Integrative genomic analysis reveals distinct transcriptional and genetic features associated with chromosome 13 deletion in multiple myeloma

Individualized dynamic risk assessment for multiple myeloma

Background

Individualized treatment decisions for patients with multiple myeloma (MM) requires accurate risk stratification that takes into account patient-specific consequences of genetic abnormalities and tumor microenvironment on disease outcome and therapy responsiveness.

Methods

Previously, SYstems Genetic Network AnaLysis (SYGNAL) of multi-omics tumor profiles from 881 MM patients generated the mmSYGNAL network, which uncovered different causal and mechanistic drivers of genetic programs associated with disease progression across MM subtypes. Here, we have trained a machine learning (ML) algorithm on activities of mmSYGNAL programs within individual patient tumor samples to develop a risk classification scheme for MM that significantly outperformed cytogenetics, International Staging System, and multi-gene biomarker panels in predicting risk of PFS across four independent patient cohorts.

Results

We demonstrate that, unlike other tests, mmSYGNAL can accurately predict disease progression risk at primary diagnosis, pre- and post-transplant and even after multiple relapses, making it useful for individualized dynamic risk assessment throughout the disease trajectory.

Conclusion

mmSYGNAL provides improved individualized risk stratification that accounts for a patient's distinct set of genetic abnormalities and can monitor risk longitudinally as each patient's disease characteristics change.

Role of 1q21 in Multiple Myeloma: From Pathogenesis to Possible Therapeutic Targets

Multiple myeloma (MM) is characterized by an accumulation of malignant plasma cells (PCs) in the bone marrow (BM). The amplification of 1q21 is one of the most common cytogenetic abnormalities occurring in around 40% of de novo patients and 70% of relapsed/refractory MM. Patients with this unfavorable cytogenetic abnormality are considered to be high risk with a poor response to standard therapies. The gene(s) driving amplification of the 1q21 amplicon has not been fully studied. A number of clear candidates are under investigation, and some of them (IL6R, ILF2, MCL-1, CKS1B and BCL9) have been recently proposed to be potential drivers of this region. However, much remains to be learned about the biology of the genes driving the disease progression in MM patients with 1q21 amp. Understanding the mechanisms of these genes is important for the development of effective targeted therapeutic approaches to treat these patients for whom effective therapies are currently lacking. In this paper, we review the current knowledge about the pathological features, the mechanism of 1q21 amplification, and the signal pathway of the most relevant candidate genes that have been suggested as possible therapeutic targets for the 1q21 amplicon.

Characterization of the role of Samsn1 loss in multiple myeloma development

The protein SAMSN1 was recently identified as a putative tumor suppressor in multiple myeloma, with re-expression of Samsn1 in the 5TGM1/KaLwRij murine model of myeloma leading to a near complete abrogation of intramedullary tumor growth. Here, we sought to clarify the mechanism underlying this finding. Intratibial administration of 5TGM1 myeloma cells into KaLwRij mice revealed that Samsn1 had no effect on primary tumor growth, but that its expression significantly inhibited the metastasis of these primary tumors. Notably, neither in vitro nor in vivo migration was affected by Samsn1 expression. Both knocking-out SAMSN1 in the RPMI-8226 and JJN3 human myeloma cell lines, and retrovirally expressing SAMSN1 in the LP-1 and OPM2 human myeloma cell lines had no effect on either cell proliferation or migration in vitro. Altering SAMSN1 expression in these human myeloma cells did not affect the capacity of the cells to establish either primary or metastatic intramedullary tumors when administered intratibially into immune deficient NSG mice. Unexpectedly, the tumor suppressive and anti-metastatic activity of Samsn1 in 5TGM1 cells were not evidenced following cell administration either intratibially or intravenously to NSG mice. Crucially, the growth of Samsn1-expressing 5TGM1 cells was limited in C57BL/6/Samsn1-/- mice but not in C57BL/6 Samsn1+/+ mice. We conclude that the reported potent in vivo tumor suppressor activity of Samsn1 can be attributed, in large part, to graft-rejection from Samsn1-/- recipient mice. This has broad implications for the design and interpretation of experiments that utilize cancer cells and knockout mice that are mismatched for expression of specific proteins.

