Researchers from City of Hope in the US and Translational Genomics Research Institute (TGen) have created a machine-learning tool for early cancer detection through liquid biopsy.
The ‘fragmentomics’ approach utilises smaller blood samples to potentially identify cancer at an earlier stage than is currently possible.
City of Hope Center for Cancer Prevention and Early Detection director Cristian Tomasetti said: “This new technology gets us closer to a world where people will receive a blood test annually to detect cancer earlier when it is more treatable and possibly curable.”
The technology, developed by City of Hope, TGen and colleagues, demonstrated the ability to identify half of the cancers in the 11 types studied, with a notable accuracy that produced a false positive in only one out of every 100 tests.
Most of the cancer samples were from early-stage patients who had minimal or no metastatic lesions at diagnosis.
The researchers employed an algorithm named Alu Profile Learning Using Sequencing (A-Plus), which was tested on 7,657 samples from 5,980 individuals, including 2,651 cancer patients.
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The algorithm analyses cell-free DNA (cfDNA) in the bloodstream, which varies in fragmentation patterns between cancerous and normal cells.
Fragmentomics requires significantly less blood than whole genome sequencing.
This year, Tomasetti is preparing to launch a clinical trial to evaluate this approach compared with standard care in detecting cancer at a more treatable stage.
TGen Integrated Cancer Genomics Division assistant professor Kamel Lahouel said: “Our technique is more practical for clinical applications as it requires smaller quantities of genomic material from a blood sample.
“Continued success in this area and clinical validation opens the door for the introduction of routine tests to detect cancer in its earliest stages.”