Non-hyperdiploid Multiple Myeloma Research Articles

Cytogenetic Changes in Multiple Myeloma Timeline: A Tertiary Center's Experience

Introduction Risk stratification in Multiple Myeloma according to cytogenetic abnormalities has been utilized clinically to guide understanding of prognosis. Specific cytogenetics abnormalities such as deletion 17p, t(14,16) and t(4,14) have been associated with more aggressive disease and poor outcome[1]. It has been postulated that genetic aberrations evolve in a temporal pattern in non-hyperploid multiple myeloma such that 13q deletion and t14q32 tend to be early events while chromosome 1 and 17 abnormalities occur as later events [2]. Furthermore, other studies have suggested higher frequencies of high risk cytogenetic abnormalities (such as deletion 17p and gain of 1q) at relapse after upfront autologous stem cell transplant [3]. Our study examined the cytogenetic profile of patients treated at a tertiary health system at the time of diagnosis and at first relapse, irrespective of bone marrow transplant and treatment Methods This is a retrospective study of patients treated at Henry Ford Cancer Institute/Henry Ford Health System. Subjects were adults diagnosed with multiple myeloma with relapse confirmed by a bone marrow biopsy between January 2014 and January 2019. The cytogenetic profile at time of diagnosis and relapse for each patient was reviewed. Exclusion criteria included history or current diagnosis of another malignancy. 145 cases were identified, of which 112 cases were excluded: 19 cases with plasmacytoma but no bone marrow relapse, 32 with other malignancy, 21 with relapse before study period, 5 with relapse after study period, and 35 with bone marrow biopsy or cytogenetics not available for review. As such, we analyzed the remaining 33 cases Results 26 of the 33 cases underwent a bone marrow transplant. A third of the patients (11) developed new mutations, which included 17p deletion (3), 13q deletion (1), chromosome 1 abnormality (1), new trisomies (5), t(11,14) (4), hypoploidy (1), and IgH/CCDN1 changes (1). Conclusion Though cytogenetic evolution in multiple myeloma has been studied, not all patients undergo a bone marrow biopsy and cytogenetic evaluation at relapse, which limits this assessment. Despite our sample size being limited as a majority of the study population did not have cytogenetics recorded at relapse, our study reveals that new mutations can arise in a significant proportion of patients at relapse, some of which are known to be associated with poor prognosis. This can be potentially prognostic, as presence of new high-risk mutations could alter the presumed disease trajectory, and these could also have therapeutic consequences. While further studies with a larger sample size are needed to determine the frequencies of each mutation, our study underscores the importance of dedicated monitoring of cytogenetics at each relapse in multiple myeloma. References Palumbo, A., et al., Revised International Staging System for Multiple Myeloma: A Report From International Myeloma Working Group. J Clin Oncol, 2015. 33(26): p. 2863-9. Jimenez-Zepeda, V.H., E. Braggio, and R. Fonseca, Dissecting karyotypic patterns in non-hyperdiploid multiple myeloma: an overview on the karyotypic evolution. Clin Lymphoma Myeloma Leuk, 2013. 13(5): p. 552-8. Merz, M., et al., Longitudinal fluorescence. Haematologica, 2017. 102(8): p. 1432-1438. Disclosures No relevant conflicts of interest to declare.

Abstract 750: Gene Signatures to Distinguish Amyloid Cardiomyopathy Risk in Multiple Myeloma Patients

Amyloid light chain (AL) amyloidosis results from tissue deposition of clonal light chains, most commonly produced by clonal plasma cells. AL amyloidosis is closely associated with multiple myeloma (MM), another disease which arises from clonal plasma cell proliferation. Here we aim to identify gene signatures to distinguish AL cardiomyopathy (AL-CM) risk in MM patients. We utilized publicly available data sets and applied Graph Cluster Perturbation approach to cluster the co-expression networks based on pathways in MM and AL-CM utilizing 225 samples from four datasets: GSE42955 study (12 dilated CM, 12 ischemic CM and 5 control LV human heart tissues), GSE95077 study (16 amiloride drug treated/untreated myeloma cell line samples), GSE24128 study (16 newly diagnosed AL with monoclonal plasma cell samples over- or under-expressing cyclin D1), and GSE6477 study (76 primary bone marrow samples from hyper-diploid myeloma patients and 80 non-hyperdiploid MM patients). From these data sets, the networks for extracellular matrix organization, immune system, innate immune system, metabolism, and neutrophil degranulation pathways for MM and amyloid CM were extracted. Summary: Ranking of the perturbed genes based on pathways similarity index in MM and AL resulted in a panel of genes: CD44, NRAS, GRAP2, CTLA4, GSN, CCND1, NFKB1, and IRF1 (p= <0.01). As shown in Figure , the high hazard ratio of this gene cluster in MM with AL-CM (3.76; p=0.021) compared to MM without CM (0.96; p=0.032) suggests the potential of this gene panel to distinguish high or low risk groups in MM based on survival.

Deciphering the Chronology of Copy Number Alterations in Multiple Myeloma (MM): What Comes First?

Deciphering the Chronology of Copy Number Alterations in Multiple Myeloma (MM): What Comes First?

