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Sensitive Tumor Detection Uses Cell-Free DNA Methylomes

By Labmedica International staff writers
Posted on 27 Nov 2018
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Image: The ability to detect cell-free circulating tumor DNA in blood provides the opportunity to develop non-invasive tests to measure tumor burden and detect molecular signatures in tumors that are associated with resistance to therapy (Photo courtesy of Huntsman Cancer Institute).
Image: The ability to detect cell-free circulating tumor DNA in blood provides the opportunity to develop non-invasive tests to measure tumor burden and detect molecular signatures in tumors that are associated with resistance to therapy (Photo courtesy of Huntsman Cancer Institute).
A combination of "liquid biopsy," epigenetic alterations and machine learning has been used to develop a blood test to detect and classify cancer at its earliest stages. The method holds promise of being able to find cancer earlier when it is more easily treated and long before symptoms ever appear.

The use of liquid biopsies for cancer detection and management is rapidly gaining prominence. Current methods for the detection of circulating tumor DNA involve sequencing somatic mutations using cell-free DNA, but the sensitivity of these methods may be low among patients with early-stage cancer given the limited number of recurrent mutations.

Scientists at the Princess Margaret Cancer Centre (Toronto, ON, Canada) and their colleagues developed a sensitive, immunoprecipitation-based protocol to analyze the methylome of small quantities of circulating cell-free DNA, and demonstrate the ability to detect large-scale DNA methylation changes that are enriched for tumor-specific patterns.

The investigators tracked the cancer origin and type by comparing 300 patient tumor samples from seven disease sites (lung, pancreatic, colorectal, breast, leukemia, bladder and kidney) and samples from healthy donors with the analysis of cell-free DNA (cfDNA) circulating in the blood plasma. In every sample, the "floating" plasma DNA matched the tumor DNA. The team has since expanded the study and has now profiled and successfully matched more than 700 tumor and blood samples from more cancer types.

By profiling epigenetic alterations instead of mutations, the team was able to identify thousands of modifications unique to each cancer type. Then, using a big data approach, they applied machine learning to create classifiers able to identify the presence of cancer-derived DNA within blood samples and to determine the cancer type. This basically turns the 'one needle in the haystack' problem into a more solvable 'thousands of needles in the haystack', where the computer just needs to find a few needles to define which haystack has needles.

This work sets the stage to establish biomarkers for the minimally invasive detection, interception and classification of early-stage cancers based on plasma cell-free DNA methylation patterns. Daniel D. De Carvalho, PhD, a professor of cancer genetics and senior author of the study, said, “We are very excited. A major problem in cancer is how to detect it early. It has been a 'needle in the haystack' problem of how to find that one-in-a-billion cancer-specific mutation in the blood, especially at earlier stages, where the amount of tumor DNA in the blood is minimal.” The study was published on November 14, 2018, in the journal Nature.

Related Links:
Princess Margaret Cancer Centre


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