Abstract
Quantification of circulating tumor DNA (ctDNA) levels in blood enables non-invasive surveillance of cancer progression. Fragle is an ultra-fast deep learning-based method for ctDNA quantification directly from cell-free DNA fragment length profiles. We developed Fragle using low-pass whole genome sequence (lpWGS) data from multiple cancer types and healthy control cohorts, demonstrating high accuracy, and improved lower limit of detection in independent cohorts as compared to existing tumor-naïve methods. Uniquely, Fragle is also compatible with targeted sequencing data, exhibiting high accuracy across both research and commercial targeted gene panels. We used this method to study longitudinal plasma samples from colorectal cancer patients, identifying strong concordance of ctDNA dynamics and treatment response. Furthermore, prediction of minimal residual disease in resected lung cancer patients demonstrated significant risk stratification beyond a tumor-naïve gene panel. Overall, Fragle is a versatile, fast, and accurate method for ctDNA quantification with potential for broad clinical utility.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Figure 2 has been updated; some mistakes were there. Also experiments related to cancer stage stratification have been added. Text has been improved. New supplementary figures added.
Data availability
Published data used in this study and their access codes are present in Suppl. Data 1. Data generated in this study have been deposited at the European Genome-phenome Archive (EGA; Dataset ID: EGAD50000000167). Data are available under restricted access and will be released subject to a data transfer agreement.