1. Introduction and Etiology of Multiple Myeloma

2. Epidemiology of Multiple Myeloma

3. Diagnosis Multiple Myeloma

4. NGS

4.3. NGS Platforms and Techniques for MM

4.3.1. Introduction

Next-generation sequencing (NGS), refers to technology of the high-throughput sequencing which enables a large number of DNA templates(millions to billions) to be sequenced at the same time, and to generate a considerable amount of genetic information in a single run [13]. Two NGS platforms (Ion Torrent and Illumina) are to mention, including NGS library preparation, data analysis, and validation. Nowadays, the choice of NGS platform is one of the first decisions a laboratory must make. In terms of installation costs, chemistry, read lengths, sequencing capacities, instrument footprints, and sequencing time.

Figure 1. Timeline evolution of NGS from 2000 to 2022 [11,12].

Figure 1. Timeline evolution of NGS from 2000 to 2022 [11,12].

4.3.2. Illumina Sequencing Platform

The Illumina sequencing platform, also known as Illumina sequencing or Illumina technology, is a widely used next-generation sequencing (NGS) technology that has revolutionized genomic research and applications. It is based on the principle of sequencing-by-synthesis, where DNA fragments are amplified and sequenced in parallel.

The Illumina sequencing platform utilizes a massively parallel sequencing approach, allowing millions of DNA fragments to be sequenced simultaneously on a single run. The process involves several key steps:

Library Preparation: DNA samples are fragmented and adapters are added to the DNA fragments. These adapters contain sequences that allow the DNA fragments to bind to the surface of the flow cell.

Cluster Generation: The adapter-ligated DNA fragments are immobilized on a flow cell surface, with each fragment occupying a distinct location known as a cluster. Through bridge amplification, the DNA clusters are amplified to create clonal clusters, where each cluster contains many identical DNA fragments.

Sequencing: The sequencing process begins by using reversible terminators. Fluorescently labeled nucleotides are added one at a time, and the incorporation of each nucleotide is detected by imaging the emitted fluorescence. After detection, the fluorescent dye and blocking group are chemically removed, allowing the next nucleotide to be added.

Image Processing and Base Calling: The fluorescent images captured during sequencing are processed to generate raw data. The intensities of the fluorescence signals are translated into DNA base calls, resulting in a sequence of DNA fragments for each cluster.

Data Analysis: The generated sequences are then aligned to a reference genome or assembled de novo to obtain the complete DNA sequence. Various bioinformatics tools and software are used for data analysis, including read mapping, variant calling, and identification of structural variations [14].

The Illumina sequencing platform offers high throughput, generating large amounts of sequencing data with high accuracy and quality. It has been widely adopted in various fields of genomics research, including whole-genome sequencing, exome sequencing, transcriptome profiling, epigenetics, metagenomics, and many other applications. The platform’s flexibility, scalability, and cost-effectiveness have made it a popular choice for diverse genomic studies.

4.3.3. Ion Torrent sequencing platform

The Ion Torrent sequencing platform, developed by Thermo Fisher Scientific, is a next-generation sequencing (NGS) technology that utilizes semiconductor-based sequencing to determine DNA or RNA sequences. It is based on the principle of detecting hydrogen ions (protons) released during DNA synthesis, enabling the identification of nucleotides.

The Ion Torrent sequencing platform operates in the following steps:

Library Preparation: Similar to other NGS technologies, the first step involves preparing the DNA or RNA sample by fragmenting it and attaching adapter sequences. These adapters facilitate the attachment of the DNA or RNA fragments to beads or microparticles.

Template Preparation: The DNA or RNA fragments are amplified through emulsion polymerase chain reaction (PCR) or clonal amplification. Each bead or microparticle carries multiple copies of the same DNA or RNA fragment, resulting in clonal populations.

Sequencing: The prepared templates are loaded onto a semiconductor chip, with each bead or microparticle occupying a separate well or ion-sensitive field-effect transistor (ISFET). During sequencing, the addition of nucleotides triggers DNA synthesis. If the added nucleotide matches the template DNA or RNA strand, a hydrogen ion is released as a byproduct. The released ions generate a detectable change in pH, which is measured by the ISFET.

