2025+EMN共识声明：二代测序技术在新诊断多发性骨髓瘤预后分层中的应用

Abstract

Given the evolving understanding of genetic risk factors in multiple myeloma (MM), this paper assesses whether next-generation sequencing (NGS) could complement or even replace fluorescence in situ hybridization (FISH) at diagnosis. A structured con-sensus process within European Myeloma Network (EMN) clinical and laboratory groups was conducted to establish re-commendations on routine clinical deployment of NGS in MM risk assessment. Four key questions were addressed: (1) should NGS be used in addition to, or alternatively to FISH in identifying prognostic genetic markers, (2) which prognostic markers are most relevant for analysis by NGS, (3) which patients should be offered NGS testing, and (4) what is the optimal timing for performing NGS. The panel reviewed current literature, evaluated available NGS technologies, and compared their performance with that of FISH-based methodologies. The paper reviews current standard NGS protocols, quality control measures, and provides practical points for the implementation of an NGS diagnosis in MM. While NGS shows promise in improving risk stratification, challenges such as cost, accessibility, and clinical workflow integration must be addressed. The consensus supports the initial incorporation of NGS as a complementary tool to FISH. Recommendations emphasize that: a broader list of genetic events should be incorporated into such a test than what currently requested by risk scores; the test should be offered at least to the fit patients who could be candidates for modern triplet or quadruplet treatments; the test should be repeated at the time relapse, especially in the future when targeted treatments may mandate the use of predictive markers of response. This consensus provides a foundation for future research and policy development, guiding the adoption of NGS in MM risk assessment.

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INTRODUCTION

Multiple myeloma(MM) is a plasma cell(PC) neoplasm arising from genetic abnormalities acquired in a germinal center mature B-lymphocyte.1.2 Initiating genetic events are categorized in two classes that are mostly, but not universally, mutually exclusive. The first includes translocations of recurrent oncogenes to the immunoglobulin heavy chain(IGH) locus in chromosome(chr) 14q32: CCND1 in chr11, NSD2(and sometimes FGFR3) in chr4, MAF in chr16, CCND3 in chr6, MAFB in chr20. The second is hyperdiploidy, consisting of at least two trisomies of the odd-numbered Chromosomes 3, 5, 7, 9, 11, 15, 19, and 21.1.3 After acquiring these initiating events, post-germinal center B-cells continue to differentiate into PCs, migrating to the bone marrow(BM)and retaining many functional properties of normal PCs. These include antibody secretion and interaction with the microenvironment,4 so that the hallmarks of MM-corresponding to its diagnostic criteria-are the presence of a monoclonal protein(MP) in the serum and sometimes in the urine, and an excess of PCs in the BM.5 In some patients, circulating PCs(cPCs) can be possibly identified and have prognostic value.Importantly, several lines of evidence suggest that decades rather than years elapse between the initial transformation of a B-cell and the appearance of a discernible MP in the serum as originally reported by Rustad et al.7-9 During this time, progressive cases will acquire sec-ondary genetic abnormalities, namely gene mutations, chromosomal deletions around tumor suppressor genes, further trisomies(collectively named copy-number abnormalities, CNAs), and other structural variants.10-12 Overall, abnormalities acquired during preclinical evolution constitute the MM genome, whose features have been described in detail since the advent of next-generation sequencing(NGS)techniques.13-15 Structural variants,CNAs,indels,and single-nucleotide variants constitute the main driver genomic abnormalities, of which each MM patient carries a median of five to six, as highlighted by tar-geted data.16,17 More recently, the catalog of driver variants with pa-thogenetic and/or prognostic significance is been significantly expanded by whole-genome sequencing(WGS) and is likely to grow even further in the future.7,12.18 This level of granularity highlighted by NGS can be used for classification purposes: initiating genetic events and identifying the main MM subgroups, and these can be further subdivided based on the presence/absence of secondary genetic events.However, while this"genomic classification" reflects the actual biology

Department of Clinical Therapeutics, School of Medicine National and Kapodistrian University of Athens, Athens, Greece

Department of Medical and Surgical Science, University of Bologna, Bologna,Italy

HOCH, Cantonal Hospital St. Gallen, Division Oncology/Hematology,St. Gallen, Switzerland

8Department of Internal Medicine II, University Hospital of Würzburg,Würzburg, Germany

Department of Hematology, Oncology and Stem Cell Transplantation, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg,Freiburg, Germany

10Department of Hematology, University Hospital of Salamanca, IBSAL, Cancer Research Center-IBMCC(USAL-CSIC), CIBERONC, Salamanca, Spain

1Department of Hematoonology, Faculty of Medicine, University of Ostrava and University Hospital Ostrava, Ostrava. Czech Republic

12Department of Biomedical Laboratory Science, Norwegian University of Science and Technology(NTNU), Trondheim, Norway

13Department of Immunology and Transfusion Medicine, St. Olav's Hospital,Trondheim, Norway

14Hemato-Oncology Unit, Hematology Department Fundacao Champalaud,Libson, Portugal

1Division of Genetics and Epidemiology, The Institute of Cancer Research,London, United Kingdom

underlying MM development and genomic makeup, it has little prog-nostic value.10,18,19 Evidence exists that a genomic MM classification can be further refined by analysis of RNA expression.20 but this is unlikely to be widely adopted in routine clinical use. On the contrary, one of the most pressing questions in newly diagnosed MM(NDMM) is whether the response to treatment and/or survival of the patient can be pre-dicted, and by which factors. While originally based on laboratory values(e.g., the International Staging System(ISS)].21 recent risk staging sys-tems mandate the identification of high-risk(HR) cytogenetic abnorm-abilities by fluorescence in situ hybridization(FISH) in CD138+ PCs purified from the BM. In particular, the revised ISS(R-ISS) classifies patients as HR if they are ISS stage III and carry t(4:14), t(14;16), del(17p), or have elevated lactic dehydrogenase.22 In the most recent re-vision, R2-ISS, the t(14;16) translocation has been eliminated, and the gain/amplification of chr(1q) has been added.23-25 Furthermore, in R2-ISS, risk stratification is additive, meaning that there is no hierarchical requirement for patients to be ISS stage III in the first place to be stratified as HR based on genetic features. Furthermore, additional risk scores exist. Many simply employ different combinations of the genetic abnormalities described above,26,27 while others look at additional markers, such as the gain of chr5 that confers a better prognosis.28

