Acquired resistance to a GPRC5D-directed T-cell engager in multiple myeloma is mediated by genetic or epigenetic target inactivation.

Bispecific antibodies targeting GPRC5D demonstrated promising efficacy in multiple myeloma, but acquired resistance usually occurs within a few months. Using a single-nucleus multi-omic strategy in three patients from the MYRACLE cohort (ClinicalTrials.gov registration: NCT03807128 ), we identified two resistance mechanisms, by bi-allelic genetic inactivation of GPRC5D or by long-range epigenetic silencing of its promoter and enhancer regions. Molecular profiling of target genes may help to guide the choice of immunotherapy and early detection of resistance in multiple myeloma.

Exploring the impact of dexamethasone on gene regulation in myeloma cells.

Among glucocorticoids (GCs), dexamethasone (Dex) is widely used in treatment of multiple myelomas. However, despite a definite benefit, all patients relapse. Moreover, the molecular basis of glucocorticoid efficacy remains elusive. To determine genomic response to Dex in myeloma cells, we generated bulk and single-cell multi-omics data and high-resolution contact maps of active enhancers and target genes. We show that a minority of glucocorticoid receptor-binding sites are associated with enhancer activity gains, increased interaction loops, and transcriptional activity. We identified and characterized a predominant enhancer enriched in cohesin (RAD21) and more accessible upon Dex exposure. Analysis of four gene-specific networks revealed the importance of the CTCF-cohesin couple and the synchronization of regulatory sequence openings for efficient transcription in response to Dex. Notably, these epigenomic changes are associated with cell-to-cell transcriptional heterogeneity, in particular, lineage-specific genes. As consequences, BCL2L11-encoding BIM critical for Dex-induced apoptosis and CXCR4 protective from chemotherapy-induced apoptosis are rather up-regulated in different cells. In summary, our work provides new insights into the molecular mechanisms involved in Dex escape.

High-dose melphalan treatment significantly increases mutational burden at relapse in multiple myeloma.

High-dose melphalan (HDM) improves progression-free survival in multiple myeloma (MM), yet melphalan is a DNA-damaging alkylating agent; therefore, we assessed its mutational effect on surviving myeloma cells by analyzing paired MM samples collected at diagnosis and relapse in the IFM 2009 study. We performed deep whole-genome sequencing on samples from 68 patients, 43 of whom were treated with RVD (lenalidomide, bortezomib, and dexamethasone) and 25 with RVD + HDM. Although the number of mutations was similar at diagnosis in both groups (7137 vs 7230; P = .67), the HDM group had significantly more mutations at relapse (9242 vs 13 383, P = .005). No change in the frequency of copy number alterations or structural variants was observed. The newly acquired mutations were typically associated with DNA damage and double-stranded breaks and were predominantly on the transcribed strand. A machine learning model, using this unique pattern, predicted patients who would receive HDM with high sensitivity, specificity, and positive prediction value. Clonal evolution analysis showed that all patients treated with HDM had clonal selection, whereas a static progression was observed with RVD. A significantly higher percentage of mutations were subclonal in the HDM cohort. Intriguingly, patients treated with HDM who achieved complete remission (CR) had significantly more mutations at relapse yet had similar survival rates as those treated with RVD who achieved CR. This similarity could have been due to HDM relapse samples having significantly more neoantigens. Overall, our study identifies increased genomic changes associated with HDM and provides rationale to further understand clonal complexity.

In-depth analysis of alternative splicing landscape in multiple myeloma and potential role of dysregulated splicing factors.

Splicing changes are common in cancer and are associated with dysregulated splicing factors. Here, we analyzed RNA-seq data from 323 newly diagnosed multiple myeloma (MM) patients and described the alternative splicing (AS) landscape. We observed a large number of splicing pattern changes in MM cells compared to normal plasma cells (NPC). The most common events were alterations of mutually exclusive exons and exon skipping. Most of these events were observed in the absence of overall changes in gene expression and often impacted the coding potential of the alternatively spliced genes. To understand the molecular mechanisms driving frequent aberrant AS, we investigated 115 splicing factors (SFs) and associated them with the AS events in MM. We observed that ~40% of SFs were dysregulated in MM cells compared to NPC and found a significant enrichment of SRSF1, SRSF9, and PCB1 binding motifs around AS events. Importantly, SRSF1 overexpression was linked with shorter survival in two independent MM datasets and was correlated with the number of AS events, impacting tumor cell proliferation. Together with the observation that MM cells are vulnerable to splicing inhibition, our results may lay the foundation for developing new therapeutic strategies for MM. We have developed a web portal that allows custom alternative splicing event queries by using gene symbols and visualizes AS events in MM and subgroups. Our portals can be accessed at http://rconnect.dfci.harvard.edu/mmsplicing/ and https://rconnect.dfci.harvard.edu/mmleafcutter/ .

