Unraveling genomic instability in multiple myeloma mechanisms, biological and clinical implications

In contrast to the selective occurrence of recurrent IgH translocations in NHRD tumors, other genetic events 17p loss or p53 mutations, RAS mutations, secondary Ig translocations, MYC t

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)

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 3 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, 55-

70% 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 1

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

The recurrent translocation breakpoints usually occur within or near switch regions, but sometimes within or near VDJ sequences, suggesting that these translocations are mediated by errors in IgH switch recombination or somatic hypermutation Since there is no evidence that IgH switch recombination or somatic

hypermutation mechanisms are active in normal PC or PC tumors, it is presumed that these translocations usually represent primary – perhaps initiating – oncogenic events as normal B cells pass through germinal centers With the exception of

FGFR3 (especially with an activating mutation) (Chesi, et al 2001) and possibly MAF (Hurt, et al 2004), the consequences of these translocations have not been

c-adequately confirmed as essential for maintenance of the tumor and/or as therapeutic targets

1.2.1.2 Cyclin D translocation group

The t(11;14) translocation in MM is unusual in that the translocation breakpoints involve VDJ and switch regions with a similar frequency By contrast, t(4;14) and t(14;16) breakpoints always and mostly, respectively, are located within

or near IgH switch regions (Bergsagel and Kuehl 2001, Gabrea, et al 2006)

1.2.1.3 MAF translocation group

The different MAF proteins are basic leucine zipper transcription factors

(Kataoka, et al 1994) MM with translocations affecting any of the three MAF genes share a very similar gene expression profile (Bergsagel, et al 2005) Inducible expression of MAF and MAFB in cell line systems has produced a largely

overlapping list of induced gene expression that has subsequently been validated using the luciferase assay Therefore, many of the genes that are up-regulated in these tumors are thought to be shared targets for all three MAF transcription factors

Notably, these putative targets include CCND2, NOTCH pathway genes and other genes (ITGB7, ARK5) that appear to affect the phenotype of the tumor cells, such as

proliferation and resistance to drug-induced apoptosis, and its potential interactions

with the bone marrow microenvironment (Hurt, et al 2004, Suzuki, et al 2005, van Stralen, et al 2009), although the transcription targets critical for MM pathogenesis

have not been determined

1.2.1.4 MMSET/FGFR3 translocation group

The prevalence of this translocation appears to be substantially lower in

MGUS/SMM than in MM (Avet-Loiseau, et al 1999a, Avet-Loiseau, et al 2002, Keats,

et al 2003, Bergsagel, et al 2005, Zhan, et al 2007a), although one study reported only a slightly decreased prevalence in MGUS/SMM compared to MM (Fonseca, et al

2002a) Although MMSET is dysregulated in all cases, nearly one third of patients

with the t(4;14) translocation do not express FGFR3 The lack of FGFR3 expression

seems to be related mainly to the loss of der(14), but in some cases der(14) is

present and other mechanisms account for the loss of FGFR3 expression (Keats, et

al 2003, Santra, et al 2003) It is unknown whether the loss of FGFR3 expression is

a primary or a secondary event, or even if dysregulation of FGFR3 is critical in pathogenesis In this regard, it may be significant that the t(4;14) is the only known mechanism that dysregulates MMSET Although it seems likely that dysregulated FGFR3 might complement dysregulated MMSET in the early stages of pathogenesis and even at later stages of pathogenesis in some tumors, there is no clear evidence that non-mutated FGFR3 is important at any stage of pathogenesis Rare tumors with t(4;14) sometimes can acquire kinase activating mutations of the dysregulated FGFR3 during tumor progression, and there is evidence that the survival and

proliferation of these tumors is dependent on the mutated FGFR3 (Chesi, et al 1997, Chesi, et al 2001) Other t(4;14) tumors have a K- or N-RAS mutation that seems mutually exclusive of FGFR3 mutations, which suggests why at least some of these tumors would not require FGFR3 (Chesi, et al 2001) The apparently invariant

dysregulation of MMSET in MM tumors with a t(4;14) suggests a critical role for dysregulated MMSET in the initiation and maintenance of these tumors In about a third of cases, the t(4;14) translocation results in loss of amino-terminal sequences of

MMSET so that translation must start from an internal methionine (Chesi, et al 1998a, Keats, et al 2005) The 1365 amino acid MMSET protein contains a SET

domain that is found in many histone methyltransferases (HMTs) and determines

their enzymatic activity (Marango, et al 2008) Recently MMSET has been shown to

have histone methyltransferase activity and knockdown studies have demonstrated that MMSET contributes to cellular adhesion, clonogenic growth and tumorigenicity

