Cancer cells
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An artificial intelligence-powered tool for detecting circulating tumor DNA (ctDNA) in blood shows unprecedented sensitivity in predicting recurrence. In one case, the method (dubbed MRD-EDGE), detected five patients who had a colorectal-cancer recurrence, without any false negatives. MRD-EDGE, the researchers say, could improve cancer care with very early detection of recurrence and close monitoring of tumor response during therapy.

This tool could thus become a key player in the liquid biopsy market, which is expected to exceed $18B within several years.  

The study appears June 14 Nature Medicine and the first author is Adam J. Widman of  the New York Genome Center. The work was led by researchers at Weill Cornell Medicine, NewYork-Presbyterian, the New York Genome Center (NYGC), and Memorial Sloan Kettering Cancer (MSKCC).

The team trained a machine learning model to analyze ctDNA based on DNA sequencing data from patient blood tests. They tested the technology in patients with lung cancer, melanoma, breast cancer, colorectal cancer, and precancerous colorectal polyps.

“We were able to achieve a remarkable signal-to-noise enhancement, and this enabled us, for example, to detect cancer recurrence months or even years before standard clinical methods did so,” said study co-corresponding author Dan Landau, of the division of hematology and medical oncology at Weill Cornell Medicine.

Liquid biopsy technology has been slow to realize its promise. In a press release, the researchers say, “Most approaches to date have targeted relatively small sets of cancer-associated mutations, which are often too sparsely present in the blood to be detected reliably, resulting in cancer recurrences that go undetected.”

Several years ago, Landau and colleagues developed an alternative approach based on whole-genome-sequencing of DNA in blood samples. They showed they could gather much more “signal” this way, enabling more sensitive—and logistically simpler—detection of tumor DNA. Since then, this approach has been increasingly adopted by liquid biopsy developers.

In the new study, the researchers used an advanced machine learning strategy to detect subtle patterns in sequencing data—in particular, to distinguish patterns suggestive of cancer from those suggestive of sequencing errors and other “noise.”

In one test, the researchers trained MRD-EDGE to recognize patient-specific tumor mutations in 15 colorectal cancer patients. Following the patients’ surgery and chemotherapy, the system predicted from blood data that nine had residual cancer. Five of these patients were found—months later, with less sensitive methods—to have cancer recurrence. But there were no false negatives: none of the patients MRD-EDGE deemed free of tumor DNA experienced recurrence during the study window.

MRD-EDGE showed similar sensitivity in studies of early-stage lung cancer and triple-negative breast cancer patients, with early detection of all but one recurrence, and tracking of tumor status during treatment.

The researchers demonstrated that MRD-EDGE can detect even mutant DNA from precancerous colorectal adenomas—the polyps from which colorectal tumors develop.

“It had not been clear that these polyps shed detectable ctDNA, so this is a significant advance that could guide future strategies aimed at detecting premalignant lesions,” said Landau, who is also a member of the Sandra and Edward Meyer Cancer Center at Weill Cornell Medicine and a hematologist/oncologist at NewYork-Presbyterian/Weill Cornell Medical Center.

Lastly, the researchers showed that even without pre-training on sequencing data from patients’ tumors, MRD-EDGE could detect responses to immunotherapy in melanoma and lung cancer patients—weeks before detection with standard X-ray-based imaging.


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