Driver Mutations That Shape Tumorigenesis and Immunoresistance of Melanoma
Abstract
Focus Area: This proposal directly addresses the Fiscal Year 2019 (FY19) Melanoma Research Program (MRP) Focus Areas of (i) Melanomagenesis, (ii) Melanoma Primary Tumor Evolution, and (iii) Therapeutic Prevention, specifically by interrupting recurrence of disease. We will analyze the DNA sequences of melanoma cancer cells to find mutations that drive melanoma and allow it to progress and avoid being killed by therapeutic intervention and by the immune cells. To achieve this, we assembled unique patient cohorts to analyze melanoma tumors both before and after treatment with immunotherapy. We anticipate that our analysis will identify novel driver mutations that promote growth and evolution of cancer clones, contribute to progression of melanoma, and enable cancer cells to evade immune cells that attempt to kill them. Such novel driver mutations, if properly detected and validated in the experiments that we propose, can (i) lead to a better understanding of how melanomas progress, (ii) help stratify patients to choose the best available therapeutic option for them, as well as (iii) serve as potential targets for future drug development to improve treatment of melanoma and prevent recurrence of the disease. Overall, these discoveries will address the FY19 MRP Challenge by finding strategies to target melanoma early and prevent its further progression, as well as therapeutic strategies to fully harness anti-melanoma immune responses. Objective and Rationale: Although precision medicine has revolutionized the treatment of melanoma, first by targeted therapies and then by immune therapies, many patients will not respond or will relapse. This is due to melanoma cancer cells acquiring more and more mutations over the course of tumor development and progression. Some of these mutations will lead to the advantage for the cancer cells (such as faster growth, resistance to cell death, immune evasion, and other)–they are called “drivers” and are the true cancer-causing mutations. In addition, a large number of background neutral mutations also accumulates in melanoma, which do not help cancer cells to grow, and these are “passenger” mutations. Distinguishing driver mutations from the amassed passengers is critical for understanding melanoma and designing strategies to kill its cancer cells (indeed, targeting drivers is a good potential strategy, while targeting passengers will not lead to a therapeutic effect). However, this task is very difficult to accurately perform in melanoma. Melanoma is a cancer type that is riddled with an extremely high number of mutations, mainly due to exposure to ultraviolet (UV) light. The majority of these are passengers, posing us a difficult challenge of accurately pinpointing the true cancer-causing drivers among them. To resolve this key problem and accurately analyze high mutation burden cancers, we recently developed a novel computational method based on a base-specific background mutability model. This unique computational tool can, unlike other similar tools, analyze tumors with such high mutation burden, due to its novel approach to predicting the background mutation frequency at each genomic position, with base-wise precision. We will now apply this tool to melanoma sequencing data in order to map the true drivers with unprecedented precision. To achieve this, we assembled the largest pre- and post-immunotherapy patient cohort that includes all published immunotherapy data, as well as a unique large patient cohort available to us at the Massachusetts General Hospital. Piloting this analysis on a pre-treatment melanoma cohort, we uncovered novel driver mutations in multiple genes targeting the RNA-processing pathway, increasing our confidence that disruption of RNA-processing pathways is important in tumor development. However, these RNA-processing candidate drivers have not been previously studied in detail, and how these mutations promote melanoma is unclear. Based on these finding
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 10, 2021
- Source ID
- W81XWH2010779
Entities
People
- Gad Getz
Organizations
- Massachusetts General Hospital
- United States Army