Next Article in Journal
Photopharmacology of Ion Channels through the Light of the Computational Microscope
Next Article in Special Issue
Nuclear RNA Exosome and Pervasive Transcription: Dual Sculptors of Genome Function
Previous Article in Journal
Multifunctional Nanomaterials: Synthesis, Properties and Applications
Previous Article in Special Issue
The miR-106b/NR2F2-AS1/PLEKHO2 Axis Regulates Migration and Invasion of Colorectal Cancer through the MAPK Pathway
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 22
Issue 21
10.3390/ijms222112070
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

The Significance of mRNA in the Biology of Multiple Myeloma and Its Clinical Implications

by
Anna Puła
1,
Paweł Robak
2,
Damian Mikulski
3 and
Tadeusz Robak
1,*
1
Department of Hematology, Medical University of Lodz, 93-510 Lodz, Poland
2
Department of Experimental Hematology, Medical University of Lodz, 93-510 Lodz, Poland
3
Department of Biostatistics and Translational Medicine, Medical University of Lodz, 92-215 Lodz, Poland
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2021, 22(21), 12070; https://doi.org/10.3390/ijms222112070
Submission received: 2 October 2021 / Revised: 28 October 2021 / Accepted: 4 November 2021 / Published: 8 November 2021
(This article belongs to the Special Issue mRNAs in Biology)
Review Reports Versions Notes

Abstract

:
Multiple myeloma (MM) is a genetically complex disease that results from a multistep transformation of normal to malignant plasma cells in the bone marrow. However, the molecular mechanisms responsible for the initiation and heterogeneous evolution of MM remain largely unknown. A fundamental step needed to understand the oncogenesis of MM and its response to therapy is the identification of driver mutations. The introduction of gene expression profiling (GEP) in MM is an important step in elucidating the molecular heterogeneity of MM and its clinical relevance. Since some mutations in myeloma occur in non-coding regions, studies based on the analysis of mRNA provide more comprehensive information on the oncogenic pathways and mechanisms relevant to MM biology. In this review, we discuss the role of gene expression profiling in understanding the biology of multiple myeloma together with the clinical manifestation of the disease, as well as its impact on treatment decisions and future directions.

1. Introduction

Multiple myeloma (MM) is a genetically complex disease resulting from a multistep transformation of normal to malignant plasma cells in the bone marrow [1]. Its precursors are believed to be monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma. However, while both lack the clinical features of organ damage presence, such as hypercalcemia, renal insufficiency, anemia, and bone lesions, they share some genetic mutations of symptomatic MM [2,3]. Further progression of the disease may lead to the proliferation of clonal plasma cells at sites outside the bone marrow, manifesting as extramedullary myeloma and plasma cell leukemia (PCL), both known to be very aggressive malignancies with inferior outcomes [4].
As MM occurs mainly in older patients, its treatment has gained prominence in today’s aging population. Its annual incidence in the United States in 2020 was estimated to be as high as 4–6 cases per 100,000, with 32,270 new cases and 12,830 deaths reported [4,5].
In the era of molecular cytogenetic methodologies such as G-band karyotyping, fluorescence in situ hybridization (FISH), comparative genomic hybridization (CGH), as well as more advanced novel genetic techniques, such as single nucleotide polymorphism (SNP) arrays and next-generation sequencing (NGS), it has become possible to better understand the molecular background of myelomagenesis [6]. Multiple myeloma is a genetically heterogeneous disease. The genetic alterations present in MM can be categorized into translocations, copy number abnormalities (CNAs), and point mutations [7,8]. The most important molecular mechanism underlying MM pathogenesis is thought to be immunoglobulin heavy chain (IgH) translocation [9]. Although the molecular mechanisms responsible for the initiation and heterogeneous evolution of MM remain largely unknown to date, the identification of driver mutations is fundamental to understanding the oncogenesis of MM and its response to therapy. However, the genetic landscape of MM is very complex, and distinguishing driver from passenger mutations is challenging. The somatic mutation rate of patients with multiple myeloma was reported to be approximately 1.6 mutations per Mb [10]. Certain genes, including KRAS, NRAS, TP53, FAM46C, DIS3, and BRAF have been reported to demonstrate frequent mutations in myeloma patients [11,12,13].
The introduction of gene expression profiling (GEP) in MM was an important step in elucidating the molecular heterogeneity of MM and its clinical relevance. Initially array-based studies, and more recently, those based on RNA sequencing (RNASeq), provided information on the transcriptomic background of myeloma, its clinical course, and prognosis. Since some mutations in MM occur in non-coding regions [14], analytical approaches based on mRNA provide more comprehensive information on the oncogenic pathways and mechanisms relevant to MM biology.
This present review discusses the role of gene expression profiling in understanding the biology of MM, together with the clinical manifestation of the disease, as well as its impact on treatment decisions and future directions for research.

2. Techniques Used for Gene Expression Analysis

The history of transcript profiling begins with early attempts of Northern blotting, reverse transcriptase quantitative PCR (RT-qPCR), and Sanger sequencing of the expressed sequence tags (ESTs), these being short nucleotide sequences generated from cDNAs [15,16,17,18]. Other early gene expression analysis techniques include serial analysis of gene expression (SAGE) [19] and DNA microarrays [20]. Both techniques are widely used for gene expression studies and novel gene identification. SAGE is based on the principle that an oligonucleotide sequence can uniquely identify a gene. It requires the isolation of mRNA and the generation of cDNA, from which unique small sequences (∼initially 10 bp), i.e., tags, are generated using restriction enzyme digestion. The frequency of a specific sequence tag determines the relative abundance of the transcript. Over the years, variations of SAGE have been devised to identify tags more accurately by increasing tag length by even as much as 26 bp [21]. DNA microarrays act by measuring the hybridization of the labeled target cDNA strands to a sample with fixed probes [22]. Although the techniques mentioned have been widely used, they both have their limitations.
The development of the high throughput sequencing RNA-seq technique has enabled even better exploration of RNA biology. The popularity of RNA-seq is driven by its large number of applications with differential gene expression analysis being the most common one. The standard workflow of RNA-seq begins with RNA extraction. This is followed by the purification of RNA from a sample since the isolated RNA is mostly ribosomal. The two most common techniques used for target enrichment are poly(A) capture for mRNA selection and ribosomal depletion. Following this, cDNA synthesis is performed and an adaptor-ligated sequencing library is prepared. Finally, the cDNA library is amplified by polymerase chain reaction (PCR) using parts of the adapter sequences as primers. When the experiment is finished, the data analysis begins: aligning and/or assembling the sequencing reads to a transcriptome, quantifying reads that overlap transcripts, filtering and normalizing between samples, and statistical modeling of significant changes in the expression levels of individual genes and/or transcripts between sample groups [23,24].
Over the years, our understanding of hematological malignancies has improved thanks to the development of next-generation sequencing (NGS), an approach comprising a range of methodologies that allow the investigation of genomics, transcriptomics, and epigenomics. An extensive review by Braggio et al. details the advances in the genomic exploration of hematological malignancies achieved through genome-wide sequence analysis [25].
Transcriptomic studies have provided important information regarding pathways and genes involved in myelomagenesis. Such gene expression profile (GEP) studies constitute a reliable prognostic tool that has been independently validated by various multiple myeloma cooperative groups. However, in daily clinical practice, no consensus has evolved to integrate GEP in multiple myeloma care.

