Grant Number:	5R01CA249981-05 Interpret this number
Primary Investigator:	Morgan, Gareth
Organization:	New York University School Of Medicine
Project Title:	Mutographs Differentiating the Inheritance and Temporal Onset of Multiple Myeloma
Fiscal Year:	2025

Inheritance based on genetic predisposition, environmental exposure or both can impact the risk of developing multiple myeloma (MM). MM is preceded by two precursor phases, monoclonal gammopathy of undetermined significance (MGUS) and smoldering myeloma (SMM) that are also increased based on inheritance. We have shown using MM samples that there is a long lag period between the genetic initiation of the disease and the time at which precursor clinical stages are detectable. It is critical to understand the genetic basis of these early evolutionary steps if we are to truly understand the excess risk of MM based on inheritance. During the evolutionary progression of MM after genetic initiation, genetic hits are accumulated providing a unique archeological fingerprint of the mutational signatures or “mutographs” over time. Using whole genome sequencing (WGS) analyzed with advanced computer algorithms based on a-priori knowledge of the timing of acquired genetic variants we have been able to extract mutographs active at different time points. This analysis has shown that MM is shaped by mutational processes variably active during the early, intermediate and late evolutionary phases of disease. A key finding of our pilot data is the identification of a mutograph occurring as a consequence of the immune response in the germinal center reaction and that this differs by inheritance. We will address the hypothesis that a major contributor to the inherited excess of MM is due to an excess immune response that can be recognized by a GC mutograph that is active in the early evolutionary phases of disease. To accurately extract early mutographs sequential samples from the same individual cases are needed. SMM, which transforms to MM at a rate of 10% per annum, provides a system where samples can be obtained at different time points in the absence of treatment. To address our hypothesis, we will generate mutographs from new WGS data from SMM and compare them to existing datasets of SMM as well as from a large pre-existing set of MM from which we will infer inheritance directly. We will also establish a longitudinal cohort study of SMM cases and study mutographs over time and compare the profiles based on inherited groups. In addition to genetic mutographs we will characterize and compare immunological mutographs of T-cell response in the bone marrow immune microenvironment identified using a flow-cytometric approach. To provide a link to the external environment we will characterize bacterial species signatures derived from 16S rRNA sequencing of the gut flora, and link findings to the genetic and T-cell mutographs. This study will identify genomic, immune and environmental signatures responsible for the inherited risk of MM and will provide new insights into the immune response in MM pathogenesis, opening the way for the generation of effective intervention strategies.