Melanoma is the most deadly skin cancer, and the incidence of the disease has been increasing steadily for about three decades. In 2016, we expect over 75,000 new diagnoses and about 10,000 deaths in the United States. Metastasized melanomas are notorious for their ability to acquire resistance to even the most promising treatments, usually resulting in a fatal recurrence. Perhaps most tragic, melanoma is among the most common cancers in young adults, ages 20 to 39. Better methods of predicting and preventing this disease would have significant impact on the lives of these patients and the economic burden of treatment.
At the root of melanoma is a cell state transition – the reprogramming of a melanocyte functioning under one specific set of operations into a different cell type that has lost some of its cellular programs and acquired others. What causes this transition to occur? Melanoma, like many cancers, has traditionally been viewed as a strictly genetic disease - progression is driven though the accumulation of genetic mutations that confer survival advantages and oncogenic phenotypes. One consequence of this progressive hit model is that treatment is reactionary – no individual cell is at greater or less risk of progression, but rather a cancer cell is only identified after it has obtained the causal mutation.
This progressive hit model is almost certainly over-simplified. Much of previous research has focused on “surviving cells” – for example, the few cells that progressed to form a tumor in a patient or mouse model, or cells that successfully transform into lines in culture. The interests of my research group can be generalized by the inverse question: “If one in ten (or more accurately, ten thousand) mutated melanocytes successfully form a melanoma – what prevented the rest from doing so and why?” Answering this question will flesh out the progressive hit model by providing both an understanding of the diversity of phenotypic consequences established oncogenic mutations can have on individual cells and by identifying the non-genetic factors that result in cells being permissive to transformation. With the goal of developing novel strategies for early diagnostics and prevention, my research group uses a combination of primary tumor sequencing, microRNA-based network dissection, CRISPR/Cas9-based engineering of melanomas from primary melanocytes, and digital holographic quantitative cytometry to study the networks of genes that prevent normal melanocytes and primary melanomas from acquiring oncogenic programs.
For more information please visit: http://judsonlab.ucsf.edu