Berkeley, CA – Multiple Myeloma is a type of cancer that grows in the plasma cells and bone marrow that frequently leads to damage in the bones, immune system, kidneys, and red blood cell count as well as death. With the growth in the prevalence of this disease, it is important to improve the methods of treatment so that they can become more personalized and precise. One important step in this process is optimizing CAR-T process design which would mean identifying T-Cell parameters for better T-Cell functionality to kill the target and the cancer cells. In addition, characterizing the relationship between patient demographics and disease state could allow improvement in prediction of patient outcomes. By identifying targets that can be used for drug design and optimizing this process, the therapies used for treatment of multiple myeloma could be greatly improved and patient care could become more personalized. 

Throughout the past few months, a team of UC Berkeley students from Data X have been working with NExtNet, a startup company focused on cell-based therapies for multiple myeloma. Steven Banerjee, the CEO of NExtNet says, “NExtNet aims to be the end-to-end data-driven digital platform for biotech and pharma pipeline addressing target discovery for immunotherapies, optimizing process design, and predicting their clinical outcomes in patients.” 

While the objective of NExtNet is somewhat broad, the Data X team worked on the more focused goal of building models that characterize response variables associated with the disease. The purpose of the models is to identify key genes that will help researchers identify drug candidates and to see how age and gender impact disease progression. With datasets taken from the Multiple Myeloma DREAM Challenge website, the team was able to gain information (including genetic data) on patients with multiple myeloma and how the disease progressed for them. The response variables that were studied include overall survival time (or time to last contact if the patient is still alive), a flag to indicate whether or not the patient survived, and the length of time from the date of diagnosis or the start of treatment for a disease until the disease begins to get worse or spread to other parts of the body. Going forward, the goal of the team is to combine the information collected from this project with other elements of the NextNet pipeline to further improve drug discovery and target identification for multiple myeloma. 

Project By: Laurel Evans, Austin Ho, Wanying Fang