Data Acquisition and Analysis: The changes in pH are converted into electrical signals and interpreted as DNA sequences. The data is processed and analyzed using bioinformatics tools to align the reads to a reference genome, detect variants, and perform other downstream analyses [15].

The Ion Torrent sequencing platform offers advantages such as speed, simplicity, and scalability. It has been used in various applications, including targeted sequencing, whole-exome sequencing, microbial genomics, cancer research, and genetic disease studies. The technology is known for its relatively low instrument cost and flexibility, making it accessible for a wide range of research laboratories and applications.

5. Methods

6. Results and Discussion

6.2. Type of Samples

Among all the publications we studied, different types of samples were used in multiple myeloma diagnosis and it is clear that the commonly used sample was BM (44.55%). This can be explained by the high concentration of tumor plasma cells in the bone marrow.

The use of peripheral blood samples is also possible in studies with NGS because of the high sensitivity of this technique to evaluate tumor cells even at low quantities and this is what made 6.02% of our articles choose peripheral blood as the sample type for their studies.

Table 1. The different types of samples used in our studied articles.

Table 1. The different types of samples used in our studied articles.

Type of samples	Percentage of use
Bone Marrow (BM)	44,55%
Unsited	35,26%
Peripheral Blood (PB)	6,02%
BM and PB	5,46%
BM, PB and Plasma Samples	2,18%
Plasma Samples	2,18%
BM and Plasma Samples	1,09%
Serum Samples	1,09%
Plasmacytoid Dendritic cells (PDCs)	1,09%
BM and Biopsies	0,54%
PB, BM and Formalin Fixed Paraffin Embedded Samples (FFPE)	0,54%

6.3. Studies Focusing on the Study of WES in Myeloma Patients by the NGS

Whole-exome sequencing (WES) by next-generation sequencing (NGS) has been utilized in several ways to study MM: identification of somatic mutations, characterization of clonal architecture, prognostic biomarker discovery or drug target discovery.

Overall, WES by NGS is a powerful tool for studying MM and has the potential to help researchers better understand the disease and develop more effective treatments.

15.38% of the studies selected in our review are interested in the study of the exome “Whole Exome Sequencing” WES data. Those studies are reported in the table below and an analysis and discussion of the results of those studies is quoted directly after the table.

Table 2. Studies focusing on the study of Whole Exome Sequencing.

Table 2. Studies focusing on the study of Whole Exome Sequencing.

	Study	Year of the study	Country	Number of patients used in the study	Type of patients	Type of samples used
1	[30]	2022	Erbil Iraq	-	-	PB
2	[31]	2021	New delhi India	62	-	-
3	[32]	2021	AIIMS, New Delhi	71	NDMM	PCs
4	[33]	2021	Helsinki, Finland	8	-	BM
5	[34]	2021	New York, NY	1154	-	-
6	[35]	2020	Galway, Ireland	291	RRMM	BM
7	[36]	2020	Tampa, FL	196	-	-
8	[37]	2019	Turin, Italy	42	RRMM	-
9	[38]	2019	Milano, Italy	40	RRMM	-
10	[39]	2018	Arkansas USA	1141	-
11	[40]	2018	Wurzburg Germany	1	-	-
12	[41]	2018	Boston, MA	629, 1144, 205	MM, NDMM, NDMM	PB
13	[42]	2017	Boston MA	186	-	-
14	[43]	2017	Adelaide Australia	10	-	-
15	[44]	2017	New York USA	19	-	-
16	[45]	2017	Boston, MA	151	MM, SMM, MGUS	PB
17	[46]	2017	Liège, Belgium	10	-	BM
18	[47]	2016	Cambridge UK	14	-	-
19	[48]	2016	Southampton UK	25	-	-
20	[49]	2016	Boston, MA	63	NDMM, RRMM, SMM, MGUS	PB
21	[50]	2016	-	1000	NDMM	-
22	[51]	2016	Little Rock, AR	2161	-	-
23	[52]	2016	Little Rock, AR	33	-	-
24	[53]	2015	Boston, MA, USA	29	-	-
25	[54]	2015	Little Rock, AR	-	-	BM
26	[55]	2014	Cambridge UK	23	-	-
27	[56]	2012	London, United Kingdom	-	-	BM
28	[57]	2011	Boston, MA, USA	-	-	-

