innovation Building, Old Road Campus, Roosevelt Drive, Headington, Oxford, UK.
² Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
³ Target Discovery Institute, Center for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK.

Abstract

Cancer is a leading cause of mortality worldwide. Over 50% of cancers are diagnosed late, rendering many treatments ineffective. Existing liquid biopsy studies demonstrate a minimally invasive and inexpensive approach for disease detection but lack parsimonious biomarker selection, exhibit poor cancer detection performance and lack appropriate validation and testing. We established a tailored machine learning pipeline, DEcancer, for liquid biopsy analysis that addresses these limitations and improved performance. In a test set from a published cohort of 1,005 patients including 8 cancer types and 812 cancer-free individuals, DEcancer increased stage 1 cancer detection sensitivity across cancer types from 48 to 90%. In addition, with a test set cohort of patients from a high dimensional proteomics dataset of 61 lung cancer patients and 80 cancer-free individuals, DEcancer's performance using a 14-43 protein panel was comparable to 1,000 original proteins. DEcancer is a promising tool which may facilitate improved cancer detection and management.

Keywords: Cancer; Diagnostics; Machine learning.