Following these advances, FISH is therefore routinely performed in NDMM patients to stratify the prognosis, but often also to identify predictive markers of response, like the presence of t(11;14). While the t(11;14) indicates standard prognosis, it represents a powerful predictive marker of response to venetoclax29 or other BCL2 inhibitors, thereby adding a possible therapeutic option for such patients. To cover prog-nostic and predictive markers, most NDMM patients would therefore be tested with 5-6 FISH probes at least. However, it is important to note that FISH is not available to 100% of patients due to technical limita-tions, such as insufficient cell yield, poor sample quality, or challenges in PC purification, particularly in low-tumor-burden cases.

Old and new evidence suggests that prognostic and predictive factors in NDMG go beyond the chromosomal abnormalities listed above and would not be captured by FISH. The most notable additional abnormality is represented by TP53 gene mutations: often co-occurring with del(17p), TP53 mutations can also be found in isolation, and ac-cording to several reports, they carry an adverse prognosis as well.17.30-32 In addition, MM is heterogeneous already at diagnosis,14 and as a result of clonal evolution, its genomic structure can change

1Department of Hematology, Oncology and Bone marrow Transplantation With Section of Pneumology, University Medical Center Hamburg-Eppendorf,Hamburg, Germany

17Hematology Department, University Hospital Hotel-Dieu, Nantes, France Department of Precision and Regenerative Medicine and Ionian Area,"Aldo Moro" University School of Medicine, Bari, Italy

19Unit of Hematology and Stem Cell Transplantation, AOUC Policlinico, Bari,Italy

20Department of Hematology, University Hospital Marqués de Valdecilla(IDIVAL), University of Cantabria, Santander, Spain

21Department of Medicine V,Hematology,Oncology and Rheumatology,Heidelberg Myeloma Center, Heidelberg University Hospital, Heidelberg,Germany

22Department of Hematology,Oslo Myeloma Center, Oslo University Hospital,Oslo, Norway

23Department of Hematology,Amsterdam UMC, Vrije Universiteit Amsterdam,Amsterdam, The Netherlands

24Department of Hematology,Erasmus MC Cancer Institute, Rotterdam,The Netherlands

25RICCS Aideno Spedallero-Universitaria di Bologna, Istituto di Ematologia“Serragnoli”, Bologna, Italy

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dramatically at relapse.20,33,34 Furthermore, novel T-cell redirecting im-munotherapies with unprecedented efficacy are now available against the cell-surface targets BCMA, GPRC5D, and FCRH5. Recent evidence suggests, at least for the first two targets, that acquired resistance could be at least in part mediated by antigen escape, indicating that sequen-cing the locus of BCMA and GPRC5D could have high predictive po-tential when considering treatment with another BCMA- or GPRC5D-directed modality, at least in the relapsed-refractory(RRMM) stages.35,36

Altogether, this evidence points to a deeper genomic character-ization of MM as a desirable option to improve prognosis and pre-diction of treatment response in MM patients. In this respect, the International Myeloma Society has recently published a consensus paper stating how an extended list of IGH translocations, CNAs, and TP53 mutations would be required to accurately prognosticate MM in the current era, and NGS is the preferred methodology to achieve this result.32 Indeed, analyses based on NGS could be more suitable in covering larger panels of chromosomal abnormalities and gene se-quences compared to traditional approaches based on FISH and polymerase chain reaction(PCR). NGS offers clear advantages in terms of a lower amount of DNA required, higher scalability,streamlining of the process, cost, and higher wealth of information provided.37 Furthermore, many centers already routinely perform NGS for other hematological malignancies, like acute myeloid leuke-mia(AML). Most of those will be able to harness the infrastructure of their molecular laboratories to implement NGS for MM as well.However, analysis of MM samples requires a specialized bioinfor-matic pipeline, particularly to identify CNAs and translocations, which are not captured by most AML NGS panels. NGS is also much less standardized, to the point that some resistance can be anticipated to its wider adoption in routine diagnostics in MM.

In this paper, we review current evidence on NGS performance,especially in comparison to FISH, with relevance to prognostic and predictive abilities in MM clinical-grade diagnosis. Based on the context of current biological and clinical knowledge, a European Myeloma Network(EMN) Expert Panel has provided a consensus statement and practical recommendations for the wider adoption of NGS, along with or in alternative to FISH in NDMM outside of clinical trials, with a look at possible future perspectives.

METHODOLOGY

The Delphi methodology was used to reach a consensus on the use of NGS in routine clinical diagnostics of MM. During the EMN Trialist meeting held in Boveino, Italy, in November 2024, the EMN Expert Panel selected key questions using the criterion of clinical relevance. Each panel expert answered independently, providing a motivated recommendation. A multistep process was then used to achieve a consensus on recommendations: one panel trialer datised to consensus statement for each key question summarizing the opi-nions of the panelists. Subsequently, this consensus was evaluated again by all panelists, who agreed on the statement or expressed their opinions and suggested changes. In this latter case, a new consensus was then drafted and reviewed by all panelists again.The stopping criterion was set as a consensus of at least 80% of the panel members.