(Lauring, et al 2008, Marango, et al 2008)

1.2.1.5 MYC translocations: a paradigm for secondary translocations

Translocations that involve a MYC gene are rare or absent in MGUS, but

occur in 15% of MM tumors, 44% of advanced tumors, and nearly 90% of HMCL

Mostly, these rearrangements involve c-MYC, but about 2% of primary tumors ectopically express N-MYC (and presumably have N-MYC translocations, as confirmed in some cases), and an L-MYC rearrangement has been identified only in

one HMCL These translocations, often heterogeneous in primary tumors, are usually complex rearrangements or insertions, sometimes involving three different

chromosomes (Shou, et al 2000, Avet-Loiseau, et al 2001b, Kuehl and Bergsagel

2002, Zhan, et al 2006a) An Ig locus is involved in 25% (Avet-Loiseau, et al 2001b)

to 60% (Gabrea A, unpublished) of these translocations The IgH locus is involved

somewhat more than the Igλ locus, but the Igκ locus is only rarely involved Thus MYC rearrangements are thought to represent a very late progression event that occurs at a time when MM tumors are becoming less stromal cell dependent and/or more proliferative, whereas bi-allelic c-MYC expression stimulated by IL-6 and other cytokines occurs at earlier phases of tumorigenesis Important questions about the role of MYC translocations in MM are raised by two observations Firstly, Avet-Loiseau and his colleagues found that MYC translocations were rare in primary PCL,

a surprising result given the high prevalence in advanced primary tumors and HMCL

that are derived from primary and secondary PCL (Avet-Loiseau, et al 2001a)

Secondly, a large study by Avet-Loiseau and colleagues showed no effect of MYC

rearrangements on prognosis (Avet-Loiseau, et al 2007) Unfortunately, they were

not able to determine if the different mechanisms of MYC re-arrangement, i.e one involving the IgH locus or not, portend different prognostic relevance

tumorigenesis, including MGUS (Bergsagel and Kuehl 2001, Fonseca, et al 2003b, Fonseca, et al 2004, Gabrea, et al 2006)

1.2.2 Divergent genetic pathways based on chromosome number

There is a clear consensus that chromosome content reflects at least two pathways of pathogenesis Approximately half of the tumors are HRD (48-75 chromosomes), and typically have multiple trisomies involving chromosomes 3, 5, 7,

9, 11, 15, 19, and 21, but only infrequently (<10%) have one of the recurrent IgH translocations NHRD tumors (<48 and/or >75 chromosomes) usually (~70%) have

one of the recurrent IgH translocations (Smadja, et al 1998, Fonseca, et al 2003b, Smadja, et al 2003, Bergsagel and Kuehl 2005) Tumors that have a t(11;14)

translocation may represent a distinct category of NHRD tumors as they are often diploid or pseudodiploid, sometimes with this translocation as the only karyotypic abnormality detected by conventional cytogenetics In contrast to the selective occurrence of recurrent IgH translocations in NHRD tumors, other genetic events

(17p loss or p53 mutations, RAS mutations, secondary Ig translocations, MYC

translocations) often occur with a similar prevalence in HRD and NHRD tumors Extra-medullary MM tumors and HMCL nearly always have a NHRD phenotype, consistent with the hypothesis that HRD tumors are more stromal cell dependent

than NHRD tumors (Avet-Loiseau, et al 2001a) We have virtually no information

about the timing, mechanism, or molecular consequences of hyperdiploidy We do not know if the extra chromosomes are accumulated one at a time in sequential steps, or as one catastrophic event For tumors that are hyperdiploid but have one of the recurrent translocations [most often a t(4;14)], we do not know if hyperdiploidy occurred before or after the translocation

1.2.3 Chromosomal gains and losses

1.2.3.1 Loss of chromosome 13/13q14 (Δ13)

About 50% of MM tumors (Avet-Loiseau, et al 1999a, Zojer, et al 2000, Facon, et al 2001, Fonseca, et al 2002c, Fonseca, et al 2003a) and 40-50% of MGUS (Avet-Loiseau, et al 1999a, Konigsberg, et al 2000, Fonseca, et al 2002a)

tumors have ∆13 in most tumor cells, suggesting that this is often an early event in

