In a keynote address at the virtual 10th Annual Individualizing Medicine Conference Oct. 8–9, Gianrico Farrugia, M.D., president and CEO of Mayo Clinic, will discuss the evolution and history of individualized medicine. He will highlight some of the top genomics and multi-omics initiatives that are accelerating discoveries and helping to precisely diagnose, treat and predict disease.
Dr. Farrugia will provide a glimpse into the Center for Individualized Medicine's past and future and its ongoing plans to further integrate individualized medicine into clinical practice. This plan is powered by research initiatives that include emerging technologies such as artificial intelligence (AI) and machine learning — all in an effort to provide patients with answers, treatment options and optimism.
He also will highlight Mayo's "Bold. Forward." strategic plan to Cure, Connect and Transform. That means curing more patients, connecting people with data to create new knowledge and transforming health care. The 2030 strategic plan includes a strong focus on innovation and new technologies.
Joining Dr. Farrugia in the panel discussion will be Mayo Clinic's Richard Weinshilboum, M.D., a Mayo Clinic pharmacologist, and Konstantinos Lazaridis, M.D., the Carlson and Nelson Endowed Executive Director for Mayo Clinic's Center for Individualized Medicine; Laura Cremona, Ph.D., with Regeneron Pharmaceuticals Inc.; and Murali Aravamudan with Nference Inc.
Putting massive genomic data sets into action
Mayo's latest cutting-edge strategies to accelerate medical breakthroughs come nearly two decades after the completion of the Human Genome Project, which mapped the 3 billion DNA letters that make up the blueprint of human life and paved the path toward individualized medicine. Dr. Farrugia will emphasize that Mayo Clinic scientists are working to reap the full potential of individualized medicine by pushing the bounds of research and by making genomics in medicine ubiquitous, automated and scalable.
Dr. Farrugia will take the virtual stage with these keynote speakers: Deborah Birx, M.D., senior fellow with the George W. Bush Institute, and Joshua Denny, M.D., CEO of the All of Us Research Program with the National Institutes of Health.
Advancing individualized medicine with AI
The Individualizing Medicine Conference will spotlight researchers' increasing development of AI tools that can help clinicians and researchers find patterns in vast amounts of multi-omic data. The advanced technology can detect genetic mutations to predict diseases and devise tailored treatments.
Here are some of the featured speakers who will explore the future of machine learning:
Arjun Athreya, Ph.D., and William Bobo, M.D.
Dr. Athreya, a Mayo Clinic computer scientist, and Dr. Bobo, a Mayo Clinic psychiatrist, recently developed a computer algorithm to accurately and efficiently predict whether a patient with depression will respond to an antidepressant. Their research, published in Neuropsychopharmacology, represents a possible step forward in individualizing treatment for major depressive disorder. It also demonstrates a collaboration between computer scientists and clinicians who are using large datasets to address challenges of individualizing medicine practices of globally devastating diseases.
Liewei Wang, M.D., Ph.D.
Dr. Wang, a Mayo Clinic pharmacogenomics researcher, is working to identify and understand how biomarkers can predict and contribute to how a person responds to a particular drug, especially drugs used to treat cancer. In one study, she is investigating a series of biomarkers related to the initial treatment for patients with estrogen receptor-positive, or ER+, breast cancer with a class of drugs called aromatase inhibitors. In the Breast Cancer Genome-Guided Therapy (BEAUTY) study, she has identified biomarkers for selection and repurposed a class of epigenetic drugs to treat chemotherapy-resistant patients.
Bradley Erickson, M.D., Ph.D.
Dr. Erickson, a Mayo Clinic diagnostic radiologist, is using AI to extract information from medical images for diagnostic, prognostic and therapeutic purposes. His research includes the development and validation of algorithms that can detect progression and regression or risk of disease, and predict molecular markers from medical images. In one recent study, Dr. Bradley used AI tools to rapidly scan MRI images and successfully identify molecular markers for patients with glioma, a type of brain cancer.
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