Primer on Medical Genomics Part IX: Scientific and Clinical Applications of DNA Microarrays—Multiple Myeloma as a Disease Model

AbstractAlthough multiple myeloma (MM) is a unique entity, a marked heterogeneity is actually observed among the patients, which has been first related to immunoglobulin (Ig) types and light chain subtypes and more recently to chromosomal abnormalities. To further investigate this genetic heterogeneity, we analyzed gene expression profiles of 92 primary tumors according to their Ig types and light chain subtypes with DNA microarrays. Several clusters of genes involved in various biologic functions such as immune response, cell cycle control, signaling, apoptosis, cell adhesion, and structure significantly discriminated IgA- from IgG-MM. Genes associated with inhibition of differentiation and apoptosis induction were up-regulated while genes associated with immune response, cell cycle control, and apoptosis were down-regulated in IgA-MM. According to the expression of the 61 most discriminating genes, BJ-MM represented a separate subgroup that did not express either the genes characteristic of IgG-MM or those of IgA-MM at a high level. This suggests that transcriptional programs associated to the switch could be maintained up to plasma cell differentiation. Several genes whose products are known to stimulate bone remodeling discriminate between κ- and λ-MM. One of these genes, Mip-1α, was overexpressed in the κ subgroup. In addition, we established a strong association (P = .0001) between κ subgroup expressing high levels of Mip-1α and active myeloma bone disease. This study shows that DNA microarrays enable us to perform a molecular dissection of the bioclinical diversity of MM and provide new molecular tools to investigate the pathogenesis of malignant plasma cells.

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Mechanisms and Clinical Applications of Genome Instability in Multiple Myeloma

BioMed Research International ◽

10.1155/2015/943096 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 6

Author(s):

Antonia Cagnetta ◽

Davide Lovera ◽

Raffaella Grasso ◽

Nicoletta Colombo ◽

Letizia Canepa ◽

...

Keyword(s):

Multiple Myeloma ◽

Genomic Instability ◽

Molecular Basis ◽

Genome Instability ◽

Dna Recombination ◽

Clinical Applications ◽

Response To Treatment ◽

Chromosomal Segregation ◽

Telomere Function ◽

Mitotic Regulation

Ongoing genomic instability represents a hallmark of multiple myeloma (MM) cells, which manifests largely as whole chromosome- or translocation-based aneuploidy. Importantly, although it supports tumorigenesis, progression and, response to treatment in MM patients, it remains one of the least understood components of malignant transformation in terms of molecular basis. Therefore these aspects make the comprehension of genomic instability a pioneering strategy for novel therapeutic and clinical speculations to use in the management of MM patients. Here we will review mechanisms mediating genomic instability in MM cells with an emphasis placed on pathogenic mutations affecting DNA recombination, replication and repair, telomere function and mitotic regulation of spindle attachment, centrosome function, and chromosomal segregation. We will discuss the mechanisms by which genetic aberrations give rise to multiple pathogenic events required for myelomagenesis and conclude with a discussion of the clinical applications of these findings in MM patients.

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RNA-Sequencing-Based Transcriptomic Score with Prognostic and Theranostic Values in Multiple Myeloma

Journal of Personalized Medicine ◽

10.3390/jpm11100988 ◽

2021 ◽

Vol 11 (10) ◽

pp. 988

Author(s):

Elina Alaterre ◽

Veronika Vikova ◽

Alboukadel Kassambara ◽

Angélique Bruyer ◽

Nicolas Robert ◽

...

Keyword(s):