Chromatin Accessibility Profiling Reveals Cis-Regulatory Heterogeneity and Novel Transcription Factor Dependencies in Multiple Myeloma

Multiple myeloma (MM) is a plasma cell malignancy characterized by clinical and genomic heterogeneity. Recurrent IgH translocations, copy number abnormalities and somatic mutations have been reported to participate in myelomagenesis; however no universal driver of the disease has been identified. Here, we hypothesize that transcriptional deregulation is critical for MM pathogenesis and the maintenance of the MM cell state. In order to capture signatures of transcription factor engagement with the myeloma epigenome, we performed the assay for transposase-accessible chromatin sequencing (ATAC sequencing), deep RNA sequencing in 23 primary myeloma samples and 5 normal plasma cell samples (NPC) from healthy donors along with whole genome sequencing and H3K27ac ChIP-seq in a cohort of these primary MM samples. We identified 22,603 variable accessible loci between MM and NPC and correlated impact of these on expression of associated genes using RNA-seq data. Together with robust differential analysis of open chromatin regions and nuclease-accessibility footprints to identify discrete transcription factor binding events, we have discerned the myeloma-specific open chromatin landscape, identified transcription factor dependencies and potential new myeloma drivers. In our dataset we observe a vast number of loci with heterogeneous chromatin states across the sample cohort, and the majority of the open chromatin sites identified are unique to a single sample. However, distinct variable chromatin accessibility signatures indicative of the MM chromatin state when compared to normal plasma cells were observed. Remarkably, we observed more frequent recurrent loss of variable accessible loci compared to gains. In addition, specific open chromatin profiles evident in hyperdiploid and non-hyperdiploid MM were also identified. Accessibility footprinting revealed MM-specific enrichment for transcription factors known to be essential for MM cell survival including Interferon Regulatory Factors (IRFs), Nuclear Factor Kappa B (NFkB), Ikaros, and Sp1. Interestingly, we also identify the myocyte enhancer factor 2 (MEF2) family of transcription factors as being specifically enriched in open chromatin regions in MM cells. Using a CRISPR-Cas9 knockout system, we identify the MEF2 family member MEF2C as essential for MM cell proliferation and survival. MEF2C is significantly overexpressed at the RNA level in our study as well as in several independent cohorts and is a central enhancer-localized transcription factor in MM core regulatory circuitry as determined by H3K27ac ChIP-sequencing profiles of primary MM samples. In order to evaluate MEF2C as a therapeutic target, we used small molecule inhibitors targeting MEF2C activity via inhibition of MEF2C phosphorylation using inhibitors of salt-induced kinases (SIK) and microtubule-associated protein/microtubule affinity regulating kinases (MARK). SIK/MARK have been described to specifically activate MEF2C. SIK and MARK inhibition resulted in both dose- and time-dependent inhibition of MM cell growth and survival in a panel of 12 MM cell lines with various genotypic and phenotypic characteristics, revealing a potential approach to targeting the dysregulated gene regulatory state of myeloma. To conclude, here we identify here an altered chromatin accessibility landscape in multiple myeloma that likely contributes to oncogenic transcription states through the activity of transcription factors such as MEF2C, representing a new MM dependency and potential therapeutic target. DisclosuresAnderson:Millennium Takeda: Consultancy; C4 Therapeutics: Equity Ownership, Other: Scientific founder; Bristol Myers Squibb: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; OncoPep: Equity Ownership, Other: Scientific founder. Young:Camp4 Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Syros Pharmaceuticals: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Omega Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Munshi:OncoPep: Other: Board of director.

Cytogenetic Abnormalities and Treatment with New Drugs on Clinical Trials Are Important Prognostic Markers for Myeloma in the Era of ISS and FISH Analysis

Background:The revised ISS staging system highlights the importance of lactate dehydrogenase (LDH), fluorescence in situ hybridization(FISH) and the International Staging System (ISS) for prognostication of multiple myeloma (MM) patients. As a result, the continued utility of conventional karyotyping, has been called into question.At the same time, novel agents have significantly improved patient outcome. However, it is still unclear if novel agents benefit all patients or a particular subgroup of patients. In particular, the impact of treatment with the latest approved drugs and early drug access through clinical trials, especially in Asian countries, is unknown.Methods:Patients who were diagnosed with MM from year 1999 at the National University Hospital, Singapore, were recruited. We evaluated the correlation between cytogenetic abnormalities (normal, hyperdiploid, and non-hyperdiploid) to the overall survival (OS) for patients with different ISS stage, Revised ISS stage, and ISS-iFISH stage. We evaluated the role of novel agents on OS for patients with ISS Stage I or II/III disease. We divided the patients into 3 groups: patients who received novel agents (bortezomib, thalidomide, lenalidomide) at induction and relapse, patients who did not receive novel agents at induction but received novel agents at relapse, and patients who never received any novel agents, and evaluated differences in OS between these groups within the ISS stage. We also evaluated the impact of access to clinical trials and treatment with the newest generations of novel agents (carfilzomib, ixazomib, pomalidomide) on OS.Results:A total of 252 patients were in our database, with 180, 105 and 132 patients evaluable for ISS, ISS-iFISH and revised ISS respectively. Karyotype significantly affected OS of patients with ISS Stage I (p=0.009), and there was a trend for patients with ISS Stage II/III (p=0.10). Karyotype significantly affected the outcome of patients with ISS-iFISH stage I or II/III (p=0.000 and 0.006 respectively). Similarly, karyotype has significant impact on OS of patients with Revised ISS stage I and II/III respectively (p=0.004 and 0.052 respective). In all cases, patients with non-hyperdiploid MM do particularly badly.For the evaluation of the benefit of novel agents, 213 patients were evaluable..There were 39 patients in ISS Stage I group and 174 patients in ISS Stage II/III group. For patients with ISS stage II/III, the difference in OS in those who received novel agents at induction, the group which did not receive novel agents at induction but received novel agents at relapse, and those who never received any novel agents was statistically significant with p-value of 0.009. Conversely, the impact of novel agents on patients with ISS Stage I is not significant (p=0.885).Patients who were enrolled in clinical trials had significantly better OS with p-value of 0.002. Similarly, patients who received newest generations of novel agents (almost all on clinical trials) had significantly better OS with p-value of 0.014.In multivariate analysis adjusting for ISS-iFISH stage, treatment history with novel agents, and access to clinical trials, karyotypic abnormalities are significantly associated with OS (p-value of 0.001). Treatment history with novel agents is the other independent factor for OS. ISS-iFISH stage and access to clinical trials are not significant factors on the multivariate analysis (p-value 0.537 and 0.609 respectively). Access to clinical trials may lose its significance on multivariate analysis because of the relatively low number of patients on clinical trials.Conclusion:Our analysis showed that cytogenetics still has an important role in MM prognostication even in the setting of FISH and ISS. In particular, non-hyperdiploid karyotype portends a very bad prognosis. Novel agents have the least impact on outcomes for ISS stage I patients. In resource poor countries, this may help to stratify treatment. Lastly, access to clinical trials and treatment with the newest generations of novel agents are important prognostic factors in our cohort. This highlights the importance of drug access, which remains a significant challenge in many Asian countries. [Display omitted] [Display omitted] [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Abstract 4424: Molecular diagnostics of drug resistant multiple myeloma cases using targeted next generation sequencing