3. Gene Expression Profile in Multiple Myeloma Biology and Prognosis

Multiple myeloma is a genetically complex and heterogeneous neoplasm in which the concurrency of multiple genomic events results in tumor development and progression. MM exists as hyperdiploid and nonhyperdiploid forms, with different karyotype [26,27]. Its most important oncogenic mechanisms are believed to be oncogene activation by IgH translocations and oncogene mutations [28]. IgH translocations are present in up to 50% of patients, and mainly involve five chromosomal loci, 11q13, 6p21, 4p16, 16q23, and 20q11, which contain the CCND1, CCND3, FGFR3/NSD2, MAF, and MAFB oncogenes [29].
The transcriptome of multiple myeloma has been evaluated in different patient cohorts [30,31,32,33]. Studies based on GEP have been widely used to better understand the biology of MM by identifying the genes involved in the molecular pathogenesis of the disease and their clinical significance, to predict survival in multiple myeloma, and to identify patients who will benefit from particular types of therapy. Some groups have even made an attempt to compare the transcriptome of MM and primary plasma cell leukemia: a more aggressive form of plasma cell dyscrasia [34]. Expression profiles of differentially expressed genes are of critical importance and have provided insights into MM biology. These genes may relate to cell cycle, cell death, autophagy, kinome, stemness, cytogenetic abnormalities, chromosome 1, homozygous deletions, and immune subnetworks [33,35,36,37,38,39,40,41,42].
GEP studies have led to the identification of Cyclin D family deregulation in MM and MGUS [30,43,44]. Deregulation of the cyclin D family (CCND1, CCND2, and CCND3) appears to be one of the key molecular events in the pathogenesis of MM [45]. It can result from the translocation of CCND1 or CCND3 with the IgH gene in the t(11;14) and the t(6;14), specific cyclin D amplification, trisomies, and other cytogenetic events. CCND2 is particularly overexpressed in t(4;14) and t(14;16) patients [30,31]. A proposed classification based on CCND1 gene expression status and 14q32 translocations divides MM patients into eight different subgroups [44].
Another attempt to use gene expression profiling in order to develop a prognostically relevant molecular classification of MM was made by Zhan et al. [32] The findings indicated the presence of seven disease subtypes that were strongly influenced by known genetic lesions including c-MAF– and MAFB-, CCND1- and CCND3-, MMSET-activating translocations and hyperdiploidy, these being CD1 [(t(11;14)], CD2 [t(11;14) and t(11;16)], MS [t(4;14)], MF [t(14;16) and t(14;20)], hyperdiploid cluster (HY), low bone disease (LB), and proliferation-associated genes (PR). Zhan et al. also identified myeloid gene expression signatures but excluded them from profiling analyses [32]. Broyl et al. confirmed the findings made by Zhan et al. and identified three novel subsets of MM: the nuclear factor kappa light chain-enhancer (NF-kB) subgroup, the cancer/testis antigen (CTA) subgroup characterized by high proliferation index, and the PRL3 subgroup characterized by up-regulation of protein tyrosine phosphatases PRL-3 and PTPRZ1 [31].
A review by Szalat et al. indicated the existence of 11 different molecular subgroups of MM based on transcriptomic studies [46]. A summary of this classification correlated with the clinical outcome is given in Table 1. Liu et al. combined data from whole-genome gene expression profiling microarrays and CytoScan HD high-resolution genomic arrays to integrate GEP with copy number variations (CNV); the findings highlighted certain molecular alterations in MM that were important for disease initiation, progression, and poor clinical outcome. In particular, eight cytogenetic driver lesions essential to the development and progression of myeloma were highlighted by the amplification of chromosome 1q: they suggest that 1q gains and the upregulated ANP32E, DTL, IFI16, UBE2Q1, and UBE2T gene expression could be responsible for MM aggressiveness [47]. These findings support those of Shaughnessy et al., who found that most of the up-regulated genes mapped to chromosome 1q, and the down-regulated genes mapped to chromosome 1p; this suggests that disease progression may be influenced by changes in the transcriptional regulation of genes mapping to chromosome 1 [40]. However, studies based on different molecular methods have yielded conflicting findings considering 1q gain as an adverse prognostic factor. Some early studies suggest it has no prognostic value [48,49], while some latest reports suggest it may be associated with an inferior outcome [50,51,52,53].
Manasach et al. compared the value of retrospective GEP data with FISH criteria to identify high-risk (HR) patients. They conclude that GEP identified more HR patients than FISH. Patients reclassified from standard-risk FISH to HR GEP presented with 1q amplification of equal to or over four copies [54]. Elsewhere, a multi-tissue transcriptome-wide association study (TWAS) aimed at exploring MM biology by Went et al. [55] identified 108 genes at 13 independent regions associated with MM risk; all of these were within 1 Mb of known MM GWAS risk variants [56,57,58,59].
It should be noted that transcriptomic approaches have rarely been employed in assessments of the risk of multiple myeloma or progression from MGUS. A number of GWAS and SNP studies have been conducted in order to explore this field, including multiple studies by the International Multiple Myeloma Research (IMMEnSE) consortium [56,57,58,59,60,61,62].

4. Gene Expression Profile and Multiple Myeloma Prognosis

Many different transcriptomic models for prognostication have been identified; however, none of them have been introduced into routine clinical practice. So far, the revised International Staging System (R-ISS) is still the first choice in MM management [63], and the older Durie-Salmon staging system is still used in some places [64]. Zhan et al. performed a microarray analysis on tumor cells from 532 newly diagnosed patients with MM in order to identify high-risk disease [32]. They report that high-risk groups presented a similar gene expression profile to human MM cell lines, whereas low-risk MM groups exhibited patterns identical to MGUS and normal plasma cells. After evaluation of the 70-gene risk model in relapse samples of 51 out of 351 of the training cohort, high-risk scores associated with poor survival were found in 39 patients. Kuiper et al. identified a 92-gene signature (EMC-92) that proved to be an independent prognostic factor of survival [65]. More recently Decaux et al. proposed a risk stratification model based on 15 different genes and note that patients with high-risk MM were characterized by the overexpression of genes involved in multiple phases of the entire cell cycle [33]. Dickens et al. limited the prognostication to six genes [41]. Similarly, Botta et al. proposed a prognostic risk score based on only six genes: IFNG, IL2, LTA, CCL2, VEGFA, and CCL3 [42]. This list was acquired from a gene expression profiling dataset of MGUS, smoldering MM, and symptomatic-MM, and identified inflammatory and cytokine/chemokine pathways as the most progressively affected during disease evolution.
Hose et al. proposed that assessment of proliferation by GEP allows the selection of patients for risk-adapted anti-proliferative treatment [66]. Liu et al. [35] constructed a multiple myeloma molecular causal network (M3CN) based on gene expression, copy number variation, and clinical data to better understand MM tumorigenesis, progression, and drug responses. The M3CN-derived prognostic subnetwork achieved demonstrated satisfactory separation between different risk groups [35]. However, the most complex approach was proposed by Katiyar et al. [67], who identified unified potential signatures for MM based on a genome-wide meta-analysis of differentially expressed genes (DEGs) and miRNAs (DEMs) in MM cells and normal plasma cells. The authors identified the top five most functionally connected hub genes (UBC, ITGA4, HSP90AB1, VCAM1, VCP) using protein–protein interactions.
In addition, transcription factor regulatory networks were determined for five seed DEGs with four or more biomarker applications (CDKN1A, CDKN2A, MMP9, IGF1, MKI67) [67]. The above studies indicate, that DEGs may influence disease pathogenesis, clinical presentation, and drug sensitivities in MM patients.
In recent years, gene expression profiling has been used to establish classifiers for prognostication. Various studies have shown that that GEP classifiers are more robust than FISH markers in identifying risk. For instance, a multivariate analysis by Kuiper et al. found that combinations of GEP with ISS, particularly SKY92 + ISS, proved superior to other combinations for stratifying MM into high-risk and low-risk categories [68]. A summary of the differences between gene expression classifiers in MM is presented in Table 2.

