Elizabeth Comen, MD
New York, New York
Assistant Attending Physician
Breast Medicine Service
Developing a minimally invasive blood test that can detect and classify early-stage breast cancer and predict response to treatment.
Mammography is the gold standard for breast cancer screening but does not distinguish between a benign mass and one that is malignant. A tissue biopsy is then needed to determine the presence or extent of breast cancer. Traditional biopsy is painful, time-consuming, and only gives a snapshot of the disease in a specific area at a moment in time. Major progress has been made in developing techniques that can detect tumor biomarkers among molecules from normal cells, including liquid biopsy, a minimally invasive diagnostic and monitoring tool that tests a fluid, typically blood, for tumor biomarkers. Liquid biopsy has the potential to identify breast cancer in its earliest stages, before a lump or tumor could be discovered, and in later stages, to monitor how the cancer is responding to therapy in real time.
Drs. Comen and Tavazoie are working to identify specific pieces of tumor genetic material that circulate in the blood and can be used as predictive biomarkers to augment mammography when a suspicious lesion is found, predict the likelihood of a breast cancer to metastasize, or monitor response to therapy. This year, the team collected blood from 50 patients with different types of breast diseases. After sequencing small RNAs isolated from these samples, they refined their machine learning algorithm by incorporating the data with their existing dataset from more than 100 patients. Drs. Comen and Tavazoie found that this strategy can stratify patients with benign and malignant breast diseases and breast cancer patients with localized or metastatic diseases with high accuracy.
Drs. Comen and Tavazoie plan to further refine their algorithm. The team plans to extend their analysis to other types of circulating small RNAs that play critical roles in breast cancer progression. Additionally, they will compare the performance of their algorithm to that of mammogram-based artificial intelligence algorithms. Drs. Comen and Tavazoie further aim to determine whether combining their algorithm with the mammogram-based artificial intelligence algorithm would outperform either alone. They expect that machine learning algorithms will be an invaluable aid to clinicians diagnosing breast cancer and informing the likelihood of a patient’s breast cancer metastasizing in the future.
Elizabeth Comen, MD is a medical oncologist at Memorial Sloan Kettering Cancer Center with a practice devoted to the study and treatment of patients with all stages of breast cancer. Dr. Comen earned her BA from Harvard College and her MD from Harvard Medical School. She completed residency at Mount Sinai Hospital and her fellowship at Memorial Sloan Kettering Cancer Center. She has presented her research many times at the American Society of Clinical Oncology (ASCO) Annual Meeting and the San Antonio Breast Cancer Symposium. She has also been awarded several peer-reviewed grants, including the Young Investigator Award from the Conquer Cancer Foundation of ASCO.
Dr. Comen’s research focuses on the mechanisms by which breast cancer metastasizes and spreads to distant organs. In particular, she collaborates with several laboratories to help translate laboratory discoveries regarding metastasis into clinically meaningful treatments for patients at risk for and with metastatic breast cancer. With her laboratory collaborators, Dr. Comen aims to identify unique biomarkers that can help identify new diagnosis of breast cancer as well as identify those women with early-stage breast cancer who are at increased risk for metastasis. For metastatic patients, the team is using laboratory methods to gain a better understanding of metastasis to develop more effective and less toxic treatments.
The Lampert Foundation Award
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