Abstract In multiple myeloma (MM), there are two distinct genetic subtypes based on copy number alterations and translocations. About half of all MM cases are a hyperdiploid type, which is characterized by multiple trisomies of chromosomes 3, 5, 7, 9 11, 15, 19 and 21 and a lower prevalence of primary translocations involving the immunoglobulin heavy chain (IgH) locus at 14q32. The remaining cases are the non-hyperdiploid type, and chromosomes 8, 13, 14, and 16 are frequently lost. Non-hyperdiploid myeloma is strongly association with translocations of IgH alleles with various partner chromosomes, such as t(11;14)(q13;q32), t(4;14)(p16.3; q32), t(14;16)(q32;q23), t(6;14)(p21;q32) or t(14;20) (q32;q11). The copy number alterations in other chromosome regions such as 1q, 6q, 8p, and 16q occur in both subtypes. Overall, non-hyperdiploid MM is associated with worse survival than hyperdiploid MM. In this study, we sequenced all exons of 409 cancer-related genes in matched tumor and normal DNA from 5 non-hyperdiploid MM patients using a next-generation semiconductor sequencing protocol. DNA was extracted from magnetic bead-enriched bone marrow CD138 positive tumor cells from the patients and CD138 negative cells were used as matched normal cells. We detected both point mutations and copy number variations (CNVs). One individual with refractory MM (IgG λ type, ISS stage II) displayed 8 non-synonymous somatic mutations in addition to numbers of CNVs including CCND1 and RB1. We observed 2 point mutations and 3 CNVs, affecting NF-kB pathway genes: IKBKB, CYLD, IKBKE, CD79B and SYK. Although the basis of NF-kB pathway activation is only partially understood, our findings greatly expand the mechanisms by which NF-kB may be activated in this patient. We also detected several gene alterations, which may be associated with poor prognosis and poor response to chemotherapy in patients with refractory MM. Although its value should be further confirmed in larger samples, the targeted next generation sequencing is a valuable tool for high-throughput genetic testing in clinical research. Citation Format: Hiroshi Ikeda, Yasushi Sasaki, Tetsuyuki Igarashi, Yuka Aoki, Toshiaki Hayashi, Tadao Ishida, Takashi Tokino, Yasuhisa Sinomura. Molecular diagnostics of drug resistant multiple myeloma cases using targeted next generation sequencing. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4424. doi:10.1158/1538-7445.AM2015-4424

Multiple Myeloma in the Afro-Caribbean Inner City Population: Conventional Cytogenetics and Survival