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

Background

Multiple 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.

Results

We 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.

Conclusions

Our 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.

Evidence of distinct tumour-propagating cell populations with different properties in primary human hepatocellular carcinoma

Background and Aims

Increasing evidence that a number of malignancies are characterised by tumour cell heterogeneity has recently been published, but there is still a lack of data concerning liver cancers. The aim of this study was to investigate and characterise tumour-propagating cell (TPC) compartments within human hepatocellular carcinoma (HCC).

Methods

After long-term culture, we identified three morphologically different tumour cell populations in a single HCC specimen, and extensively characterised them by means of flow cytometry, fluorescence microscopy, karyotyping and microarray analyses, single cell cloning, and xenotransplantation in NOD/SCID/IL2Rγ^−/− mice.

Results

The primary cell populations (hcc-1, -2 and -3) and two clones generated by means of limiting dilutions from hcc-1 (clone-1/7 and -1/8) differently expressed a number of tumour-associated stem cell markers, including EpCAM, CD49f, CD44, CD133, CD56, Thy-1, ALDH and CK19, and also showed different doubling times, drug resistance and tumorigenic potential. Moreover, we found that ALDH expression, in combination with CD44 or Thy-1 negativity or CD56 positivity identified subpopulations with a higher clonogenic potential within hcc-1, hcc-2 and hcc-3 primary cell populations, respectively. Karyotyping revealed the clonal evolution of the cell populations and clones within the primary tumour. Importantly, the primary tumour cell population with the greatest tumorigenic potential and drug resistance showed more chromosomal alterations than the others and contained clones with epithelial and mesenchymal features.

Conclusions

Individual HCCs can harbor different self-renewing tumorigenic cell types expressing a variety of morphological and phenotypical markers, karyotypic evolution and different gene expression profiles. This suggests that the models of hepatic carcinogenesis should take into account TPC heterogeneity due to intratumour clonal evolution.

Molecular heterogeneity of multiple myeloma: pathogenesis, prognosis, and therapeutic implications

Multiple myeloma (MM) is characterized by a significant heterogeneity at the molecular level. The first level is the chromosomal one. Although cytogenetics is difficult to assess in MM, patients can be divided into two categories: hyperdiploidy and non-hyperdiploidy (about half in each group). Using molecular cytogenetic techniques, several subgroups of patients are identified, particularly on the basis of 14q32 translocations. This chromosomal heterogeneity is confirmed by genomic techniques (gene expression profiling or single nucleotide polymorphism/comparative genomic hybridization arrays). Unsupervised analyses of gene expression profiles identified several subgroups of patients, essentially on the basis of chromosomal abnormalities such as hyperdiploidy or 14q32 translocations. However, these analyses failed to separate MM into subentities, which could lead to specific therapeutic approaches, as is the case for non-Hodgkin's lymphomas. Nevertheless, these chromosomal/genomic data can be used for prognostication of patients. Specific chromosomal changes, such as loss of the short arm of chromosome 17, or specific gene expression profiles clearly identify patients with short survival. No molecular change so far has been associated with long survival or even cure, probably because of the short follow-up observed in all studies. So far, it is unclear how to use this massive amount of data to treat patients. Because of the complex and heterogeneous picture of the molecular profiles, it is unexpected that targeted therapies might play a role in MM. The only recognized indication is to propose bortezomib-based approaches for the treatment of patients displaying the translocation t(4;14).

