Multiple Myeloma Research Consortium Research Articles

A pleiotropic variant in DNAJB4 is associated with multiple myeloma risk.

Pleiotropy, which consists of a single gene or allelic variant affecting multiple unrelated traits, is common across cancers, with evidence for genome-wide significant loci shared across cancer and noncancer traits. This feature is particularly relevant in multiple myeloma (MM) because several susceptibility loci that have been identified to date are pleiotropic. Therefore, the aim of this study was to identify novel pleiotropic variants involved in MM risk using 28 684 independent single nucleotide polymorphisms (SNPs) from GWAS Catalog that reached a significant association (P < 5× 10-8 ) with their respective trait. The selected SNPs were analyzed in 2434 MM cases and 3446 controls from the International Lymphoma Epidemiology Consortium (InterLymph). The 10 SNPs showing the strongest associations with MM risk in InterLymph were selected for replication in an independent set of 1955 MM cases and 1549 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and 418 MM cases and 147 282 controls from the FinnGen project. The combined analysis of the three studies identified an association between DNAJB4-rs34517439-A and an increased risk of developing MM (OR=1.22, 95%CI 1.13-1.32, P=4.81× 10-7 ). rs34517439-A is associated with a modified expression of the FUBP1 gene, which encodes a multifunctional DNA and RNA-binding protein that it was observed to influence the regulation of various genes involved in cell cycle regulation, among which various oncogenes and oncosuppressors. In conclusion, with a pleiotropic scan approach we identified DNAJB4-rs34517439 as a potentially novel MM risk locus.

Does a Multiple Myeloma Polygenic Risk Score Predict Overall Survival of Patients with Myeloma?

Genome-wide association studies (GWAS) of multiple myeloma in populations of European ancestry (EA) identified and confirmed 24 susceptibility loci. For other cancers (e.g., colorectum and melanoma), risk loci have also been associated with patient survival. We explored the possible association of all the known risk variants and their polygenic risk score (PRS) with multiple myeloma overall survival (OS) in multiple populations of EA [the International Multiple Myeloma rESEarch (IMMEnSE) consortium, the International Lymphoma Epidemiology consortium, CoMMpass, and the German GWAS] for a total of 3,748 multiple myeloma cases. Cox proportional hazards regression was used to assess the association between each risk SNP with OS under the allelic and codominant models of inheritance. All analyses were adjusted for age, sex, country of origin (for IMMEnSE) or principal components (for the others) and disease stage (ISS). SNP associations were meta-analyzed. SNP associations were meta-analyzed. From the meta-analysis, two multiple myeloma risk SNPs were associated with OS (P &lt; 0.05), specifically POT1-AS1-rs2170352 [HR = 1.37; 95% confidence interval (CI) = 1.09-1.73; P = 0.007] and TNFRSF13B-rs4273077 (HR = 1.19; 95% CI = 1.01-1.41; P = 0.04). The association between the combined 24 SNP MM-PRS and OS, however, was not significant. Overall, our results did not support an association between the majority of multiple myeloma risk SNPs and OS. This is the first study to investigate the association between multiple myeloma PRS and OS in multiple myeloma.

A polygenic risk score for multiple myeloma risk prediction

There is overwhelming epidemiologic evidence that the risk of multiple myeloma (MM) has a solid genetic background. Genome-wide association studies (GWAS) have identified 23 risk loci that contribute to the genetic susceptibility of MM, but have low individual penetrance. Combining the SNPs in a polygenic risk score (PRS) is a possible approach to improve their usefulness. Using 2361 MM cases and 1415 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium, we computed a weighted and an unweighted PRS. We observed associations with MM risk with OR = 3.44, 95% CI 2.53–4.69, p = 3.55 × 10−15 for the highest vs. lowest quintile of the weighted score, and OR = 3.18, 95% CI 2.1 = 34–4.33, p = 1.62 × 10−13 for the highest vs. lowest quintile of the unweighted score. We found a convincing association of a PRS generated with 23 SNPs and risk of MM. Our work provides additional validation of previously discovered MM risk variants and of their combination into a PRS, which is a first step towards the use of genetics for risk stratification in the general population.