CURRENT STRATEGIES TO IMPLEMENT NGS IN CLINICAL-GRADE DIAGNOSIS OF MM

Different approaches have been published on NGS for the genomic characterization of MM, with varying degrees of clinical validation.Most clinically oriented studies are based on targeted DNA

sequencing38-44 with variability in terms of aims and consequently in selected target regions. Most efforts are aimed at cataloging gene mutations,CNAs,and IGH translocations in one panel.

As far as gene mutations are concerned, some"reductionist"approaches have targeted only the six most frequent mutations in myeloma,41 others over 200 genes.44 Validation efforts have been conducted, indicating a high concordance of variant allele frequency between targeted NGS and WGS or droplet digital PCR(ddPCR)(R2=0.90 and R2=0.98, respectively).44

CNAs can also be screened in several different ways. By NGS,CNAs can be detected by using probes centered on single-nucleotide polymorphisms(SNPs) that are evenly distributed across the genome: this would allow detection of a CNA based on their coverage as compared to that of the diploid regions in the same sample, but also track zygosity of SNPs across the region to increase sensitivity of detection of CNAs and even events of copy-neutral loss of heterozygosity(LOH). To increase sensitivity in small regions of recurrent CNAs, probes could be tiled more densely in such re-gions, and/or the exonic footprint of the genes commonly affected by CNAs could be used.40,42,44 Using this approach, an NGS panel had a sensitivity of 94.89% and specificity of 99.68% compared to a gold-standard method such as Multiplex Ligation-dependent Probe Amplification across 22 regions of recurrent CNAs.44 An alternative approach could be separating the NGS workflow and generating,from the same sample, a second library for ultra-low pass WGS(ULP-WGS): this would be a cheaper approach to detect genome-wide CNAs, although more cumbersome from a technical point of view due to the duplication of library preparation and analysis pi-pelines. Furthermore, lower sensitivity was reported for subclonal lesions and for small deletions.45 As a more attractive option,reads mapping off-target regions generated during the capture itself can be leveraged for such purposes. Their random distribution generates a de facto genome-wide shallow sequencing that provides sufficient signal to reliably detect CNAs, while reducing the panel's target dimensions46 yet allowing a single, accurate analytical workflow with sensitivity and specificity of 87.8% and 98.4%,respectively.

Among the most important aspects of NGS applications in MM is its ability to detect translocations involving the immunoglobulin genes. To detect MM-related IGH translocations, the challenge lies in effectively covering the IGH locus, located on chromosome 14q32.Each B-cell(and therefore each MM clone) goes through extensive rearrangements during its development to generate antibody di-versity. These consist of widespread deletions, nucleotide insertions,and point mutations that generate a cell-specific sequence very dif-ferent from the germline status of the locus, all in the context of a highly repetitive region. Therefore, this region is poorly amenable to capturing with anticomplementary probes, which can only be de-signed around the germline status of the locus. Furthermore, mapping back the captured regions onto the germline genome reference is also challenging for the same reasons. To identify IGH translocations, most commonly employed approaches rely on the comprehensive targeting of the entire IGH locus.40,42,44 With this approach, the accuracy compared to WGS was 100%. Interestingly, NGS apparently per-formed less well compared to FISH, with a sensitivity of 94.92% and a specificity of 87.88% in 92 patients. However, NGS captured six IGH translocations that FISH failed to identify.44 Alternative approaches have targeted the IGH regions most frequently involved in chromo-somal breakpoints derived from previous sequencing efforts to re-duce the size of the capture panel and increase its efficiency. In one such approach, the sensitivity and the specificity of the NGS assay to detect IGH translocations were 88.6% and 99.5%, respectively, for 44 events.46

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Either way, all approaches have only targeted the IGH region and never that of partner chromosomes. This is because, with paired-end sequencing, at least some reads would span the breakpoint and show discordant mapping on Chromosome 14 and on one of the recurrent partners, eliminating the need to sequence regions on both ends of the translocation. This significantly reduces the footprint of the panel and allows for an unbiased identification of whatever oncogene is involved in the event. One notable exception is the t(8;14)(q24;q32)translocation, involving MYC, which is notoriously hard to target. This is because the MYC locus is large(0.5 megabases), breakpoints on chr8 can span megabases away from the gene, and the IGH break-points are less well characterized and may differ from those involved in the IGH translocations described above. Furthermore, MYC could also be translocated to the IGK or IGL regions, necessitating the use of substantially larger panels.44 Therefore, because of technical lim-itations and the absence of a clearly defined prognostic value, even though MYC has a known pathogenic role, its translocations are rarely assessed using NGS panels.

A radically different approach has been proposed where WGS and RNA-seq have been combined to increase the sensitivity and robustness of NGS analysis in MM as compared to FISH.47 This study showed 96% concordance with FISH for IGH translocations and 92%concordance for CNAs. The use of WGS as opposed to targeted panels allowed a much higher rate of detection of MYC transloca-tions, while RNA-seq allowed the correct categorization of IGH-translocated patients and their classification into classical mole-cular subgroups.48 Despite the need for a large amount of starting DNA(1μg)47 and high sequencing costs at the time of the publica-tion, the procedure proved sufficiently sensitive to be considered reliable in a clinical context.

Overall, the above evidence highlights the potential of a genome-wide approach to provide, in a single experiment, the data necessary for clinical patient management while also offering valuable in-formation for research purposes.

EMN RECOMMENDATIONS ON THE USE OF NGS IN MM

Would you support the use of NGS as an alternative to FISH, or complementary to FISH, for the prognostication of NDMM?