MM pathogenesis In most cases, ∆13 represents whole chromosome monosomy

(Avet-Louseau, et al 2000, Fonseca, et al 2001b), but in a subset of tumors the common deleted region seems to be located at 13q14 (Dewald, et al 1985, Sawyer,

et al 1995, Avet-Louseau, et al 2000, Shaughnessy, et al 2000, Fonseca, et al 2001b, Sawyer, et al 2001), although no critical molecular abnormality has yet been confirmed The RB gene (RB1) falls within the most common minimally deleted

region, however inactivating mutations of the remaining allele are not commonly

detected Haploinsufficiency for RB1 is being investigated as a possible mechanism (Chng, unpublished), and as a target it provides a possible explanation for the

observation that ∆13 is more commonly seen in Cyclin D2-expressing than in Cyclin D1-expressing tumors (∆13 occurs in 80-90% of tumors that have either a t(4;14) or

t(14;16) translocation, but in 30-40% of other tumors (Fonseca, et al 2001a, Loiseau, et al 2002)) Although Cyclin D1 and Cyclin D2 have largely redundant

Avet-functional capabilities, Cyclin D1 has been shown to have an additional ability to antagonize RB, suggesting that CCND2 tumors may be more critically dependent on

the level of RB protein (Baker, et al 2005)

1.2.3.2 Gain of chromosome 1q21

A number of laboratories have determined, by a combination of FISH, array comparative genomic hybridization (aCGH), and gene expression profiling (GEP), that there is a gain of sequences - and corresponding increased gene expression - at 1q21 in 30-40% of tumors These gains are concentrated substantially in those tumors that have a t(4;14) or t(14;16), or have a high proliferation expression index

(Chang, et al 2006a, Fonseca, et al 2006, Hanamura, et al 2006) Although not

formally proven by examination of paired samples, the gain of chromosome 1q21

sequences may occur de novo in tumors with t(4;14) or t(14;16) translocations, but is

associated with tumor progression and an increased proliferative capacity in other tumors It has been proposed that the increased proliferation in tumors with gain of

1q21 sequences is due to the increased expression of CKS1B as a result of an increased copy number (Zhan, et al 2007b) One might expect to find other mechanisms, such as localized amplification or a translocation, if increased CKS1B

expression is a cause of increased proliferation, but there is no evidence of other

mechanisms increasing CKS1B expression Furthermore, CKS1B expression

correlates closely with the expression of a number of proliferation genes in a wide variety of tumors where it appears to be a consequence rather than a cause of the proliferation Hence it remains unclear which gene is being targeted by the gain of 1q21 sequences

1.2.3.3 Chromosome 17p loss and abnormalities of p53 gene (TP53)

Deletion (mainly mono-allelic) of 17p13, the locus containing TP53 amongst

others, as detected by interphase FISH, occurs in about 10% of MM tumors and

approximately 40% of PCL and HMCL (Drach, et al 1998, Fonseca, et al 2002c)

However, it should be noted that there is no definitive evidence that the critical

chromosome 17p loss involves TP53 Certainly, TP53 is contained within the minimal

deleted region on 17p13 in an analysis of 67 MM patients by aCGH from the Mayo Clinic Occasionally, these deletions can be quite small, and thus not always detected by interphase FISH using BAC probes Although almost all 17p13 deletions

detected are mono-allelic, there has been no definitive analysis of TP53 mutation of the remaining allele in these patients to conclusively implicate TP53 as the critical

gene Mutations of TP53 are relatively rare in newly diagnosed MM, occurring in

approximately 5% of tumors However, the frequency of mutations appears to increase with disease stage, and is about 30% in PCL and 65% in HMCL

(Preudhomme, et al 1992, Neri, et al 1993, Corradini, et al 1994, Chng, et al 2007c)

In a large study comprising 268 MM patients entered into Eastern Cooperative Oncology Group (ECOG) combination chemotherapy studies, only 5 of 31 (16%)

patients with the 17p13 deletion had a mutation of the remaining TP53 allele (Chng,

et al 2007c) However, the use of DNA from whole bone marrow aspirates may have

resulted in reduced sensitivity in this study In a smaller study (24 newly diagnosed

MM patients) using purified CD138+ plasma cells, no TP53 mutations were detected, but it is unclear whether these samples also had the 17p13 deletion (Xiong, et al 2006) Therefore, current evidence does not exclude TP53 as the critical gene

deleted on 17p13 Furthermore, the actual impact of 17p13 mono-allelic deletion on the p53 pathway and whether other cooperating deregulation, e.g epigenetic silencing of p53 or increased expression of MDM2, of various components of the pathway are involved needs to be further clarified

1.2.4 Genetic mutations

1.2.4.1 Activating RAS mutations

The prevalence of activating N- or K-RAS mutations is about 30-40% of newly

diagnosed MM tumors, with only a small increase occurring during tumor progression