Some of these studies aim to investigate the genetic landscape of multiple myeloma, like the study carried out by Kakoo et al. which included 22 MM patients from Iranian population, the WES analysis revealed 6,959 somatic mutations across all patients and the most commonly mutated genes in MM patients were TP53, KRAS, NRAS, DIS3, and FAM46C.

This study recognized potential novel driver genes in MM, including ARID1A, RYR2, and ARID2 and also highlighted the potential clinical implications of the identified mutations, such as the association between TP53 mutations and worse overall survival in MM patients.

The authors also found that the mutational landscape of MM patients in the Iranian population was distinct from other populations, with some specific mutations being more prevalent in Iranian MM patients [58]. The study conducted by Munshi et al. identified recurrent mutations commonly seen in multiple myeloma cells. These recurring mutations included: the TP53, KRAS and NRAS genes which were found in up to 20% of multiple myeloma cases studied in this study, the DIS3 gene: which is involved in the degradation of messenger RNA and which has been identified in up to 13% of cases of multiple myeloma, the FAM46C which is involved in the regulation of protein synthesis and which has been mutated in up to 15% of cases, finally, the IRF4 gene, which plays a role in regulating immune responses, has been found to be mutated in approximately 20% of cases [57]. In the study conducted by Bolli et al., these genes were investigated as recurring mutations. KRAS and NRAS mutations were detected in 36% of patients, TP53 mutations were found in 28% of patients, which is a common mutation observed in various types of cancer, including MM. DIS3 mutations were identified in 20% of patients, FAM46C mutations were observed in 16% of patients, and TRAF3 mutations were found in 12% of patients. TRAF3 is a gene that has also been associated with the development of MM [47].

KRAS, NRAS, TP53, DIS3 and FAM46C are also the most frequently mutated genes in the study carried by Manier et al., this study which involved 45 patients with MM and sequenced their cfDNA samples using both WES and targeted deep sequencing panels. The research detected a total of 4714 somatic mutations in all patients, with an average of 104 mutations per patient [49]. In the study conducted by Miller et al., specific gene mutations were linked to the duration of progression-free survival (PFS) in multiple myeloma (MM) patients. KRAS and NRAS mutations were associated with longer PFS, while TP53 mutations were associated with shorter PFS. This particular study focused on analyzing exome sequencing data from 765 MM patients with the aim of investigating the relationship between the burden of somatic mutations, neoantigen load, and progression-free survival (PFS) in MM patients [50].

The study by Walker, Samur, et al. and that performed by Munshi et al. joins the set of previous studies in identifying TP53, KRAS and NRAS as genes frequently mutated in affected patients and which are associated with a poor prognosis and higher-risk molecular subtypes. The DIS3, FAM46C and CYLD genes also recurrently mutated and which are involved in the pathogenesis of MM [51], [59].

The study conducted by Zielinska et al. study developed a high-throughput pipeline for NGS analysis of multiple myeloma samples. The study found that the NextSeq 500 platform performed well for identifying known gene variants in multiple myeloma samples. The high-throughput pipeline developed in the study was also found to identify various gene variants in multiple myeloma samples, including mutations in the TP53, KRAS, NRAS, and BRAF genes [54].

Another study conducted by Bolli et al. sequenced the exomes of tumor cells from 50 MM patients and matched germline DNA from 44 of them. Additionally, they sequenced the IGH locus and performed copy number analysis on the tumor cells.

The study identified a range of mutations and copy number changes in the MM tumor cells, including mutations in genes such as KRAS, NRAS, TP53, and DIS3, as well as copy number gains or losses in chromosomes 1, 6, 8, 9, 11, 12, 13, 15, 16, 17, and 19. The study also detected IGH translocations in 35 out of the 50 MM patients.