EMN experts unanimously agree on the added value that NGS offers for the prognostication of NDMM in terms of the genomic information provided, and believe that NGS should be brought to routine, clinical-grade diagnostics. The most immediate benefit of doing so would be to genotype the TP53 gene and, provided that mutations are identified,capture an additional adverse risk factor for prognostication. While TP53 mutations could also be detected by conventional PCR and se-quencing, all experts agreed that NGS would be the preferred method to carry out this analysis. Another benefit of NGS would be to provide a“one-step” solution to score the individual risk of each patient by also providing information on recurrent IGH translocations and CNAs, and thus replacing FISH analysis. As for how to implement this, the majority of the experts recommend starting to use NGS as complementary to FISH, while the minority suggests NGS could replace FISH straight away. Issues preventing immediate replacement of FISH by NGS in-clude a lack of standardization, a lack of accessibility, and cost/country-specific reimbursement issues.

The recommendation of the EMN experts is therefore to start using NGS in laboratories/countries where this is available at least for the analysis of TP53 mutations. The experts, however, recommend

that the NGS panel should be designed to also potentially capture CNAs and IGH translocations, that is, del(1p), gain/amp(1q), del(17p),t(4;14), and t(14;16). This latter analysis could initially complement FISH to allow the laboratory to perform internal validation of the NGS methodology. In the longer term, provided adequate validation and standardization have been performed, NGS could replace FISH for routine clinical diagnosis, based on accessibility.

Should the NGS panel only cover current prognostic markers(i.e., R2-ISS, International Myeloma Society criteria), or would you also like to know about potential future biomarkers of response(e.g., t(11;14),genome-wide CNAs, and BCMA mutations)?

The experts unanimously recommend that clinical-grade NGS diag-nostics should be designed to cover currently used risk factors, even if used along with FISH to start with. However, the experts unan-imously state that a clear added value of NGS over FISH would be to provide information on additional prognostic/predictive markers with a clinical role beyond the currently used risk staging systems. While at the moment there are no such validated markers, the experts re-comment and that a limited set of additional alterations should also be investigated through NGS. These include the following: bi/mono-allelic deletions and mutations in BCMA and GPRC5D as predictive markers of response to T-cell redirecting therapies; BRAF as a drug-gable target in a limited set of myeloma patients49; and t(11;14) to identify patients that may be candidates for BCL2-inhibition ther-apy.2 Going forward, novel prognostic or predictive markers in NDMM are likely to be identified that could be potentially in-corporated into future iterations of the NGS gene panel.

Would you use this panel in every patient at diagnosis or only in subcategories?

Most of the experts believe that the panel should be used in every NDMM patient where prognostic information can potentially guide therapeutic decisions. Clearly, therapeutic decisions would change based on the patient risk only in a subgroup of patients: those eligible for current or future HR NDMM clinical trials/treatments, those fit enough to undergo intensive treatments, or even in less fit patients in the presence of specific abnormalities that suggest sensitivity or re-sistance to specific therapies. Also, while there is a clear added value in knowing the prognosis in every patient, the risk stratification might not be accurate or overall informative in very elderly patients, that is,those with an age range beyond the upper limit of studies used to build the prognostic score itself, or those for whom treatment deci-sion will not be affected by NGS results. Therefore, experts suggest not to run the test in very elderly/frail patients, similar to FISH.50

Overall, the experts recommend that the NGS panel be used in the NDMM patients where FISH would currently be performed for clinical-grade prognostication.

Would you repeat the testing at relapse?

Most experts believe that the test should be repeated at relapse in patients undergoing active treatment, especially if treated with T-cell redirecting therapies or BCL2-inhibition therapy such as venetoclax.Good clinical practice already recommends that FISH is repeated in RRMM,limited to secondary abnormalities(i.e.,del(1p),gain/amp(1q),and del(17p)). Similarly, a future NGS approach should be performed again in these patients. For this purpose, as detailed above, the NGS

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NGS vs FISH?

Use NGS to complement FISH initially.Replace once validated(e.g.,TP53,IGH,CNAs).

What should NGS panel include?

Current prognostic markers(R2-ISS, IMS).Predictive markers: BCMA, GPRC5D,BRAF, t(11;14).

Use for all NDMM patients?

Yes,except in very elderly/frail patients.Follow current FISH indications.

Repeat at relapse?

Yes,if eligible for active treatment(CAR-T,BCL2i).Include relapse-relevant markers.

FIGURE 1 Graphic representation and concise summary of the four consensus statements from the European Myeloma Network. CNAs, copy-number abnormalities; FISH, fluorescence in situ hybridization; IGH,immunoglobulin heavy chain; IMS, International Myeloma Society; NDMM,newly diagnosed multiple myeloma; NGS, next-generation sequencing; R-ISS,revised International Staging System.

panel should cover the above abnormalities, including the TP53 gene as well as current(i.e., BCMA, GPRC5D) and future targets of CAR-T and bispecific antibodies, given that biallicle loss, or monoallelic loss plus mutation, NGS could be a precision and resistance.35,51,52 Novel T-cell redirecting therapies are extremely expensive, and with appropriate validation, NGS could be a precision medicine tool to ensure treat-ment is only administered to likely responders.

Hence, the recommendation of the experts is to perform NGS, provided it is reimbursed for this indication, in patients at relapse eligible for active treatment with CAR-T,T-cell redirecting therapies, antibody-drug conjugates, or BCL2 inhibitors, and generally in those patients fit enough to receive intensive treat-ment where FISH would be performed to inform clinical decisions(Figure 1).

PRACTICAL ASPECTS OF NGS ANALYSIS IN MM

The key challenge in designing NGS panels lies in balancing the pa-nel's target size with its manageability and cost, while also considering the availability of DNA and the effort required for data analysis,aiming at capturing all the disease-related critical genomic informa-tion in a single assay. Members of the EMN laboratory consortium were asked to provide their advice on how to practically implement the design and analysis of an NGS-based diagnostic test for prog-nostication in MM (Figure 2).