Gene expression profiling for molecular classification of multiple myeloma in newly diagnosed patients

To identify molecularly defined subgroups in multiple myeloma, gene expression profiling was performed on purified CD138+ plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/GMMG-HD4 trial. Hierarchical clustering identified 10 subgroups; 6 corresponded to clusters described in the University of Arkansas for Medical Science (UAMS) classification, CD-1 (n = 13, 4.1%), CD-2 (n = 34, 1.6%), MF (n = 32, 1.0%), MS (n = 33, 1.3%), proliferation-associated genes (n = 15, 4.7%), and hyperdiploid (n = 77, 24.1%). Moreover, the UAMS low percentage of bone disease cluster was identified as a subcluster of the MF cluster (n = 15, 4.7%). One subgroup (n = 39, 12.2%) showed a myeloid signature. Three novel subgroups were defined, including a subgroup of 37 patients (11.6%) characterized by high expression of genes involved in the nuclear factor kappa light-chain-enhancer of activated B cells pathway, which include TNFAIP3 and CD40. Another subgroup of 22 patients (6.9%) was characterized by distinct overexpression of cancer testis antigens without overexpression of proliferation genes. The third novel cluster of 9 patients (2.8%) showed up-regulation of protein tyrosine phosphatases PRL-3 and PTPRZ1 as well as SOCS3. To conclude, in addition to 7 clusters described in the UAMS classification, we identified 3 novel subsets of multiple myeloma that may represent unique diagnostic entities.

Identification of microRNA expression patterns and definition of a microRNA/mRNA regulatory network in distinct molecular groups of multiple myeloma

To date, little evidence of miRNA expression/deregulation in multiple myeloma has been reported. To characterize miRNA in the context of the major multiple myeloma molecular types, we generated miRNA expression profiles of highly purified malignant plasma cells from 40 primary tumors. Furthermore, transcriptional profiles, available for all patients, were used to investigate the occurrence of miRNA/predicted target mRNA pair anticorrelations, and the miRNA and genome-wide DNA data were integrated in a subset of patients to evaluate the influence of allelic imbalances on miRNA expression. Differential miRNA expression patterns were identified, which were mainly associated with the major IGH translocations; particularly, t(4;14) patients showed specific overexpression of let-7e, miR-125a-5p, and miR-99b belonging to a cluster at 19q13.33. The occurrence of other lesions (ie, 1q gain, 13q and 17p deletions, and hyperdiploidy) was slightly characterized by specific miRNA signatures. Furthermore, the occurrence of several allelic imbalances or loss of heterozygosity was found significantly associated with the altered expression of miRNAs located in the involved regions, such as let-7b at 22q13.31 or miR-140-3p at 16q22. Finally, the integrative analysis based on computational target prediction and miRNA/mRNA profiling defined a network of putative functional miRNA-target regulatory relations supported by expression data.

A SNP microarray and FISH-based procedure to detect allelic imbalances in multiple myeloma: an integrated genomics approach reveals a wide gene dosage effect

Multiple myeloma (MM) is characterized by marked genomic heterogeneity. Beyond structural rearrangements, a relevant role in its biology is represented by allelic imbalances leading to significant variations in ploidy status. To elucidate better the genomic complexity of MM, we analyzed a panel of 45 patients using combined FISH and microarray approaches. We firstly generated genome-wide profiles of 41 MMs and four plasma cell leukemias, using a self-developed procedure to infer exact local copy numbers (CNs) for each sample. Our analysis allowed the identification of a significant fraction of patients showing near-tetraploidy. Furthermore, a conventional hierarchical clustering analysis showed that near-tetraploidy, 1q gain, hyperdiploidy, and recursive deletions at 1p and chromosomes 13, 14, and 22 were the main aberrations driving samples grouping. Moreover, mapping information was integrated with gene expression profiles of the tumor samples. A multiclass analysis of transcriptional profiles characterizing the different clusters showed marked gene-dosage effects, particularly concerning 1q transcripts; this finding was also confirmed by a nonparametric analysis between normalized gene expression levels and local CN variations (1027 highly-significant correlated genes). Finally, we identified several loci in which gene expression correlated with the occurrence of loss of heterozygosity. Our results provide insights into the composite network linking genome structure and transcriptional features in MM.

