General MM

Molecular lesions in MM

With the development of NGS (Next Generation Sequencing) at both the DNA and RNA level, better characterization of genetic abnormalities in MM is now possible. In a review written by Sébastien Robiou du Pont and colleagues from L’Institut Universitaire du Cancer, Oncopole, Toulouse, France, as well as collaborators at the Centre National de la Recherche Scientifique, Montpellier University, France, and The Dana-Farber Cancer Institute, Boston, USA, the genomics of Multiple Myeloma (MM) is discussed. The review was published in Journal of Clinical Oncology in March 2017.

Key Highlights

Gene Expression Profiling (GEP):

  • GEP identified 8 subsets, partially confirmed by HOVON (Haemato Oncology Foundation for Adults in the Netherlands) group, but not substantiated fully by later studies, which identified three more relevant groups. Therefore, failed to identify real sub-entities
  • GEP data identified high-risk (13% to 25% of pts) vs standard risk subgroups, but GEP not optimized for routine use to assess prognosis

DNA copy number changes:

  • Karyotyping often uninformative, so knowledge of unbalanced chromosomal changes comes from single nucleotide polymorphism (SNP)/comparative genomic hybridization (CGH) array studies which allow identification of molecular karyotypes
  • Most karyotypes are usually highly complex, with 2 exceptions: pts with no detectable abnormality at the chromosomal level (10%) and pts with the t(11;14) translocation (approximately 15%-20%)
  • Numerous changes observed (in approximately 70% of pts); two groups identified:
    • pts with gains of odd chromosomes (3, 5, 7, 9, 11, 15, 19, and 21) – which define hyperdiploidy (50%)
    • pts with many structural changes (gains and losses) which define pseudo- or hypodiploidy (approximately 20% of patients). This group commonly include 14q32 translocations that target the IGH gene and partners such as FGFR3/MMSET on chromosome 4 or, less commonly, MAF (chromosome 16), MAFB (Chromosome 20) or CCND3 (chromosome 6)
  • Common recurrent unbalanced changes observed = gains and losses at 1p, 6q, 8p, 13q, 14q, 16q, and 17p
  • Links with prognosis:
    • hyperdiploidy - associated with longer survival
    • chromosomes 3 and 5 - good outcome; trisomy 21 worsens prognosis
    • high-risk abnormalities - t(4;14),  t(14;16), del(17p),del (1p32), and 1q gains
  • Molecular targets of losses and gains largely unknown, but the following are:
    • del(17p): minimal deleted region includes the TP53 gene, but is not mutated on the remaining allele in all cases; other nearby genes might be important for prognosis
    • del(1p32): minimal deleted region targets two genes, FAF1 and CDKN2C (unpublished data)
    • 1q gains: majority are gains of the whole long arm, which prevents the identification of specific target genes.
  • Routine practice uses interphase FISH on sorted PCs to analyse these abnormalities

DNA Sequencing

  • Hundreds of primary tumors have been exome sequenced
  • MM has no single unique mutation, is middle landscape in terms of mutation number and heterogeneous landscape in terms of mutation type
  • Most frequently mutated genes:
    • KRAS and NRAS (~20% each)
    • TP53, DIS3, FAM46C, and BRAF (10%)
    • All other mutations (5%)
    • Some pts present two or more mutations in genes involved in the same pathway (eg, KRAS, NRAS, or BRAF in the MAPK pathway), although biologic significance unknown
  • Several mutational signatures identified:
    • deamination of methylated cytosines: leads to C>T transitions at CpG sites; generic mutational process
    • kataegis
    • apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC), signature: characterized by C>T, C>G, and C>A mutations at TpC sites, predominantly occurring in cases of MAF/MAFB translocations
    • somatic hypermutations: driven by activation-induced deaminase (AID)
    • AID-driven mutations: observed in several genes involved in immunoglobulin translocations, such as MYC or CCND1
  • Relationship between most of these mutations and prognosis is unknown
  • Mutations in TP53 significantly affect survival
  • Whole genome sequencing has identified a large mutation dataset; further research required to separate passenger vs driver mutations
  • Prognostic value of the main chromosomal abnormalities:
    • Negative: t(4;14), Del(17p), Del(1p32), 1q gains, t(14;16)
    • Positive (specific gains?): Hyperdiploidy
    • Neutral: t(11;14)


  • MM shows heterogeneity at the molecular level, suggesting Darwinian evolution
  • Mutational process is dynamic, driver mutations (eg. KRAS< RAS, BRAF) can occur late in evolution
  • Two types of subclonal evolution: linear evolution with acquisition of novel mutations in the clone over time, and branching evolution driven by subclonal acquisition of novel mutations leading to divergence of subclones
  • Relationship between therapeutic pressure or the natural history of each patient is unknown
  • Oligoclonality is a key factor in choosing targeted drug regimens, eg. if only 50% of clones would respond

With improvements in genetic profiling methodologies and an increased number of patients enrolled in clinical trials, the search for a link between genetics and prognosis in MM should become easier. This will be increasingly important to help steer more tailored treatment regimens in the future.


Multiple myeloma (MM) is characterized by wide variability in the chromosomal/genetic changes present in tumor plasma cells. Genetically, MM can be divided into two groups according to ploidy and hyperdiploidy versus nonhyperdiploidy. Several studies in gene expression profiling attempted to identify subentities in MM without convincing results. These studies mostly confirmed the cytogenetic data and subclassified patients according to 14q32 translocations and ploidy. More-recent data that are based on whole-exome sequencing have confirmed this heterogeneity and show many gene mutations but without a unifying mutation. These newer studies have shown the frequent alteration of the mitogen-activated protein kinase pathway. The most interesting data have demonstrated subclonality in all patients with MM, including subclonal mutations of supposed driver genes KRAS, NRAS, and BRAF.

  1. Robiou du Pont S. et al. Genomics of Multiple Myeloma. J Clin Oncol. 2017 Mar 20;35(9):963-967. DOI: 10.1200/JCO.2016.70.6705. Epub 2017 Feb 13.
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