Jacobs, Lauren

Abstract Given the frequent and largely incurable occurrence of multiple myeloma, identification of germline genetic mutations that predispose cells to multiple myeloma may provide insight into disease etiology and the developmental mechanisms of its cell of origin, the plasma cell (PC). Here, we identified familial and early-onset multiple myeloma kindreds with truncating mutations in lysine-specific demethylase 1 (LSD1/KDM1A), an epigenetic transcriptional repressor that primarily demethylates histone H3 on lysine 4 and regulates hematopoietic stem cell self-renewal. In addition, we found higher rates of germline truncating and predicted deleterious missense KDM1A mutations in patients with multiple myeloma unselected for family history compared with controls. Both monoclonal gammopathy of undetermined significance (MGUS) and multiple myeloma cells have significantly lower KDM1A transcript levels compared with normal PCs. Transcriptome analysis of multiple myeloma cells from KDM1A mutation carriers shows enrichment of pathways and MYC target genes previously associated with myeloma pathogenesis. In mice, antigen challenge followed by pharmacologic inhibition of KDM1A promoted PC expansion, enhanced secondary immune response, elicited appearance of serum paraprotein, and mediated upregulation of MYC transcriptional targets. These changes are consistent with the development of MGUS. Collectively, our findings show that KDM1A is the first autosomal-dominant multiple myeloma germline predisposition gene providing new insights into its mechanistic roles as a tumor suppressor during post-germinal center B-cell differentiation. Significance: KDM1A is the first germline autosomal dominant predisposition gene identified in multiple myeloma and provides new insights into multiple myeloma etiology and the mechanistic role of KDM1A as a tumor suppressor during post-germinal center B-cell differentiation. Cancer Res; 78(10); 2747–59. ©2018 AACR.

Abstract Massively parallel next generation sequencing data (NGS) has proved to be useful in identification of rare Mendelian disorders. Efforts are on-going to use NGS for discovery of novel rare variants in common disease etiology. Distinguishing rare private mutations from causal variants remains a major challenge in complex disorders. With decreasing cost, investigators are generating NGS data for common cancers. However, power and other considerations suggest that a consortia approach of pooling data is more likely to succeed where individual efforts fail. Combining data within such consortia brings its own level of challenges. We enumerate these challenges and suggest recommendations based on our experience of combining and harmonizing NGS data on 358 breast cancer samples from four centers; (City of Hope n=8, Mayo Clinic n=221, MSKCC n=96 and University of Pennsylvania n=33). Major concerns were ability of individual datasets to discover known and novel variations, depth of coverage across the exome, and quality of variants. Using pre- and post-processed data, we demonstrate an increased power for detecting variants when using a joint-calling method, quality control (QC) and quality assurance pre- and post-merge, as well as the advantages of combined annotations and filtering procedures. To generate a filtered list of high confident calls, we subset the data on Depth per Allele (DP), Genotyping Quality (GQ), Allele balance, Phred likelihood score (PL) as well as using frequency filters on the alternate allele both within the data and comparing against convenience control summaries. We describe general principles and QC measures to be adopted when NGS data is used from diverse sources, specifically applicable to large consortia. Note: This abstract was not presented at the meeting. Citation Format: Joseph Vijai, Steven Hart, Tinu Thomas, Bradley Wubbenhorst, Lucia Guidugli, Kasmintan Schrader, Kara Maxwell, Lauren Jacobs, Danylo Villano, Robert Klein, Steven Lipkin, Susan Neuhausen, Jeffrey Weitzel, David Altshuler, Fergus Couch, Kenneth Offit, Katherine Nathanson. Harmonization of next generation sequencing data within consortia for gene discovery in familial breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2378. doi:10.1158/1538-7445.AM2014-2378

Abstract Background:BRCA1 and BRCA2 mutation carriers are at substantially increased risk for developing breast and ovarian cancer. The incomplete penetrance coupled with the variable age at diagnosis in carriers of the same mutation suggests the existence of genetic and nongenetic modifying factors. In this study, we evaluated the putative role of variants in many candidate modifier genes. Methods: Genotyping data from 15,252 BRCA1 and 8,211 BRCA2 mutation carriers, for known variants (n = 3,248) located within or around 445 candidate genes, were available through the iCOGS custom-designed array. Breast and ovarian cancer association analysis was performed within a retrospective cohort approach. Results: The observed P values of association ranged between 0.005 and 1.000. None of the variants was significantly associated with breast or ovarian cancer risk in either BRCA1 or BRCA2 mutation carriers, after multiple testing adjustments. Conclusion: There is little evidence that any of the evaluated candidate variants act as modifiers of breast and/or ovarian cancer risk in BRCA1 or BRCA2 mutation carriers. Impact: Genome-wide association studies have been more successful at identifying genetic modifiers of BRCA1/2 penetrance than candidate gene studies. Cancer Epidemiol Biomarkers Prev; 24(1); 308–16. ©2014 AACR.