The Multiple Myeloma Genome Project: Developing a Risk Stratification System for Multiple Myeloma

An update from the Multiple Myeloma Genome Project (MMGP), presented at the 16th International Myeloma Workshop (IMW), revealed several associations between the development of multiple myeloma (MM) and specific molecular abnormalities, that could be therapeutic targets. These results could aid in the development of improved, genomic-based risk stratification systems for the disease, according to MMGP researchers.

“MM is a heterogenous disease with many genomic alterations, and segmenting it into subgroups with distinct pathogenesis and clinical behavior is important to understand the molecular subtypes and advance therapy,” presenter Mehmet Samur, PhD, of the Dana-Farber Cancer Institute, told ASH Clinical News. “However, previous studies have evaluated different aspects of the MM genome with a limited number of samples. The [MMGP] has created one of the largest genomics and clinical data sets in MM to redefine molecular groups to advance our understanding of the disease.”

MMGP is a global initiative to create a repository of molecular profiling data and associated clinical outcome data from multiple sources, including the Myeloma XI trial, Intergroupe Francophone du Myeloma/Dana-Farber Cancer Institute, the UAMS Myeloma Institute, and the Multiple Myeloma Research Foundation.

In the analysis presented at this year’s IMW, Dr. Samur and colleagues collected genomic data from more than 2,000 patients, then categorized genetic abnormalities into five major translocation groups (t[4;14], t[6;14], t[11;14], t[14;16] and t[14;20]) and recurrent copy number changes (deletion of CDKN2C and TP53). They then evaluated clinical data for outcomes associated with “mutational, chromosomal, and gene-expression alterations to develop a classification system to segment MM into therapeutically meaningful subgroups.”

Genomic data were collected from 2,161 patients via:

  • whole-exome sequencing (n=1,436)
  • whole-genome sequencing (n=708)
  • targeted panel sequencing (n=993)
  • expression data from RNA-seq and expression arrays (n=1,497)

Integrated analyses revealed 28 significantly mutated genes that were present in samples from newly diagnosed patients (17 genes in >2% of samples). The main recurrent mutations included KRAS, NRAS, and negative regulators of the NF-κB pathway. The researchers also identified novel genes and recurrent copy number abnormalities, which could be potential therapeutic targets, and a subset of high-risk patients with specific copy number abnormalities and single nucleotide variants, including inactivation of CDKN2C, RB1, and TP53.

Though this is one of the largest genomic data sets in MM, Dr. Samur commented that the study’s findings were limited by the variation among the data sources and missing information from included clinical trials.


Reference
Samur M, Ashby C, Wardell C, et al. The Multiple Myeloma Genome Project: development of a molecular segmentation strategy for risk stratification of multiple myeloma. Abstract #521. Presented at the 16th International Myeloma Workshop, March 4, 2017; New Delhi, India.

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