5. mRNA and Drug Resistance

Despite recent advancements in the design of novel anti-myeloma drugs, the acquisition of anti-cancer drug resistance is a major limitation of MM therapy. The mechanisms underlying drug resistance are diverse and include both genetic and epigenetic abnormalities. The topic of drug resistance in multiple myeloma has been widely reviewed by Robak et al. [73]. However, for the purpose of this review, we would like to briefly mention the mechanisms associated with altered mRNA expression.
Mitra et al. [74] developed a gene expression signature that predicts response specific to proteasome inhibitor (PI) treatment in MM on human myeloma cell lines (HMCLs). They created a 42-gene expression signature that could distinguish good and poor PI response in the HMCL panel and could be successfully applied to four different clinical data sets on MM patients undergoing PI-based chemotherapy to distinguish between good and poor outcomes [74].
In a study of the functional role of ABCB1 overexpression in MM, Besse et al. [75] found this to be the most significant change in carfilzomib-resistant MM cells compared to bortezomib-resistant cells. This change enhances the p-glycoprotein-mediated export of therapeutic drugs. The authors identified nelfinavir and lopinavir as approved drugs that could overcome resistance to carfilzomib by modulating P-glycoprotein function [75]. In addition, they observed that ABCB1 overexpression reduces the proteasome-inhibiting activity of carfilzomib but not of bortezomib.
Tang et al. [67]. identified 2099 long non-coding mRNAs that were deregulated in exosomes of bortezomib-resistant patients. Of these, 78 mRNAs in drug resistance-related pathways were enriched, with mammalian targets: rapamycin, platinum drug resistance, the cAMP, and phosphoinositide 3-kinase/Akt signaling pathways being key examples [76].
A recent study by Robak et al. [77] compared the mRNA expression of nine previously described genes that may affect resistance to multiple myeloma (ABCB1, CXCR4, MAF, MARCKS, POMP, PSMB5, RPL5, TXN, and XBP1) by bortezomib-refractory and bortezomib-sensitive patients [77]. The analysis was performed on 73 MM patients and 11 healthy controls. It was reported that RPL5 was significantly downregulated in MM patients, and that POMP was significantly upregulated in MM patients refractory to bortezomib. A multivariate analysis found high expression of PSMB5 and CXCR and autologous stem cell transplantation to be independent predictors of progression-free survival, while high expression of POMP and RPL5 was associated with shorter overall survival [77].

6. mRNA in CAR-T Cell Therapy

When reviewing the role of messenger RNA in the biology of multiple myeloma, it is important to include the latest achievement in the field of chimeric antigen receptor (CAR) T cell therapy. Despite the introduction of many novel therapeutic strategies, multiple myeloma remains incurable and requires continued intervention for disease control. However, a promising recent development is the design of an engineered T-cell product, Descartes-08, that transiently modifies a purified population of autologous CD8 + T-cells with anti-B cell maturation antigen (BCMA) CAR mRNA, as reported by Lin et al. [78]. Descartes-08 is engineered by mRNA transfection to express anti-BCMA CAR for a defined length of time. The mRNA is synthesized by in vitro transcription from a linearized DNA plasmid [78]. The development of this virus-free CAR-T cell technology has recently led to the initiation of the first clinical trial [79].

7. Conclusions

Gene expression profiling studies provide important information regarding the biology of multiple myeloma and may serve as a tool to predict outcomes and guide therapy. In the era of personalized medicine, the future lies in enabling therapy to be chosen based on the presence of specific mutations and gene expression profiles. However, the complexity of the MM genome and transcriptome still requires further investigation.

Author Contributions

A.P. wrote the paper. A.P., P.R., T.R. and D.M. examined the available data, reviewed, and revised the manuscript and provided their approval of the final version of the manuscript. All authors agree to be accountable for all aspects of the work. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a grant from the Polish Ministry of Science and Higher Education (MNiSW 0085/DIA/2018/47).

Acknowledgments

We thank Edward Lowczowski from the Medical University of Lodz for editorial assistance.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; interpretation of data, writing of the manuscript, or in the decision to publish the review.

Abbreviations

BCMAB cell maturation antigen
CGHComparative genomic hybridization
CANCopy number abnormality
CARChimeric antigen receptor
CICentrosome Index
CNVCopy number variations
CTACancer testis antigen
DEGsDifferentially expressed genes
DEMsDifferentially expressed miRNAs
FISHFluorescent in situ hybridization
GEPGene expression profiling
HRHigh risk
HMCLsHuman myeloma cell lines
HYHyperdiploid cluster
IgHImmunoglobulin heavy chain
LBLow bone disease
miRNAsMicroRNAs
M3CNMolecular causal network
MGUSMonoclonal gammopathy of unknown significance
MMMultiple myeloma
NGSNext-generation sequencing
NF-kBNuclear factor kappa light chain-enhancer
PAGProliferation-associated genes
PCsPlasma cells
PCLPlasma cell leukemia
PCR Polymerase chain reaction
PIProteasome inhibitor
qRT-PCRQuantitative real-time polymerase chain reaction
R-ISSRevised International Staging System
RNASeqRNA sequencing
RT-qPCRReverse transcriptase quantitative PCR
SAGESerial analysis of gene expression
SNPSingle nucleotide polymorphism
TWASTranscriptome-wide association study

