Characterization of Germline Variants in Multiple Myeloma

Multiple myeloma (MM) is an incurable hematological malignancy characterized by the clonal proliferation of malignant plasma cells in the bone marrow. Like other cancers, MM is a genetically complex and heterogeneous disease. One of its distinctive characteristics is that it is preceded by a pre-malignant condition known as monoclonal gammopathy of undetermined significance (MGUS), which then progresses to asymptomatic (smoldering) multiple myeloma (SMM) and, ultimately, to late-stage MM. Its progression through these stages is determined by a sequence of genomic aberrations, starting with germline events that predispose to the disease, followed by early initiating events and the later acquisition of mutations that contribute to disease progression.

Although considerable progress has been made in the past 6 years in cataloguing somatic events underlying MM development and progression, little is known about its genetic predisposition. Therefore, large-scale germline genomic variant studies are urgently needed. Recently, our group has published the largest-scale pan-cancer study of >10K adult and >1K pediatric cases that revealed new insights on germline predisposition variants across 33 cancer types (853 pathogenic or likely pathogenic variants) (Huang et al., 2018). Here, we aim to apply a similar strategy to MM cases.

The CoMMpass study, promoted by MMRF (Multiple Myeloma Research Foundation) is a longitudinal, prospective observational study involving the collection and analysis of sequencing and clinical data from >1K MM patients at diagnosis and relapse. We performed germline variant calling on 808 normal samples from this dataset using GenomeVIP (https://github.com/ding-lab/GenomeVIP), which integrates multiple tools: VarScan2 and Genome Analysis ToolKit (GATK) for the identification of single nucleotide variants (SNVs) and indels; and Pindel for indel prediction. Variants were limited to coding regions of full length transcripts obtained from Ensembl release 70 plus the additional two base pairs flanking each exon that cover splice donor/acceptor sites. SNVs were based on the union of raw GATK and VarScan calls. Indels were required to be called by at least two out of the three callers (GATK, Pindel, VarScan). Variant calls from all tools were merged, filtered (allelic depth ≥ 5 for the alternative allele; rare variants with allele frequency ≤ 0.01 in 1000 Genomes and ExAC), and annotated using Variant Effect Predictor (VEP), resulting in an average of 1,653 variants per sample.

Further, we applied CharGer (Characterization of Germline Variants, https://github.com/ding-lab/CharGer) to classify the identified germline variants as pathogenic, likely pathogenic, and prioritized variants of unknown significance (VUS). CharGer is an automatic variant classification pipeline developed by our group which adopts ACMG-AMP guidelines specifically for rare variants in cancer. Here, we were able to classify a total of 635 germline variants as pathogenic and 150 as likely pathogenic, affecting 90% of samples. Among pathogenic variants, 28 were found in known cancer predisposition genes including BRCA1 and BRCA2 - which have been previously associated with MM risk - BRIP1, CHEK2, TP53, TERT, and PMS2. Ongoing analyses include: functional characterization of these variants, identifying genes with enriched pathogenic or likely pathogenic variants in our dataset; investigation of LOH and two-hit (biallelic) events; gene and protein expression analyses in carriers of pathogenic germline variants of the respective gene; scanning for rare, germline copy number variations (CNVs); and identification of variants in post-translational modification sites that may affect protein signaling. Additionally, we are currently working on improving our CharGer tool by integrating new tumor associated data, such as DNA-Seq, RNA-Seq, Methyl-Seq and MS proteomics data, to improve variant classification.

The preliminary results and analysis strategies described here will allow for efficient and cost-effective discovery of genetic changes relevant to MM etiology. Ultimately, we hope this work will impact our overall understanding of the genetics underlying MM predisposition, allowing for the development of better prevention and early detection strategies.