A Phase 2 Study of Extended Daratumumab, Carfilzomib, Lenalidomide, and Dexamethasone in Newly Diagnosed Multiple Myeloma

A Phase 2 Study of Extended Daratumumab, Carfilzomib, Lenalidomide, and Dexamethasone in Newly Diagnosed Multiple Myeloma

P-052: Identification of novel genetic loci for risk of multiple myeloma by functional annotation

<h3>Background</h3> Genetic factors have proven to have an impact on multiple myeloma (MM) susceptibility, with genome-wide association studies (GWAS) discovering 23 loci associated with MM risk. However, much of the heritability of MM remains unexplained. The stringent significance threshold used in GWAS (p<5×10^-8) accounts for the numerous statistical tests being performed but is prone to the risk of false negatives. One strategy for reducing the number of tests is to consider only SNPs with heightened prior probabilities of association, according to meaningful biological criteria. The best candidate SNPs identified with this approach can then be tested in additional MM cases and controls from independent populations. We aimed at surveying the effect of single nucleotide polymorphisms (SNPs), predicted to have a functional role, on MM risk. <h3>Methods</h3> The association study consisted of two GWAS as discovery datasets, namely the InterLymph consortium and the German GWAS and a replication dataset, namely the the International Multiple Myeloma rESEarch (IMMEnSE) consortium, for an overall total of 5442 MM cases and 6174 controls. SNPs were first ranked according to p-value and concordance of the association between the two discovery datasets, then ranked by functional annotation, using bioinformatic tools and databases. We considered the following classes of functional SNPs: missense, synonymous and non-sense SNPs, expression quantitative trait loci (eQTLs), splicing quantitative trait loci (sQTLs),), SNPs in SNPs affecting function of long non-coding RNAs (lncRNA), and SNPs modifying transcription factor binding sites. We prioritized the resulting SNPs for replication in IMMEnSE by p-values for association in InterLymph and German GWAS and by evidence for a functional role. <h3>Results</h3> In the two discovery datasets, 136 SNPs fit the criteria of association with MM risk with p<10^-4 and did not map close to known MM risk loci. After pruning for linkage disequilibrium, four SNPs (rs12038685, rs2664188, rs12652920, rs29794) were chosen for replication in IMMEnSE. Among these, rs2664188 showed to be significantly associated also in the replication dataset (OR=1.30, 95% CI=1.16-1.46, p=0.001). The final meta-analysis including the three datasets, using a random-effect model, confirmed the association (OR=1.18, 95% CI=1.07-1.30, p=0.0007). <h3>Conclusion</h3> The G-allele of rs2664188, that showed a consistent association with increased risk of developing MM in all phases of our analysis is an eQTL, according to GTEx., The G-allele is associated with increased expression of the N4BP2 gene in whole blood. N4BP2 encodes a protein which binds to B-cell leukemia/lymphoma 3 (BCL-3), a well-known proto-oncogene, known to play a role in cell proliferation and apoptosis inhibition in myeloma cell lines.

The Multiple Myeloma Research Consortium (MMRC): A Model For Accelerating Development Of Novel Therapies For Multiple Myeloma

The Multiple Myeloma Research Consortium (MMRC): A Model For Accelerating Development Of Novel Therapies For Multiple Myeloma

Utility of Clinical-Grade Sequencing of Relapsed Multiple Myeloma Patients; Interim Analysis of the Multiple Myeloma Research Foundation (MMRF) Molecular Profiling Protocol

Utility of Clinical-Grade Sequencing of Relapsed Multiple Myeloma Patients; Interim Analysis of the Multiple Myeloma Research Foundation (MMRF) Molecular Profiling Protocol

Interim analysis of a phase 2 minimal residual disease (MRD)-adaptive trial of elotuzumab, carfilzomib, lenalidomide, and dexamethasone (Elo-KRd) for newly diagnosed multiple myeloma (MM).

