NDLI: Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

Content Provider	Springer Nature : BioMed Central
Author	Wan, Nathan Weinberg, David Liu, Tzu-Yu Niehaus, Katherine Ariazi, Eric A. Delubac, Daniel Kannan, Ajay White, Brandon Bailey, Mitch Bertin, Marvin Boley, Nathan Bowen, Derek Cregg, James Drake, Adam M. Ennis, Riley Fransen, Signe Gafni, Erik Hansen, Loren Liu, Yaping Otte, Gabriel L. Pecson, Jennifer Rice, Brandon Sanderson, Gabriel E. Sharma, Aarushi St. John, John Tang, Catherina Tzou, Abraham Young, Leilani Putcha, Girish Haque, Imran S.
Abstract	Background Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer. Methods Whole-genome sequencing was performed on cfDNA extracted from plasma samples (N = 546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validations to assess generalization performance. Results In a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91–0.93) with a mean sensitivity of 85% (95% CI 83–86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance. Conclusions A machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.
Related Links	https://bmccancer.biomedcentral.com/counter/pdf/10.1186/s12885-019-6003-8.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	14712407
DOI	10.1186/s12885-019-6003-8
Journal	BMC Cancer
Issue Number	1
Volume Number	19
Language	English
Publisher	BioMed Central
Publisher Date	2019-08-23
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Surgical Oncology Health Promotion and Disease Prevention Biomedicine Medicine Public Health Cell-free DNA Colorectal cancer Screening Whole-genome sequencing Early-stage cancer Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Cancer Research Oncology Genetics
Journal Impact Factor	3.4/2023
5-Year Journal Impact Factor	3.8/2023

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