Background:Multiple Myeloma (MM) is the second most common hematologic malignancy in the United States. African Americans have among the highest risks of MM and MGUS with several distinct features compared to existing literature. Furthermore, the prevalence of MM is even higher in the Afro-Caribbean population. Cytogenetic and molecular genetic abnormalities predict outcome in patients with MM. Hyperdiploid MM (H-MM) generally has a better prognosis than nonhyperdiploid MM (NH-MM). In addition, patients with additional chromosome 1 abnormalities, loss of chromosome 13, translocation t(14;16) and t(4;14) tend to have a worse survival while patients with translocations t(11;14) are associated with improved survival. In our patient population, the most common cytogenetic abnormalities and their effect on survival remain unknown.Objective:This study was performed to establish a profile of Afro-Caribbean patients with newly diagnosed Multiple Myeloma in order to gain further insight into unique cytogenetic abnormalities and their effects on survival.Methods:Patients with Multiple Myeloma at Kings County Hospital Center and University Hospital at Brooklyn from 2000-2013 were identified by our tumor registries (n=311). We included all the newly diagnosed patients from 2000-2013 who underwent a bone marrow biopsy and conventional cytogenetic by chromosome banding and FISH (n= 173). Patients who did not have a cytogenetic analysis were excluded. Data was collected at the time of initial presentation to include demographics and cytogenetic abnormalities. Survival data was obtained from Social Security Death Index. Differences in frequency of each cytogenetic abnormality by mortality status were examined using Chi-Square or Fisher’s Exact Tests. Two sets of age-adjusted logistic regression models were used to examine potential cytogenetic correlates of both poor (less than two years) and good (4 years or more) survival. Data analysis was performed using SPSS Advanced Statistics.Results:The median age at the time of diagnosis was 65 (Range 36-90). Chromosome banding and FISH showed abnormal cytogenetics in 46% of our patients (n=79). These patients were also found to have multiple abnormal clones. NH-MM was found in 24% (n=19) and H-MM was found in 39% (n=31) of the 79 patients. The most commonly affected abnormalities were trisomiesof odd-numbered chromosomes; +1 (47%), +3 (19%), +5 (21%), +7 (24%), +9 (47%), +11 (42%), +15 (44%), +17 (9%) and +19 (29%).Thirty five percent of 173 patients have expired (n=60). The median survival in the deceased patients was 6.2 years (Range 0.34-12.9). When we examined all patients who lived greater than four years post-diagnosis (n=152), we found significant abnormalities including +5 (p=0.052), NH-MM (p=0.009) and t(11;14) (p=0.03) (See Table 1). Indicators of poor prognosis including 1q gain (p=0.13), loss of chromosome 13 (p=0.21) and del17 (p=0.08) were not significant. In patients who are living, 19% (n=29) have not yet reached the four-year post-diagnosis survival. Less than ten percent underwent autologous stem cell transplantation.Excludes patients who lived less than 3 months post diagnosis August 5 2014Table 1:Age-Adjusted Logistic Regression Models Predicting Good Survival (lived 4 years or more post-diagnosis)Chromosome abnormality ( + gain, - loss)Age-Adjusted Odds Ratio (95% CI) N=152P-value1+0.77 (0.26, 2.29)0.631-2.91 (0.58, 14.57)0.193+1.05 (0.35, 3.17)0.935+0.47 (0.22, 1.00)0.0527+0.39 (0.14, 1.10)0.0811+0.80 (0.36, 1.75)0.5714+2.07 (0.62, 6.91)0.2415+0.74 (0.34,1.60)0.4419+1.20 (0.46, 3.13)0.71X-0.42 (0.11, 1.50)0.18Y-0.40 (0.13, 1.26)0.12Hyperdiploidy0.88 (0.39, 2.00)0.88Nonhyperdiplody0.24 (0.08, 0.70)0.009t(4;14)0.76 (0.27, 2.15)0.60t(11;14)0.18 (0.04, 0.86)0.03Conclusion:In this group of Afro-Caribbean patients, median survival (6 years) was higher than Surveillance, Epidemiology, and End Results (SEER) data and more recent review of literature. Gain of chromosome 5 and t(11;14) are consistent with existing data for good prognosis. However, NH-MM which is usually an indicator of poor prognosis was also highly significant in the four-year post-diagnosis survival. This further supports the notion that prognostic value of cytogenetic analysis in this population requires further exploration. DisclosuresNo relevant conflicts of interest to declare.

Widespread Genetic Heterogeneity in Multiple Myeloma: Implications for Targeted Therapy

We performed massively parallel sequencing of paired tumor/normal samples from 203 multiple myeloma (MM) patients and identified significantly mutated genes and copy number alterations and discovered putative tumor suppressor genes by determining homozygous deletions and loss of heterozygosity. We observed frequent mutations in KRAS (particularly in previously treated patients), NRAS, BRAF, FAM46C, TP53, and DIS3 (particularly in nonhyperdiploid MM). Mutations were often present in subclonal populations, and multiple mutations within the same pathway (e.g., KRAS, NRAS, and BRAF) were observed in the same patient. In vitro modeling predicts only partial treatment efficacy of targeting subclonal mutations, and even growth promotion of nonmutated subclones in some cases. These results emphasize the importance of heterogeneity analysis for treatment decisions.

The Genetic Architecture of Multiple Myeloma

Multiple myeloma is a malignant proliferation of monoclonal plasma cells leading to clinical features that include hypercalcaemia, renal dysfunction, anaemia, and bone disease (frequently referred to by the acronym CRAB) which represent evidence of end organ failure. Recent evidence has revealed myeloma to be a highly heterogeneous disease composed of multiple molecularly-defined subtypes each with varying clinicopathological features and disease outcomes. The major division within myeloma is between hyperdiploid and nonhyperdiploid subtypes. In this division, hyperdiploid myeloma is characterised by trisomies of certain odd numbered chromosomes, namely, 3, 5, 7, 9, 11, 15, 19, and 21 whereas nonhyperdiploid myeloma is characterised by translocations of the immunoglobulin heavy chain alleles at chromosome 14q32 with various partner chromosomes, the most important of which being 4, 6, 11, 16, and 20. Hyperdiploid and nonhyperdiploid changes appear to represent early or even initiating mutagenic events that are subsequently followed by secondary aberrations including copy number abnormalities, additional translocations, mutations, and epigenetic modifications which lead to plasma cell immortalisation and disease progression. The following review provides a comprehensive coverage of the genetic and epigenetic events contributing to the initiation and progression of multiple myeloma and where possible these abnormalities have been linked to disease prognosis.