References

  1. Morgan, G.J.; Walker, B.A.; Davies, F.E. The genetic architecture of multiple myeloma. Nat. Rev. Cancer 2012, 12, 335–348. [Google Scholar] [CrossRef] [PubMed]
  2. Corral, L.L.; Gutierrez, N.; Vidriales, M.-B.; Mateos, M.V.; Rasillo, A.; Garcia-Sanz, R.; Paiva, B.D.L.; Miguel, J.S. The Progression from MGUS to Smoldering Myeloma and Eventually to Multiple Myeloma Involves a Clonal Expansion of Genetically Abnormal Plasma Cells. Clin. Cancer Res. 2011, 17, 1692–1700. [Google Scholar] [CrossRef] [Green Version]
  3. Rajkumar, S.V.; Dimopoulos, M.A.; Palumbo, A.; Blade, J.; Merlini, G.; Mateos, M.-V.; Kumar, S.; Hillengass, J.; Kastritis, E.; Richardson, P.; et al. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014, 15, e538–e548. [Google Scholar] [CrossRef]
  4. Palumbo, A.; Anderson, K. Multiple Myeloma. N. Engl. J. Med. 2011, 364, 1046–1060. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2020. CA Cancer J. Clin. 2020, 70, 7–30. [Google Scholar] [CrossRef] [PubMed]
  6. Cardona-Benavides, I.J.; de Ramón, C.; Gutiérrez, N.C. Genetic Abnormalities in Multiple Myeloma: Prognostic and Therapeutic Implications. Cells 2021, 10, 336. [Google Scholar] [CrossRef]
  7. Walker, B.A.; Mavrommatis, K.; Wardell, C.P.; Ashby, C.; Bauer, M.; Davies, F.; Rosenthal, A.; Wang, H.; Qu, P.; Hoering, A.; et al. Identification of novel mutational drivers reveals oncogene dependencies in multiple myeloma. Blood 2018, 132, 587–597. [Google Scholar] [CrossRef] [PubMed]
  8. Maura, F.; Bolli, N.; Angelopoulos, N.; Dawson, K.J.; Leongamornlert, D.; Martincorena, I.; Mitchell, T.J.; Fullam, A.; Gonzalez, S.; Szalat, R.; et al. Genomic landscape and chronological reconstruction of driver events in multiple myeloma. Nat. Commun. 2019, 10, 3835. [Google Scholar] [CrossRef] [Green Version]
  9. Ross, F.M.; Avet-Loiseau, H.; Ameye, G.; Gutiérrez, N.C.; Liebisch, P.; O’Connor, S.; Dalva, K.; Fabris, S.; Testi, A.M.; Jarosova, M.; et al. Report from the European Myeloma Network on interphase FISH in multiple myeloma and related disorders. Haematologica 2012, 97, 1272–1277. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Lawrence, M.S.; Stojanov, P.; Mermel, C.; Robinson, J.T.; Garraway, L.A.; Golub, T.R.; Meyerson, M.; Gabriel, S.B.; Lander, E.S.; Getz, G. Discovery and saturation analysis of cancer genes across 21 tumour types. Nat. Cell Biol. 2014, 505, 495–501. [Google Scholar] [CrossRef] [Green Version]
  11. Lohr, J.G.; Stojanov, P.; Carter, S.L.; Cruz-Gordillo, P.; Lawrence, M.S.; Auclair, D.; Sougnez, C.; Knoechel, B.; Gould, J.; Saksena, G.; et al. Widespread Genetic Heterogeneity in Multiple Myeloma: Implications for Targeted Therapy. Cancer Cell 2014, 25, 91–101. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Bolli, N.; Avet-Loiseau, H.; Wedge, D.; Van Loo, P.; Alexandrov, L.; Martincorena, I.; Dawson, K.J.; Iorio, F.; Nik-Zainal, S.; Bignell, G.R.; et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat. Commun. 2014, 5, 2997. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Walker, B.; Boyle, E.M.; Wardell, C.; Murison, A.; Begum, D.B.; Dahir, N.M.; Proszek, P.Z.; Johnson, D.C.; Kaiser, M.F.; Melchor, L.; et al. Mutational Spectrum, Copy Number Changes, and Outcome: Results of a Sequencing Study of Patients With Newly Diagnosed Myeloma. J. Clin. Oncol. 2015, 33, 3911–3920. [Google Scholar] [CrossRef] [PubMed]
  14. Hoang, P.H.; Dobbins, S.E.; Cornish, A.J.; Chubb, D.; Law, P.; Kaiser, M.; Houlston, R.S. Whole-genome sequencing of multiple myeloma reveals oncogenic pathways are targeted somatically through multiple mechanisms. Leukemia 2018, 32, 2459–2470. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Adams, M.D.; Kelley, J.M.; Gocayne, J.D.; Dubnick, M.; Polymeropoulos, M.H.; Xiao, H.; Merril, C.R.; Wu, A.; Olde, B.; Moreno, R.F.; et al. Complementary DNA Sequencing: Expressed Sequence Tags and Human Genome Project. Science 1991, 252, 1651–1656. [Google Scholar] [CrossRef] [Green Version]
  16. Marra, M.A.; Hillier, L.; Waterston, R.H. Expressed sequence tags—ESTablishing bridges between genomes. Trends Genet. 1998, 14, 4–7. [Google Scholar] [CrossRef]
  17. Alwine, J.C.; Kemp, D.J.; Stark, G.R. Method for detection of specific RNAs in agarose gels by transfer to diazobenzyloxymethyl-paper and hybridization with DNA probes. Proc. Natl. Acad. Sci. USA 1977, 74, 5350–5354. [Google Scholar] [CrossRef] [Green Version]
  18. Becker-André, M.; Hahlbrock, K. Absolute mRNA quantification using the polymerase chain reaction (PCR). A novel approach by aPCR aidedtranscipttitration assay (PATTY). Nucleic Acids Res. 1989, 17, 9437–9446. [Google Scholar] [CrossRef]
  19. Velculescu, V.E.; Zhang, L.; Vogelstein, B.; Kinzler, K.W. Serial Analysis of Gene Expression. Science 1995, 270, 484–487. [Google Scholar] [CrossRef] [Green Version]
  20. Lockhart, D.J.; Dong, H.; Byrne, M.C.; Follettie, M.T.; Gallo, M.V.; Chee, M.S.; Mittmann, M.; Wang, C.; Kobayashi, M.; Norton, H.; et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat. Biotechnol. 1996, 14, 1675–1680. [Google Scholar] [CrossRef]
  21. Matsumura, H.; Ito, A.; Saitoh, H.; Winter, P.; Kahl, G.; Reuter, M.; Krüger, D.H.; Terauchi, R. SuperSAGE. Cell. Microbiol. 2004, 7, 11–18. [Google Scholar] [CrossRef]
  22. Gresham, D.; Dunham, M.J.; Botstein, D. Comparing whole genomes using DNA microarrays. Nat. Rev. Genet. 2008, 9, 291–302. [Google Scholar] [CrossRef]
  23. Stark, R.; Grzelak, M.; Hadfield, J. RNA sequencing: The teenage years. Nat. Rev. Genet. 2019, 20, 631–656. [Google Scholar] [CrossRef] [PubMed]
