||5R01CA249981-02 Interpret this number
||New York University School Of Medicine
||Mutographs Differentiating the Racial and Temporal Incidence of Multiple Myeloma
African Americans (AA) have a higher incidence of multiple myeloma (MM) compared to European Americans
(EA) due to genetic predisposition, environmental exposure or both. MM is preceded by two precursor phases,
monoclonal gammopathy of undetermined significance (MGUS) and smoldering myeloma (SMM) that are also
increased in AA. We have shown using European 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 in AA. 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 in EA 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 this
differs by race. We will address the hypothesis that a major contributor to the observed excess of MM in AA
compared to EA 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 AA SMM and compare them to
existing datasets of EA with SMM as well as from a large pre-existing set of MM from which we will infer
ancestry directly. We will also establish a longitudinal cohort study of SMM cases and study mutographs over
time and compare the profiles between AA and EA. 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 higher risk of MM observed among AAs and will provide new insights into the immune response in MM
pathogenesis, opening the way for the generation of effective intervention strategies.
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