8011 Background: The addition of a monoclonal antibody to triplet induction regimens in patients (pts) with MM with intent for autologous stem cell transplant (ASCT) has resulted in higher overall and deep response rates. In this study we are investigating the impact of the addition of Elo to KRd on complete response (CR) and/or MRD-negative rates in newly diagnosed MM regardless of transplant eligibility. Methods: Pts were enrolled from four MM Research Consortium sites into this phase 2 study. All patients receive 12 cycles of Elo-KRd in 28-day cycles: Elo per standard dosing, K 20/56/70 mg/m2 days 1, 8 and 15, R 25 mg days 1-21, and dexamethasone 40 mg days 1, 8, 15, 22. ASCT eligible candidates can undergo stem cell collection after cycle 4 and then resume treatment; pts who elect to proceed to ASCT are censored for response at that time. Pts MRD(-) (&lt;10-5) by NGS after cycles 8 (C8) and 12 (C12) proceed to Elo-Rd until progression. Patients who convert from MRD(+) to MRD(-) between C8 and C12 receive an additional 6 cycles of Elo-KRd (total 18 cycles) followed by Elo-Rd, and pts MRD(+) after C12 receive an additional 12 cycles of Elo-KRd (total 24) followed by Elo-Rd. The primary endpoint of the study is sCR and/or MRD(-) rate after C8 E-KRd. MRD status was determined by ClonoSEQ next generation sequencing (NGS, &lt;10-5) [Adaptive Biotechnologies]. An improvement in the sCR and/or MRD(-) rate by NGS from a historical 30% to 50% at the end of C8 will be considered promising. Results: 44 pts are enrolled, 39 of whom are evaluable for response (cutoff Jan 10 2021). Median age is 62 years (range 43-81, 23% age &gt;70) and 23 (52%) have high-risk cytogenetic abnormalities (HRCA) including 13 (30%) with &gt;2 high-risk abnormalities (6 pts unknown cytogenetics). 34/39 (87%) have MRD trackable by clonoSEQ. The rate of sCR and/or MRD(-) by NGS at the end of C8 is 19/33 (58%), meeting the statistical threshold for establishing efficacy (2 pts censored for elective ASCT before C8 and 4 pts receiving therapy but have not reached C8). With a median follow-up of 24 months, estimated 2-year progression free survival is 87% (100% for standard risk, 79% for HRCA) and estimated 2-year overall survival is 89% (82% for HRCA). No pt who was MRD(-) by NGS after C8 has progressed, including 6 pts with HRCA. Serious adverse events occurred in 30 pts (68%). 89% experienced treatment emergent AEs, the most common (&gt;10%) of which was pneumonia (14%). One pt had grade 5 myocardial infarction. Conclusions: Elo-KRd demonstrates tolerability consistent with known toxicities of these agents and met the primary endpoint of sCR and/or MRD(-) of &gt;50% after 8 cycles. With longer follow-up, the study results may validate that an MRD-adaptive design for de-escalation of therapy in MM can generate deep responses while reducing treatment exposure. Clinical trial information: NCT02969837.

Genetically determined telomere length and multiple myeloma risk and outcome

Telomeres are involved in processes like cellular growth, chromosomal stability, and proper segregation to daughter cells. Telomere length measured in leukocytes (LTL) has been investigated in different cancer types, including multiple myeloma (MM). However, LTL measurement is prone to heterogeneity due to sample handling and study design (retrospective vs. prospective). LTL is genetically determined; genome-wide association studies identified 11 SNPs that, combined in a score, can be used as a genetic instrument to measure LTL and evaluate its association with MM risk. This approach has been already successfully attempted in various cancer types but never in MM. We tested the “teloscore” in 2407 MM patients and 1741 controls from the International Multiple Myeloma rESEarch (IMMeNSE) consortium. We observed an increased risk for longer genetically determined telomere length (gdTL) (OR = 1.69; 95% CI 1.36–2.11; P = 2.97 × 10−6 for highest vs. lowest quintile of the score). Furthermore, in a subset of 1376 MM patients we tested the relationship between the teloscore and MM patients survival, observing a better prognosis for longer gdTL compared with shorter gdTL (HR = 0.93; 95% CI 0.86–0.99; P = 0.049). In conclusion, we report convincing evidence that longer gdTL is a risk marker for MM risk, and that it is potentially involved in increasing MM survival.

Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients.

Gene expression profiling can be used for predicting survival in multiple myeloma (MM) and identifying patients who will benefit from particular types of therapy. Some germline single nucleotide polymorphisms (SNPs) act as expression quantitative trait loci (eQTLs) showing strong associations with gene expression levels. We performed an association study to test whether eQTLs of genes reported to be associated with prognosis of MM patients are directly associated with measures of adverse outcome. Using the genotype-tissue expression portal, we identified a total of 16 candidate genes with at least one eQTL SNP associated with their expression with P < 10-7 either in EBV-transformed B-lymphocytes or whole blood. We genotyped the resulting 22 SNPs in 1327 MM cases from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and examined their association with overall survival (OS) and progression-free survival (PFS), adjusting for age, sex, country of origin and disease stage. Three polymorphisms in two genes (TBRG4-rs1992292, TBRG4-rs2287535 and ENTPD1-rs2153913) showed associations with OS at P < .05, with the former two also associated with PFS. The associations of two polymorphisms in TBRG4 with OS were replicated in 1277 MM cases from the International Lymphoma Epidemiology (InterLymph) Consortium. A meta-analysis of the data from IMMEnSE and InterLymph (2579 cases) showed that TBRG4-rs1992292 is associated with OS (hazard ratio=1.14, 95% confidence interval 1.04-1.26, P=.007). In conclusion, we found biologically a plausible association between a SNP in TBRG4 and OS of MM patients.

Common gene variants within 3′‐untranslated regions as modulators of multiple myeloma risk and survival

We evaluated the association between germline genetic variants located within the 3'-untranlsated region (polymorphic 3'UTR, ie, p3UTR) of candidate genes involved in multiple myeloma (MM). We performed a case-control study within the International Multiple Myeloma rESEarch (IMMEnSE) consortium, consisting of 3056 MM patients and 1960 controls recruited from eight countries. We selected p3UTR of six genes known to act in different pathways relevant in MM pathogenesis, namely KRAS (rs12587 and rs7973623), VEGFA (rs10434), SPP1 (rs1126772), IRF4 (rs12211228) and IL10 (rs3024496). We found that IL10-rs3024496 was associated with increased risk of developing MM and with a worse overall survival of MM patients. The variant allele was assayed in a vector expressing eGFP chimerized with the IL10 3'-UTR and it was found functionally active following transfection in human myeloma cells. In this experiment, the A-allele caused a lower expression of the reporter gene and this was also in agreement with the in vivo expression of mRNA measured in whole blood as reported in the GTEx portal. Overall, these data are suggestive of an effect of the IL10-rs3024496 SNP on the regulation of IL10 mRNA expression and it could have clinical implications for better characterization of MM patients in terms of prognosis.

Concurrent lipidomics and proteomics on malignant plasma cells from multiple myeloma patients: Probing the lipid metabolome.

BackgroundMultiple myeloma (MM) is a hematological malignancy characterized by the clonal expansion of malignant plasma cells. Though durable remissions are possible, MM is considered incurable, with relapse occurring in almost all patients. There has been limited data reported on the lipid metabolism changes in plasma cells during MM progression. Here, we evaluated the feasibility of concurrent lipidomics and proteomics analyses from patient plasma cells, and report these data on a limited number of patient samples, demonstrating the feasibility of the method, and establishing hypotheses to be evaluated in the future.MethodsPlasma cells were purified from fresh bone marrow aspirates using CD138 microbeads. Proteins and lipids were extracted using a bi-phasic solvent system with methanol, methyl tert-butyl ether, and water. Untargeted proteomics, untargeted and targeted lipidomics were performed on 7 patient samples using liquid chromatography-mass spectrometry. Two comparisons were conducted: high versus low risk; relapse versus newly diagnosed. Proteins and pathways enriched in the relapsed group was compared to a public transcriptomic dataset from Multiple Myeloma Research Consortium reference collection (n = 222) at gene and pathways level.ResultsFrom one million purified plasma cells, we were able to extract material and complete untargeted (~6000 and ~3600 features in positive and negative mode respectively) and targeted lipidomics (313 lipids), as well as untargeted proteomics analysis (~4100 reviewed proteins). Comparative analyses revealed limited differences between high and low risk groups (according to the standard clinical criteria), hence we focused on drawing comparisons between the relapsed and newly diagnosed patients. Untargeted and targeted lipidomics indicated significant down-regulation of phosphatidylcholines (PCs) in relapsed MM. Although there was limited overlap of the differential proteins/transcripts, 76 significantly enriched pathways in relapsed MM were common between proteomics and transcriptomics data. Further evaluation of transcriptomics data for lipid metabolism network revealed enriched correlation of PC, ceramide, cardiolipin, arachidonic acid and cholesterol metabolism pathways to be exclusively correlated among relapsed but not in newly-diagnosed patients.ConclusionsThis study establishes the feasibility and workflow to conduct integrated lipidomics and proteomics analyses on patient-derived plasma cells. Potential lipid metabolism changes associated with MM relapse warrant further investigation.