  24. Van Den Berge, K.; Hembach, K.M.; Soneson, C.; Tiberi, S.; Clement, L.; Love, M.I.; Patro, R.; Robinson, M.D. Annual review of biomedical data science RNA sequencing data: Hitchhiker’s guide to expression analysis introduc-tion: Overview of the RNA sequencing assay. Annu. Rev. Biomed. Data Sci. 2019, 2, 139–173. [Google Scholar] [CrossRef] [Green Version]
  25. Braggio, E.; Egan, J.B.; Fonseca, R.; Stewart, A.K. Lessons from next-generation sequencing analysis in hematological malignancies. Blood Cancer J. 2013, 3, e127. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Du Pont, S.R.; Cleynen, A.; Fontan, C.; Attal, M.; Munshi, N.; Corre, J.; Avet-Loiseau, H. Genomics of Multiple Myeloma. J. Clin. Oncol. 2017, 35, 963–967. [Google Scholar] [CrossRef] [PubMed]
  27. Manier, S.; Salem, K.; Glavey, S.V.; Roccaro, A.M.; Ghobrial, I.M. Genomic Aberrations in Multiple Myeloma. Cancer Treat. Res. 2016, 169, 23–34. [Google Scholar] [CrossRef]
  28. Nishida, K.; Tamura, A.; Nakazawa, N.; Ueda, Y.; Abe, T.; Matsuda, F.; Kashima, K.; Taniwaki, M. The Ig Heavy chain gene is frequently involved in chromosomal translocations in multiple myeloma and plasma cell leu-kemia as detected by in situ hybridization. Blood 1997, 90, 526–534. [Google Scholar] [CrossRef] [PubMed]
  29. Chesi, M.; Bergsagel, P.L. Advances in the pathogenesis and diagnosis of multiple myeloma. Int. J. Lab. Hematol. 2015, 37, 108–114. [Google Scholar] [CrossRef]
  30. Bergsagel, P.L.; Kuehl, W.M.; Zhan, F.; Sawyer, J.; Barlogie, B.; Shaughnessy, J.J. Cyclin D dysregulation: An early and unifying pathogenic event in multiple myeloma. Blood 2005, 106, 296–303. [Google Scholar] [CrossRef] [Green Version]
  31. Broyl, A.; Hose, D.; Lokhorst, H.; de Knegt, Y.; Peeters, J.; Jauch, A.; Bertsch, U.; Buijs, A.; Stevens-Kroef, M.; Beverloo, H.B.; et al. Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients. Blood 2010, 116, 2543–2553. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Zhan, F.; Huang, Y.; Colla, S.; Stewart, J.P.; Hanamura, I.; Gupta, S.; Epstein, J.; Yaccoby, S.; Sawyer, J.; Burington, B.; et al. The molecular classification of multiple myeloma. Blood 2006, 108, 2020–2028. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Decaux, O.; Lodé, L.; Magrangeas, F.; Charbonnel, C.; Gouraud, W.; Jézéquel, P.; Attal, M.; Harousseau, J.-L.; Moreau, P.; Bataille, R.; et al. Prediction of Survival in Multiple Myeloma Based on Gene Expression Profiles Reveals Cell Cycle and Chromosomal Instability Signatures in High-Risk Patients and Hyperdiploid Signatures in Low-Risk Patients: A Study of the Intergroupe Francophone du Myélome. J. Clin. Oncol. 2008, 26, 4798–4805. [Google Scholar] [CrossRef] [PubMed]
  34. Rojas, E.A.; Corchete, L.A.; Mateos, M.V.; García-Sanz, R.; Misiewicz-Krzeminska, I.; Gutiérrez, N.C. Transcriptome analysis reveals significant differences between primary plasma cell leukemia and multiple myeloma even when sharing a similar genetic background. Blood Cancer J. 2019, 9, 1–13. [Google Scholar] [CrossRef] [PubMed]
  35. Liu, Y.; Yu, H.; Yoo, S.; Lee, E.; Laganà, A.; Parekh, S.; Schadt, E.E.; Wang, L.; Zhu, J. A Network Analysis of Multiple Myeloma Related Gene Signatures. Cancers 2019, 11, 1452. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. De Boussac, H.; Bruyer, A.; Jourdan, M.; Maes, A.; Robert, N.; Gourzones, C.; Vincent, L.; Seckinger, A.; Cartron, G.; Hose, D.; et al. Kinome expression profiling to target new therapeutic avenues in multiple myeloma. Haematologica 2019, 105, 784–795. [Google Scholar] [CrossRef]
  37. Zhu, F.; Wang, X.; Zeng, H.; Yin, Z.; Ye, Z. A predicted risk score based on the expression of 16 autophagy-related genes for multiple myeloma survival. Oncol. Lett. 2019, 18, 5310–5324. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Bai, H.; Chen, B. A 5-Gene Stemness Score for Rapid Determination of Risk in Multiple Myeloma. OncoTargets Ther. 2020, ume 13, 4339–4348. [Google Scholar] [CrossRef]
  39. Zhou, Y.; Zhang, Q.; Stephens, O.; Heuck, C.J.; Tian, E.; Sawyer, J.R.; Cartron-Mizeracki, M.-A.; Qu, P.; Keller, J.; Epstein, J.; et al. Prediction of cytogenetic abnormalities with gene expression profiles. Blood 2012, 119, e148–e150. [Google Scholar] [CrossRef] [Green Version]
  40. Shaughnessy, J.D.; Zhan, F.; Burington, B.E.; Huang, Y.; Colla, S.; Hanamura, I.; Stewart, J.P.; Kordsmeier, B.; Randolph, C.; Williams, D.R.; et al. A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. Blood 2007, 109, 2276–2284. [Google Scholar] [CrossRef] [Green Version]
  41. Dickens, N.; Walker, B.A.; Leone, P.E.; Johnson, D.C.; Brito, J.L.; Zeisig, A.; Jenner, M.; Boyd, K.; Gonzalez, D.; Gregory, W.M.; et al. Homozygous Deletion Mapping in Myeloma Samples Identifies Genes and an Expression Signature Relevant to Pathogenesis and Outcome. Clin. Cancer Res. 2010, 16, 1856–1864. [Google Scholar] [CrossRef] [Green Version]
  42. Botta, C.; DI Martino, M.T.; Ciliberto, D.; Cucè, M.; Correale, P.; Rossi, M.; Tagliaferri, P.; Tassone, P. A gene expression inflammatory signature specifically predicts multiple myeloma evolution and patients survival. Blood Cancer J. 2016, 6, e511. [Google Scholar] [CrossRef] [PubMed]
  43. Miura, K.; Lida, S.; Hanamura, I.; Kato, M.; Banno, S.; Ishida, T.; Kusumoto, S.; Takeuchi, G.; Miwa, H.; Nitta, M.; et al. Frequent occurrence of CCND1 deregulation in patients with early stages of plasma cell dyscrasia. Cancer Sci. 2003, 94, 350–354. [Google Scholar] [CrossRef] [PubMed]
