Chapter 1. Multiple Myeloma (MM): A B-cell malignancy characterized by complex genetic changes 1.1 MM MM is an incurable plasma cell (PC) malignancy. In 2009, it is estimated that 20,580 new cases will be diagnosed, with 10,580 patients succumbing to the disease (Jemal, et al 2007). In many instances, it is preceded by a pre-malignant tumor called monoclonal gammopathy of undetermined significance (MGUS), which is the most common lymphoid tumor in humans, occurring in approximately 3% of individuals over the age of 50 (Kyle, et al 2006). Both MM and non-IgM MGUS show a similar markedly increased prevalence with age. Significantly, the prevalence of both MM and non-IgM MGUS is about two-fold higher in African Americans compared to Caucasians, whereas it appears that the frequency of progression from non-IgM MGUS to MM is similar in these two populations (Landgren, et al 2006). Despite some evidence for familial clustering of MM and non-IgM MGUS, the effects of genetic background and environment remain to be clarified (Lynch, et al 2005). 1.1.1 MM is a plasmablast/plasma cell tumor of post-GC B-cells. Post-germinal center (post-GC) B cells that have undergone somatic hypermutation, antigen selection, and IgH switching can generate plasmablasts (PB), which typically migrate to the bone marow (BM) microenvironment that enables differentiation into long-lived PC (Shapiro-Shelef and Calame 2005). Importantly, non-IgM MGUS and MM are monoclonal tumors that are phenotypically similar to PB/long-lived PC, including a strong dependence on the BM microenvironment for survival and growth (Kuehl and Bergsagel 2002). In contrast to normal long-lived PC, non-IgM MGUS and MM tumors retain some potential for an extremely low rate of proliferation, usually with no more than a few percent of cycling cells until advanced stages of MM (Rajkumar, et al 1999). 1.1.2 Stages of MM It is thought that a PC tumor must include about 109 cells to produce enough monoclonal immunoglobulin (M-Ig) or monoclonal immunoglobulin light chain (M-IgL) to be detected by serum and/or urine electrophoresis (Salmon 1973). However, the recent development of a serum free light chain assay has significantly reduced the number of PC tumor cells that can be detected (Katzmann, et al 2005). This includes an increased ability to detect low levels of M-Ig, or M-IgL in the approximately 15% of MGUS and MM tumors that express only IgL (Bradwell, et al 2003). For MGUS, serum M-Ig usually is 0.5 to g/dL, with the tumor cells comprising no more than 10% of the mononuclear cells in the BM (The International Myeloma Working Group 2003). Depending on the level of M-Ig, presumably a surrogate for tumor mass, MGUS can progress sporadically to MM expressing the same M-Ig with a probability of about 0.6-3% per year (Kyle, et al 2002). For a given M-Ig level, an increased level of serum free-IgL is also associated with an increased probability of progression to MM (Rajkumar, et al 2005). Unfortunately, there are no unequivocal genetic or phenotypic markers, despite a recent report of extensive gene expression profiling, that can distinguish MGUS from MM tumor cells (Zhan, et al 2007a). Moreover, it is still not known to what extent intrinsic genetic or epigenetic changes in the MGUS tumor cell versus extrinsic changes in non-tumor cells affect progression. Therefore, it is still not possible to predict if and when this progression will occur. Smoldering multiple myeloma (SMM), which has a stable BM tumor content of 10-30% but no osteolytic lesions, anemia, or other secondary manifestations of malignant MM, has a high probability of sporadic progression to frankly malignant MM. Extramedullary MM is a more aggressive tumor that often is called secondary or primary plasma cell leukemia (PCL), depending on whether or not preceding intramedullary MM has been recognized. Human myeloma cell lines (HMCL), which are presumed to include most oncogenic events involved in tumor initiation and progression of the corresponding tumor, have been generated mainly from a subset of extramedullary MM tumors (Kuehl and Bergsagel 2002). 