Science at the Edge

                                                                                                          Friday April 12, 2019                            

                                                                                                          11:30 1400 BPS

                                                                                                          Refreshments at 11:00         

Sorin Draghici

                                                                                                                          Wayne State University

Discovering disease subtypes through the integration of multiple types of omics data

A recent paper in The New England Journal of Medicine  and a follow-up in The New York Times revealed that 1.4 million women/year receive unnecessary cancer treatments  costing the society $32.2 billion/year for breast cancer alone. Furthermore, the personal costs in pain and suffering are tremendous. At the same time, some patients do not receive needed treatment. For instance, chemotherapy is not routinely recommended for patients with early stage lung cancer. However, the disease will recur in a large number of these patients, leading to additional suffering and premature death. The ability to correctly identify disease subtypes and patient subgroups is a pre-condition to the ability to distinguish between patients who are in danger and need the most aggressive treatments,  and those who will never progress, recur, or develop resistance. Clearly, we do not have this ability at this time.  Here, we will present an approach that is able to integrate multiple types of omics data (methylation, gene expression and micro RNA) and distinguish between more and less aggressive types of tumors  based on their molecular profiles alone.  This technique has the potential to significantly reduce the health care costs while simultaneously improve the patient  care by helping select the correct treatment for  each patient.