At the present time, NGS should be performed on genomic DNA from CD138-purified BM PCs. Since this is the starting material for FISH studies, in some cases, cell number can be a limiting factor.While at first, priority should be given to FISH studies in such in-stances, in the future, the lower amount of cells required for NGS studies will likely eliminate this problem.

The consensus is that an NGS-based diagnostic effort in MM at this time should be based on a targeted pulldown approach through hybridization capture using antisense baits. This approach ensures maximal flexibility in design and reduces sequencing, analysis, and storage costs. As stated above, the design of a target region should allow analysis of recurrent gene mutations, recurrent CNAs, and re-current IGH translocations.

Tumor suppressor genes should be targeted for their whole coding sequence, while oncogenes could only be targeted for hot-spots, although the cost-effectiveness of doing so is questionable,so that most members of the consortium prefer to sequence the whole coding region of all genes in the NGS panel. Detection of CNAs could be performed according to different strategies, as de-scribed above. While each approach is reasonable and could be used without specific issues, it seems preferable to rely on off-target reads for CNA calls. This approach ensures a minimal target region,the least hands-on time for sample preparation, and yet returns highly accurate data.46

The detection of recurrent IGH translocations can be more challenging due to the repetitive nature of the IGH locus and to the wide range of breakpoints in the partner chromosomes. If the whole IGH region is targeted, repeat repeat regions should be avoided with strin-gent masking. Failing to do so will greatly increase off-target se-quencing and will not allow good-quality mapping of the additional regions anyway. Furthermore, IGH-translocation breakpoints cluster to limited segments of the IGH locus, and targeting the whole region is unlikely to increase the diagnostic yield. The recommendation is to only target the hotspot regions, while at the same time creating and continuously updating a catalog of such positions from the thousands of samples sequenced to improve the design of the NGS panel. This approach has proven very accurate in recent studies. 46 Because of the variability of the breakpoint position in partner chromosomes,the advice is to only target the IGH side of the translocation and rely on discordantly mapping reads to identify the translocation partner.

Sequencing should be performed in NGS instruments allowing paired-end sequencing, at a median on-target depth of at least 200x.The number of gigabases required to achieve this depth of coverage should be calculated based on the size of the capture region, ac-counting for the predicted or calculated on-target capture efficiency,also factoring in a small margin of error.

Following standard practices for quality control of the reads,alignment, and removal of PCR duplicates, several algorithms can be used to identify somatic events. Analysis of the sequencing data will vary greatly based on the pipeline in use in each laboratory. In general,we suggest employing at least two callers for mutations and CNAs, and only report events called by the two algorithms. In the case of trans-locations, we favor the use of two callers and report any event called by at least one, since their sensitivity is lower but specificity is high.

While cutoffs for allelic frequency of the identified events are not standardized, the panel suggests a conservative approach to minimize the risk of identifying false positives. At the suggested depth, a safe variant allelic frequency cutoff for mutations could be set at around 5%. For CNAs, the identification itself by current algorithms would suggest a clonality of at least 20%, and therefore, all identified events should be reported. For IGH translocations, reads mapping on the 14q32 locus will be of low quality, but paired reads mapping to the partner chromosome must be of higher quality. Under these cir-cumstances, if the breakpoint is close to the known oncogene, even 10 supportive reads should be accepted as a true positive result.40

Clearer indications on read cutoffs to support translocations will be defined from the internal validation efforts derived from each laboratory, comparing NGS data with FISH. One potential caveat could be the possibility that NGS identifies small, interstitial deletions

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 FIGURE 2 Flow chart of the recommendations of European Myeloma Network(EMN) for next-generation sequencing(NGS) in multiple myeloma(MM) and considerations for its future development.BM,bone marrow; CNAs, copy-number abnormalities; FISH, fluorescence in situ hybridization; IGH, immunoglobulin heavy chain; PC, plasma cell; VAF, variant allele frequency; WGS, whole-genome sequencing.

that are not identified by FISH. This can be particularly the case for deletions in chr1p32.32

Finally, it is important to note that, to date, no paper on the MM NGS panel has thoroughly addressed the strategy for reporting re-sults. According to international guidelines, this should be as com-prehensive and explicative as possible. This includes information on the patient and sample characteristics, the sample-specific assay performance, the observed alterations, and a section for genomic data interpretation for diagnosis, prognosis, and therapy selection.53 Indeed, the output of NGS panels, especially in the context of PC dyscrasias, requires further discussion to precisely define which information should be reported and how it should be presented.

FUTURE APPROACHES

Evidence and recommendations provided above delineate an approach to NGS diagnostics in routine practice. However, it is fair to say that this approach is currently used in a few centers only, while most centers rely on FISH. Undoubtedly, with time, laboratories will acquire confidence in the accuracy of NGS methods as compared to FISH, and with time, the former methodology will become standard.Validation requirements to certify the performance of NGS will likely

vary from country to country, such that hard cutoffs for sensitivity and specificity as compared to FISH cannot be provided. However,NGS performed comparably to, or even better than, FISH in all published studies so far,38-44 so there is little doubt about its value.

NGS also provides a more "future-proof" approach to diagnosis as it allows higher scalability than FISH. In this respect, it is already possible to outline topics that will be a matter of future developments on NGS diagnosis in MM (Figure 2).