1.2 Genetics of MM 1.2.1 Ig translocations are present in a majority of MM tumors Like other post-GC B cell tumors, translocations involving the IgH locus (14q32) or one of the IgL loci (κ, 2p12 or λ, 22q11) are common (Kuppers 2005). Mostly, they are mediated by errors in one of the three B cell specific DNA modification mechanisms: VDJ recombination, IgH switch recombination, or somatic hypermutation. With rare exceptions, these translocations result in dysregulated or increased expression of an oncogene that is positioned near one or more of the strong Ig enhancers on the derivative 14 translocated chromosome (Bergsagel and Kuehl 2001, Pasqualucci, et al 2001). However, translocations involving an IgH switch region uniquely dissociate the intronic region(s) from one or both 3’ IgH enhancers, so that an oncogene might be juxtaposed to an IgH enhancer on either or both of the derivative chromosomes, as first demonstrated for FGFR3 on der(14) and WHSC1 (MMSET) on der(4) in MM (Kuehl and Bergsagel 2002). These IgH translocations are efficiently detected by fluorescence in situ hybridization (FISH) analyses. Large studies from several different groups show that the prevalence of IgH translocations increase with disease stage: about 50% in MGUS or SMM, 5570% for intramedullary MM, 85% in PCL, and >90% in HMCL (Avet-Loiseau, et al 2001a, Avet-Loiseau, et al 2002, Fonseca, et al 2002a, Kuehl and Bergsagel 2002, Fonseca, et al 2003a). Other studies indicate that Igλ translocations are present in about 10% of MGUS/SMM tumors, and about 15 to 20% of intramedullary MM tumors and HMCL. Translocations involving an Igκ locus are rare, occurring in only to 2% of MM tumors and HMCL (Fonseca, et al 2002a, Kuehl and Bergsagel 2002). 1.2.1.1 Seven recurrent IgH translocations appear to represent primary oncogenic events Recently, IgH translocations involving CCND2 and MAFA have been reported (Hanamura, et al 2005). Thus, there are now seven recurrent chromosomal partners and oncogenes that are involved in IgH translocations in approximately 40% of MM tumors. There are three recurrent IgH translocation groups (Table 1.1) (Chesi, et al 1996, Chesi, et al 1997, Chesi, et al 1998a, Chesi, et al 1998b, Hanamura, et al 2001, Shaughnessy, et al 2001, Kuehl and Bergsagel 2002) Table 1.1. Prevalence of recurrent IgH translocations. IgH Translocation gene Protein Deregulated % of all MM Translocation group partner 11q13 (CCND1) Cyclin D1 15% Cyclin D 12p13 (CCND2) Cyclin D2 z al , the Enrichment Score of this pathway signature in this particular sample is the Enrichment Score of a pathway signature of l genes is determined by its top ranking k genes subset. These k genes are called “leading edge genes”, which are presumably “core” genes related to the pathway of interest. For each pathway/geneset in the curated (c2) genesets MSigDB database and MM sample pair, we applied this one-vs-all style GSEA and obtained a geneset enrichment score matrix, whose rows represent genesets and columns represent MM samples. 204 Appendix Figure 1. Clustering of samples and enriched genesets. Each row is a geneset and each column, a sample. The color coding in the heatmap is based on the enrichment score of each geneset. The major clusters of genesets (main branches of the dendogram on the left) are subjected to leading edge analysis. 205 We further identified the common leading edge genes to see which genes cause the activation of pathways in each cluster. These are the “core” genes for each pathway signature. Supplemental Methods Table 1. Genes Constituting Core Pathway Signatures INTERFERON_CORE MYC_CORE MX1 LDHA MX2 ENO1 STAT1 HSPE1 IFI27 HSPD1 IFITM1 NPM1 ISG15 FKBP4 IFIT3 NME1 OAS1 OAS2 CELL_CYCLE_COREMETABOLIC_CORE PROTEASOME_CORETRNA_CORE MCM2 JTV1 PSMA1 TARS MCM3 CCT5 PSMA2 IARS MCM6 C1QBP PSMA3 NARS MCM7 MRPS12 PSMA4 RARS CCNB1 RAN PSMA5 YARS CCNB2 SSBP1 PSMA6 GARS CDC2 AASDHPPT PSMA7 EPRS TYMS TYMS PSMB1 QARS BUB1 CCT2 PSMB2 KARS BUB1B MCM2 PSMB3 CARS HGMB2 G3BP PSMB4 WARS2 PCNA RPL26L1 PSMB5 MARS MAD2L1 IHPK2 PSMB6 HARS KIF11 IK PSMB7 WARS PTTG1 RNF7 PSMC2 KIAA0101 KEAP1 PSMC3 RPA3 LSM5 PSMD1 RRM2 DKC1 PSMD6 PRC1 PSMD8 PSMD13 CKS2 NDUFA13 FIBP EIF3S4 BARD1 We then re-performed the iGSEA analysis using these pathway core signatures (see main text, Figure 3a). iPASA The idea of our iPASA (individual Pathway Activity Score Analysis) originated from the fact that using the average expression value of a handful of “core” genes for a pathway often gives very good estimation of the pathway activity. However, to find the “core” genes that can represent a pathway is a laborious and difficult task which needs the help of clinic experts with in-depth understanding of the pathway of interest. iPASA is designed to tackle this problem. To start with, iPASA takes a predefined pathway signature of k gene names as input. These k genes (usually hundreds) are not necessarily “core” or verified genes related to the pathway, and are usually curated gene lists from literature or other high throughput gene mutation experiments. The key idea is that if a subset of “core” genes within these k genes actually indicates the pathway activities, there should be a reasonable degree of correlation among the expression values of these genes. Furthermore, genes which have higher correlations are likely to be the “core” genes, and therefore deserve a higher weight in the algorithm. Principal Component Analysis (PCA) is a suitable mathematical tool for this task. Hence, iPASA uses the first principal component (PC1) as the predicted pathway activity score. 