(Liu, et al 1996, Bezieau, et al 2001) The prevalence is about 45% in HMCL (Chesi,

et al 2001) Importantly, less than 5% of MGUS tumors have RAS mutations, consistent with the hypothesis that RAS mutations may mark, if not mediate, the MGUS to MM transition (Bezieau, et al 2001, Rasmussen, et al 2005) Recent studies indicate that the prevalence of RAS mutations is substantially higher in

tumors that express Cyclin D1 compared to tumors that express Cyclin D2, with

t(4;14) tumors having a particularly low prevalence of RAS mutations (Rasmussen, et

al 2005) Two recent large, unpublished studies differ in that Fonseca et al (Fonseca,

et al 2003c) found N-RAS and K-RAS mutations, respectively, in 17% and 6% of tumors, whereas Kuehl and Shaughnessy found N-RAS and K-RAS mutations, respectively, in 14% and 17% of tumors (personal communication) The latter group also found that the prevalence of N-RAS mutations was substantially higher in tumors that express Cyclin D1, whereas K-RAS mutations occurred with a similar

prevalence in tumors that expressed Cyclin D1 or Cyclin D2

1.2.4.2 NFKB pathway mutations

It has been suggested in the past that activation of the NFKB pathway is important in the pathogenesis of MM, but little is known about the prevalence of, or mechanisms that cause NFKB activation Recently a promiscuous array of mutations that result in constitutive activation of the NFKB pathway have been identified in about 20% of MM patient samples, and 45% of HMCL The most common event is

inactivating mutations of TRAF3 in 13% 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) (Annunziata, et al 2006), CD40, LTBR, TACI, NFKB1, NFKB2 were also reported (Bergsagel, et al

2006) Although activation of both the canonical and non-canonical pathways is seen, the preponderance of mutations results most directly in the increased processing of NFKB2 p100 to p52 (i.e., activation of the non-canonical pathway) Depletion of NIK with shRNAs directed against NIK results in an inhibition of both the classical and alternative NFKB pathways, and also growth inhibition Half of primary MM tumors have an expression signature of NFKB target genes, with activating mutations identified in less than half of these patients Presumably either other mutations, or ligand-dependent interactions in the bone-marrow microenvironment are responsible for the NFKB activation in the remaining patients

1.2.5 Dysregulation of a Cyclin D gene: A unifying and early oncogenic event

in MGUS and MM

It has been proposed that dysregulation of a Cyclin D gene is a unifying, early

oncogenic event in MGUS and MM (Bergsagel, et al 2005) About 25% of MM

tumors have an IgH translocation that directly dysregulates a Cyclin D gene or a MAF gene encoding a transcription factor that markedly upregulates Cyclin D2 Although

MM tumors with a t(4;14) express moderately high levels of Cyclin D2, the cause of increased Cyclin D2 expression remains unknown Despite the fact that normal BM

PC express little or no detectable Cyclin D1, nearly 40% of MGUS and MM tumors that do not have a t(11;14), but are HRD, bi-allelically express Cyclin D1 by a yet to

be determined mechanism Most other tumors express increased levels of Cyclin D2 compared to normal BM PC, although the mechanism that causes this is also unknown Only a small percentage of MM tumors do not express increased levels of

a Cyclin D gene compared to normal PC, but many of these tumors appear to represent samples that are substantially contaminated by normal cells and another

large fraction of these tumors express little or no RB1, eliminating the necessity of

expressing a Cyclin D gene

1.2.6 Genetic differences between MGUS and MM

The main primary translocations that have been described in MM tumors, including t(4;14), t(11;14), t(14;16) as well as the trisomies of HRD MM, have also been described in MGUS The prevalence of t(11;14) translocations is approximately

15% in both MGUS Loiseau, et al 1999a, Fonseca, et al 2002a) and MM Loiseau, et al 2002, Fonseca, et al 2002b) The prevalence of chromosomal trisomies of hyperdiploidy in MGUS (about 40%) is similar to that of myeloma (Chng,

(Avet-et al 2005) The prevalences of t(14;16) and t(4;14) are lower in MGUS/SMM compared to MM (Avet-Loiseau, et al 2002) The prevalence of t(4;14) was reported

to be 2% in MGUS in 2 studies (Avet-Loiseau, et al 1999a, Avet-Loiseau, et al 2002)

and 9% in a Mayo Clinic study whereas the prevalence in MM is closer to 15%

(Fonseca, et al 2002a) For t(14;16), the prevalence in MGUS was reported by a French study to be less than 1% (Avet-Loiseau, et al 2002) but was reported to be 5% in a Mayo Clinic study (Fonseca, et al 2002a) The discrepancy in prevalence

may relate to methodology and sample size The French performed FISH on plasma cell populations purified using CD138 magnetic beads (>90% purity), whereas the Mayo group used cIg-FISH on unpurified cells, where the clonotypic light chain is concurrently detected using IF and only cells staining positive for the clonotypic light chains are scored The higher prevalence of both t(4;14) and t(14;16) in MM compared to MGUS suggest that these primary abnormalities lead to clonal advantage and selection whereas the similar prevalences of t(11;14) and hyperdiploidy in MGUS and MM suggest that these abnormalities and additional cooperating secondary events, may be important for transformation from MGUS to