Molecular Signature of 18F-FDG PET Biomarkers in Newly Diagnosed Multiple Myeloma Patients: A Genome-Wide Transcriptome Analysis from the CASSIOPET Study.

The International Myeloma Working Group recently fully incorporated 18F-FDG PET into multiple myeloma (MM) diagnosis and response evaluation. Moreover, a few studies demonstrated the prognostic value of several biomarkers extracted from this imaging at baseline. Before these 18F-FDG PET biomarkers could be fully endorsed as risk classifiers by the hematologist community, further characterization of underlying molecular aspects was necessary. Methods: Reported prognostic biomarkers (18F-FDG avidity, SUVmax, number of focal lesions, presence of paramedullary disease [PMD] or extramedullary disease) were extracted from 18F-FDG PET imaging at baseline in a group of 139 patients from CASSIOPET, a companion study of the CASSIOPEIA cohort (ClinicalTrials.gov identifier NCT02541383). Transcriptomic analyses using RNA sequencing were realized on sorted bone marrow plasma cells from the same patients. An association with a high-risk gene expression signature (IFM15), molecular classification, progression-free survival, a stringent clinical response, and minimal residual disease negativity were explored. Results:18F-FDG PET results were positive in 79.4% of patients; 14% and 11% of them had PMD and extramedullary disease, respectively. Negative 18F-FDG PET results were associated with lower levels of expression of hexokinase 2 (HK2) (fold change, 2.1; adjusted P = 0.04) and showed enrichment for a subgroup of patients with a low level of bone disease. Positive 18F-FDG PET results displayed 2 distinct signatures: either high levels of expression of proliferation genes or high levels of expression of GLUT5 and lymphocyte antigens. PMD and IFM15 were independently associated with a lower level of progression-free survival, and the presence of both biomarkers defined a group of "double-positive" patients at very high risk of progression. PMD and IFM15 were related neither to minimal residual disease assessment nor to a stringent clinical response. Conclusion: Our study confirmed and extended the association between imaging biomarkers and transcriptomic programs in MM. The combined prognostic value of PMD and a high-risk IFM15 signature may help define MM patients with a very high risk of progression.

The DNA methylation landscape of multiple myeloma shows extensive inter- and intrapatient heterogeneity that fuels transcriptomic variability.

BACKGROUND: Cancer evolution depends on epigenetic and genetic diversity. Historically, in multiple myeloma (MM), subclonal diversity and tumor evolution have been investigated mostly from a genetic perspective. METHODS: Here, we performed an analysis of 42 MM samples from 21 patients by using enhanced reduced representation bisulfite sequencing (eRRBS). We combined several metrics of epigenetic heterogeneity to analyze DNA methylation heterogeneity in MM patients. RESULTS: We show that MM is characterized by the continuous accumulation of stochastic methylation at the promoters of development-related genes. High combinatorial entropy change is associated with poor outcomes in our pilot study and depends predominantly on partially methylated domains (PMDs). These PMDs, which represent the major source of inter- and intrapatient DNA methylation heterogeneity in MM, are linked to other key epigenetic aberrations, such as CpG island (CGI)/transcription start site (TSS) hypermethylation and H3K27me3 redistribution as well as 3D organization alterations. In addition, transcriptome analysis revealed that intratumor methylation heterogeneity was associated with low-level expression and high variability. CONCLUSIONS: We propose that disrupted DNA methylation in MM is responsible for high epigenetic and transcriptomic instability allowing tumor cells to adapt to environmental changes by tapping into a pool of evolutionary trajectories.

