A SNP microarray and FISH-based procedure to detect allelic imbalances in multiple myeloma: An integrated genomics approach reveals a wide gene dosage effect (original) (raw)
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Blood, 2016
Multiple myeloma (MM) is a neoplasm that exhibits a broad heterogeneity in both biological behavior and clinical presentation. Specific copy number abnormalities (CNAs) such as hyperdiploidy, 1p loss, 1q gain, 13q loss and 17p loss (including the TP53 gene), and IGH translocations, such as t(4;14)(p16;q32) and t(14;16)(q32;q23), provide important information regarding prognosis and treatment response. Interphase fluorescence in situ hybridization (FISH) on enriched plasma cells, currently used in clinical diagnostics of MM, is a targeted test aimed at specific genomic loci. However, it is laborious and provides only genetic information of the probe targets. Microarray-based genomic profiling is a high-resolution tool that enables genome-wide analyses for copy number alterations (CNA), including focal CNA (<5 Mb) and regions of copy neutral loss of heterozogosity (CNLOH) that cannot be identified by FISH. A limitation of SNP-based array is its inability to identify balanced transl...
2012
Supplemental experimental procedures Validation of the prognostic significance of NMF groups K1 and K2 in an independent set of tumors Rationale It is possible that aCGH in a new sample set, or by different technique, could yield NMF components different from those obtained in this study. Nonetheless, the association of the K1 and K2 groups with prognosis makes a strong prediction for the effect of ch13 loss and ch1q gain in tumors with an odd-chromosome gained (hyperdiploid) background. We attempted to validate the prognostic power of these principle features defining K1 and K2 in an independent set of samples using a combination of FISH and expression data as surrogates for aCGH. We first used the corresponding gene-expression profiles of the 67 MM cases to develop an approach that would simulate validation on aCGH data. Based on the fact that most of the observed copy number heterogeneity among various k subclasses is of large genomic regions or chromosomal arms, and such regional copy number alterations are known to have a measurable influence on the average expression level of the resident genes, we converted geneexpression profiles into "pseudo-CGH" (pCGH) by averaging expression values of all probes by chromosome arms (which effectively reduces the otherwise-dominant effects of gene expression regulation by mechanisms other than copy-number). We then conducted NMF classification using pCGH profiles but were only able to achieve 70% or less accuracy when compared to classifying these 67 samples by true high-resolution aCGH profiles, a disappointingly poor performance. In other words, gene-expression data alone cannot be used as a direct substitute for CGH for purpose of validation of genomic classification. However, as expected, we found that pCGH was very accurate (>95%) at predicting odd-chromosome gain patterns (ch3,5,7,9,15,19). We therefore explored the possibility of combining pCGH (based on gene-expression profiles) for defining the hyperdiploid pattern plus FISH probing alterations of 1q and ch13. Derivation of "pseudo-CGH" in a validation set of 281 samples Affymetrix expression profiles (U133plus2) were obtained for 281 clinically-annotated samples of MM treated with TT2. The data were normalized and converted to expression level via MAS 5.0 software. Profiles were converted to log 2 expression and individual genes standardized by subtracting the mean and dividing by the standard deviation. These values were averaged across all genes on each of the chromosomal arms yielding single values. For each sample, the values were centered by the average of the most invariant chromosomes in multiple myeloma (ch2, 4, 6, 8, 10, 12, 17, 18 and 20). Finally, for each chromosomal arm, the distributions across all 281 samples were then centered to the mode (or to the peak mode for multimodal distributions). The result is a pCGH measure for each chromosomal arm, which is zero for normal copy number, and positive/negative for gain/loss, respectively. Validation of pCGH using FISH pCGH estimates of gain/loss were compared to FISH results obtained in the same samples for probes located on 13q (n=244) and 1q (n=191). The results were reasonably concordant, establishing pCGH as a reasonable surrogate for detecting both losses (13q) and gains (1q) (Figure S4B).