A Network Analysis of Multiple Myeloma Related Gene Signatures.

Multiple myeloma (MM) is the second most prevalent hematological cancer. MM is a complex and heterogeneous disease, and thus, it is essential to leverage omics data from large MM cohorts to understand the molecular mechanisms underlying MM tumorigenesis, progression, and drug responses, which may aid in the development of better treatments. In this study, we analyzed gene expression, copy number variation, and clinical data from the Multiple Myeloma Research Consortium (MMRC) dataset and constructed a multiple myeloma molecular causal network (M3CN). The M3CN was used to unify eight prognostic gene signatures in the literature that shared very few genes between them, resulting in a prognostic subnetwork of the M3CN, consisting of 178 genes that were enriched for genes involved in cell cycle (fold enrichment = 8.4, p value = 6.1 × 10−26). The M3CN was further used to characterize immunomodulators and proteasome inhibitors for MM, demonstrating the pleiotropic effects of these drugs, with drug-response signature genes enriched across multiple M3CN subnetworks. Network analyses indicated potential links between these drug-response subnetworks and the prognostic subnetwork. To elucidate the structure of these important MM subnetworks, we identified putative key regulators predicted to modulate the state of these subnetworks. Finally, to assess the predictive power of our network-based models, we stratified MM patients in an independent cohort, the MMRF-CoMMpass study, based on the prognostic subnetwork, and compared the performance of this subnetwork against other signatures in the literature. We show that the M3CN-derived prognostic subnetwork achieved the best separation between different risk groups in terms of log-rank test p-values and hazard ratios. In summary, this work demonstrates the power of a probabilistic causal network approach to understanding molecular mechanisms underlying the different MM signatures.

Inching towards precision medicine for multiple myeloma with causal network models.

e19526 Background: Multiple myeloma (MM) is the 2nd most prevalent blood cancer. For each molecular subtype of MM defined by molecular profiles, mechanisms underlying prognosis and drug response are largely unknown. Elucidating the mechanisms underlying differences in prognosis and drug response will enable a more personalized, precision medicine (PM) approach to treating patients. Methods: Big multiomics data are now widely available and contain the ingredients necessary to build causal models to uncover mechanisms of prognosis and drug response. However, data from resources like the MM Research Consortium (MMRC) dataset may contain errors (eg, sample mislabeling) that diminish modeling accuracy. We developed a probabilistic data matching method ( proMODMatcher) to correct such errors and applied it to the MMRC data. We identified labeling errors in 10 patients, including swaps in RNA and CNV profiles. Given the corrected MMRC data, we characterized recurrent genomic aberrations in MM. We found &gt; 8000 genes significantly associated (FDR &lt; 0.01) with CNVs spanning the gene locations. To distinguish expression correlations driven by causal biological relationships from those driven by coincident CNV influence, we constructed a causal MM network based on the MMRC dataset. Results: Overlaps among multiomic prognostic or drug response biomarkers are sparse. To identify common mechanisms of different biomarkers, we projected them onto our MM network to define the causal context in which they occur. Prognostic biomarkers were enriched in subnetworks associated with mitotic regulation and lipid metabolism, and predicted by our network to be regulated by ZWINT, BUB1B, DTL, TPX2 and NOP16. Proteasome inhibitor and immunomodulating drug responses were mediated by amino acid biosynthesis and cell surface protein complex subnetworks, respectively, with MTHFD2 and TMC8 inferred as the master regulators of these subnetworks, respectively. Regulators include genes (eg, BUB1B and MTHFD2) known to associate with tumor growth and drug response and novel therapeutic control points. Conclusions: Causal models can elucidate the molecular mechanisms underlying prognosis and drug response, enabling the design of more personalized MM treatments.

