iScience (May 2023)

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

Journal volume & issue
Vol. 26, no. 5
p. 106610

Abstract

Read online

Summary: 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