DNA hydroxymethylation is associated with disease severity and persists at enhancers of oncogenic regions in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a heterogeneous plasma cell malignancy that remains challenging to cure. Global hypomethylation correlates with an aggressive phenotype of the disease, while hypermethylation is observed at particular regions of myeloma such as B cell-specific enhancers. The recently discovered active epigenetic mark 5-hydroxymethylCytosine (5hmC) may also play a role in tumor biology; however, little is known about its level and distribution in myeloma. In this study, we investigated the global level and the genomic localization of 5hmC in myeloma cells from 40 newly diagnosed patients, including paired relapses, and of control individuals. RESULTS: Compared to normal plasma cells, we found global 5hmC levels to be lower in myeloma (P < 0.001). Higher levels of 5hmC were found in lower grades of the International Staging System prognostic index (P < 0.05) and tend to associate with a longer overall survival (P < 0.1). From the hydroxymethylome data, we observed that the remaining 5hmC is organized in large domains overlapping with active chromatin marks and chromatin opening. We discovered that 5hmC strongly persists at key oncogenic genes such as CCND1, CCND2 and MMSET and characterized domains that are specifically hydroxymethylated in myeloma subgroups. Novel 5hmC-enriched domains were found at putative enhancers of CCND2 and MYC in newly diagnosed patients. CONCLUSIONS: 5hmC level is associated with clinical aspects of MM. Mapping 5hmC at a genome-wide level provides insights into the disease biology directly from genomic DNA, which makes it a potent mark to study epigenetics on large patient cohorts.

Genome-Wide Somatic Alterations in Multiple Myeloma Reveal a Superior Outcome Group.

PURPOSE: Multiple myeloma (MM) is accompanied by heterogeneous somatic alterations. The overall goal of this study was to describe the genomic landscape of myeloma using deep whole-genome sequencing (WGS) and develop a model that identifies patients with long survival. METHODS: We analyzed deep WGS data from 183 newly diagnosed patients with MM treated with lenalidomide, bortezomib, and dexamethasone (RVD) alone or RVD + autologous stem cell transplant (ASCT) in the IFM/DFCI 2009 study (ClinicalTrials.gov identifier: NCT01191060). We integrated genomic markers with clinical data. RESULTS: We report significant variability in mutational load and processes within MM subgroups. The timeline of observed activation of mutational processes provides the basis for 2 distinct models of acquisition of mutational changes detected at the time of diagnosis of myeloma. Virtually all MM subgroups have activated DNA repair-associated signature as a prominent late mutational process, whereas APOBEC signature targeting C>G is activated in the intermediate phase of disease progression in high-risk MM. Importantly, we identify a genomically defined MM subgroup (17% of newly diagnosed patients) with low DNA damage (low genomic scar score with chromosome 9 gain) and a superior outcome (100% overall survival at 69 months), which was validated in a large independent cohort. This subgroup allowed us to distinguish patients with low- and high-risk hyperdiploid MM and identify patients with prolongation of progression-free survival. Genomic characteristics of this subgroup included lower mutational load with significant contribution from age-related mutations as well as frequent NRAS mutation. Surprisingly, their overall survival was independent of International Staging System and minimal residual disease status. CONCLUSION: This is a comprehensive study identifying genomic markers of a good-risk group with prolonged survival. Identification of this patient subgroup will affect future therapeutic algorithms and research planning.

Whole genome copy number analysis in search of new prognostic biomarkers in first line treatment of mantle cell lymphoma. A study by the LYSA group.

Mantle cell lymphoma (MCL) is a lymphoproliferative disorder characterized by the t(11;14)(q13;q32) CCND1/IGH translocation. This lymphoma is however extremely heterogeneous in terms of molecular alterations. Moreover, the course of the disease can vary greatly between indolent forms with slow progression and aggressive conditions rapidly pejorative. The identification of early markers allowing to predict individual patients outcome has however been unsuccessful so far. The LyMa trial treated homogeneously a cohort of young MCL patients. This appeared as a good opportunity to search for biomarkers of response to therapy. DNA extracted from diagnostic paraffin-embedded lymph node biopsies from 100 patients with newly diagnosed MCL, homogeneously treated in this prospective clinical trial, were investigated for copy number alterations and copy neutral loss of heterozygosity using the Oncoscan SNP-array scanning the whole genome. An independent confirmatory cohort was used to strengthen the possibly relevant anomalies observed. Here we describe the recurrent anomalies identified with this technique. Deletions of 17p(TP53) and 9p(CDKN2A) were more frequent in refractory or early relapsing patients (10%), but had no significant impact in univariate analysis on progression-free (PFS) or overall survival (OS). Regardless of the presence of TP53 or CDKN2A deletions, gains in 7p22 (8,5%) were associated with better PFS in univariate but not in multivariate analysis including MCL International Prognostic Index and treatment. Gains of 11q(CCDN1), suggesting gains of the CCND1/IGH fusion, were associated with worse OS and PFS in univariate and multivariate analyses. This worse prognosis impact was confirmed by FISH in an independent confirmatory cohort. This work, using a whole genome approach, confirms the broad genomic landscape of MCL and shows that gains of the CCND1/IGH fusion can be considered as a new prognostic structural variant. Genomic abnormalities of prognostic impact could be useful to strengthen or de-escalate treatment schedules or choosing targeted therapies or CART-cells.