Carfilzomib-Lenalidomide-Dexamethasone Versus Bortezomib-Lenalidomide-Dexamethasone in Patients with Newly Diagnosed Multiple Myeloma: Results from the Prospective, Longitudinal, Observational Commpass Study

Carfilzomib-Lenalidomide-Dexamethasone Versus Bortezomib-Lenalidomide-Dexamethasone in Patients with Newly Diagnosed Multiple Myeloma: Results from the Prospective, Longitudinal, Observational Commpass Study

Identification of miRSNPs associated with the risk of multiple myeloma.

Multiple myeloma (MM) is a malignancy of plasma cells usually infiltrating the bone marrow, associated with the production of a monoclonal immunoglobulin (M protein) which can be detected in the blood and/or urine. Multiple lines of evidence suggest that genetic factors are involved in MM pathogenesis, and several studies have identified single nucleotide polymorphisms (SNPs) associated with the susceptibility to the disease. SNPs within miRNA-binding sites in target genes (miRSNPs) may alter the strength of miRNA-mRNA interactions, thus deregulating protein expression. MiRSNPs are known to be associated with risk of various types of cancer, but they have never been investigated in MM. We performed an in silico genome-wide search for miRSNPs predicted to alter binding of miRNAs to their target sequences. We selected 12 miRSNPs and tested their association with MM risk. Our study population consisted of 1,832 controls and 2,894 MM cases recruited from seven European countries and Israel in the context of the IMMEnSE (International Multiple Myeloma rESEarch) consortium. In this population two SNPs showed an association with p < 0.05: rs286595 (located in gene MRLP22) and rs14191881 (located in gene TCF19). Results from IMMEnSE were meta-analyzed with data from a previously published genome-wide association study (GWAS). The SNPs rs13409 (located in the 3'UTR of the POU5F1 gene), rs1419881 (TCF19), rs1049633, rs1049623 (both in DDR1) showed significant associations with MM risk. In conclusion, we sought to identify genetic polymorphisms associated with MM risk starting from genome-wide prediction of miRSNPs. For some mirSNPs, we have shown promising associations with MM risk.

Management of multiple myeloma: the impact of ixazomib's approval in Canada.

Donna E Reece speaks to Laura Dormer, Commissioning Editor: Dr Donna E Reece is a Professor of Medicine and Director of the Program for Multiple Myeloma and Related Diseases in the Department of Medical Oncology and Hematology at Princess Margaret Hospital/University of Toronto. She earned a Bachelor of Arts degree at the University of Texas, Austin, and graduated as valedictorian with a medical degree from Baylor College of Medicine, Houston, Texas. She completed an internship in Internal Medicine at the University of Colorado Affiliated Hospitals, a residency and Chief Residency in Internal Medicine at Jewish Hospital, St Louis, and a Fellowship in Hematology/Oncology at Barnes Hospital, Washington University, St Louis, Missouri. She was a fellow and later a leukemia/stem cell transplant staff physician at Vancouver General Hospital/University of British Columbia for over 10 years. She then served as Director of the Outpatient Leukemia/Stem Cell Transplant Program, and later interim director, of the Blood and Marrow Transplant Program of the Markey Cancer Center at the University of Kentucky, Lexington, Kentucky until her appointment to Princess Margaret Hospital in Toronto in 2001. Dr Reece received the David and Molly Bloom Chair in Myeloma Research in 2009. She is currently the co-chair of the Multiple Myeloma Clinical Trials Group of the National Cancer Institute of Canada, member of the Scientific Advisory Board of the International Myeloma Foundation, and member of the Project Review Committee of the MMRC (Multiple Myeloma Research Consortium). She is also the Chief Medical Officer of the Myeloma Canada Research Network and serves on the board of directors of Myeloma Canada. Her career focus has been in the areas of hematopoietic stem cell transplantation, lymphoid malignancies and plasma cell dyscrasias. She has published numerous articles in these areas.