206 Connection between iGSEA and iPASA There exist intrinsic mathematical connections between iGSEA and iPASA. 207 [...]... complexity in MM suggests ongoing genomic instability, the mechanism underlying this is unclear Genomic instability in cancer is broadly categorized into mutational instability, e.g., microsatellite instability (MIN) and chromosome instability (CIN) CIN, characterized by unstable aneuploidy, is the most common form of genetic instability in human cancer Several pathways are operative in the generation of CIN... discrepancies in the results from UAMS and the Mayo Clinic in terms of the independent prognostic impact of 1q21 gain by FISH may be related to differences in the factors included in the Cox proportional hazard analysis In the Mayo Clinic analysis, the prognostic impact of 1q21 gain was no longer significant when the plasma cell labeling index and t(4;14) was included in the modeling, suggesting that much... aberrations occurring in < 10% of all samples are indicated in black [This figure was originally published in Leukemia Chng WJ et al Correlation between array-comparative genomic hybridization-defined genomic gains and losses and survival: identification of 1p31-32 deletion as a prognostic factor in myeloma Leukemia 2010;24:833-42 © Nature Publishing Group.] The frequent gains observed in chromosomes... biological and clinical implications of centrosome abnormalities, which could be a major mechanism of chromosome instability in MM Rationale and Hypothesis MM is characterized by chromosomal abnormalities that are hallmarks of chromosomal instability Abnormalities in the centrosome, a cellular organelle that forms the mitotic spindle during mitosis, have been proposed as a mechanism leading to chromosome instability. .. differentially expressed genes in terms 16 of functional genesets may provide insights into pathways and transcriptional programs that are dysregulated during MGUS to MM transformation The understanding of early events in MM pathogenesis may provide important diagnostic and therapeutic information 17 Chapter 2 Chromosomal instability in MM 2.1 Landscape of genomic aberrations in MM MM cells are characterized... by the Mayo Clinic to define high-risk patients (Stewart, et al 2007) 1.4 Specific aims Against this background, my present work has the following specific aims: a) Describe the landscape and clinical relevance of genomic complexity using aCGH Rationale and Hypothesis Current understanding of MM genetics is hampered by inadequacies of techniques formerly available Conventional karyotyping allows a global... gains and losses in the autosomes amongst all the samples analyzed The probes are sequentially aligned according to the genomic loci from left to right Each column represents a chromosome and the dashed lines within the column separate the p- from the q-arm of the chromosome The chromosome number is indicated at the bottom The bars going up in green represent gain of a probe whereas bars going down in. .. indicates the color-coding for gains and losses Peaks in the legend indicated frequencies of log(base2) ratios The probes are aligned vertically according to their chromosome position as indicated by the alternating black and grey bar on the left The colored bars at the top of the heatmap codes for different clinical parameters For clinical phase, yellow represent newly diagnosed cases and cyan represent... indicated in red whilst losses (log2 ratios < -0.25) are indicated in green The bottom panel represents the Chi-Squared test of independence for frequencies of gains and losses between HRD and NHRD MM The y-axis represents the clone statistics The cyan dotted line going across represent a pvalue cut-off of 0.1, the blue dotted line represent 0.05 and the red line 0.01 These p-values correspond to multiple- testing... reproducible international staging system (ISS) applicable across geographical regions, comprising of two routine clinical tests: serum albumin and beta-2 microglobulin (Greipp, et al 2005) However, genetic factors were not fully assessed in this study A large study from the Intergroupe Francophone du Myelome (IFM) group comprising more than 900 patients entered into clinical trials showed that high-risk . Clinic and the Multiple Myeloma Genomics Portal at the MM Research Consortium (MMRC) (http://www.broad.mit.edu/mmgp). The clinical and genomic information for these patients are appended in. of patients. In addition inactivating mutations of TRAF2, cIAP1/2, and CYLD were identified. Chromosome translocations and amplifications resulting in activation of NFB inducing kinase (NIK). depending on whether or not preceding intramedullary MM has been recognized. Human myeloma cell lines (HMCL), which are presumed to include most oncogenic events involved in tumor initiation and