Researchers from Weill Cornell Medicine, NewYork-Presbyterian, the New York Genome Center (NYGC), and Memorial Sloan Kettering Cancer Center (MSK) have developed an artificial intelligence-powered method for detecting tumor DNA in blood with unprecedented sensitivity. Their study, published in Nature Medicine, demonstrated that a machine learning model could detect circulating tumor DNA (ctDNA) in patients with lung cancer, melanoma, breast cancer, colorectal cancer, and precancerous colorectal polyps with high accuracy. This technology has the potential to significantly improve cancer care by enabling the early detection of cancer recurrence and monitoring of tumor response during therapy.
The study’s co-corresponding author, Dr. Dan Landau, described the technology as achieving a remarkable signal-to-noise enhancement, allowing for the detection of cancer recurrence months or even years before standard clinical methods. The researchers, including co-first author Dr. Adam Widman, were able to detect patient-specific tumor mutations in colorectal cancer patients using their system, MRD-EDGE, which predicted residual cancer in nine patients after surgery and chemotherapy. Subsequent follow-up confirmed that five of these patients experienced cancer recurrence, demonstrating the system’s high sensitivity in detecting tumor DNA.
Liquid biopsy technology has not yet realized its full potential due to the limited detection of cancer-associated mutations in the blood. However, Dr. Landau and his colleagues developed an alternative approach based on whole-genome sequencing of DNA in blood samples, which enabled more sensitive detection of tumor DNA. By using an advanced machine learning strategy, the researchers were able to detect subtle patterns in sequencing data to distinguish patterns indicative of cancer from sequencing errors and other noise. This approach, similar to popular AI applications like ChatGPT, showed promising results in detecting cancer recurrence and tracking tumor status during treatment in patients with lung cancer and triple-negative breast cancer.
Furthermore, MRD-EDGE was able to detect mutant DNA from precancerous colorectal adenomas, representing a significant advance in detecting premalignant lesions that could guide future strategies for early cancer detection. The researchers also demonstrated that MRD-EDGE could detect responses to immunotherapy in melanoma and lung cancer patients weeks before detection with standard X-ray imaging. Dr. Landau highlighted the potential of MRD-EDGE in addressing the unmet need for more sensitive and accurate cancer detection methods, expressing excitement about working with industry partners to deliver this technology to patients.
Overall, the researchers’ innovative approach to detecting tumor DNA in blood using artificial intelligence has the potential to revolutionize cancer care by enabling early detection of cancer recurrence and monitoring of tumor response to therapy. This study, supported in part by the National Cancer Institute, represents a significant advancement in liquid biopsy technology and demonstrates the power of combining AI with genomics to improve cancer outcomes. The researchers are optimistic about the future impact of MRD-EDGE in guiding clinical decision-making and improving patient outcomes in the field of oncology.