  44. Bergsagel, P.L.; Kuehl, W.M. Molecular Pathogenesis and a Consequent Classification of Multiple Myeloma. J. Clin. Oncol. 2005, 23, 6333–6338. [Google Scholar] [CrossRef] [PubMed]
  45. Waheed, S.; Mitchell, A.; Usmani, S.; Epstein, J.; Yaccoby, S.; Nair, B.; Van Hemert, R.; Angtuaco, E.; Brown, T.; Bartel, T.; et al. Standard and novel imaging methods for multiple myeloma: Correlates with prognostic laboratory variables including gene expression profiling data. Haematologica 2012, 98, 71–78. [Google Scholar] [CrossRef]
  46. Szalat, R.; Avet-Loiseau, H.; Munshi, N.C. Gene Expression Profiles in Myeloma: Ready for the Real World? Clin. Cancer Res. 2016, 22, 5434–5442. [Google Scholar] [CrossRef] [Green Version]
  47. Li, C.; Wendlandt, E.; Darbro, B.; Xu, H.; Thomas, G.; Tricot, G.; Chen, F.; Shaughnessy, J.; Zhan, F. Genetic Analysis of Multiple Myeloma Identifies Cytogenetic Alterations Implicated in Disease Complexity and Progression. Cancers 2021, 13, 517. [Google Scholar] [CrossRef]
  48. Fonseca, R.; Van Wier, S.A.; Chng, W.J.; Ketterling, R.; Lacy, M.Q.; Dispenzieri, A.; Bergsagel, P.L.; Rajkumar, S.V.; Greipp, P.R.; Litzow, M.R.; et al. Prognostic value of chromosome 1q21 gain by fluorescent in situ hybridization and increase CKS1B expression in myeloma. Leukemia 2006, 20, 2034–2040. [Google Scholar] [CrossRef] [Green Version]
  49. Avet-Loiseau, H.; Li, C.; Magrangeas, F.; Gouraud, W.; Charbonnel, C.; Harousseau, J.-L.; Attal, M.; Marit, G.; Mathiot, C.; Facon, T.; et al. Prognostic Significance of Copy-Number Alterations in Multiple Myeloma. J. Clin. Oncol. 2009, 27, 4585–4590. [Google Scholar] [CrossRef] [Green Version]
  50. Shah, V.; Csg, O.B.O.T.N.H.-O.; Sherborne, A.L.; Walker, B.A.; Johnson, D.C.; Boyle, E.M.; Ellis, S.; Begum, D.B.; Proszek, P.Z.; Jones, J.R.; et al. Prediction of outcome in newly diagnosed myeloma: A meta-analysis of the molecular profiles of 1905 trial patients. Leukemia 2018, 32, 102–110. [Google Scholar] [CrossRef] [Green Version]
  51. Shah, G.L.; Landau, H.; Londono, D.; Devlin, S.M.; Kosuri, S.; Lesokhin, A.M.; Lendvai, N.; Hassoun, H.; Chung, D.J.; Koehne, G.; et al. Gain of chromosome 1q portends worse prognosis in multiple myeloma despite novel agent-based induction regimens and autologous transplantation. Leuk. Lymphoma 2017, 58, 1823–1831. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Giri, S.; Huntington, S.F.; Wang, R.; Zeidan, A.M.; Podoltsev, N.A.; Gore, S.; Ma, X.; Gross, C.P.; Davidoff, A.J.; Neparidze, N. Chromosome 1 abnormalities and clinical outcomes in multiple myeloma in the era of novel agents. J. Clin. Oncol. 2019, 37, 8044. [Google Scholar] [CrossRef]
  53. Abdallah, N.; Greipp, P.; Kapoor, P.; Gertz, M.A.; Dispenzieri, A.; Baughn, L.B.; Lacy, M.Q.; Hayman, S.R.; Buadi, F.K.; Dingli, D.; et al. Clinical characteristics and treatment outcomes of newly diagnosed multiple myeloma with chromosome 1q abnormalities. Blood Adv. 2020, 4, 3509–3519. [Google Scholar] [CrossRef] [PubMed]
  54. Manasanch, E.E.; Berrios, D.; Fountain, E.; Claussen, C.M.; Chuang, T.; Kaufman, G.; Amini, B.; Bashir, Q.; Nieto, Y.; Qazilbash, M.; et al. Gene expression profiling predicts relapse-free and overall survival in newly diagnosed myeloma patients treated with novel therapies. Br. J. Haematol. 2021, 192, e115–e120. [Google Scholar] [CrossRef] [PubMed]
  55. Went, M.; Kinnersley, B.; Sud, A.; Johnson, D.C.; Weinhold, N.; Försti, A.; Van Duin, M.; Orlando, G.; Mitchell, J.S.; Kuiper, R.; et al. Transcriptome-wide association study of multiple myeloma identifies candidate susceptibility genes. Hum. Genom. 2019, 13, 1–8. [Google Scholar] [CrossRef]
  56. Went, M.; The PRACTICAL Consortium; Sud, A.; Försti, A.; Halvarsson, B.-M.; Weinhold, N.; Kimber, S.; Van Duin, M.; Thorleifsson, G.; Holroyd, A.; et al. Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma. Nat. Commun. 2018, 9, 3707. [Google Scholar] [CrossRef]
  57. Mitchell, J.S.; Li, N.; Weinhold, N.; Försti, A.; Ali, M.; Van Duin, M.; Thorleifsson, G.; Johnson, D.C.; Chen, B.; Halvarsson, B.-M.; et al. Genome-wide association study identifies multiple susceptibility loci for multiple myeloma. Nat. Commun. 2016, 7, 12050. [Google Scholar] [CrossRef]
  58. Chubb, D.; Weinhold, N.; Broderick, P.; Chen, B.; Johnson, D.C.; Försti, A.; Vijayakrishnan, J.; Migliorini, G.; Dobbins, S.E.; Holroyd, A.; et al. Common variation at 3q26.2, 6p21.33, 17p11.2 and 22q13.1 influences multiple myeloma risk. Nat. Genet. 2013, 45, 1221–1225. [Google Scholar] [CrossRef]
  59. Swaminathan, B.; Thorleifsson, G.; Jöud, M.; Ali, M.; Johnsson, E.; Ajore, R.; Sulem, P.; Halvarsson, B.-M.; Eyjolfsson, G.; Haraldsdottir, V.; et al. Variants in ELL2 influencing immunoglobulin levels associate with multiple myeloma. Nat. Commun. 2015, 6, 7213. [Google Scholar] [CrossRef] [Green Version]
  60. Broderick, P.; Chubb, D.; Johnson, D.C.; Weinhold, N.; Försti, A.; Lloyd, A.; Olver, B.; Ma, Y.P.; Dobbins, S.E.; Walker, B.; et al. Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk. Nat. Genet. 2011, 44, 58–61. [Google Scholar] [CrossRef] [Green Version]
  61. Giaccherini, M.; Macauda, A.; Orciuolo, E.; Rymko, M.; Gruenpeter, K.; Dumontet, C.; Raźny, M.; Moreno, V.; Buda, G.; Beider, K.; et al. Genetically determined telomere length and multiple myeloma risk and outcome. Blood Cancer J. 2021, 11, 1–10. [Google Scholar] [CrossRef]
