IU Simon Comprehensive Cancer Center Indiana University Melvin and Bren Simon Comprehensive Cancer CenterA National Cancer Institute Comprehensive Cancer Center

Dr. Walker's research interests include:

RESEARCH ACTIVITIES A. Copy number alterations in Multiple Myeloma During my post-doc I performed gene expression and copy number arrays on primary patient material to identify genes that are commonly dysregulated. I have utilized SNP-based mapping and expression arrays to identify novel regions and genes involved in the pathogenesis of myeloma. This work identified several new genes, including WWOX, CYLD, FAM46C, CDKN2C, and FAF1, which are frequently deleted in myeloma at presentation. I characterized the genome of myeloma leading to the identification of potential new targets for treatment by integrating SNP-based mapping arrays with gene expression profiling to identify dysregulation of these key tumor suppressor genes. My work also detected and identified novel mechanisms resulting in the inactivation of these tumor suppressor genes, such as copy number neutral loss of heterozygosity, the first time this was reported in myeloma. B. Translocations and dysregulated gene expression In myeloma, structural abnormalities are common, and we estimate that they account for up to 84% of the risk associated with known genetic abnormalities. Key structural abnormalities include translocations into the immunoglobulin loci, which are primary events found in all myeloma cells. These translocations were thought to only occur through class-switch recombination in the post-germinal center B cell. However, I recently showed using next-generation sequencing that up to 25% of translocations involve the V, D or J segments of the IGH loci, indicating that RAG1/RAG2 are involved in a subset of patients. I also investigated the breakpoints surrounding secondary translocations, the most common of which affects MYC on chromosome 8. I showed that in primary patient samples MYC translocations with the IG loci only occur in 30% of cases, and the remaining MYC rearrangements are with novel partners. These novel partners are not random and are highly expressed in myeloma cells and the rearrangement results in enhancer elements juxtaposed to MYC. Identifying the mechanisms of these rearrangements has led to the application of bromodomain inhibitors, which disrupt enhancers, in these patients. Additionally, I have shown that MYC translocations occur through a different mechanism than the primary IGH translocations. From the analysis of translocation breakpoints, primary translocations occur through class-switch recombination and non-homologous end joining, whereas MYC translocations occur through microhomology mediated end joining and when the IGH loci is involved they do not occur in the switch regions. This indicates a secondary mechanism of gene disruption that is independent from the primary events and is associated with disease progression. C. The mutational landscape of multiple myeloma I have utilized primary patient material, mostly within clinical trials, to perform whole-exome sequencing on several large datasets, including the largest dataset published to date. This allowed us to comprehensively characterize myeloma for SNVs, CNVs, and SVs. This work resulted in several high impact papers describing the correlation between abnormalities and identifying prognostically important mutations. For example, we identified several mutations in genes that are associated with prognosis in newly diagnosed patients including CCND1, TP53 and ATM. We were also the first to show that bi-allelic inactivation of TP53 is solely responsible for the poor prognosis associated with del(17p). We also identified that the poor prognosis of the t(14;16) sub-group is associated with a high mutational burden and a mutational signature caused by APOBEC cytidine deaminases. Using these data we have investigated the sub-clonal structure of myeloma at the mutational level. We were the first to use single-cell genomics in myeloma to identify the sub-clonal structure showing that different cells contain unique mutations. We have also performed whole genome sequencing on samples covering the multi-step pathogenesis of myeloma, determining that MGUS is a less complex disease state, while high-risk smoldering multiple myeloma (SMM) is very similar to myeloma. Understanding the genomics associated with high risk SMM may enable successful treatment of these patients whilst leaving low risk SMM untreated as they have a stable disease and are associated with a longer time to progression to myeloma. The understanding of the sub-clonal structure of myeloma is key to understanding the efficacy of treatments and time to relapse. FUTURE PLANS A. The non-coding genome of multiple myeloma The genomic background of the tumor significantly contributes to patient outcome, where prognosis is associated with translocation groups, copy number changes, and protein coding mutations. The non-coding regions of the MM genome have been studied in a limited number of samples, and so there is insufficient data on non-coding mutations in MM to determine if these contribute to prognosis and disease progression. Non-coding regions could contribute to outcome through mutations in promoters, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs), which can alter gene expression. Therefore, I hypothesize that non-coding mutations in MM contribute to outcome, through gene expression deregulation. Having performed whole-genome sequencing on approximately 100 newly diagnosed myeloma patients we have the power to identify hotspots of mutation in non-coding regions of the genome. Although it is more difficult to define hotspots in the non-coding regions, due to the lack of annotations in these regions we have implemented several