The first point certainly concerns the breadth of coverage that would be ideally pursued within a genotyping approach in MM.Indeed, targeted panels have the advantage of requiring a lower amount of sequencing bases per patient, thus also reducing the complexity and cost of downstream analyses. On the other hand, the production of probes is costly and requires time, and the enrichment step adds variability to the uniformity of coverage. Advances in the knowledge of genomic drivers in MM will eventually highlight novel prognostic and predictive markers that will require periodic updates of the NGS diagnostic panels. With the advent of more efficient protocols requiring very little starting material,54,55 more processive sequencers, and the decreased storage-computing costs, a more practical strategy could be performing WGS for every patient.This would require a matched germline sample, more processive

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sequencing machines, and a more powerful IT infrastructure. How-ever, WGS would have the advantage of simplifying the pre-analytical steps(i.e., no need for target capture) and providing a more“future-proof" approach for prognostic and predictive scores to come. For example, most targeted panels are not amenable to the identification of MYC,56 IGL, or IGK translocations,57 nor mutational signatures.58,59 These events are currently recognized as prognostic, although not captured in current models. On the contrary, WGS would easily pro-vide this additional information and more. For example, the sequence of some drug targets like CD38 that can be mutated in cases resistant to daratumab,60 or the hypervariable region of the B-cell receptor locus that could later be used for minimal residual disease detection.Lastly, while hyperdiploid chromosomes have long been considered chromosome-wide events, more recent NGS studies have highlighted how only a fraction of a hyperdiploid chromosome can be gained,10 an event not visible by FISH. Whether the widespread use of NGS will help refine the definition of major MM cytogenetic categories is an open issue.

Another future approach will be genomic profiling of tumor DNA from peripheral blood. This can be done, for example, through ana-lysis of the few cPCs at diagnosis.61,62 Still technically challenging due to hurdles in the isolation of cPCs and in working with the little genomic DNA they would provide, this approach has proven as in-formative and potentially more so than BM analysis when cutting-edge genomic techniques are applied.55 Alternatively, cell-free DNA(cfDNA) could be used as a source of tumor DNA. This approach has been trialed by many authors63-66 with varying results. Although promising and certainly more comfortable for the patient, more va-lidation and higher sensitivity are needed before these approaches can be considered for routine, clinical-grade diagnosis. Thanks to technical advances allowing the sequencing of whole genomes at high depth and accuracy,67 analysis of tumor cfDNA is gaining momentum and will potentially provide a more established role in cancer diag-nostics in the future.

A crucial issue of an NGS approach in the prognostication of MM is the standardization of the sequencing analysis and the harmoni-zation of mutation reporting. While the current recommendations are only intended to provide a consensus, several practical aspects re-main unanswered. It is likely that most laboratories will adopt slightly different sequencing reagents and analysis tools to perform NGS in MM. However, such tools are becoming more and more accurate and easy to use,37,46 and there is a wide convergence on which tools to use for the detection of mutations(e.g., among others Mutect2,68 Strelka2,69 Varscan2,70 and Freebayes71), CNAs(e.g., CNVkit,72 CopywriteR73), and IGH translocations(e.g., Manta,74 Delly75). In this context, it would be advisable that an external quality assessment service be adopted that certifies a minimum standard across labora-tories; for example, through sequencing exercises of standardized samples. EMN provides the largest clinical network of MM in Europe,and its structure also encompasses centralized laboratories with the capability to perform clinical-grade NGS within EMN clinical trials.Certainly, gaining from this advantageous position, EMN could foster the homogeneous development of clinical-grade NGS diagnosis in MM in routine clinical practice outside of clinical trials within its network.

Another open question is whether prognostication based on RNA-sequencing will ever find a place in routine clinical practice in MM. Indeed, RNA-based classification of NDMM was proposed well before the advent of NGS.76 In the subsequent years, several RNA-based risk scores have been proposed in NDMM,77-79 but their widespread use has never gained traction outside of clinical trials due to a lack of standardization and concordance, as well as to the nui-sances of working with RNA rather than DNA. However, RNA-based prognostication has a clear added value to DNA-based sequencing

and is being routinely employed in settings like breast cancer80 and acute lymphoblastic leukemia.81 Advantages range from identification of risk biomarkers that are not captured at the DNA level,82 to the potential of identifying IGH, IGL, or IGK translocations based on ex-pression,83 to the identification of predictive markers based on their expression, for example, increased expression of BCL2 correlating with sensitivity to venetoclax and other BCL2 inhibitors.84,85 Indeed,the feasibility of risk assessment through a centralized gene-expression platform has been recently shown in a successful clinical trial for HR myeloma.82

Finally, another issue will regard the application of NGS diag-nostics in the wider field of PC dyscrasias. While understandably NDMM has been the subject of the most intense studies, relapsed and/or refractory disease could benefit from advanced diagnostics as well. In the near future, BCMA or GPRC5D mutations or deletions could become a predictive marker of response to CAR-T-cells or bis-pecific antibodies, managing repeated testing at relapse to allow personalization of treatment.35,51,86 More in general, HR features are enriched for at relapse, and prognosis could change based on their presence even in the RRMM setting. Furthermore, sufficient evidence suggests that NGS can be applied in smoldering MM(SMM) to predict the risk of progression.87-90 Indeed, prognostication of SMM is per-formed with different strategies ranging from biochemical, radiological pathological,immunophenotypic, and FISH approaches, with sig-nificant discordance.91-93 In times when treatment of HR SMM is debated as a strategy to delay progression and prolong survival,94-98 a consensus on a more refined stratification of the risk of progression could come from the adoption of NGS diagnostics, benefitting the unambiguous identification of SMM patients who could be candidates for treatment.99 Indeed, in this setting where the fraction of clonal PCs is lower and the CD138* sample can be contaminated by a significant amount of normal PCs,100 NGS is likely to perform better than FISH.Importantly, it should be noted that in SMM, genomic prognostic factors for progression are substantially different than those in NDMM. For example, canonical translocations of MYC to IGH, IGK,IGL, and noncanonical translocations(such as to the FAM46C) locus,mutations in KRAS,NRAS, and DNA-damage response genes have all been implicated with prognosis in SMM.54,89,101

Similarly, initial evidence suggests that genomic lesions can be used to improve prognostication for primary PC leukemia,102 while evidence for AL amyloidosis is still immature.103 However, it is likely that NGS diagnostics could be applied to these diseases as well in the future.

