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Ultrasensitive liquid biopsy technique detects cancer sooner than standard methods

Ultrasensitive liquid biopsy technique detects cancer sooner than standard methods

 


An artificial intelligence-based method to detect tumor DNA in blood has shown unprecedented sensitivity in predicting cancer recurrence, according to a study led by researchers at Weill Cornell Medicine, NewYork-Presbyterian, the New York Genome Center (NYGC), and Memorial Sloan Kettering Cancer Center (MSK). The new technology has the potential to improve cancer care by detecting recurrence at a very early stage and closely monitoring tumor response during treatment.

The study, published June 14, Nature MedicineThe researchers trained a machine learning model, a type of artificial intelligence platform, to demonstrate that it can detect circulating tumor DNA (ctDNA) with very high sensitivity and accuracy based on DNA sequence data from patients' blood tests. They successfully demonstrated the technology in patients with lung cancer, melanoma, breast cancer, colorectal cancer, and precancerous colorectal polyps.

“We were able to achieve a remarkable improvement in the signal-to-noise ratio, which could enable us to detect, for example, cancer recurrence months or even years earlier than standard clinical methods,” said Dan Landau, PhD, co-corresponding author of the study and professor in the departments of hematology and medical oncology at Weill Cornell Medical College and core faculty at the New York Genome Center.

The co-first and co-corresponding author of the study is Dr. Adam Widmann, a postdoctoral researcher in the Landau lab and a breast cancer oncologist at MSK. Other co-first authors are Minita Shah of NYGC, Dr. Amanda Friedendahl of Aarhus University, and Daniel Halmos of NYGC and Weill Cornell Medicine.

Liquid biopsy technologies have been slow to realise their vast potential: most approaches to date have targeted a relatively small set of cancer-associated mutations that are often too sparsely present in the blood to be reliably detected, leaving cancer recurrences undetected.

A few years ago, Dr. Landau and his colleagues developed an alternative approach based on whole genome sequencing of DNA from blood samples. They showed that this method allowed them to collect much more “signal” and detect tumor DNA more sensitively and logistically easier. Since then, this approach has been increasingly adopted by liquid biopsy developers.

In the new study, the researchers go a step further by using advanced machine learning strategies (similar to ChatGPT and other popular AI applications) to detect subtle patterns in the sequence data, and in particular to distinguish between patterns that suggest cancer and those that suggest sequencing errors or other “noise.”

In one test, the researchers trained a system they called MRD-EDGE to recognize patient-specific tumor mutations in 15 patients with colorectal cancer. After the patients' surgery and chemotherapy, the system predicted from their blood data that nine patients had residual cancer. Five of these patients were found to have cancer recurrence several months later using a less sensitive method. However, there were no false negatives; none of the patients who MRD-EDGE determined to have no tumor DNA experienced a recurrence during the study period.

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

The researchers demonstrated that MRD-EDGE can even detect mutated DNA from precancerous colorectal adenomas, the polyps from which colorectal cancer develops.

“It was not clear whether these polyps shed detectable ctDNA, so this is an important advance that may guide future strategies aimed at detecting precancerous lesions,” said Dr. Landau, a member of Weill Cornell Medicine's Sandra and Edward Meyer Cancer Center and a hematologist/oncologist at NewYork-Presbyterian/Weill Cornell Medical Center.

Finally, the researchers showed that even without pre-training on sequence data from patients' tumors, MRD-EDGE can detect responses to immunotherapy in melanoma and lung cancer patients weeks before detection by standard X-ray-based imaging.

“Overall, MRD-EDGE addresses a great need, we're excited about its potential, and we look forward to working with our industry partners to bring it to patients,” Dr. Landau said.

Research in this story was supported in part by the National Cancer Institute, part of the National Institutes of Health, through grant number R01 CA266619.

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2/ https://www.sciencedaily.com/releases/2024/06/240614141855.htm

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