2006
Multiple myeloma is characterized by genomic alterations frequently involving gains and losses of chromosomes. Single nucleotide polymorphism (SNP)-based mapping arrays allow the identification of copy number changes at the sub-megabase level and the identification of loss of heterozygosity (LOH) due to monosomy and uniparental disomy (UPD). We have found that SNP-based mapping array data and fluorescence in situ hybridization (FISH) copy number data correlated well, making the technique robust as a tool to investigate myeloma genomics. The most frequently identified alterations are lo- . LOH is found in these large regions and also in smaller regions throughout the genome with a median size of 1 Mb. We have identified that UPD is prevalent in myeloma and occurs through a number of mechanisms including mitotic nondisjunction and mitotic recombination. For the first time in myeloma, integration of mapping and expression data has allowed us to reduce the complexity of standard gene expression data and identify candidate genes important in both the transition from normal to monoclonal gammopathy of unknown significance (MGUS) to myeloma and in different subgroups within myeloma. We have documented these genes, providing a focus for further studies to identify and characterize those that are key in the pathogenesis of myeloma. (Blood. 2006;108: 1733-1743)
Cancers
Multiple myeloma (MM) is a genetically heterogeneous disease characterized by genomic chaos making it difficult to distinguish driver from passenger mutations. In this study, we integrated data from whole genome gene expression profiling (GEP) microarrays and CytoScan HD high-resolution genomic arrays to integrate GEP with copy number variations (CNV) to more precisely define molecular alterations in MM important for disease initiation, progression and poor clinical outcome. We utilized gene expression arrays from 351 MM samples and CytoScan HD arrays from 97 MM samples to identify eight CNV events that represent possible MM drivers. By integrating GEP and CNV data we divided the MM into eight unique subgroups and demonstrated that patients within one of the eight distinct subgroups exhibited common and unique protein network signatures that can be utilized to identify new therapeutic interventions based on pathway dysregulation. Data also point to the central role of 1q gains and t...
Cancer Cell, 2006
To identify genetic events underlying the genesis and progression of multiple myeloma (MM), we conducted a high-resolution analysis of recurrent copy number alterations (CNAs) and expression profiles in a collection of MM cell lines and outcome-annotated clinical specimens. Attesting to the molecular heterogeneity of MM, unsupervised classification using nonnegative matrix factorization (NMF) designed for array comparative genomic hybridization (aCGH) analysis uncovered distinct genomic subtypes. Additionally, we defined 87 discrete minimal common regions (MCRs) within recurrent and highly focal CNAs. Further integration with expression data generated a refined list of MM gene candidates residing within these MCRs, thereby providing a genomic framework for dissection of disease pathogenesis, improved clinical management, and initiation of targeted drug discovery for specific MM patients.
Genes Chromosomes & Cancer, 2005
Chromosomal translocations involving the immunoglobulin heavy chain (IGH) locus and various partner loci frequently are associated with multiple myeloma (MM). We investigated the expression profiles of the FGFR3/MMSET, CCND1, CCND3, MAF, and MAFB genes, which are involved in t(4;14)(p16.3;q32), t(11;14)(q13;q32), t(6;14)(p21;q32), t(14;16)(q32;q23), and t(14;20)(q32;q12), respectively, in purified plasma cell populations from 39 MMs and six plasma cell leukemias (PCL) by DNA microarray analysis and compared the results with the presence of translocations as assessed by dual-color FISH or RT-PCR. A t(4;14) was found in 6 MMs, t(11;14) in 9 MMs and 1 PCL, t(6;14) in 1 MM, t(14;16) in 2 MMs and 1 PCL, and t(14;20) in 1 PCL. In all cases, the translocations were associated with the spiked expression of target genes. Furthermore, gene expression profiling enabled the identification of putative translocations causing dysregulation of CCND1 (1 MM and 1 PCL) and MAFB (1 MM and 1 PCL) without any apparent involvement of immunoglobulin loci. Notably, all of the translocations were mutually exclusive. Markedly increased MMSET expression was found in 1 MM showing associated FGFR3 and MMSET signals on an unidentified chromosome. Our data suggest the importance of using combined molecular cytogenetic and gene expression approaches to detect genetic aberrations in MM. © 2004 Wiley-Liss, Inc.