Overall, the broader scope of advanced diagnostics is that of benefitting patients through improved therapeutic approaches. In myeloma, therapeutic progress in the past decade has been spectra cular, and for the first time, clinical studies have been successfully performed to treat HR disease upfront with intensive quadruplet combinations including anti-CD38 antibodies.82,104 Furthermore, we do have evidence suggesting that HR MM should be dealt with dif-ferently when it comes to transplant, especially in the context of less intensive induction regimens.105-107 Even more importantly, we know that maintenance should not be stopped or should even be intensified in HR patients.108,109 In the near future, also thanks to a new con-sensus on the definition of risk,32 even more studies will be per-formed on the first-line treatment of HR MM. This will lead to new recommendations on personalized treatments based on risk at diag-nosis, mandating advanced diagnostics that would benefit from the adoption of NGS.

ACKNOWLEDGMENTS

We thank the numerous MM, NGS, and diagnostic/FISH experts for advice on this paper. Dr Sonia Fabris, Dr Marzia Barbieri, Dr Stefania Pioggi, Dr Giancarlo Castellano, and Dr Akihiro Maeda, from

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Fondazione IRCS Ca' Granda Ospedale Maggiore Policlinico. Dr Marta Lionetti, Dr Marghera Scopetti, and Dr Alessio Marrella from the University of Milan.Open access publishing facilitated by Uni-versa degli Studi di Milano, as part of the Wiley- CRUI-CARE agreement.

Dr Andrea Poletti, Dr Barbara Tauraisr, Dr Gaia Mazzocchetti,and Dr Marina Martello from the University of Bologna.

Prof.Dr Ralph Wasch, Dr Dietmar Pfeifer, Dr Miguel Waterhouse,Dr Michael Hassner, and Dr Milpan Piance from University of Freiburg.

AUTHOR CONTRIBUTIONS

Niccolò Bolli: Conceptualization; writing-original draft; writing-review and editing; supervision; funding acquisition; resources. Matta D'Agostino: Methodology; writing-review and editing; con-ceptualization. Tina Barugati: Methodology; writing-review and editing. Mario Bocaddoro: Writing-review and editing; methodology;funding acquisition; conceptualization; Michele Cavo: Methodology;writing-review and editing. Microsoft and editing. Christoph Driessen: Methodology;writing-review and editing. Hermann Einsele: Methodology; writing-review and editing. Monika Engelhardt: Methodology: writing-review and editing. Francesca Gay: Methodology; writing-review and editing. Renama C. Gutierrez: Methodology; writing-review and editing. Roman Hajek: Methodology; writing-review and editing. Toril Holien: Meth-odology; writing-review and editing. Cristina Jao: Methodology;writing-review and editing. Martin Kaiser: Methodology; writing-review and editing. K. Martin Kortim: Writing-review and editing; methodology. methodology; Lepredt: Methodology; writing-review and edit-ing. Philippe Moreau: Methodology; writing-review and editing.Pellegrino Musto: Methodology; writing-review and editing. Enrique M. Ocio: Methodology; writing-review and editing. Marc S. Raab:Methodology; writing-review and editing. Leo Rasche: Methodology;writing-review and editing. Fredrik Schjesvold: Methodology;writing-review and editing. Terese Vscivickova: Methodology; writing-review and editing. Evangelos Terpos: Methodology; writing-review and editing and editing. Cylze Touzeau: Methodology; writing-review and edit-ing. Niels W. C. J. Van de Donk: Methodology; writing-review and editing. Mark van Duin: Methodology; writing-review and editing. Katja Weisel: Methodology; writing-review and editing. Elena Zamagni: Methodology; writing-review and editing. Tom Cupodo:Methodology; writing-review and editing; conceptualization; Pieter Sonneveld: Conceptualization; methodology; writing-review and editing; funding acquisition. Carolina Terragna: Conceptualization;writing-original draft; methodology; writing-review and editing; funding acquisition; resources.

CONFLICT OF INTEREST STATEMENT

All authors declare no conflict of interest with the contents of the manuscript. N.B. received honoraria from Amgen, GSK, J&J, Jazz,Menarini, Oconpeptides, Pfizer, Sanofi, and Takeda; unrestricted re-search support from J& paid to the institution.

T.C.received research support from J&J.

C.D.received honoraria from Amgen,GSK,J&J,Pfizer, and Sanofi.M.S.R. has received research support from Janssen Pharmaceuticals,Celgene,and Sanofi,and serves on advisory boards for Janssen Phar-maceuticals,Amgen,Celgene,BMS,Sanofi,and AbbVie, all paid to the institution, and received honoraria from Janssen, Sanofi, and BMS.

E.T.:advisory boards for Amgen, AstraZeneca, EUSA Pharma,BMS,BMS,GSK,J&J. Menarini/Steline, Pfizer, Sanofi, and Takeda; horona from Amgen, Antengene, AstraZeneca, EUSA Pharma, BMS,Forus,GSK,J&J. Menarini/Steline, Novartis, Pfizer, Sanofi, Swixk,and Takeda; research funding from Amgen, GSK, J&J, Sanofi, and Takeda; travel expenses from Takeda;

N.W.C.J.V.D.d. has received research support from Janssen Phar-maceuticals,Amgen,Celgene,Novartis,Cellectis, and BMS, and serves on advisory boards for Janssen Pharmaceuticals, Amgen, Celgene, BMS,Sanofi, Takeda, Roche, Novartis, Bayer, Adaptive, Galapagos, Kite Pharma,Merck,Pfizer,AbbVie, and Servier, all paid to the institution.