Genomic landscape and chronological reconstruction of driver events in multiple myeloma.

The multiple myeloma (MM) genome is heterogeneous and evolves through preclinical and post-diagnosis phases. Here we report a catalog and hierarchy of driver lesions using sequences from 67 MM genomes serially collected from 30 patients together with public exome datasets. Bayesian clustering defines at least 7 genomic subgroups with distinct sets of co-operating events. Focusing on whole genome sequencing data, complex structural events emerge as major drivers, including chromothripsis and a novel replication-based mechanism of templated insertions, which typically occur early. Hyperdiploidy also occurs early, with individual trisomies often acquired in different chronological windows during evolution, and with a preferred order of acquisition. Conversely, positively selected point mutations, whole genome duplication and chromoplexy events occur in later disease phases. Thus, initiating driver events, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of evolution that are biologically relevant but heterogeneous across patients.

Deciphering the chronology of copy number alterations in Multiple Myeloma.

Multiple myeloma (MM) and its precursor condition MGUS are characterized by chromosomal aberrations. Here, we comprehensively characterize the order of occurrence of these complex genomic events underlying MM development using 500 MGUS, and MM samples. We identify hyperdiploid MM (HMM) and non-HMM as genomically distinct entities with different evolution of the copy number alterations. In HMM, gains of 9,15 or 19 are the first and clonal events observed as clonal even at MGUS stage. These events are thus early and may underlie initial transformation of normal plasma cells to MGUS cells. However, CNAs may not be adequate for progression to MM except in 15% of the patients in whom the complex subclonal deletion events are observed in MM but not MGUS. In NHMM, besides the driver translocations, clonal deletion of 13 and 1q gain are early events also observed in MGUS. We combined this information to propose a timeline for copy number alteration.

Genomic patterns of progression in smoldering multiple myeloma.

We analyzed whole genomes of unique paired samples from smoldering multiple myeloma (SMM) patients progressing to multiple myeloma (MM). We report that the genomic landscape, including mutational profile and structural rearrangements at the smoldering stage is very similar to MM. Paired sample analysis shows two different patterns of progression: a "static progression model", where the subclonal architecture is retained as the disease progressed to MM suggesting that progression solely reflects the time needed to accumulate a sufficient disease burden; and a "spontaneous evolution model", where a change in the subclonal composition is observed. We also observe that activation-induced cytidine deaminase plays a major role in shaping the mutational landscape of early subclinical phases, while progression is driven by APOBEC cytidine deaminases. These results provide a unique insight into myelomagenesis with potential implications for the definition of smoldering disease and timing of treatment initiation.

Long intergenic non-coding RNAs have an independent impact on survival in multiple myeloma.

Although long intergenic non-coding RNAs (lincRNA) role in various cancers is described, their significance in Multiple Myeloma (MM) remains poorly defined. Here we have studied the lincRNA profile and their clinical impact in MM. We performed RNA-seq on MM cells from 308 newly diagnosed and uniformly treated patients, 16 normal plasma cells and utilized RNA-seq data from 532 newly diagnosed patients from CoMMpass study to analyze for lincRNAs. We observed 869 differentially expressed lincRNAs in MM compared to normal plasma cells. We identified 14 lincRNAs associated with PFS and calculated a risk score to stratify patients. The median PFS between high vs low-risk groups was 17 months vs not-reached (NR); and OS 30 months vs NR, respectively (p < 0.0001 for both). In the independent validation dataset between high and low-risk groups, PFS was 27 vs 42 months (HR 2.06 [1.44-2.96]; p < 0.0005); and 4-year OS 62% vs 86% (HR 2.76 [1.51-5.05]; p < 0.0005) confirming significant clinical relevance of lincRNA in MM. Importantly, lincRNA signature was able to further identify patients with significant differential outcomes within each low and high-risk categories identified using standard risk categorization including cytogenetic/FISH, ISS, and MRD negative or positive. Our results suggest that lincRNAs have an independent effect on MM outcome and provide a rationale to evaluate its molecular and biological impact.

Constraints on signaling network logic reveal functional subgraphs on Multiple Myeloma OMIC data.