The shaping and functional consequences of the dosage effect landscape in multiple myeloma

BackgroundMultiple myeloma (MM) is a malignant proliferation of plasma B cells. Based on recurrent aneuploidy such as copy number alterations (CNAs), myeloma is divided into two subtypes with different CNA patterns and patient survival outcomes. How aneuploidy events arise, and whether they contribute to cancer cell evolution are actively studied. The large amount of transcriptomic changes resultant of CNAs (dosage effect) pose big challenges for identifying functional consequences of CNAs in myeloma in terms of specific driver genes and pathways. In this study, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs.ResultsWe propose gene-wise dosage effect score and genome-wide karyotype plot as tools to measure and visualize concordant copy number and expression changes across cancer samples. We find that dosage effect in myeloma is widespread yet variable, and it is correlated with gene expression level and CNA frequencies in different chromosomes. Our analysis suggests that despite the enrichment of differentially expressed genes between hyperdiploid MM and non-hyperdiploid MM in the trisomy chromosomes, the chromosomal proportion of dosage sensitive genes is higher in the non-trisomy chromosomes. Dosage-sensitive genes are enriched by genes with protein translation and localization functions, and dosage resistant genes are enriched by apoptosis genes. These results point to future studies on differential dosage sensitivity and resistance of pro- and anti-proliferation pathways and their variation across patients as therapeutic targets and prognosis markers.ConclusionsOur findings support the hypothesis that recurrent CNAs in myeloma are selected by their functional consequences. The novel dosage effect score defined in this work will facilitate integration of copy number and expression data for identifying driver genes in cancer genomics studies. The accompanying R code is available at http://www.canevolve.org/dosageEffect/.

Transcription factor-pathway coexpression analysis reveals cooperation between SP1 and ESR1 on dysregulating cell cycle arrest in non-hyperdiploid multiple myeloma

Multiple myeloma is a hematological cancer of plasma B-cells and remains incurable. Two major subtypes of myeloma, hyperdiploid (HMM) and non-hyperdiploid myeloma (NHMM), have distinct chromosomal alterations and different survival outcomes. Transcription factors (TrFs) have been implicated in myeloma oncogenesis but their dysregulation in myeloma subtypes are less studied. Here we develop a TrF-pathway co-expression analysis to identify altered co-expression between two sample types. We apply the method to the two myeloma subtypes and the cell cycle arrest pathway, which is significantly differentially expressed between the two subtypes. We find that TrFs MYC, NF-κB and HOXA9 have significantly lower co-expression with cell cycle arrest in HMM, co-occurring with their over-activation in HMM. In contrast, TrFs ESR1, SP1 and E2F1 have significantly lower co-expression with cell cycle arrest in NHMM. SP1 ChIP targets are enriched by cell cycle arrest genes. These results motivate a cooperation model of ESR1 and SP1 in regulating cell cycle arrest, and a hypothesis that their over-activation in NHMM disrupts proper regulation of cell cycle arrest. Co-targeting ESR1 and SP1 shows a synergistic effect on inhibiting myeloma proliferation in NHMM cell lines. Therefore, studying TrF-pathway co-expression dysregulation in human cancers facilitates forming novel hypotheses towards clinical utility.

Dissecting Karyotypic Patterns in Non-Hyperdiploid Multiple Myeloma: An Overview on the Karyotypic Evolution

Multiple myeloma (MM) is a plasma cell disorder characterized by the presence of specific genetic and cytogenetic aberrations that define unique subgroups with different outcomes. On the basis of the ploidy status, MM can be subdivided into hyperdiploid MM (H-MM) and non-hyperdiploid MM (NH-MM). NH-MM is an entity that encompasses hypodiploid, pseudodiploid, and near tetraploid MM and is associated with a higher number of immunoglobulin heavy-chain (IgH) translocations. We have systematically analyzed the structure of the karyotypic evolution in NH-MM and identified several genetic features of their complex karyotypic patterns. On the basis of statistical models used in complex karyotypes, we were able to identify the temporal order in which the genetic aberrations occur in NH-MM. In this analysis, whole chromosome losses and IgH translocations were commonly seen, and -13/13q- and t14q32 were defined as early genetic events in the karyotypic evolution of NH-MM. Furthermore, chromosome 1 and 17 abnormalities were associated with a late karyotypic phase of evolution consistent with the recognized pattern of acquired events deemed to be associated with these type of genetic aberrations. Accumulation of genetic aberrations in NH-MM above a threshold results in malignant transformation.

Hypodiploid multiple myeloma is characterized by more aggressive molecular markers than non-hyperdiploid multiple myeloma