E.O. discloses honoraria/consulting fees from AbbVie, Amgen,AstraZeneca,BMS,GSK,Janssen, Menarini,Oncopeptides,Pfizer,Regeneron, Sanofi, and Takeda.

K.M.K. received honoraria from AbbVie, BMS, GSK, Janssen,Novartis,Pfizer,Sanofi,Takeda,and Stemline,and is supported by the Stifterverband.

M.D. has received honoraria from GlaxoSmithKline, Sanofi, and Janssen; has served on the advisory boards for GlaxoSmithKline,Sanofi, Bristol Myers Squibb, and Adaptive Biotechnologies; and has received research support from Janssen.

LL. has received research support from AbbVie, GSK(both to the institution), has served on advisory boards for Janssen Pharmaceu-ticals,Sanofi,GSK,and Pfizer, and received honoraria from Adaptive,AbbVie, Janssen Pharmaceuticals, BMS/Celgene, Pfizer, Sanofi,GSK,Takeda, and AstraZeneca.

M.E. received honoraria from Amgen, GSK, J&J, Menarini,Oncopeptides, Pfizer, Sanofi, and Takeda.

L.R. has received research support from BMS, has served in ad-visory boards for Amgen, BMS, Roche, Johnson& Johnson, Sanofi,GSK, and Pfizer, and received honoraria from Amgen, BMS, Roche,Johnson& Johnson, Sanofi, GSK, Pfizer, and Takeda.

C.J. received honoraria from Amgen, GSK, J&J, Pfizer, Sanofi, Roche, AbbVie, and Beigen.

F.S. received honoraria from Amgen, BMS, Takeda, AbbVie, J&J,Sanofi, Pfizer, GSK, Menarini, Regeneron, GSK, Oncopeptides, and Galapagos.

R.H. has served in a consultancy/advisory role for Janssen,Amgen,Celgene,AbbVie,Bristol Myers Squibb,Novartis,PharmaMar,and Takeda and Hased and has received honoraria from Janssen, Amgen,Celgene, Bristol Myers Squibb, PharmaMar, and Takeda.

N.C.G.received honoraria from Janssen,Amgen, and Sanofi.

DATA AVAILABILITY STATEMENT

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

FUNDING

Evidence summarized in this work was supported by the European Research Council under the European Union's Horizon 2020 research and innovation program(grant agreement no. 817997) and by Associazione Italiana Ricerca sul Cancro(IG25739) and, in part by Italian Ministry of Health-Current research IRCCS to N.B. AIRC IG2018-22059 and AIL Bologna to C.T.

H.E. was funded by the European Union's Horizon 2020 research Innovation program(ELUMMY) and the MMRF MAC Program Grant.

R.H. was supported by the European Union project LERCO(No. CZ.10.03.01/00/22_003/0000 003); by the Operational Pro-gram Just Transition: SALVAGE project CZ.02.01.01/00/22_008/0004644, supported by OP JAK.

Open access publishing facilitated by Universita degli Studi di Milano, as part of the Wiley- CRUI-CARE agreement.

ORCID

Niccollo Boli 11 https://orcid.org/0000-0002-1018-5139

Tina Bagratuni https://orcid.org/0000-0001-8695-0493

Martin Kaiser https://orcid.org/0000-0002-3677-4804

HemaSphere

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K. Martin Kortüm https://orcid.org/0000-0002-7011-0286

Lisa Lypoldt https://orcid.org/0000-0002-9248-588X

Evangelos Terpos https://orcid.org/0000-0001-5133-1422

Niels W. C. J. Van de Donk https://orcid.org/0000/0000-0002-

7445-2603

Tom Cupedo https://orcid.org/0000-0003-1095-4273

Carolina Terragna https://orcid.org/0000-0002-4948-4785

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82. Kaiser MF, Hall A, Walker K, et al. Daratumumab, cyclophope-sphamide, bortezomb, lenalidomide, and dexamethasone as in-duction and extended consolidation improves outcome in ultra-high-risk multiple myeloma. J Clin Oncol. 2023;41(23):3945-3955.

83. Kaiser MF, Walker BA, Hockley SL, et al. A TC classification-based predictor for multiple myeloma using multiplexed real-time quan-titative PCR. Leukemia.2013;27(8):1754-1757.

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89. Bustoros M, Sklavenitis-Pistofidis R, Park J,et al. Genomic profiling of smoldering multiple myeloma identifies patients at a high risk of disease progression. J Clin Oncol.2020;38(21):2380-2389.

90.Bustoros M, Anand S, Sklavenitis-Pistofidis R, et al. Genetic sub-types of smoldering multiple myeloma are associated with distinct pathogenic phenotypes and clinical outcomes. Nat Commun.2022;13(1):3449.

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105.Cavo M,Gay F,BekscAs M,et al. Autologous haematopoietic stem-cell transplantation versus bortezomib-melpabolan-predisone,with or without bortezomib-lenalidomide-dexamethasone con-solidation therapy, and lenalidomide maintenance for newly diagnosed multiple myeloma(EMN02/HO95): a multicentre, ran-domised, open-label, phase 3 study. Lancet Haematol.2020;7(6):456.

106. Hari P, Pasquini MC, Stadtmauer EA, et al. Long-term follow-up of BMT CTN 0702(STaMINA) of postautologous hematopoietic cell transplantation(autoHCT) strategies in the upfront treatment of multiple myeloma(MM). J Clin Oncol.2020;38(15_suppl):8506.

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