MM These secondary events are currently not known A strong candidate is

activating RAS mutations, which are rarely seen in MGUS but seen in 30-40% of

newly diagnosed MM Of interest, these mutations are also commonly associated with tumors over-expressing Cyclin D1, such as t(11;14) and HRD-MM (see above

section on RAS mutation)

1.3 Prognostic implications of genetic abnormalities

Besides providing insights into the biology of disease pathogenesis and evolution, genetic abnormalities are also powerful prognostic factors in MM

(Fonseca, et al 2004, Stewart and Fonseca 2005) A recent large international study

developed a 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 genetic abnormalities such as t(4;14) and 17p13 deletion significantly dichotomize survival in each of the ISS stages, showing conclusively that genetic factors are powerful prognostic factors that should be incorporated into routine

clinical practice (Avet-Loiseau, et al 2007)

1.3.1 Primary translocations

It is now well established that t(4;14) is associated with adverse prognosis following conventional chemotherapy or high dose therapy with stem cell transplant

(HDT) (Moreau, et al 2002, Fonseca, et al 2003a, Keats, et al 2003, Winkler, et al

2003, Chang, et al 2004, Gertz, et al 2005, Avet-Loiseau, et al 2007) The reason for

the adverse outcome appears to be early relapse as treatment response is not

different from that of other genetic subtypes (Jaksic, et al 2005) An earlier study showed that there is no difference in survival between t(4;14) with or without FGFR3 expression (Keats, et al 2003) However, an analysis of a larger dataset treated on

total therapy II and III (TTII and TTIII) showed that there is a strong trend towards

shorter survival for those who have lost FGFR3 expression (L Bergsagel, unpublished) This results needs to be confirmed with longer follow-up This would fit with the hypothesis that the loss of der(14) is a secondary progression event

Recently, several studies have shown that treatment with bortezomib overcomes the poor prognosis associated with t(4;14) in both newly diagnosed and

relapsed patients (Chang, et al 2006b, Mateos, et al 2006, Zhan, et al 2006a) Furthermore, specific inhibitors of FGFR3, one of the genes deregulated by the translocations, have shown efficacy in vitro and in vivo and are currently in clinical testing (Paterson, et al 2004, Trudel, et al 2004, Trudel, et al 2005, Trudel, et al

2006) For these reasons, t(4;14) should be assessed routinely for all patients as it provides important prognostic information and offers opportunities to tailor therapy, for example, using a bortezomib-containing regimen rather than HDT

The prognostic significance of t(14;16) is less well established as the numbers are often too small for meaningful interpretation However, existing evidence suggests that t(14;16), and presumably translocations deregulating other

MAF genes (MAFA and MAFB) are associated with adverse prognosis In a large

ECOG study of patients treated with combination chemotherapy, patients with

t(14;16) have survival as short as patients with t(4;14) (Fonseca, et al 2003a) In a GEP study, patients with spiked expression of MAF genes indicating IgH translocations involving MAF genes, have significantly shorter survival when treated

on both TTII and TTIII (Zhan, et al 2006a)

Newly diagnosed patients with t(11;14) have better prognosis than patients with the other two afore-mentioned primary translocations Early data suggest that this group of patients may particularly benefit from HDT resulting in a significantly

better survival compared to all other genetic subtypes (Fonseca, et al 2002b, Moreau, et al 2002), but recent larger studies failed to confirm this observation (Fonseca, et al 2003a, Chang, et al 2005b, Gertz, et al 2005, Avet-Loiseau, et al

2007) In contrast, these patients seemed to have inferior survival at relapse In an analysis of relapsed patients entered into the Apex trial, t(11;14) patients have significantly worse prognosis compared to other genetic subtype regardless of treatment received (dexamethasone or bortezomib), despite similar response rate

and time from diagnosis to trial entry (Chng, et al 2007b) These results suggest that

the prognostic impact of the genetic subtypes may be different at relapse

1.3.2 Chromosome 1q21 gain

A large study from the University of Arkansas Medical School (UAMS) established that 1q21 amplification detected by FISH is a significant and independent

poor prognostic factor (Hanamura, et al 2006) However, another study from the

Mayo Clinic showed that while significantly associated with poor prognosis on univariate analysis, 1q21 gain was not an independent prognostic factor on Cox

proportional hazard analysis (Fonseca, et al 2006) The 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 of the prognostic impact of 1q21 gain on univariate analysis was mediated through its close association with poor risk

genetics and proliferative disease (Fonseca, et al 2006)

