Abstract
Over 100,000 people are diagnosed with cutaneous melanoma each year in the United States. Despite recent advancements in metastatic melanoma treatment, such as immunotherapy, there are still over 7000 melanoma-related deaths each year. Melanoma is a highly heterogenous disease, and many underlying genetic drivers have been identified since the introduction of next-generation sequencing. Despite clinical staging guidelines, the prognosis of metastatic melanoma is variable and difficult to predict. Bioinformatic and machine learning analyses relying on genetic, clinical, and histopathologic inputs have been increasingly used to risk stratify melanoma patients with high accuracy. This literature review summarizes the key genetic drivers of melanoma and recent applications of bioinformatic and machine learning models in the risk stratification of melanoma patients. A robustly validated risk stratification tool can potentially guide the physician management of melanoma patients and ultimately improve patient outcomes.
Highlights
Melanoma Risk Assessment and Cutaneous melanoma is the most aggressive form of skin cancer and the fifth most common cancer in the United States [1]
The incidence of cutaneous melanoma has been rising in the past few decades, with over 100,000 new cases diagnosed in the United States each year [1]
Despite recent advancements in advanced melanoma therapy, including targeted therapy (e.g., BRAF/MEK inhibitors) and immunotherapy (e.g., PD-1 inhibitors), there are over 7000 melanoma-related deaths each year in the United States, as the most advanced stage melanoma patients have recurrence after initial therapy [1,2,3]
Summary
Melanoma Risk Assessment and Cutaneous melanoma is the most aggressive form of skin cancer and the fifth most common cancer in the United States [1]. Bioinformatic analyses, including machine learning, are increasingly utilized to predict prognosis, risk stratify, and inform personalized treatment in cutaneous melanoma. Given the massive catalog of bioinformatics and machine learning studies in the field of melanoma genomics and risk stratification, we attempt to summarize the currently established key drivers of melanoma that have utilized bioinformatics in its discovery. We provide an overview of key findings, algorithms, and the predictive accuracy of recent studies applying bioinformatic and machine learning algorithms to melanoma risk stratification. In 2015, the Cancer Genome Atlas Skin Cutaneous Melanoma (TCGA) used WES to confirm previously identified melanoma mutations in BRAF, NRAS, CDKN2A, TP53, and PTEN [15].
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