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Updated: Jul 12, 2026
Target Cell Pre-enrichment and Whole Genome Amplification for Single Cell Downstream Characterization
Published on: May 15, 2018
Fragmentomic analysis of circulating tumor DNA-targeted cancer panels
K T Helzer1, M N Sharifi2, J M Sperger3
1Department of Human Oncology, University of Wisconsin, Madison.
Background:
The isolation of cell-free DNA (cfDNA) from the bloodstream can be used to detect and analyze somatic alterations in circulating tumor DNA (ctDNA), and multiple cfDNA-targeted sequencing panels are now commercially available for Food and Drug Administration (FDA)-approved biomarker indications to guide treatment. More recently, cfDNA fragmentation patterns have emerged as a tool to infer epigenomic and transcriptomic information. However, most of these analyses used whole-genome sequencing, which is insufficient to identify FDA-approved biomarker indications in a cost-effective manner.
Patients And Methods:
We used machine learning models of fragmentation patterns at the first coding exon in standard targeted cancer gene cfDNA sequencing panels to distinguish between cancer and non-cancer patients, as well as the specific tumor type and subtype. We assessed this approach in two independent cohorts: a published cohort from GRAIL (breast, lung, and prostate cancers, non-cancer, n = 198) and an institutional cohort from the University of Wisconsin (UW; breast, lung, prostate, bladder cancers, n = 320). Each cohort was split 70%/30% into training and validation sets.
Results:
In the UW cohort, training cross-validated accuracy was 82.1%, and accuracy in the independent validation cohort was 86.6% despite a median ctDNA fraction of only 0.06. In the GRAIL cohort, to assess how this approach performs in very low ctDNA fractions, training and independent validation were split based on ctDNA fraction. Training cross-validated accuracy was 80.6%, and accuracy in the independent validation cohort was 76.3%. In the validation cohort where the ctDNA fractions were all <0.05 and as low as 0.0003, the cancer versus non-cancer area under the curve was 0.99.
Conclusions:
To our knowledge, this is the first study to demonstrate that sequencing from targeted cfDNA panels can be utilized to analyze fragmentation patterns to classify cancer types, dramatically expanding the potential capabilities of existing clinically used panels at minimal additional cost.
Insights
Machine learning models analyzing cell-free DNA (cfDNA) fragmentation patterns can accurately detect cancer types using standard targeted sequencing panels. This cost-effective approach expands the utility of existing cfDNA tests for cancer diagnostics.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Cell-free DNA (cfDNA) analysis, particularly circulating tumor DNA (ctDNA), aids in detecting cancer biomarkers.
- cfDNA fragmentation patterns offer insights into epigenomic and transcriptomic information.
- Whole-genome sequencing for cfDNA analysis is often not cost-effective for biomarker identification.
Purpose of the Study:
- To develop and validate machine learning models using cfDNA fragmentation patterns for cancer detection and classification.
- To assess the feasibility of using standard targeted cfDNA sequencing panels for this analysis.
- To determine if this approach can identify cancer types and subtypes cost-effectively.
Main Methods:
- Machine learning models were trained on cfDNA fragmentation patterns from the first coding exon.
- Standard targeted cancer gene cfDNA sequencing panels were utilized.
- Two independent cohorts (GRAIL and University of Wisconsin) were used for training and validation, with subsets analyzed based on ctDNA fraction.
Main Results:
- The University of Wisconsin cohort achieved 82.1% training accuracy and 86.6% validation accuracy.
- The GRAIL cohort showed 80.6% training accuracy and 76.3% validation accuracy.
- In validation cohorts with very low ctDNA fractions (<0.05), the cancer vs. non-cancer AUC reached 0.99.
Conclusions:
- This study demonstrates the novel use of targeted cfDNA sequencing panels for analyzing fragmentation patterns to classify cancer types.
- This approach significantly expands the capabilities of existing clinical cfDNA panels.
- The method offers a cost-effective way to enhance cancer detection and classification using established sequencing technologies.