As mentioned earlier, CKS1B has been implicated as the candidate gene on

1q21 mediating biological and prognostic impact However, when the relative

prognostic strength of 1q21 copy gain and increased CKS1B expression were

analyzed in a multivariate model, 1q21 copy gain was the more significant prognostic

factor (Fonseca, et al 2006) Therefore, the overall evidence that a critical gene

located on 1q21 may be causatively involved in mediating progression and prognosis

is weak Instead, it appears more likely that chromosome 1q amplification is a marker

of more clonally advanced and genomically unstable tumors that are more likely to progress

1.3.3 Δ13

∆13 is one of the first established genetic prognostic factors in MM patients

treated with combination chemotherapy or HDT (Fonseca, et al 2004) As mentioned

above, the presence of ∆13 is strongly associated with the presence of one of the poor prognosis translocations such as t(4;14) or t(14;16), therefore one important question arising is whether the prognostic importance of ∆13 is due to its association with poor-risk genetic subtypes or whether it has intrinsic prognostic properties Several recent large studies conclusively showed that the former is true The large IFM study showed that the prognostic value of ∆13 was entirely dependent on its frequent association with t(4;14) and chromosome 17p13 deletion In patients lacking these 2 abnormalities, ∆13 was no longer significantly associated with survival (Avet-

Loiseau, et al 2007) Likewise, another study of 260 patients treated with HDT from

the Spanish GEM/PATHEMA group also showed that the presence of ∆13 (using a

FISH probe centered on RB1) without other FISH-based genetic abnormalities is not associated with adverse prognosis (Gutierrez, et al 2007) Furthermore, an analysis

of only HRD patients showed that ∆13 have no prognostic impact in patients of this

genetic subtype (Chng, et al 2006c) Therefore, there seems to be no role for routine

detection of ∆13 by FISH in clinical practice

1.3.4 17p13 Deletion

The prognostic importance of 17p13 deletion detected by FISH had been

demonstrated in several large studies (Fonseca, et al 2003a, Chang, et al 2005a, Avet-Loiseau, et al 2007, Gutierrez, et al 2007) The large French study confirmed

that it is a powerful and independent prognostic factor In fact 17p13 deletion, together with t(4;14), are the only genetic factors included in their prognostic model

(Avet-Loiseau, et al 2007) A recent analysis from UAMS suggests that the expression of TP53 is correlated with 17p13 deletion by FISH using a cut-off TP53

expression that identified 10% of patients with the lowest expression, they found that

low TP53 gene expression is an independent factor associated with poor prognosis

(Xiong, et al 2006) This supports the possibility that TP53 is the important gene

deleted within 17p13 As mentioned earlier, this issue will need to be resolved in the

future The recent ECOG study showed that despite its rarity, TP53 mutation is

associated with significantly shorter survival, with the median survival of patients with

TP53 mutation being only one and a half years (Chng, et al 2007c)

1.3.5 Ploidy

A number of studies have consistently showed that HRD MM generally has better prognosis than NHRD myeloma Within the latter group, those with cytogenetically defined hypodiploidy in particular appear to have very poor prognosis

(Calasanz, et al 1997, Smadja, et al 2001, Fassas, et al 2002), and this is one of the factors used 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

Current understanding of MM genetics is hampered by inadequacies of techniques formerly available Conventional karyotyping allows a global view

of abnormalities in each tumor but suffers from poor resolution and the need for metaphases which are only available in 30% of MM and almost never in MGUS FISH negates the need for metaphase as it can be done on interphase cells but requires specific knowledge of abnormalities to be detected and hence cannot be used as a discovery tool The advent of aCGH overcomes many of these problems as it only needs DNA, offers a high resolution global view of copy number abnormalities, and therefore serves as

a useful discovery tool to survey the global structural genetic changes in MM The use of such a technique will allow a comprehensive description of genetic abnormalities in MM, enumeration of the number of genetic abnormalities per tumor, and clarification of the prognostic relevance of both the genetic complexity as well as specific genetic locus

Rationale and Hypothesis

b) Study the biological and clinical implications of centrosome abnormalities, which could be a major mechanism of chromosome instability in MM

