When cells die, they disintegrate, releasing part of their DNA material into the bloodstream. This cell-free DNA (cfDNA) contains cancer signals. The cfDNA from healthy cells breaks down into standard-sized fragments, whereas cancerous cfDNA fragments disintegrate at different locations, often in the genome's repetitive regions. Instead of searching for specific DNA mutations, which is like finding a single misarranged letter in billions of letters, researchers have developed a novel machine-learning method. This method detects variations in fragmentation patterns between cancerous and normal cfDNA in these repetitive regions of cancer. This groundbreaking technique could potentially allow for earlier cancer detection in patients through smaller blood samples, as it requires approximately eight times less blood than what is needed for whole genome sequencing.

The algorithm called Alu Profile Learning Using Sequencing (A-Plus) was developed by researchers at City of Hope (Duarte, CA, USA) and Translational Genomics Research Institute (TGen, Phoenix, AZ, USA). The researchers tested the algorithm on 7,657 samples from 5,980 individuals, 2,651 of whom were diagnosed with cancers like breast, colon and rectum, esophagus, lung, liver, pancreas, ovary, or stomach cancer. They discovered that A-Plus could identify about half of the cancers across the 11 types studied. The test proved to be highly accurate, yielding only one false positive for every 100 tests conducted.

Significantly, most of the cancer samples came from individuals with early-stage disease, who had little to no metastatic lesions at the time of diagnosis. Going forward, a clinical trial is set to begin in summer 2024 to compare the effectiveness of this fragmentomics blood testing approach against the standard-of-care in adults aged 65-75. The aim is to assess how well this biomarker panel can detect cancer at an earlier, more treatable stage.

“A huge body of evidence shows that cancer caught at later stages kills people,” said Cristian Tomasetti, Ph.D., corresponding author of the new study and director of City of Hope’s Center for Cancer Prevention and Early Detection. “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.”

“Our technique is more practical for clinical applications as it requires smaller quantities of genomic material from a blood sample,” added Kamel Lahouel, Ph.D., an assistant professor in TGen’s Integrated Cancer Genomics Division and the study’s co-first author. “Continued success in this area and clinical validation opens the door for the introduction of routine tests to detect cancer in its earliest stages.”

Source:
https://www.labmedica.com/molecular-diagnostics/articles/294800090/innovative-fragmentomics-approach-to-enable-earlier-detection-of-cancer-using-smaller-blood-draws.html