approaches to identify them. Concentrating on those in promoter regions and non-coding RNAs we will identify key regulatory regions where mutation results in a change in gene expression by integrating mutation data with expression data. By performing targeted sequencing on a larger set of patients we will identify those mutational hotspots associated with a short time to progression. Additionally, we will examine the effect of non-coding mutations on disease progression by performing targeted sequencing on SMM patient samples. Together these datasets will determine the impact of non-coding mutations on disease progression, prognosis, and on gene regulatory networks. This project was submitted to the Leukemia & Lymphoma Society as a Translational Research Project and has been funded for three years. B. Chromosomal translocations and gene dysregulation Although the primary translocations at the IGH locus are the most frequent, the myeloma genome is unstable and there are many other translocations present per sample. These other translocations are not well defined and their function is not well understood. We hypothesize that a subset of these translocations is involved in gene dysregulation through super-enhancer hijacking, where a super-enhancer active at one site in the genome is translocated next to a proto-oncogene resulting in its over-expression. Using the whole genome sequencing data from 100 newly diagnosed myeloma patients we will identify recurrent regions with breakpoints and determine the genes dysregulated by them. We will determine the effect of the translocation on gene expression, which may identify new drivers of disease. We will examine the frequency of these events at the SMM disease stage to determine if they are related to disease progression. Complex translocations, which may involve multiple chromosomes, are also found in myeloma. These can be defined as chromoplexy or chromothripsis, depending on the reararrangements seen. These are present in up to 30% of newly diagnosed myeloma patients and is associated with a poor prognosis. We will investigate the molecular fingerprint of these breakpoints to determine the likely mechanism in which they were generated as well as the effect on the expression of the surrounding genes. This project was submitted to the Leukemia & Lymphoma Society as a Translational Research Project and has been funded for three years. The non-coding and translocation grants are separate. C. Epigenetics of myeloma Translocations result in the rearrangement of topologically associated domains (TADs) where enhancer and super-enhancers are more likely to affect expression of genes within the same TAD. Few datasets are available on TADs in myeloma, and even fewer with associated super-enhancer and chromatin maps. Given the heterogeneity of the mutational landscape, DNA methylation levels, copy number changes, and translocations we expect that there will be heterogeneity in the chromatin accessibility in myeloma patients. We aim to use patient-derived xenografts to grow up sufficient material to perform comprehensive genomic analysis, encompassing whole genome sequencing, Hi-C for TAD interactions, ChIP-seq for key histone marks and CTCF binding sites, bisulfite sequencing for DNA methylation, ATAC-seq for chromatin accessibility, and RNA-seq for gene expression. Performing this in a set of PDXs from different IGH translocation backgrounds will enable us to identify the heterogeneity in the chromatin landscape, and how it is affected by translocations. Our preliminary data indicates that super-enhancers at translocation breakpoints spreads onto the other chromosome, resulting in over-expression of the partner oncogene. We have also identified gene cassettes that are over-expressed which contain super-enhancers and the TAD boundaries are demarcated by CTCF-binding sites. The overall aim of this project is to inform the biology of the different cytogenetic subgroups of myeloma using the chromatin landscape to identify mechanisms of gene dysregulation that could be used as a therapeutic target. D. Precision Health Initiative Myeloma Pillar I will play a key part in the Indiana University Precision Health Initiative (PHI) Myeloma pillar, bringing genomics and translational expertise to the project. I will collaborate closely with the other members of the myeloma pillar, namely Dr. Abonour, Roodman, Abu Zaid, Farag, Suvannasankha, and Perna. Our collective expertise in patient treatment, bone marrow microenvironment, transplantation, immunotherapy, and genomics will enable us to fulfill the aims of the project. We will study the genomic composition of myeloma cells and changes in the microenvironment through different stages of therapy utilizing state of the art next-generation sequencing and other molecular technologies. Single-cell technologies will be key in determining clonal outgrowth and the effect on the microenvironment. The genomics can also identify new drivers of disease, which may include good targets for novel therapies such as CAR-T cells. A key phase of the PHI myeloma pillar is the prevention of myeloma through identifying factors associated with the pre-malignant disease stages MGUS and SMM. A part of this will be the genomics of predisposition to myeloma, as the incidence of myeloma is two-times higher in African Americans and this population is associated with a poor outcome compared to other ethnic groups. This key departmental collaboration is likely to be fruitful and provide preliminary data for many future grants for all those involved.

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Post-doctoral Fellowship - The Institute of Cancer Research, UK 09/2008

Post-doctoral Fellowship - Institute for Animal Health, UK 09/2004

Ph.D. - Imperial College, University of London, UK 09/2000