The authors also compared their NGS results with those obtained using traditional techniques, for example fluorescence in situ hybridization (FISH) and Sanger sequencing. They found that NGS provided a more comprehensive analysis of genomic abnormalities and enabled the detection of mutations that would have been missed by other methods.

Overall, the study demonstrates the potential of NGS for identifying clinically relevant gene variants in MM and suggests that this approach could be used to guide personalized treatment strategies for MM patients [55].

So, these studies performed on myeloma patients highlight the importance of the KRAS, NRAS, BRAF, TP53, DIS3 and FAM46C as regularly mutated genes in the pathology of MM and suggest their potential as therapeutic targets or prognostic biomarkers, and in studies investigating smoldering multiple myeloma SMM, mutations in the TP53, BRAF, DIS3, and ATM genes, among others. provide high-risk abnormalities in these patients and can help identify patients who might benefit from early intervention as mentioned in the study by Bustoros et al. for example [42].

6.5. The Use of WGS and In-House Panels in the Diagnosis of MM

Whole genome sequencing allows for the comprehensive analysis of an individual’s complete genetic code, including mutations and variations in the DNA sequence. In multiple myeloma, WGS has been used to identify somatic mutations and chromosomal aberrations that are associated with the disease. These genetic changes can be used to develop personalized treatment plans and improve patient outcomes.

In addition to WGS, Gene panels are a targeted sequencing approach that focuses on a panel of genes that are known to be associated with MM or other hematologic malignancies. Gene panels typically include genes involved in oncogenesis, DNA repair, cell cycle regulation, and other biological processes that are relevant to cancer development and progression.

A large cohort of 2161 was studied by Walker, Samur, et al. (2016) and focused on the use of WGS, WES and RNAseq to establish a strategy for the Clinical Classification of Multiple Myeloma to segment the disease into therapeutically meaningful subgroups. Actually 5 translocation groups effecting prognosis have been elucidated: t(4;14), t(6;14), t(11;14), t(14;16) and t(14;20) but minor translocation and mutational groups need a sensitive technology as WGS to be detected [51].

Another study of Ashby et al. (2018) included a cohort of 1141 NDMM and used WGS as well as WES based on previous karyotype information in order to study the presence of hyperhaploidy related to poor prognosis (Double-Hit Bi-Allelic Inactivation of TP53). [39].

One study made by Raab et al. (2020) about the phase II clinical trial investigated the effectiveness of a combination of BRAF/MEK inhibitors in patients with relapsed/refractory multiple myeloma (RRMM) who had activating BRAF V600E mutations. This study used WGS and phospho-IHC to explore the therapeutic efficacy of the combination between Encorafenib and Binimetinib. The results showed that phospho-IHC was more effective in proving that pharmacodynamic markers reveal suppression of BRAF/MEK signaling et cycle 1/day 28 and restoration of expression at the time of the relapse [68].

Table 3. Studies that have used WGS for Diagnosis of MM.

Table 3. Studies that have used WGS for Diagnosis of MM.

Study	Year	Country	Number of cases	Type of patients	Type of samples	Type of NGS investigation
[69]	2021	New York, NY	1154	-	-	WGS and WES
[68]	2020	Heidelberg, Germany	15	RRMM	-	Exploratory biomarker assessments include cytogenetics, genomic analysis (WGS, RNAseq) and phospho-IHC.
[70]	2019	New York, USA	154	-	BM	WGS with myTYPE panel
[71]	2018	New York, USA	-	-	BM	WGS using myTYPE panel
[39]	2018	Arkansas USA	1141			WGS, WES, and targeted panel sequencing
[72]	2018	Little Rock, AR	439	NDMM	-	WGS, WES or targeted panel (TP) modalities.
[73]	2017	St. Louis MO	995	-	-	WGS (were identified using custom Seq-FISH software on long-insert whole genome sequencing data.)
[42]	2017	Boston MA	186	-	-	WES and WGS libraries were constructed with Agilent SureSelect XT2 library prep kit,
[51]	2016	Little Rock, AR	2161	-	-	WGS, WES, targeted panel sequencing, expression data from RNA-Seq and Gene Expression array
[74]	2016	New York, USA	-	-	-	WGS
[59]	2015	Boston, MA, USA	29	-	-	WGS, (22 patients) WES (17 patients)
[75]	2014	Phoenix, AZ	-	-	-	WGS
[76]	2012	London, UK	13	MM, SMM	BM	WGS
[56]	2012	London, UK	-	-	BM	WGS, WES, SNVs