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 in solid tumors We therefore hypothesized that similar abnormalities may underlie chromosome instability

in MM Furthermore, such abnormalities may have important clinical importance in terms of prognosis and may be potential therapeutic targets Rationale and Hypothesis

c) Do a comprehensive study in HRD MM patients

HRD MM is the most common genetic subtypes of MM but hitherto little was known about this type of MM because its diagnosis requires karyotypic analysis with its inherent limitations There is thus a need to develop an interphase-based technique that can be widely applied in a practical manner From the data accrued, we can then try to understand the molecular mechanism(s) underlying HRD MM and the potential molecular heterogeneity within HRD MM In addition, the origin and evolution of genetic abnormalities

in HRD MM as well as the clinical characteristics of HRD MM are unknown

We therefore aim to understand these aspects of HRD MM by examining a large clinical dataset and a large cytogenetic dataset respectively

Rationale and Hypothesis

d) Identify some important mechanism(s) or molecular abnormalities leading to transformation of MGUS to MM

All the primary genetic abnormalities are already present in MGUS, suggesting secondary events are required for transformation to MM The

nature of these secondary events is still currently unclear Activating RAS

mutations represent a most consistently described difference between MGUS and newly diagnosed MM and may therefore represent one of these

secondary events However, RAS mutations are known to be present in only

about one third of MM We hypothesize that comparing GEPs of MM to those

of MGUS will allow us to detect global transcriptional differences between MM and MGUS and that analyzing these differentially expressed genes in terms Rationale and Hypothesis

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

Chapter 2 Chromosomal instability in MM

2.1 Landscape of genomic aberrations in MM

MM cells are characterized by complex genetic aberrations suggesting

underlying genomic instability (Fonseca, et al 2004) However, previous studies are

hampered by some important limitations Karyotypically defined survival associated genetic aberrations are limited to 30% or less of patients that produced informative

karyotype (Lai, et al 1995) Furthermore, the resolution of G-banding is low, and

some abnormalities are karyotypically invisible FISH-based assay on interphase cells allows acquisition of useful information on larger number of patients but is hampered by its obvious limitations as a discovery tool Therefore its application so far is limited to detecting recurrent abnormalities previously defined by karyotyping or molecular methods

aCGH negates many of these limitations by having significantly higher resolution, not requiring metaphase cells, and allowing detection of genome-wide copy number changes It therefore lends itself as a powerful discovery tool Indeed several previously unappreciated genetic abnormalities have already been

discovered in MM using this type of platform (Carrasco, et al 2006, Largo, et al 2006, Keats, et al 2007, Largo, et al 2007) Therefore, we aim to survey the landscape of

genetic aberrations in MM using aCGH, and define recurrent genomic aberrations that are significantly associated with shorter survival

2.1.1 Prevalence of genomic aberrations

We examined aCGH data from 100 MM patients from the Mayo 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 Appendix 1 aCGH was performed on the Agilent platform

using either the 44k or higher resolution 244k DNA microarray chip For this analysis, only probes common to the 2 chips were used (37,500 probes)

Results from our genomic scan using aCGH revealed a pattern of genomic aberrations that generally fit in with current knowledge of myeloma genetics The known recurrent chromosomal abnormalities were recapitulated, but in addition, we detected other genetic abnormalities that were common but previously not well appreciated The expected pattern of trisomies of chromosome 3, 5, 7, 9, 11, 15, 19 and 21 was observed in 50-60% of cases similar to commonly reported prevalence of

HRD-MM (Smadja, et al 1998) ∆13 was seen in about 40% of cases and 1q gain in

about 45% of patients, findings which were very consistent with published reports Other relatively common regions of DNA copy aberrations (>10%) that were less commonly reported elsewhere included 6p gain, 8q gain, 18 gain, 1p loss, 4 loss, 6q loss and 8p loss (which affect >30% of patients), 14 loss, 16q loss, 17p loss, 20p loss

and 22q loss (Carrasco, et al 2006, Walker, et al 2006, Largo, et al 2007) (Figure

2.1a)

