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Performance of a Cell-Free DNA-Based Multi-cancer Detection Test in Individuals Presenting With Symptoms Suspicious
Alan H Bryce1, David D Thiel2, Michael V Seiden3
1Mayo Clinic, Phoenix, AZ.
JCO Precision Oncology
|July 19, 2023
Summary
This multi-cancer detection test shows high specificity and accuracy in symptomatic individuals, with improved sensitivity for GI cancers. The test may help streamline diagnosis and assess cancer risk in those with concerning symptoms.
Area of Science:
- Oncology
- Genomics
- Machine Learning
Background:
- Multi-cancer detection tests are crucial for early cancer diagnosis.
- Targeted methylation assays combined with machine learning offer promising screening capabilities.
- Validation in symptomatic individuals is key to assessing clinical utility.
Purpose of the Study:
- To validate and optimize a multi-cancer detection test in symptomatic individuals.
- To assess the test's performance in facilitating diagnostic evaluation.
- To evaluate the test's ability to predict cancer signal origin (CSO).
Main Methods:
- A targeted methylation assay and machine learning classifiers were used.
- Test performance (sensitivity, specificity, CSO prediction accuracy) was evaluated in 2,036 cancer and 1,472 noncancer participants.
- Overall survival (OS) analysis was conducted comparing detected vs. not detected cancer signals.
Main Results:
- High specificity (99.5%) was observed in noncancer participants.
- Overall sensitivity was 64.3% in participants with clinically presenting cancers (CPCs).
- Sensitivity was notably higher for GI cancers (84.1%) and CSO prediction accuracy was 90.3%.
Conclusions:
- The multi-cancer detection test demonstrates high specificity and CSO accuracy, with moderate sensitivity in symptomatic individuals.
- The test shows particular strength in detecting GI cancers.
- Survival analysis suggests prognostic insights, potentially aiding in risk stratification and workup for symptomatic patients.