BACKGROUND: The integration of gene expression profiles (GEPs) and large-scale biological networks derived from pathways databases is a subject which is being widely explored. Existing methods are based on network distance measures among significantly measured species. Only a small number of them include the directionality and underlying logic existing in biological networks. In this study we approach the GEP-networks integration problem by considering the network logic, however our approach does not require a prior species selection according to their gene expression level. RESULTS: We start by modeling the biological network representing its underlying logic using Logic Programming. This model points to reachable network discrete states that maximize a notion of harmony between the molecular species active or inactive possible states and the directionality of the pathways reactions according to their activator or inhibitor control role. Only then, we confront these network states with the GEP. From this confrontation independent graph components are derived, each of them related to a fixed and optimal assignment of active or inactive states. These components allow us to decompose a large-scale network into subgraphs and their molecular species state assignments have different degrees of similarity when compared to the same GEP. We apply our method to study the set of possible states derived from a subgraph from the NCI-PID Pathway Interaction Database. This graph links Multiple Myeloma (MM) genes to known receptors for this blood cancer. CONCLUSION: We discover that the NCI-PID MM graph had 15 independent components, and when confronted to 611 MM GEPs, we find 1 component as being more specific to represent the difference between cancer and healthy profiles.

Logic programming reveals alteration of key transcription factors in multiple myeloma.

Innovative approaches combining regulatory networks (RN) and genomic data are needed to extract biological information for a better understanding of diseases, such as cancer, by improving the identification of entities and thereby leading to potential new therapeutic avenues. In this study, we confronted an automatically generated RN with gene expression profiles (GEP) from a cohort of multiple myeloma (MM) patients and normal individuals using global reasoning on the RN causality to identify key-nodes. We modeled each patient by his or her GEP, the RN and the possible automatically detected repairs needed to establish a coherent flow of the information that explains the logic of the GEP. These repairs could represent cancer mutations leading to GEP variability. With this reasoning, unmeasured protein states can be inferred, and we can simulate the impact of a protein perturbation on the RN behavior to identify therapeutic targets. We showed that JUN/FOS and FOXM1 activities are altered in almost all MM patients and identified two survival markers for MM patients. Our results suggest that JUN/FOS-activation has a strong impact on the RN in view of the whole GEP, whereas FOXM1-activation could be an interesting way to perturb an MM subgroup identified by our method.

A Genome-Wide Association Study Identifies a Novel Locus for Bortezomib-Induced Peripheral Neuropathy in European Patients with Multiple Myeloma.

PURPOSE: Painful peripheral neuropathy is a frequent toxicity associated with bortezomib therapy. This study aimed to identify loci that affect susceptibility to this toxicity. EXPERIMENTAL DESIGN: A genome-wide association study (GWAS) of 370,605 SNPs was performed to identify risk variants for developing severe bortezomib-induced peripheral neuropathy (BiPN) in 469 patients with multiple myeloma who received bortezomib-dexamethasone therapy prior to autologous stem cell in randomized clinical trials of the Intergroupe Francophone du Myelome (IFM) and findings were replicated in 114 patients with multiple myeloma of the HOVON-65/GMMG-HD4 clinical trial. RESULTS: An SNP in the PKNOX1 gene was associated with BiPN in the exploratory cohort [rs2839629; OR, 1.89, 95% confidence interval (CI), 1.45-2.44; P = 7.6 × 10(-6)] and in the replication cohort (OR, 2.04; 95% CI, = 1.11-3.33; P = 8.3 × 10(-3)). In addition, rs2839629 is in strong linkage disequilibrium (r(2) = 0.87) with rs915854, located in the intergenic region between PKNOX1 and cystathionine-ß-synthetase (CBS) Expression quantitative trait loci mapping showed that both rs2839629 and rs915854 genotypes have an impact on PKNOX1 expression in nerve tissue, whereas rs2839629 affects CBS expression in skin and blood. CONCLUSIONS: The use of GWAS in multiple myeloma pharmacogenomics has identified a novel candidate genetic locus mapping to PKNOX1 and in the immediate vicinity of CBS at 21q22.3 associated with the severe bortezomib-induced toxicity. The proximity of these two genes involved in neurologic pain whose tissue-specific expression is modified by the two variants provides new targets for neuroprotective strategies. Clin Cancer Res; 22(17); 4350-5. ©2016 AACR.

Understanding the role of hyperdiploidy in myeloma prognosis: which trisomies really matter?