Multiple Myeloma ◽

Stem Cell ◽

Risk Score ◽

Dna Microarrays ◽

Kinase Inhibitors ◽

Plasma Cells ◽

Aurora Kinase ◽

Interferon Response ◽

High Dose ◽

Rna Seq

Multiple myeloma (MM) is the second most frequent hematological cancer and is characterized by the clonal proliferation of malignant plasma cells. Genome-wide expression profiling (GEP) analysis with DNA microarrays has emerged as a powerful tool for biomedical research, generating a huge amount of data. Microarray analyses have improved our understanding of MM disease and have led to important clinical applications. In MM, GEP has been used to stratify patients, define risk, identify therapeutic targets, predict treatment response, and understand drug resistance. In this study, we built a gene risk score for 267 genes using RNA-seq data that demonstrated a prognostic value in two independent cohorts (n = 674 and n = 76) of newly diagnosed MM patients treated with high-dose Melphalan and autologous stem cell transplantation. High-risk patients were associated with the expression of genes involved in several major pathways implicated in MM pathophysiology, including interferon response, cell proliferation, hypoxia, IL-6 signaling pathway, stem cell genes, MYC, and epigenetic deregulation. The RNA-seq-based risk score was correlated with specific MM somatic mutation profiles and responses to targeted treatment including EZH2, MELK, TOPK/PBK, and Aurora kinase inhibitors, outlining potential utility for precision medicine strategies in MM.

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The α-emitter astatine-211 targeted to CD38 can eradicate multiple myeloma in a disseminated disease model

Blood ◽

10.1182/blood.2019001250 ◽

2019 ◽

Vol 134 (15) ◽

pp. 1247-1256 ◽

Cited By ~ 10

Author(s):

Shyril O’Steen ◽

Melissa L. Comstock ◽

Johnnie J. Orozco ◽

Donald K. Hamlin ◽

D. Scott Wilbur ◽

...

Keyword(s):

Multiple Myeloma ◽

Disease Model ◽

Murine Models ◽

Cell Clones ◽

Key Points ◽

Cell Kill ◽

Disseminated Disease

Key Points 211At targeted to CD38 eliminates MM cell clones in murine models of low-burden disease. 211At deposits ≥500 times more energy than β-emitters and provides a mechanism of uniform cell kill unique among MM therapeutics.

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A Clinically Usable Gene Expression Report for Risk Assessment and Identification of Possible Therapeutic Targets in Multiple Myeloma.

Blood ◽

10.1182/blood.v114.22.1829.1829 ◽

2009 ◽

Vol 114 (22) ◽

pp. 1829-1829

Author(s):

Tobias Meiβner ◽

Anja Seckinger ◽

Thomas Hielscher ◽

Thierry Rème ◽

Jerome Moreaux ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Risk Stratification ◽

Open Source ◽

Dna Microarrays ◽

Conflicts Of Interest ◽

Therapeutic Targets ◽

Proliferation Index ◽

Final Report ◽

Clinical Routine

Abstract Abstract 1829 Poster Board I-855 Introduction In addition to current clinical and cytogenetic risk factors, several highly predictive gene expression based risk stratifications have been proposed in multiple myeloma. At the same time, putative drugable targets have been identified which are only expressed in a subpopulation of myeloma patients (e.g. AURKA). Whereas assessment of both works well within a clinical trial or an experimental setting, they can currently not readily be applied to clinical routine. Methods As reference a group of 300 Affymetrix U133 Plus 2.0 DNA microarrays from patients with multiple myeloma is preprocessed using GC-RMA. Quality control of the DNA microarrays is implemented according to the MACQ-Project. Gene expression based prediction of sex, immunoglobulin- and light chain type is used as sample identity-test within a multicenter-setting. Gene expression based risk stratification (IFM-score, 70-gene high risk score, gene expression based proliferation index) and molecular classifications are assessed as published, as are individual target genes e.g. AURKA. To classify a patient within a prospective clinical routine setting, the documentation by value strategy (Kostka & Spang, 2008) was adapted for GC-RMA preprocessing and is used for documenting the quantitative preprocessing information of the reference group. The gene expression based report is developed in the open source language R, containing a GUI based on Gtk2, and the final report is created as a PDF-file. Results We present here our publicly available (http://code.google.com/p/gep-r) open source software-framework (GEP-R) that allows creating a gene expression based report from Affymetrix raw-data. The risk stratification of an individual patient is assessed and based on saved preprocessing information of a reference cohort by treating the individual patient's expression data as being part of this group, assuring comparable risk stratification. Results can be interpreted and commented within the report and a PDF based document be created. The generation of the report can be performed within short time on a standard computer. Conclusion Gene expression reporting allows validated assessment of risk and of individual therapeutic targets in myeloma patients within a clinical routine setting. Disclosures No relevant conflicts of interest to declare.

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