Multiple myeloma can be categorized into hyperdiploid or non-hyperdiploid myeloma based on the number of chromosomes found in the tumor clone. Among the non-hyperdiploid myelomas, the hypodiploid subtype has the most aggressive clinical phenotype, but the genetic differences between groups are not completely defined. In order to understand the genetic background of hypodiploid multiple myeloma better, we compared the genomic (array-based comparative genomic hybridization) and transcriptomic (gene expression profiling) background of 49 patients with hypodiploid myeloma with 50 other non-hyperdiploid and 125 hyperdiploid myeloma patients. There were significant chromosomal and gene expression differences between hyperdiploid patients and non-hyperdiploid and hypodiploid patients. Non-hyperdiploid and hypodiploid patients shared most of the chromosomal abnormalities; nevertheless a subset of these abnormalities, such as monosomies 13, 14 and 22, was markedly increased in hypodiploid patients. Furthermore, deletions of 1p, 12p, 16q and 17p, all associated with poor outcome or progression in multiple myeloma, were significantly enriched in hypodiploid patients. Molecular risk-stratification indices reinforce the worse prognosis associated with hypodiploid multiple myeloma compared with non-hyperdiploid multiple myeloma. Gene expression profiling clustered hypodiploid and non-hyperdiploid subgroups closer than hyperdiploid myeloma but also highlighted the up-regulation of CCND2, WHSC1/MMSET and FGFR3 in the hypodiploid subtype. In summary, hypodiploid multiple myeloma is genetically similar to non-hyperdiploid multiple myeloma but characterized by a higher prevalence of genetic alterations associated with poor outcome and disease progression. It is provocative to hypothesize that hypodiploid multiple myeloma is an advanced stage of non-hyperdiploid multiple myeloma.

Classify Hyperdiploidy Status of Multiple Myeloma Patients Using Gene Expression Profiles

Multiple myeloma (MM) is a cancer of antibody-making plasma cells. It frequently harbors alterations in DNA and chromosome copy numbers, and can be divided into two major subtypes, hyperdiploid (HMM) and non-hyperdiploid multiple myeloma (NHMM). The two subtypes have different survival prognosis, possibly due to different but converging paths to oncogenesis. Existing methods for identifying the two subtypes are fluorescence in situ hybridization (FISH) and copy number microarrays, with increased cost and sample requirements. We hypothesize that chromosome alterations have their imprint in gene expression through dosage effect. Using five MM expression datasets that have HMM status measured by FISH and copy number microarrays, we have developed and validated a K-nearest-neighbor method to classify MM into HMM and NHMM based on gene expression profiles. Classification accuracy for test datasets ranges from 0.83 to 0.88. This classification will enable researchers to study differences and commonalities of the two MM subtypes in disease biology and prognosis using expression datasets without need for additional subtype measurements. Our study also supports the advantages of using cancer specific characteristics in feature design and pooling multiple rounds of classification results to improve accuracy. We provide R source code and processed datasets at www.ChengLiLab.org/software.

Molecular pathogenesis of multiple myeloma: basic and clinical updates

Multiple myeloma is divided into two distinct genetic subtypes based on chromosome content. Hyperdiploid myeloma is characterized by multiple trisomies of chromosomes 3, 5, 7, 9 11, 15, 19 and 21, and lacks recurrent immunoglobulin gene translocations. Non-hyperdiploid myeloma in contrast is characterized by chromosome translocations t(4;14), t(14;16), t(14;20), t(6;14) and t(11;14). A unifying event in the pathogenesis of multiple myeloma is the dysregulated expression of a cyclin D gene, either directly by juxtaposition to an immunoglobulin enhancer, as a result of ectopic expression of a MAF family transcription factor, or indirectly by as yet unidentified mechanisms. Secondary genetic events include rearrangements of MYC, activating mutations of NRAS, KRAS or BRAF, a promiscuous array of mutations that activate NFkB and deletions of 17p. Among the poor-risk genetic features are t(4;14), t(14;16), t(14;20), del 17p and gains of 1q. Available evidence supports the use of a risk-stratified approach to the treatment of patients with multiple myeloma, with the early and prolonged use of bortezomib particularly in patients with t(4;14) and del 17p.

Integrating Gene and Mir Expression Profiles and Regulatory Network Structures to Define Aberrent Feed Forward Loops with Functional and Clinical Implications in Myeloma.

AbstractAbstract 2386Multiple myeloma (MM) is a clonal plasma cell malignancy with a heterogeneous genetic background. Extensive gene expression profile analysis have provided interesting insight into the disease biology and its correlation with clinical outcome; however, we have begun to realize the significant limitations of expression profile data alone. Therefore there is a growing understanding that additional genomic correlates need to be incorporated to develop an integrated oncogenomic analysis. We have here developed and defined multi-gene transcriptional and post-transcriptional feed-forward loop (FFL) These conceptual FFLs consist of a master TF which regulates a miR and together with it controls a set of specific common gene/s. These recurrent and important network motifs form functional nodes in the larger regulatory network, and are considered linchpins of disease causing genomic alterations in cancer and MM in particular. We have developed a comprehensive novel integrative analysis method, dChip-GemiNI (Gene and miRNA Network-based Integration), which combines gene and miR expression profiles, and also incorporates regulatory network structure in the form of computationally identified TF–miRNA FFLs. The dChip-GemiNI method statistically ranks computationally predicted FFLs by their explanatory power to account for differential gene and miRNA expression. We have next applied dChip-GemiNi to a training dataset of 60 MM patients and 5 normal plasma cells (NPCs) with both gene expression (GE) and miR profiles (dataset GSE16558) in order to identify FFLs containing TF-miR-gene networks with loss of negative feedback regulation in MM, supporting the uncontrolled growth, anti-apoptosis and/or other oncogenic effects. We have identified 20 FFLs significantly aberrant between NPC and MM cells. Prominent FFLs involve known MM dysregulated TFs such as MYC, TP53 and Sp1. In addition, we have utilized 3 available myeloma datasets with both miR and GE profiles (GSE16558, GSE17306, GSE17498), and classified MM samples into hyperdiploid MM (HMM) and non-hyperdiploid MM (NHMM) subtype groups by GE profiles at an accuracy >85%. These two groups have different survival outcomes (p-value < 0.01). We have identified 55 FFLs altered between these two MM subtypes. In particular we have observed that the FFL involving CREB1- miR-20a and target genes RRAGD, PIP4K2A, RHOC and CCND2 is altered between HMM and NHMM and is common between the 3 datasets. We have now begun to statistically ranks computationally predicted FFLs and develop a motif score to develop an integrated risk stratification model. In conclusion, FFLs form critical regulatory loops driving the functional behavior of MM cells. Analyzing the molecular impact of FFLs as a unit combining the aggregate impact of TF-miR and the target gene/s in MM will be instrumental in understanding the biology of the disease, developing clinically relevant integrated risk models, and translating basic research into targeted therapy. The ultimate goal is to develop strategies to regulate homeostatic control of these loops and overcome their oncogenic effects that drive the malignant phenotype. Disclosures:Munshi:Celgene: Consultancy; Millenium: Consultancy; Merck: Consultancy; Onyx: Consultancy.

