Genomic adaptability of the tumor cells is the major obstacle in achieving successful cancer treatment. We have studied both tumor cells and their microenvironment in multiple myeloma (MM), a plasma cell malignancy. The immune microenvironment in MM exhibit considerable dysfunction1. In our previous work, we showed that dysfunctional regulatory T helper cells (Tregs)2, iNKT cells3, and elevated Th17 cells and IL-17A, increase MM cell growth and survival and suppress immune responses and induce bone disease4,5. Also, by targeting IL-17A in myeloma using a human anti-IL-17A monoclonal antibody (AIN457) we showed significant inhibition of MM cell growth5. However, the vast genomic changes in MM cells provide them the ability to survive and adapt to the therapeutic and immune micro-environmental influences. Our recent study has defined the mutational spectrum in MM at the time of initial diagnosis and found heterogeneity across patients6. We have recently identified biologically distinct mutations (APOBEC signature) responsible for tumor evolution and heterogeneity6. This clonal evolution is driven by a combination of factors, leads to both clonal selection and formation of new clones. We hypothesize that a combination of micro-environmental influences along with evolving genomic changes drives the tumor clone leading to progressive disease. In this regards, having studied immune status and function in previous funding period, we will now focus on identifying and validating mechanisms driving clonal changes in myeloma along with evaluation of the impact of immune microenvironment on these mechanisms. Towards this goal, we will pursue following specific Aims: Specific Aim 1: To identify molecular markers of progression in MM by investigating mutational signatures, expression profile and APOBEC activity in paired diagnosis and relapse samples We hypothesize that continued acquisition of mutational changes, driven by specific mutational processes, underlies progression of disease in myeloma from newly-diagnosed disease to relapsed state. We will analyze genome sequencing data from archived paired MM cell samples collected at the time of diagnosis and then at relapse following initial therapy, for overall mutational spectrum, types of mutational signatures inducing mutations at diagnosis and then driving the evolution, as well as overall clonal dynamics to identify patterns associated with progression. Based on our preliminary data, we will also evaluate if APOBEC activity and expression in these samples correlates with specific genomic signatures, overall genomic instability and/or progression. Specific Aim 2: To functionally evaluate the role of APOBECs in driving genomic instability in MM. We hypothesize that dysregulated APOBEC activity may be in part responsible for acquisition of new mutational changes associated with progression in myeloma. We will therefore perform loss- and gain-of-function studies using MM cell lines to investigate immediate an...