Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups

Abstract

In multiple myeloma, next-generation sequencing (NGS) has expanded our knowledge of genomic lesions, and highlighted a dynamic and heterogeneous composition of the tumor. Here we used NGS to characterize the genomic landscape of 418 multiple myeloma cases at diagnosis and correlate this with prognosis and classification. Translocations and copy number abnormalities (CNAs) had a preponderant contribution over gene mutations in defining the genotype and prognosis of each case. Known and novel independent prognostic markers were identified in our cohort of proteasome inhibitor and immunomodulatory drug-treated patients with long follow-up, including events with context-specific prognostic value, such as deletions of the PRDM1 gene. Taking advantage of the comprehensive genomic annotation of each case, we used innovative statistical approaches to identify potential novel myeloma subgroups. We observed clusters of patients stratified based on the overall number of mutations and number/type of CNAs, with distinct effects on survival, suggesting that extended genotype of multiple myeloma at diagnosis may lead to improved disease classification and prognostication.

Somatic Genomic Mosaicism in Multiple Myeloma

“…A recent cancer genome analysis has illustrated that the profile of chromosome aberrations is much more useful than gene mutation profiles when correlated with clinical outcomes either as prognostic or predictive markers (Davoli et al, 2017;Jamal-Hanjani et al, 2017). This result was also confirmed in MM, as karyotypic events have a stronger impact on prognosis than mutations (Bolli et al, 2018). In fact, chromosomal profiles have extensively been associated with prognosis in MM, based on specific translocation, hyperdiploidy, chromosomal amplification/deletion, and chromosomal copy number abnormalities (Garcia-Sanz et al, 1995;Avet-Loiseau et al, 2007, 2009Walker et al, 2010;Shah et al, 2018).…”

“…In addition to the karyotypic level of mosaicism discussed, different types of somatic mosaicism include copy number variations (CNVs) (Walker et al, 2010(Walker et al, , 2015Lohr et al, 2014;Bolli et al, 2018;Aktas Samur et al, 2019), gene mutations (both driver and passenger) (Chapman et al, 2011;Egan et al, 2012;Keats et al, 2012;Bolli et al, 2014Bolli et al, , 2018Lohr et al, 2014;Walker et al, 2015), and non-genetic variations (e.g., epigenetic variations) (Huang, 2009;Heng, 2019). Together, the multiple levels of genetic variation represent the high degree of somatic genomic mosaicism in MM.…”

“…Equally important, since different subpopulations can be molecularly profiled, especially after becoming dominant clones, a huge number of molecular mechanisms can be characterized. Data from recent studies illustrate diverse genetic variations in MM disease evolution Keats et al, 2012;Bolli et al, 2014Bolli et al, , 2018Pawlyn and Morgan, 2017;Aktas Samur et al, 2019;Maura et al, 2019). A better way to understand MM is to study the evolutionary mechanism of cancer (Ye et al, 2009), rather than continue identifying individual molecular mechanisms: when there are so many, the clinical prediction of any single mechanism is low due to highly dynamic evolutionary processes.…”

From single gene analysis to single cell profiling: a new era for precision medicine

“…In this context, Daniele Caracciolo from the Magna Graecia University, Catanzaro (Italy) reported data that highlight the relevance of genomic instability in multiple myeloma (MM), which is characterized by the presence of plasma cells harboring numerous and progressive karyotypic aberrations. Indeed, previous data have highlighted mutation or deletion of genes implicated in DNA damage response in 22% of MM patients [28]. Further investigations by Caracciolo and coworkers have provided evidence that increased expression of the DNA ligase III (LIG3) correlates with worst prognosis in MM and is crucial for genomic instability and survival of multiple myeloma cells.…”

Patient Similarity Network of Multiple Myeloma Identifies Patient Sub-groups with Distinct Genetic and Clinical Features

“…Our recent co-expression network model of newly diagnosed MM, MMNet, revealed a clear molecular separation between patients with Ig translocations and hyperdiploidy and identified three novel subtypes characterized by cytokine signaling (CK), immune signatures (IMM) and MYC translocations (MYC) 12 . Another recent study investigated novel MM subtypes based on a targeted DNA panel and revealed a large cluster comprising most HD and IgH translocated patients, and two smaller clusters, one enriched for IgH translocations and one mostly composed of HD patients 13 .…”