Promiscuous Cryptic Rearrangements of the MYC Locus Cis-Dysregulate MYC Expression and Are Present in the Majority of Patients with Hyperdiploid Myeloma

AbstractAbstract 724 Background:Chromosome content identifies two pathogenic pathways, each occurring in about half of patients with MGUS and multiple myeloma (MM). Hyperdiploid MM (HRD) has 48–75 chromosomes with multiple trisomies selectively involving chromosomes 3, 5, 7, 9, 11, 15, 19 and 21; only 10% of these HRD tumors have primary IgH translocations and no frequent focal genetic mutations have been identified. In contrast primary IgH translocations are identified in over 70% of non-hyperdiploid MM (NHRD). Rearrangements of MYC have been detected by FISH in only 16% of untreated MM, but over 90% of MM cell lines, identifying a late role for MYC in the progression of MM. The introduction of a MYC transgene into a mouse strain predisposed to MGUS results in mice that uniformly develop MM, suggesting a distinct early role of MYC in the progression of MGUS to MM. We report here that rearrangements in a 4Mb region surrounding MYC are present in 70% of HRD, representing the most frequent focal genetic mutation in this genetic subtype of MM. Results:We analyzed the MMRC reference collection of gene expression (Affymetrix Hu133Plus2) and copy number (Agilent 244k CGH) data and performed FISH to identify MYC rearrangements with IgH or IgL loci in 218 patients with untreated and relapsed MM. We found MYC rearrangements in 48% of MM (identified only by FISH in 5%, only by aCGH in 33%, and by both FISH and aCGH in 10%), including 43% of untreated, and 51% of relapsed MM. Using a hyperdiploid index calculated from the median copy number of the chromosomes involved in trisomies we determined that rearrangements of MYC were present in 70% of the top third, 35% of the middle third, and 25% of the bottom third. Using the paired gene expression data we found that the expression of MYC was approximately two-fold higher in the samples with rearrangements compared to those without rearrangements (p<0.001) and about three-fold higher in MM tumors without rearrangements compared to MGUS (p<0001). Using paired RNA and DNA from the MMRC reference collection we determined in 22 informative patients that MYC rearrangements are associated with monoallelic expression of MYC (p<0.01), consistent with cis-dysregulation of MYC. Analysis of the various changes on aCGH, and fine mapping of the genetic architecture of the rearrangements using next generation sequencing identifies a promiscuous array of rearrangements that often result in the introduction of an enhancer within the MYC locus, resulting in its cis-dysregulation. Since they cannot be comprehensively identified by either CGH or FISH alone, more sensitive techniques, such as next generation sequencing approaches, will be required to comprehensively identify all MYC rearrangements in MM. Conclusions:Rearrangements of MYC are the most frequent focal genetic mutation in untreated MM and are particularly prevalent in hyperdiploid MM. While only one third involve an immunoglobulin locus, they all result in cis-dysregulated expression of MYC, and may be one mechanism responsible for the progression of MGUS to MM. Tumors lacking MYC rearrangements bi-allelically over-express MYC by a trans mechanism including potentially inactivating mutations of BLIMP1/PRDM1, or activating mutations of IRF4. We propose two largely non-overlapping pathogenic pathways in MM: HRD associated with frequent MYC rearrangements, and NHRD associated with frequent primary IgH translocations. The prevalence of MYC rearrangements increases with tumor progression, identifying a role for MYC both early and late in tumorigenesis. As therapies that have been reported to target MYC (e.g., IMiDs®, bortezomib, bromodomain inhibitors) are used in the clinic, it will be important to associate their effect with the presence or absence of MYC rearrangements. Disclosures:Bergsagel:Constellation Pharmaceuticals: Consultancy. Keats:Tgen: Employment.