The molecular characterization and clinical management of multiple myeloma in the post-genome era

Cancer-causing mutations disrupt coordinated, precise programs of gene expression that govern cell growth and differentiation. Microarray-based gene-expression profiling (GEP) is a powerful tool to globally analyze these changes to study cancer biology and clinical behavior. Despite overwhelming genomic chaos in multiple myeloma (MM), expression patterns within tumor samples are remarkably stable and reproducible. Unique expression patterns associated with recurrent chromosomal translocations and ploidy changes defined molecular classes with differing clinical features and outcomes. Combined molecular techniques also dissected two distinct, reproducible forms of hyperdiploid disease and have molecularly defined MM with high risk for poor clinical outcome. GEP is now used to risk-stratify patients with newly diagnosed MM. Groups with high-risk features are evident in all GEP-defined MM classes, and GEP studies of serial samples showed that risk increases over time, with relapsed disease showing dramatic GEP shifts toward a signature of poor outcomes. This suggests a common mechanism of disease evolution and potentially reflects preferential expansion of therapy-resistant cells. Correlating GEP-defined disease class and risk with outcomes of therapeutic regimens reveals class-specific benefits for individual agents, as well as mechanistic insights into drug sensitivity and resistance. Here, we review modern genomics contributions to understanding MM pathogenesis, prognosis, and therapy.

Neurobeachin (NBEA) is a target of recurrent interstitial deletions at 13q13 in patients with MGUS and multiple myeloma

OBJECTIVE

Chromosome 13 deletions (del[13]), detected by metaphase cytogenetics, predict poor outcomes in multiple myeloma (MM), but the gene(s) responsible have not been conclusively identified. We sought to identify tumor-suppressor genes on chromosome 13 using a novel array comparative genomic hybridization (aCGH) strategy.

MATERIALS AND METHODS

We identified DNA copy number losses on chromosome 13 using genomic DNA isolated from CD138-enriched bone marrow cells (tumor) from 20 patients with MM, monoclonal gammopathy of undetermined significance, or amyloidosis. We used matched skin biopsy (germline) genomic DNA to control for copy number polymorphisms and a novel aCGH array dedicated to chromosome 13 to map somatic DNA gains and losses at ultra-high resolution (>385,000 probes; median probe spacing 60 bp). We analyzed microarray expression data from an additional 262 patient samples both with and without del[13].

RESULTS

Two distinct minimally deleted regions at 13q14 and 13q13 were defined that affected the RB1 and NBEA genes, respectively. RB1 is a canonical tumor suppressor previously implicated in MM. NBEA is implicated in membrane trafficking in neurons, protein kinase A binding, and has no known role in cancer. Noncoding RNAs on chromosome 13 were not affected by interstitial deletions. Both the RB1 and NBEA genes were deleted in 40% of cases (8 of 20; 5 patients with del[13] detected by traditional methods and 3 patients with interstitial deletions detected by aCGH). Forty-one additional MM patient samples were used for complete exonic sequencing of RB1, but no somatic mutations were found. Along with RB1, NBEA gene expression was significantly reduced in cases with del[13].

CONCLUSIONS

The NBEA gene at 13q13, and its expression are frequently disrupted in MM. Additional studies are warranted to evaluate the role of NBEA as a novel candidate tumor-suppressor gene.

Role of genetics in prognostication in myeloma

As in other hematological malignancies, cytogenetics is becoming a major prognostic parameter in myeloma. Myeloma differs from other hemopathies particularly in technical aspects related to low proliferation and partial infiltrates. Thus, fluorescence in-situ hybridization (FISH) is probably the best method for cytogenetic assessment in myeloma, but it requires the identification of the malignant cells (morphologically, immunologically or through sorting). Several chromosomal abnormalities have been identified. Among them, the t(4;14) and t(14;16) translocations and the del(17p) are the most important for outcome prediction, all of them predicting a short overall survival. However, even in these genetically defined subgroups, an outcome heterogeneity is observed, suggesting the role of other factors (genetic or otherwise) in disease evolution.