The prognosis of multiple myeloma is mainly dependent upon chromosomal changes. The 2 major abnormalities driving poor outcome are del(17p) and t(4;14). However, the outcome of these high-risk patients is not absolutely uniform, with some patients presenting long survival. We hypothesized that these better outcomes might be related to concomitant "good-risk" chromosomal changes exploring hyperdiploidy. We analyzed a large series of 965 myeloma patients, including 168 patients with t(4;14) and 126 patients with del(17p), using high-throughput single-nucleotide polymorphism arrays after plasma cell sorting. As expected, trisomic chromosomes were highly associated. Using the LASSO model, we found that only chromosome 3, when trisomic, was associated with a longer progression-free survival and that 3 trisomies modulated overall survival (OS) in myeloma patients: trisomies 3 and 5 significantly improved OS, whereas trisomy 21 worsened OS. In patients with t(4;14), trisomies 3 and/or 5 seemed to overcome the poor prognosis. For the first time, using a specific modeling approach, we show that not all trisomies display the same prognostic impact. This finding could be important for routine assessment of prognosis in myeloma, and some high-risk patients with a traditional evaluation could in fact be standard-risk patients.

Lenalidomide Enhances Immune Checkpoint Blockade-Induced Immune Response in Multiple Myeloma.

PURPOSE: PD-1/PD-L1 signaling promotes tumor growth while inhibiting effector cell-mediated antitumor immune responses. Here, we assessed the impact of single and dual blockade of PD-1/PD-L1, alone or in combination with lenalidomide, on accessory and immune cell function as well as multiple myeloma cell growth in the bone marrow (BM) milieu. EXPERIMENTAL DESIGN: Surface expression of PD-1 on immune effector cells, and PD-L1 expression on CD138(+) multiple myeloma cells and myeloid-derived suppressor cells (MDSC) were determined in BM from newly diagnosed (ND) multiple myeloma and relapsed/refractory (RR) multiple myeloma versus healthy donor (HD). We defined the impact of single and dual blockade of PD-1/PD-L1, alone and with lenalidomide, on autologous anti-multiple myeloma immune response and tumor cell growth. RESULTS: Both ND and RR patient multiple myeloma cells have increased PD-L1 mRNA and surface expression compared with HD. There is also a significant increase in PD-1 expression on effector cells in multiple myeloma. Importantly, PD-1/PD-L1 blockade abrogates BM stromal cell (BMSC)-induced multiple myeloma growth, and combined blockade of PD-1/PD-L1 with lenalidomide further inhibits BMSC-induced tumor growth. These effects are associated with induction of intracellular expression of IFNγ and granzyme B in effector cells. Importantly, PD-L1 expression in multiple myeloma is higher on MDSC than on antigen-presenting cells, and PD-1/PD-L1 blockade inhibits MDSC-mediated multiple myeloma growth. Finally, lenalidomide with PD-1/PD-L1 blockade inhibits MDSC-mediated immune suppression. CONCLUSIONS: Our data therefore demonstrate that checkpoint signaling plays an important role in providing the tumor-promoting, immune-suppressive microenvironment in multiple myeloma, and that PD-1/PD-L1 blockade induces anti-multiple myeloma immune response that can be enhanced by lenalidomide, providing the framework for clinical evaluation of combination therapy.

Role of additional chromosomal changes in the prognostic value of t(4;14) and del(17p) in multiple myeloma: the IFM experience.

In multiple myeloma, cytogenetic changes are important predictors of patient outcome. In this setting, the most important changes are deletion 17p, del(17p), and translocation of chromosomes 4 and 14, t(4;14), conferring a poor outcome. However, a certain degree of heterogeneity is observed in the survival of these high-risk patients. We hypothesized that other chromosomal changes may impact the outcome. We retrospectively analyzed a large series of 242 patients displaying either t(4;14) (157 patients) or del(17p) (110 patients), 25 patients presenting both abnormalities, using single nucleotide polymorphism array. In patients with t(4;14), del(1p32), del22q, and >30 chromosomal structural changes negatively impacted progression-free survival (PFS). For overall survival (OS), del(13q14), del(1p32), and the number of chromosomal structural changes worsened the prognosis of patients. For patients with del(17p), del6q worsened the prognosis of patients, whereas trisomy 15 and monosomy 14 were found to have a protective effect on PFS. For OS, del(1p32) worsened the prognosis of patients, whereas having >8 numerical changes was found to have a protective effect on survival. This study, which is the largest series of high-risk patients analyzed with the most modern genomic technique, identified 1 main factor negatively impacting survival: del(1p32).