spontaneous Evolution and Clonal heterOgeneity in MoNoclonal Gammopathies: from mechanisms of progression to clinical management

ended

As an onco-hematologist with a strong expertise in genomics, I significantly contributed to the understanding of multiple myeloma (MM) heterogeneity and its evolution over time, driven by genotypic and phenotypic features carried by different subpopulations of cells. MM is preceded by prevalent, asymptomatic stages that may evolve with variable frequency, not accurately captured by current clinical prognostic scores. Supported by preliminary data, my hypothesis is that the same heterogeneity is present early on the disease course, and identification of the biological determinants of evolution at this stage will allow better prediction of its evolutionary trajectory, if not its control. In this proposal I will therefore make a sharp change from conventional approaches and move to early stages of MM using unique retrospective sample cohorts and ambitious prospective sampling. To identify clonal MM cells in the elderly before a monoclonal gammopathy can be detected, I will collect bone marrow (BM) from hundreds of hip replacement specimens, and analyze archive peripheral blood samples of thousands of healthy individuals with years of annotated clinical follow-up. This will identify early genomic alterations that are permissive to disease initiation/evolution and may serve as biomarkers for clinical screening. Through innovative, integrated single-cell genotyping and phenotyping of hundreds of asymptomatic MMs, I will functionally dissect heterogeneity and characterize the BM microenvironment to look for determinants of disease progression. Correlation with clinical outcome and mini-invasive serial sampling of circulating cell-free DNA will identify candidate biological markers to better predict evolution. Last, aggressive modelling of candidate early lesions and modifier screens will offer a list of vulnerabilities that could be exploited for rationale therapies. These methodologies will deliver a paradigm for the use of molecularly-driven precision medicine in cancer.