A common variant within the HNF1B gene is associated with overall survival of multiple myeloma patients: results from the IMMEnSE consortium and meta-analysis.

Diabetogenic single nucleotide polymorphisms (SNPs) have recently been associated with multiple myeloma (MM) risk but their impact on overall survival (OS) of MM patients has not been analysed yet. In order to investigate the impact of 58 GWAS-identified variants for type 2 diabetes (T2D) on OS of patients with MM, we analysed genotyping data of 936 MM patients collected by the International Multiple Myeloma rESEarch (IMMENSE) consortium and an independent set of 700 MM patients recruited by the University Clinic of Heidelberg. A meta-analysis of the cox regression results of the two sets showed that rs7501939 located in the HNF1B gene negatively impacted OS (HRRec= 1.44, 95% CI = 1.18–1.76, P = 0.0001). The meta-analysis also showed a noteworthy gender-specific association of the SLC30A8rs13266634 SNP with OS. The presence of each additional copy of the minor allele at rs13266634 was associated with poor OS in men whereas no association was seen in women (HRMen-Add = 1.32, 95% CI 1.13–1.54, P = 0.0003). In conclusion, these data suggest that the HNF1Brs7501939 SNP confers poor OS in patients with MM and that a SNP in SLC30A8 affect OS in men.

The International Multiple Myeloma Research (IMMEnSE) Consortium: Genetics of Multiple Myeloma Risk and Prognosis

We established the IMMEnSE (International Multiple Myeloma rESEarch) consortium, to increase our understanding of the genetic determinants of multiple myeloma (MM) risk, response to therapy and survival. At present we have DNA samples of over 3200 MM cases and 3000 healthy controls from 10 countries, mainly in Europe. For the majority of the cases clinical data on known prognostic factors, therapy outcome and survival have been also collected.We already performed several association studies in the context of the IMMEnSE consortium. In particular, associations were found between MM risk and SNPs in the ABCB1 gene, which encodes for an efflux pump that has a key role in protecting cells from chemical damage (rs10264990: odds ratio (OR) =0.79, 95% confidence interval (CI) 0.68-0.91; p=0.001, and rs17327442: OR=1.99; 95%CI 1.32-3.02; p=0.001). We also investigated the 8q24 region, which has been shown to harbor multiple loci of susceptibility to various cancers, and found an association between a SNP mapping in this region and MM risk (rs2456449: OR=1.37; 95%CI 1.12-1.68; p=0.0022). In addition, IMMEnSE cases and controls were also genotyped for three MM risk SNPs from the first genome-wide association study (GWAS), and the association of two of them (rs4487645 on chromosome 7p15.3 and rs6746082 on chromosome 2p23.3) was confirmed. Finally, SNPs of key telomere-related genes were genotyped and telomere length was measured in MM cases and controls, and a pleiotropic and functional variant of the TERT gene was found to be associated with reduced MM risk (rs2242652: OR=0.81; 95%CI 0.72-0.92; p=0.001). A suggestive association between longer telomeres and increased MM risk was also found (ptrend=0.01).We will study new SNPs emerging as promising candidates from ongoing GWAS on MM risk and survival, as well as SNPs of key genes involved in the pathogenesis of MM. Finally, we plan to study methylation status of key genes involved in MM etiology, and mitochondrial copy number. The role of all these factors will be investigated in relation to MM risk and prognosis.Collection of samples and data of MM cases and controls is ongoing, as well as of subjects with monoclonal gammopathy of undetermined significance (MGUS).Table 1MM cases and healthy controls collected in the IMMEnSE consortiumCountryCasesMedian age(5th-95th percentile)ControlsMedian age(5th-95th percentile)Control typeItaly23263 (46-78)23759 (42-76)General populationPoland1,28663 (42-82)20068 (43-79)Blood donorsSpain32264 (46-82)1,13166 (43-84)Hospitalized subjectsFrance64257 (37-68)19148 (18-63)Blood donorsPortugal7068 (45-82)10058 (53-79)Blood donorsHungary14868 (34-90)10574 (55-87)Hospitalized subjectsDenmark34856 (43-65)1,00063 (52-73)Blood donorsIsrael10960 (41-77)95-Blood donorsCanada6258 (42-70)---Japan5166 (47-84)---Total3,27063 (37-84)3,05963 (18-92) [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

