Genomic and epigenomic interactions of complex structural variants affecting outcome in multiple myeloma
Brian Walker
PhDIndiana University
Project Term: October 1, 2024 - September 30, 2027
Multiple myeloma is characterized by severe changes in chromosomes that result in gains or losses of genetic material. Several key events disrupt the genome of myeloma cells and are important in defining poor patient outcome, but the biological mechanisms of how they cause high-risk disease is not known. We will perform comprehensive genomic studies, involving six different cutting-edge techniques, to examine the interactions of these high-risk events and identify the mechanisms leading to them.
Multiple myeloma is a cancer of cells that reside in the bone marrow. The DNA inside these cells has been extensively studied, and abnormalities that are associated with patient outcome have been described. Two of these abnormalities are increased copies of chromosome 1q and complex events that involve rearranging multiple chromosomes.
From existing data, the presence of extra copies of 1q is known to affect ~40% of patients but in 10% of patients there can be many extra copies (amplification of 1q). Despite being such a common abnormality there is little agreement on why chromosome 1q is associated with poor outcome as no definitive gene has been identified. Also, the location of the extra copies of 1q in the genome is not routinely investigated and current technologies struggle to determine their location. We have generated tools using patient-derived material that represents the different states of 1q (normal, whole arm gain, focal gain or amplification) and we will use several cutting-edge technologies to examine the location of the extra copies and the impact these extra copies have on the rest of the genome. Data will be integrated from six different data types to show that 1q can jump around the genome, knocking out other important genes that regulate cell growth, and determine the molecular mechanisms involved.
For those complex events that rearrange multiple chromosomes, we will use the same cutting-edge technologies to look at the new interactions formed across chromosomes and determine which genes are dysregulated by them and how that is achieved. Using sequential samples we will track how these complex events change over time, leading to the acquisition of new events and further disrupting the genome. These longitudinal studies will be used to determine if cells at relapse are selected for these complex events due to their increased risk status.
In both instances, we will use these technologies to piece together the genome of the cells after they have been rearranged by these important events. The data generated here will be applied to other larger datasets so that we can utilize previous knowledge for patient benefit.