  62. Pertesi, M.; Vallée, M.; Wei, X.; Revuelta, M.V.; Galia, P.; Demangel, D.; Oliver, J.; Foll, M.; Chen, S.; Perrial, E.; et al. Exome sequencing identifies germline variants in DIS3 in familial multiple myeloma. Leukemia 2019, 33, 2324–2330. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Palumbo, A.; Avet-Loiseau, H.; Oliva, S.; Lokhorst, H.M.; Goldschmidt, H.; Rosinol, L.; Richardson, P.; Caltagirone, S.; Lahuerta, J.J.; Facon, T.; et al. Revised International Stag-ing System for Multiple Myeloma: A Report from International Myeloma Working Group. J. Clin. Oncol. 2015, 33, 2863–2869. [Google Scholar] [CrossRef]
  64. Durie, B.G.; Salmon, S.E. A Clinical Staging System for Multiple Myeloma. Correlation of Measured Mye-loma Cell Mass with Presenting Clinical Features, Response to Treatment, and Survival. Cancer 1975, 36, 842–854. [Google Scholar] [CrossRef]
  65. Kuiper, R.; Broyl, A.; De Knegt, Y.; Van Vliet, M.H.; Van Beers, E.H.; Van Der Holt, B.; El Jarari, L.; Mulligan, G.; Gregory, W.; Morgan, G.; et al. A gene expression signature for high-risk multiple myeloma. Leukemia 2012, 26, 2406–2413. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Hose, D.; Rème, T.; Hielscher, T.; Moreaux, J.; Messner, T.; Seckinger, A.; Benner, A.; Shaughnessy, J.D.; Barlogie, B.; Zhou, Y.; et al. Proliferation is a central independent prognostic factor and target for personalized and risk-adapted treatment in multiple myeloma. Haematologica 2011, 96, 87–95. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Katiyar, A.; Kaur, G.; Rani, L.; Jena, L.; Singh, H.; Kumar, L.; Sharma, A.; Kaur, P.; Gupta, R. Genome-wide identification of potential biomarkers in multiple myeloma using meta-analysis of mRNA and miRNA expression data. Sci. Rep. 2021, 11, 1–15. [Google Scholar] [CrossRef]
  68. Kuiper, R.; van Duin, M.; van Vliet, M.H.; Broijl, A.; van der Holt, B.; el Jarari, L.; van Beers, E.H.; Mulligan, G.; Avet-Loiseau, H.; Gregory, W.M.; et al. Prediction of high- and low-risk multiple myeloma based on gene expression and the International Staging System. Blood 2015, 126, 1996–2004. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Shaughnessy, J.D.; Qu, P.; Usmani, S.; Heuck, C.J.; Zhang, Q.; Zhou, Y.; Tian, E.; Hanamura, I.; Van Rhee, F.; Anaissie, E.; et al. Pharmacogenomics of bortezomib test-dosing identifies hyperexpression of proteasome genes, especially PSMD4, as novel high-risk feature in myeloma treated with Total Therapy 3. Blood 2011, 118, 3512–3524. [Google Scholar] [CrossRef]
  70. Rème, T.; Hose, D.; Theillet, C.; Klein, B. Modeling risk stratification in human cancer. Bioinformatics 2013, 29, 1149–1157. [Google Scholar] [CrossRef] [Green Version]
  71. Chng, W.J.; Kumar, S.; VanWier, S.; Ahmann, G.; Price-Troska, T.; Henderson, K.; Chung, T.-H.; Kim, S.; Mulligan, G.; Bryant, B.; et al. Molecular Dissection of Hyperdiploid Multiple Myeloma by Gene Expression Profiling. Cancer Res. 2007, 67, 2982–2989. [Google Scholar] [CrossRef] [Green Version]
  72. Chng, W.J.; Braggio, E.; Mulligan, G.; Bryant, B.; Remstein, E.; Valdez, R.; Dogan, A.; Fonseca, R. The centrosome index is a powerful prognostic marker in myeloma and identifies a cohort of patients that might benefit from aurora kinase inhibition. Blood 2008, 111, 1603–1609. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Robak, P.; Drozdz, I.; Szemraj, J.; Robak, T. Drug resistance in multiple myeloma. Cancer Treat. Rev. 2018, 70, 199–208. [Google Scholar] [CrossRef] [PubMed]
  74. Mitra, A.; Harding, T.; Mukherjee, U.K.; Jang, J.S.; Li, Y.; Hongzheng, R.; Jen, J.; Sonneveld, P.; Kumar, S.; Kuehl, W.M.; et al. A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma. Blood Cancer J. 2017, 7, e581. [Google Scholar] [CrossRef] [PubMed]
  75. Besse, A.; Stolze, S.C.; Rasche, L.; Weinhold, N.; Morgan, G.; Kraus, M.; Bader, J.; Overkleeft, H.S.; Besse, L.; Driessen, C. Carfilzomib resistance due to ABCB1/MDR1 overexpression is overcome by nelfinavir and lopinavir in multiple myeloma. Leukemia 2018, 32, 391–401. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Tang, J.; Chen, Q.; Li, Q.; He, Y.; Xiao, D. Exosomal mRNAs and lncRNAs involved in multiple myeloma resistance to bortezomib. Cell Biol. Int. 2021, 45, 965–975. [Google Scholar] [CrossRef]
  77. Robak, P.; Jarych, D.; Mikulski, D.; Dróżdż, I.; Węgłowska, E.; Kotkowska, A.; Misiewicz, M.; Smolewski, P.; Stawiski, K.; Fendler, W.; et al. The Prognostic Value of Whole-Blood PSMB5, CXCR4, POMP, and RPL5 mRNA Expression in Patients with Multiple Myeloma Treated with Bortezomib. Cancers 2021, 13, 951. [Google Scholar] [CrossRef] [PubMed]
  78. Lin, L.; Cho, S.-F.; Xing, L.; Wen, K.; Li, Y.; Yu, T.; Hsieh, P.A.; Chen, H.; Kurtoglu, M.; Zhang, Y.; et al. Preclinical evaluation of CD8+ anti-BCMA mRNA CAR T cells for treatment of multiple myeloma. Leukemia 2021, 35, 752–763. [Google Scholar] [CrossRef]
  79. Prommersberger, S.; Reiser, M.; Beckmann, J.; Danhof, S.; Amberger, M.; Quade-Lyssy, P.; Einsele, H.; Hudecek, M.; Bonig, H.; Ivics, Z. CARAMBA: A first-in-human clinical trial with SLAMF7 CAR-T cells prepared by virus-free Sleeping Beauty gene transfer to treat multiple myeloma. Gene Ther. 2021, 28, 560–571. [Google Scholar] [CrossRef]
Table
Table 1. The identification of 11 molecular subgroups of incorrectly expressed genes using gene expression profiling.
Table 1. The identification of 11 molecular subgroups of incorrectly expressed genes using gene expression profiling.
PrognosisSubgroupCytogeneticsCyclin D ExpressionUpregulated GenesDownregulated GenesFrequency
Low riskCD1t(11;14)CCND1INHBE
ETV1
MACROD2		CD9
NOTCH2NL		4–9%
CD2t(11;14)