ILF2 Is a Regulator of RNA Splicing and DNA Damage Response in 1q21-Amplified Multiple Myeloma

In the last decade, significant effort has been directed toward the stratification of multiple myeloma (MM) patients for targeted therapy, and many studies have shown that some genetic alterations, especially t(4;14) translocation, loss of the short arm of chromosome 17, and amplification of chromosome 1q21, are associated with a poor outcome. The 1q21 amplicon spans a region of 10-15 Mb and contains a large number of possible candidate genes; it is among the most frequent chromosomal aberrations in patients with MM and is associated with poor prognosis, disease progression, and drug resistance. Therefore, the identification of critical 1q21 genes may yield potential therapeutic targets for this high-risk MM subgroup and provide a rationale for patient stratification.In an effort to accomplish this goal, we first identified a high-priority list of 78 copy number-driven 1q21 MM-relevant genes by integrating high-resolution array comparative genomic hybridization (aCGH) and matched expression profiling of the 254 MM samples deposited in the Multiple Myeloma Research Consortium (MMRC) database. Then, we performed a high-throughput systematic shRNA screen in vitroto identify 1q21 genes whose loss of function resulted in the selective death and/or growth inhibition of MM cells carrying the 1q21 amplification. We used shRNA targeting (excluding shRNAs that displayed cytotoxic activity regardless of 1q21 amplification) and a GFP competitive growth assay to identify 1q21-resident targets whose downregulation significantly decreased the percentage of GFP-positive MM cells with 1q21 amplification over a time of 7 days. These assays identified UBAPL2, INTS3, LASS2, KRTCAP2, and ILF2 as key targets for further analysis.Secondary validation experiments in the MM cell lines JJN3 and H929 confirmed that the downregulation of all of our top five candidate genes induced significant levels of apoptosis, inhibition of proliferation, and cell cycle arrest. Integration of copy number analysis, expression profiling, and clinical outcome indicated that only UBAPL2 and ILF2 were highly significant prognostic genes, and target validation in NOD-SCID mice showed that ILF2, but not UBAPL2, downregulation had a significant impact on in vivo survival. Therefore, we sought to further characterize ILF2’s role in 1q21-amplified MM.ILF2 encodes NF45, the regulatory subunit of NF90/NF110 complexes, which are involved in mitotic control, DNA break repair, and RNA splicing regulation. Downregulation of ILF2 in MM cells with 1q21 amplification resulted in multinucleated phenotypes and abnormal nuclear morphologies (nucleoplasmic bridges and buds and micronuclei) that were associated with a significant accumulation of phospho-H2AX foci and DNA damage response activation, increased sensitivity to the DNA damaging agent melphalan, and impaired activation of DNA repair pathways. Experiments of immunoprecipitation combined with mass spectometry showed that ILF2 interacts with numerous RNA binding proteins directly implicated in DNA repair or regulation of DNA damage response by modulating alternative splicing and stability of specific pre-mRNAs. Accordingly, RNA-seq analysis of ILF2-depleted MM cells, when compared to cells carrying scrambled shRNAs, identified specific changes in RNA splicing patterns both before and after treatment with melphalan.In conclusion, our studies have revealed an unanticipated link between 1q21 amplification, DNA damage response, and RNA splicing. We identified ILF2 as a key driver of this interaction, and our findings support the development of strategies designed to modulate ILF2 expression in patients with high-risk MM carrying 1q21 amplification. DisclosuresNo relevant conflicts of interest to declare.