Genes Chromosomes & Cancer, 2007
To investigate the patterns of genetic lesions in a panel of 23 human multiple myeloma cell lines (HMCLs), we made a genomic integrative analysis involving FISH, and both gene expression and genome-wide profiling approaches. The expression profiles of the genes targeted by the main IGH translocations showed that the WHSC1/MMSET gene involved in t(4;14)(p16;q32) was expressed at different levels in all of the HMCLs, and that the expression of the MAF gene was not restricted to the HMCLs carrying t(14;16)(q32;q23). Supervised analyses identified a limited number of genes specifically associated with t(4;14) and involved in different biological processes. The signature related to MAF/MAFB expression included the known MAF target genes CCND2 and ITGB7, as well as genes controlling cell shape and cell adhesion. Genome-wide DNA profiling allowed the identification of a gain on chromosome arm 1q in 88% of the analyzed cell lines, together with recurrent gains on 8q, 18q, 7q, and 20q; the most frequent deletions affected 1p, 13q, 17p, and 14q; and almost all of the cell lines presented LOH on chromosome 13. Two hundred and twenty-two genes were found to be simultaneously overexpressed and amplified in our panel, including the BCL2 locus at 18q21.33. Our data further support the evidence of the genomic complexity of multiple myeloma and reinforce the role of an integrated genomic approach in improving our understanding of the molecular pathogenesis of the disease. This article contains Supplementary Material available at http://www.interscience.wiley.com/jpages/1045-2257/suppmat. © 2006 Wiley-Liss, Inc.
Genes, Chromosomes and Cancer, 2016
The genomic profile of multiple myeloma (MM) has prognostic value by dividing patients into a good prognosis hyperdiploid group and a bad prognosis nonhyperdiploid group with a higher incidence of IGH translocations. This classification, however, is inadequate and many other parameters like mutations, epigenetic modifications, and genomic heterogeneity may influence the prognosis. We performed a genomic study by array-based comparative genomic hybridization on a cohort of 162 patients to evaluate the frequency of genomic gains and losses. We identified a high frequency of X chromosome alterations leading to partial Xq duplication, often associated with inactive X (Xi) deletion in female patients. This partial X duplication could be a cytogenetic marker of aneuploidy as it is correlated with a high number of chromosomal breakages. Patient with high level of chromosomal breakage had reduced survival regardless the region implicated. A higher transcriptional level was shown for genes with potential implication in cancer and located in this altered region. Among these genes, IKBKG and IRAK1 are members of the NFKB pathway which plays an important role in MM and is a target for specific treatments.
Genomic Basis of Multiple Myeloma Subtypes from the MMRF CoMMpass Study
2021
Multiple myeloma is a treatable, but currently incurable, hematological malignancy of plasma cells characterized by diverse and complex tumor genetics for which precision medicine approaches to treatment are lacking. The MMRF CoMMpass study is a longitudinal, observational clinical study of newly diagnosed multiple myeloma patients where tumor samples are characterized using whole genome, exome, and RNA sequencing at diagnosis and progression, and clinical data is collected every three months. Analyses of the baseline cohort identified genes that are the target of recurrent gain- and loss-of-function events. Consensus clustering identified 8 and 12 unique copy number and expression subtypes of myeloma, respectively, identifying high- risk genetic subtypes and elucidating many of the molecular underpinnings of these unique biological groups. Analysis of serial samples showed 25.5% of patients transition to a high-risk expression subtype at progression. We observed robust expression o...
Molecular Dissection of Hyperdiploid Multiple Myeloma by Gene Expression Profiling
Cancer Research, 2007
Hyperdiploid multiple myeloma (H-MM) is the most common form of myeloma. In this gene expression profiling study, we show that H-MM is defined by a protein biosynthesis signature that is primarily driven by a gene dosage mechanism as a result of trisomic chromosomes. Within H-MM, four independently validated patient clusters overexpressing nonoverlapping sets of genes that form cognate pathways/networks that have potential biological importance in multiple myeloma were identified. One prominent cluster, cluster 1, is characterized by high expression of cancer testis antigen and proliferation-associated genes. Tumors from these patients were more proliferative than tumors in other clusters (median plasma cell labeling index, 3.8; P < 0.05). Another cluster, cluster 3, is characterized by genes involved in tumor necrosis factor/nuclear factor-KB signaling and antiapoptosis. These patients have better response to bortezomib as compared with patients within other clusters (70% versus 29%; P = 0.02). Furthermore, for a group of patients generally thought to have better prognosis, a cluster of patients with short survival (cluster 1; median survival, 27 months) could be identified. This analysis illustrates the heterogeneity within H-MM and the importance of defining specific cytogenetic prognostic factors. Furthermore, the signatures that defined these clusters may provide a basis for tailoring treatment to individual patients. [Cancer Res 2007;67 :