t(6;14)		CCND1
CCND3		cd79a
cd20		 CCND2 11–17%
LB-CCND1
CCND2		EDN1
IL6R
SMAD1		DKK1
STAT1
STAT2		12–17%
HYHDCCND1TRAIL
DKK1
CCR5		 CCND2
CD52
TAGLN2
CKS1B
OPN3		26–32%
NF-κBHDCCND1
CCND2		CD40
BCL10
IL8		TRAF3
CCR2
MAT2A		11%
PRL3HDCCND2SOX3
PTP4A3
PTPRZ1		CD44
DUSP6		2–3%
Myeloid-CCND1
CCND2		CD163
CA1
LIZ		PRMT1
DUSP5
SMAD7		12%
High riskMFt(14;16)
t(14;20)		CCND2IL6R
c-MAF
MAFB		DKK1
CCND1 		6–10%
MSt(4;14)
1q gain		CCND2MMSET
FGFR3
PBX1		 CCND1
DUSP2
SYK PAX5		15–17%
PR1q gainCCND2
CCND1		CCNB1
MCM2
CDC2
BIRC5
CCNB2
AURKA		CXCR4
CD27		11%
CTA1q gainCCND1
CCND2		Cancer testis antigen
AURKA		MALAT17%
Table
Table 2. Summary of differences between gene expression classifiers in MM.
Table 2. Summary of differences between gene expression classifiers in MM.
Classifiers NameNo. of Genes InvolvedGene Names InvolvedNo. of Patients Tested in Validation in the Original PublicationNo. of Risk-Groups IdentifiedReported OutcomeRef.
UAMS7070FABP5, PDHA1, TRIP13, AIM2, SELI, SLCI19A1, LARS2, OPN3, ASPM, CCT2, UBE2I, STK6, FLJ13052, LAS1L, BIRC5, RFC4, CKS1B, CKAP1, MGC57827, DKFZp779O175, PFN1, ILF3, IFI16, TBRG4, PAPD1, EIF2C2, MGC4308, ENO1, DSG2, C6orf173, EXOSC4, TAGLN2, RUVBL1, ALDOA, CPSF3, NA, MGC15606, LGALS1, RAD18, SNX5, PSMD4, RAN, KIF14, CBX3, TMPO, DKFZP586L0724, WEE1, ROBO1, TCOF1, YWHAZ, MPHOSPH1, GNG10, NA, PNPLA4, NA, KIAA1754, AHCYL1, MCLC, EVI5, AD-020, NA, PARG1, CTBS, UBE2R2, FUCA1, RFP2, FLJ20489, NA, LTBP1, TRIM334127Low-risk groups
3-year EFS:
84% in LB, 72% HY, 82% in CD1, and 86% in CD2
3-year OS:
81% in CD1, 84% in HY, 87% in LB, and 88% in CD2		[32]
High risk groups
3-year EFS:
44% in PR, 39% in MS and 50% in MF
3-year OS:
55% in PR, 69% in MS, 71% in MF
UAMS1717KIF14, SLC19A1, CKS1B, YWHAZ, MPHOSPH1, TMPO, NADK, LARS2, TBRG4, AIM2, 242488_at, ASPM, AHCYL1, CTBS, MCLC, LTBP1, 1557277_a_at1812High-risk vs. low-risk
5-year EFS: 18% vs. 60%, p < 0.001; HR = 4.51
5-year OS:
28% vs. 78%, p < 0.001; HR = 5.16		[40]
UAMS8080COX6C, NOLA1, COPS5, SOD1, TUBA6, HNRPC, PSMB2, PSMC4, LOC400657, C1orf31, FUNDC1, SUMO1, PSMB4, PSMB3, ENSA, PSMB4, COMMD8, MRPL47, PSMC5, HNRPC, PSMA4, PSMD4, NMT1, PSMB7, NXT2, SLC25A14, PSMD4, PSMD2, SNRPD1, PSMD4, CHORDC1, PSMD14, LAP3, PSMA7, UBPH, BIRC5, STAU2, ALDOA, TMC8, C1orf128, FLNA, HIST1H3B, FOSB, LOC644250, C17orf60, LZTR2, PDE4B, STAU2, PDE4B, GABARAPL1, TAGAP, LOC643318, CISH, NR4A1, MGC61598, ANKRD37, KIAA1394, ACVR1C, TBC1D9, CRYGS, PDE4B, CISH, ZNF710, RBM33, STX11, KIAA1754, CISH, RPL41, WIRE, LAPTM4A, KLHL7, C9orf130, C14orf100, 1561060_at, 229388_at, 239343_at, 236986_at, 227524_at, 239082_at, 226399_at1282Low-risk vs. high-risk
2-year OS: 92% vs. 60%
2-year PFS: 87% vs. 53% 		[69]
IFM1515CNDP2, STMN1, AFG3L2, STK38, PARP1, CPSF6, LOC151162, TOX2, FRY, FLJ21438, MGST1, ALDH2, CTSF, ATF4, FAM49A85323-year OS
Low-risk: 90.5% (95% CI, 85.6% to 95.3%)
High-risk: 47.4% (95% CI, 33.5% 60.1%)		[33]
MRCIX66BUB1B, HDAC3, CDC2, FIS1, RAD21, ITM2B8002Median OS: 13 vs. 45 months
Median PFS 11 vs. 22 months		[41]
HM1919BUB1B, HEATR2, CENPW, NUDT11, EHD4, EZH2, DLGAP5, HJURP, CDCA8, TMEM48, CDC42BPA, DEPDC1B, FAM83D, PGM2L1, NUSAP13453OS at 48 months (%): 92 vs. 72 vs. 20[70]
GPI5050ASPM, AURKA, AURKB, BIRC5, BRCA1, BUB1, BUB1B, CCNA2, CCNB1, CCNB2, CDC2, CDC20, CDC25C, CDC6, CDCA8, CDKN3, CEP55 (C10orf3), CHEK1, CKS1B, CKS2, DLG7, ESPL1, GINS1, GTSE1, KIAA1794, KIF11, KIF15, KIF20A, KIF2C, KNTC2, MAD2L1, MCM10, MCM6, MKI67, NCAPD3, NCAPG, NCAPG2, NEK2, NPM1, PCNA, PGAM1, PLK4, PTTG1, RACGAP1, SMC2, SPAG5, STIL, TPX2, UBEC2C, ZWINT3453EFS 12.7 vs. 26.2 vs. 40.6 months p < 0.001[66]
SKY92/EMC9292SLC30A7, AK2, SYF2, S100A6, NUF2, DARS2, ARPC5, DTL, ANGEL2, LBR, TARBP1, GGPS1,LTBP1, FAM49A, MCM6, ACVR2A, GRB14, ITGA6, DHRS9, STAT1, SPATS2L, BCS1L, SFMBT1, ARL8B, POLQ, MCM2, CCRL1, SEC62, GABRA4, PGM2, NCAPG, FGFR3, SEPT11, AIMP1, CENPE, IL7R 5, DHFR 5, SAR1B, PCDHB7, ATP6V0E1, MCM3, TUBB, TUBB, MARCKS, SLC17A5, NCUBE1, SUN1/GET4, DNAJB9, 208232_x_at, RAB2A, TRAM1, ZNF252, HNRNPK, MRPL41, ZWINT, 243018_at, FANCF, EHBP1L1, C11orf85, PPP2R1B, ROBO3, 238780_s_at, C1S, ESPL1, ITM2B, ZBTB25, NPC2, ATPBD4, C15orf38, FANCI, SMG1, DYNLRB2, TMEM97, SPAG5, TOP2A, BIRC5, C18orf10, TSPAN16, RPS28, RPS11, NOP56, FTL 19, CDH22, DONSON 21, PFKL, ST13, DUX4, RPS4X, KIF4A, HMGN5, HMGB3, MAGEA6757 in validation4HR 3.4 (95% CI:2.19–5.29, p = 5.7 × 10−8) in high-risk patients[68]
CTA28ASPM, BIRC5,
C4A, CDC2, CENPA, CST3, CTAG2, DLG7, FLJ110719, FLJ21841, GAGE5, GAGED2,
KIF20A, MAGEA1, MAGEA12, MAGEA3, MAGEA6, NEK2, RACGAP1, RRM2, SGK,
SSX1, SSX2, SSX3, SSXT/SSX4 fusion, TOP2A, TTK, TYMS.		53 patients overall2Median survival of 27 months in the high-risk cluster 1[71]
CI4CETN2, TUBG1, PCNT1 PCNT25392High CI vs. low CI
Median OS 30.6 vs. 45.6 months, log-rank p = 0.04
Median PFS 2.8 vs. 4.9 months, log−rank p = 0.02		[72]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Puła, A.; Robak, P.; Mikulski, D.; Robak, T. The Significance of mRNA in the Biology of Multiple Myeloma and Its Clinical Implications. Int. J. Mol. Sci. 2021, 22, 12070. https://doi.org/10.3390/ijms222112070

AMA Style

Puła A, Robak P, Mikulski D, Robak T. The Significance of mRNA in the Biology of Multiple Myeloma and Its Clinical Implications. International Journal of Molecular Sciences. 2021; 22(21):12070. https://doi.org/10.3390/ijms222112070

Chicago/Turabian Style

Puła, Anna, Paweł Robak, Damian Mikulski, and Tadeusz Robak. 2021. "The Significance of mRNA in the Biology of Multiple Myeloma and Its Clinical Implications" International Journal of Molecular Sciences 22, no. 21: 12070. https://doi.org/10.3390/ijms222112070

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop