B.J. Nickoloff, M.D., Ph.D. is currently Professor and Director of the Nicholas V. Perricone M.D. Division of Dermatology at Michigan State University College of Human Medicine, and Director (acting) of the Center for Translational Medicine at the Van Andel Institute. He is also the Medical Director of Dermatopathology at the Lack Cancer Center, St. Mary’s Hospital, and Trinity Health System in Grand Rapids, MI. He has published over 365 articles and numerous book chapters and is the editor of three books, and presented lectures worldwide.


Malignant melanoma (MM) contains thousands of somatic DNA alterations. As aff ordability and faster turn-around times for identifi cation of druggable targets specifi c for each patient improves, together with limited number of therapeutic agents available, a growing gap is emerging as to identifi cation of the right drug(s) for the patient at the right time. To further complicate personalized medicine, frequent acquired drug resistance arises, and failure to kill all tumor cells using single agents. Indeed, MM is notorious for resistance to killing. Th us, rational therapeutic combinations promoting rapid onset of apoptosis and preventing drug resistance is highly desirable. Using patient-derived tumorgraft s it is possible to study responses of MM within in vivo three-dimensional context. Using a collection of such tumorgraft s spanning the genomic landscape, together with agents such as BRAF inhibitors and MEK inhibitors,  responses, including acquired drug resistance can be modeled. Th ese models are now being used for identifi cation of drug resistance switches, and ideal combination therapy regimens that prevent drug resistance. As dozens of “avatar” mice can be created from a single patient sample, various drug combinations, scheduling, etc. can be more rapidly tested than would be achievable in a clinical trial setting. We envision results from tumorgraft models actually serving as informative tools for clinical trial design. Ideally, a suffi cient number of tumorgraft models genomically characterized, and biologically tested with rationally designed and optimized therapeutic cocktails could be used to “best-fit” models from previous patients aligned with newly diagnosed MM patients approach to guide molecularly informed treatment protocols.

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