DNA Copy Number Changes Have Gene Dosage Effects with Consequent Impact On Disease Biology and Prognosis in Multiple Myeloma

AbstractAbstract 3984Copy number alterations, deletions and amplifications, are very frequent in multiple myeloma (MM), however, it is less clear how these alterations affect gene expression. We performed a genome-wide analysis of 170 newly-diagnosed uniformly treated MM patients using high-density SNP arrays and Exon ST 1.0 gene expression arrays, and evaluated how copy number alterations affect gene expression in MM. Using SNP array data just over 40% patients had hyperdiploid MM (HMM) while the rest had non-hyperdiploid MM (N-HMM). We used two-step procedure to identify dosage effect scores of genes. At first, for each gene, percentage of copy number altered samples was calculated. Then for each gene percentage of samples that had dosage effect was calculated. Finally dosage effect score for each gene was calculated as a ratio of dosage effect samples percentage to copy number alteration sample percentage. We show that dosage effect in MM is wide-spread and some chromosomal locations are affected by dosage effect more compared to other locations. The dosage effect tracks can be observed at trisomy chromosomes and chromosome 1q, but most explicitly at chromosome 9, 11, 15 and 19. Also for deleted genes, dosage effect can be mostly observed at chromosome 13 and 16q. Separate analysis of HMM and N-HMM patients also showed that HMM patients have higher dosage effect especially in chromosome 15 compared to the others. In addition, relation between dosage effect and gene expression analysis show that the highly expressed genes have significantly higher dosage effect compared to the lowly expressed genes. Also function enrichment analysis showed that genes involved with crucial biological processes including translation, RNA processing and transcription factor genes are enriched in genes with higher dosage effect. Interstingly, dosage resistant genes are enriched in cell death and GTPase processes. These results help us understand the impact of aneuploidy in MM on global gene expression changes. In conclusion, our analysis identifies concordant and discordant gene expression changes associated with DNA copy number alterations, identifying genes and pathways that may play an important role in myeloma disease behavior as well as prognosis. Disclosures:No relevant conflicts of interest to declare.

Whole Genome Sequencing Reveals Novel Recurring Somatic Mutations Affecting HUWE1 and DIAPH2 Genes in Multiple Myeloma

Abstract 320Multiple myeloma (MM) is an incurable malignancy of antibody secreting plasma B-cells whose etiology is still poorly understood. We used whole genome sequencing (WGS) and deep-read capture validation to thoroughly characterize the mutation landscape in four newly diagnosed MM patients and further examined mutation recurrence in 89 additional MM patients. WGS cases were selected to be racially diverse and represent both hyperdiploid and non-hyperdiploid MM with otherwise “simple” karyotypes. All studies to date have used peripheral blood as controls, however, abnormal B cells and circulating tumor cells frequently contaminate the peripheral blood of MM patients and these studies may have missed the early genetic events potentially important in disease pathogenesis. We therefore chose to use matched skin samples as normal controls. The use of skin DNA controls and deep read count capture validation of single nucleotide variants (SNV) allowed us, for the first time, to demonstrate clear separation between normal and malignant cell populations. Our analysis pipeline detected both somatic SNVs and structural variants (SV, ie. translocations, deletions, insertions). In all four patients, we observed chromosomal translocations at the Ig heavy chain locus, VDJ recombination, and Ig locus somatic hypermuation. We found somatic mutations affecting the E3 ubiquitin ligase HUWE1 in 4% (4/94) of cases, as well as recurring deletions affecting the Rho pathway regulator DIAPH2 (recurrence data will be presented). RNA sequencing was preformed for 3 of the WGS patients to explore transcriptional effects of mutations observed. Statistical analysis identified significantly mutated genes, i.e. mutation hotspots, including ROBO2, BCL6 and cadherin/catenin genes. Genes involved in cell polarity, cell adhesion and axon guidance were affected in all four WGS patients. Chromosomal translocations present in all four WGS patients involved both known oncogenes (CCND1, MYC and MAFB) and putative oncogenes. A novel translocation implicated KCNT2, encoding a sodium-activated potassium channel, as a potential oncogene in MM. Recurrent point mutations were relatively rare in MM, suggesting that SVs are more common driver mutations and/or that SNVs in MM disrupt a diverse array of genes to affect key pathways. For genomic studies in MM moving forward, our results emphasize the importance of matched normal controls uncontaminated by tumor cells, and suggest that careful analysis of SVs be included to find novel oncogenes and important clinical correlations. Disclosures:No relevant conflicts of interest to declare.

Identification of unbalanced genome copy number abnormalities in patients with multiple myeloma by single-nucleotide polymorphism genotyping microarray analysis

Single-nucleotide polymorphism genotyping microarray (SNP array) analysis provides detailed information on chromosomal copy number aberrations. To obtain detailed information on genomic abnormalities related to pathogenesis or prognosis of multiple myeloma (MM), we performed 250K SNP array analysis in 39 MM patients and 11 cell lines. We identified an accumulation of deletions and uniparental disomies at 22q12.1. Among the hyperdiploid MM cases, chromosomal imbalance at this locus was associated with poor prognosis. On sequencing, we also found a mutation in the seizure-related 6 homolog (mouse)-like (SEZ6L) gene located at ch.22q12.1 in an MM cell line, NOP1. We further found isolated deletions in 17 genes, five of which are known tumor suppressor genes. Of these, deletion of protein tyrosine phosphatase, receptor type D (PTPRD) was found in three samples, including two patients. Consistent with previous reports, non-hyperdiploid MM, deletion of 13q (del13q) and gain of 1q in non-hyperdiploid MMs were predictive of poor prognosis (p = 0.039, p = 0.049, and p = 0.013, respectively). However, our analysis revealed that unless accompanied by gain of 1q, the prognosis of non-hyperdiploid MM was as good as that of hyperdiploid MM. Thus, SNP array analysis provides significant information useful to understanding the pathogenesis and prognosis of MM.