The studies identified several genes that were frequently mutated in multiple myeloma, including TP53, KRAS, NRAS, and BRAF.

Overall, practice of WGS in the diagnosis and management of multiple myeloma has shown great promise. As the technology continues to advance, it is likely that WGS will become an increasingly important tool in the diagnosis and treatment of this complex disease.

Table 4. Studies that have used myTYPE Panel for Diagnosis of MM.

Table 4. Studies that have used myTYPE Panel for Diagnosis of MM.

Study	Year	Country	Number of cases	type of samples	Type of NGS investigations
[77]	2022	Stockholm Sweden	159	-	NGS MRD assay with myTYPE panel
[78]	2020	New York USA	74	-	NGS-based assay with myTYPE panel
[70]	2019	New York, USA	154	BM	WGS with myTYPE panel
[71]	2018	New York, USA	-	BM	WGS using myTYPE panel
[79]	2018	New York USA	147	BM	NGS assay based myTYPE panel
[80]	2018	Trondheim, Norway	177	BM	LymphoTrack® VDJ assay NGS based myTYPE panel assay

Three studies were leaded by Hultcrantz et Al in 2018, 2020 and 2022 about usage of MyType Panel multiple myeloma diagnosis (Table 5). The first study of 2018 published in Blood Journal [79] compared the adaptive NGS VDJ assay and the internal NGS panel myTYPE and concluded that the assay was less sensitive in samples with insufficient DNA content. The last study of 2022 published in Clinical Cancer Research journal [77] has been conducted at Memorial Sloan Kettering Cancer Center (MSK) between 2010 and 2017 and has also focused on the comparison of MyType Panel with Adaptive next generation sequencing (NGS) MRD. The results were similar to the first study( MyType panel had significantly higher V(D)J clonotype detection rates in univariate and multivariate analysis.

Two studies published in the Blood Cancer Journal in 2019 and 2018 by Yellapantula et Al used both whole genome sequencing and MyType Panel. The first study of 2018 [71] compared in one hand MyType Panel and WGS especially in deletions of 1p, 13p, 16q, 17p and gains of 1q, 11q and concluded a total concordance of these aberrations with a percentage of 100% identified by both assays. In The second hand, this study compared MyTYPE panel with FISH and the results were additional t(11;14) translocations identified uniquely by myTYPE. FISH was also used in deletions of 17q, 13q, 1p and 1q gain. All aberrations identified by FISH were identified in myTYPE but 13q- in four samples and 1p- in one sample were uniquely identified by myTYPE and concluded that evaluation of specificity and sensitivity require larger clinical cohort. This team leaded then a larger.

Therefore, considering the findings of these investigations collectively, it can be inferred that NGS is a remarkably accurate and precise method for identifying minimal residual disease (MRD) in individuals with multiple myeloma.

Some studies have shown that NGS-based assays have a high success rate in characterizing clonality and detecting MRD in plasma cell neoplasms (C. Ho et al., 2018; Rustad et al., 2018) and others demonstrated that NGS can achieve a sensitivity of 10-6 or better, which is superior to other techniques such as PCR and flow cytometry (Yao et al., 2019).

Furthermore, several studies have compared the performance of NGS with other MRD detection methods, such as flow cytometry and PCR, and found that NGS provides higher sensitivity and specificity for MRD detection (Cho et al., 2022b; Medina et al., 2020b).

Overall, these studies suggest that NGS is a highly accurate and reliable method for MRD detection in multiple myeloma patients, and may be used to guide treatment decisions and predict patient outcomes.

7. Conclusion

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