When the samples were clustered according to their aCGH abnormalities, it was clear that HRD and NHRD MM form distinct clusters There appeared to be more relapsed cases amongst the NHRD MM but this was not statistically significant Almost all the D1 and half of D2 tumors as defined by the TC classification were HRD 11q13 cases tended to be diploid or with the addition of 1q gain and ∆13 alongside a few other aberrations The 2 cases of NHRD MM clustered together with the HRD cases had partial gains of several of the typically trisomic chromosomes but also deletion of chromosome 13 and 14 such that in the final chromosome count they were not HRD One of these cases had t(4;14)

t(11;14) patients had two patterns of genomic aberrations One group had very stable genomes with few aberrations, whereas the other group had much more complex genomic changes suggesting possible underlying genomic instability These two groups could be separated on the basis of ∆13 It is conceivable that acquisition

of ∆13 in t(11;14) patients destabilizes the genome of the tumor cells

A)

in red represent loss of a probe The height or depth of these bars represents the prevalence of each aberration as a fraction of the total cohort studied Data from each of the 37,500 probes are

represented B) Unsupervised clustering of samples based on DNA copy gains and losses Ward’s

method of unsupervised clustering was used for aggregation of samples Each column represents a sample, and each row a probe The probes are aligned sequentially from top to bottom, based on their chromosomal locations Data from all 37,500 probes used for the analysis and all 100 samples assayed are represented The legend at the top 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 relapsed cases For ploidy, red represents HRD cases and purple HRD cases For TC class, green represents 11q13, white represents unknown (no gene expression data), blue represents D1, red D2, black D1+D2, yellow D3, cyan Maf and purple 4p16 Sample names are indicated at the bottom of the plot The chromosomes are represented by the black and grey bars on

the left

2.1.2 Genomic aberrations according to ploidy categories

The genetic aberrations detected by aCGH were then compared statistically between HRD and NHRD MM, which revealed additional chromosome aberrations that were different between these two broad subtypes of MM Some of these aberrations were almost exclusively found in one ploidy sybtype and may contribute

to the pathology of the subtypes Gains of the trisomic chromosomes, chromosomes

3, 5, 7, 9, 11, 15, 19 and 21 were significantly more common in HRD compared to NHRD MM Among these, trisomy 9, 15 and 19 were the most common In addition, gain of 6p was also significantly more common in HRD MM On the other hand, gain

of chromosome 4, loss of 6q and loss of chromosome 13 were significantly more common in NHRD MM 1p loss, 8q loss, 10 loss, 12 loss, 14 loss, 16p, 16q, 20p and 20q loss were clearly more prevalent in NHRD MM, although these did not reach statistical significance Of note, 8p loss, 10 loss, 12 loss and 16p loss were seen almost exclusively in NHRD MM Other relatively common aberrations such as 1q gain (44% and 48% respectively) and 8p loss (27% and 28% respectively) had almost identical prevalence in both HRD and NHRD MM

Figure 2.2 Penetrance plot of chromosome gains and losses by ploidy The top panel is the

penetrance plot for HRD MM The middle panel is the penetrance plot for NHRD MM Gains (log2 ratios

> 0.25) are 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 p- value 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 corrected values

2.1.3 Chromosome instability in MM assessed using NAPT defined by aCGH

To assess the degree of chromosome complexities, as an indicator of possible chromosomal instability, we counted the number of aberrations as defined

by aCGH This number constitutes the number of abnormalities per tumor (NAPT) The assumption is that the higher the NAPT, the more complex the genome Since the initiating events of most MM are similar and belong to a small number of defined genetic aberrations such as t(4;14), t(11;14) and trisomies, the subsequent aberrations acquired must represent ongoing chromosomal instability either from the primary clone or sub-clones, and the higher the accumulated aberrations, the more genomically unstable the clone

The samples were initially clustered according to their NAPT In this analysis, the more genetically complex cases were enriched for NHRD tumors, and relapsed cases All the cases of D1+D2 had complex karyotypes, and half of D2 tumors had complex karyotypes Amongst the high-risk genetic groups (4p16 and MAF), it was interesting that 4p16 tumors tended to be genetically complex whereas MAF tumors were genetically simpler Chromosomal 1 abnormalities were almost universally present in the tumors with high NAPT In addition, the number of aberrations on chromosome 1 also increased with increasing NAPT

Figure 2.3 Samples are clustered according to the number of aberrations for each chromosome

Ward’s method was used as above The color bar on the top left corner represents the number of aberrations in a particular chromosome for each sample The color-coding for clinical phase, ploidy and

TC class is as per above (see Figure 2.1a)

The distribution of NAPT had a bi-modal pattern A cut-off of 26 was selected

as the edge of the second mode and was designated as high complexity for subsequent survival analysis

Figure 2.4 Frequency distribution of number of abnormalities per tumor (NAPT)

2.2 Clinical Implications of Genomic Instability in MM

2.2.1 Degree of genomics complexity as measured by NAPT is a prognostic factor in MM

Patients with more complex karyotypes (NAPT>26) have significantly shorter

survival (Figure 2.5a) In fact, the degree of genetic complexity has a prognostic impact which is independent of ploidy and relapsed status (Figure 2.5b-e)

A)

B)

C)

D)

E)

Figure 2.5 Survival according to NAPT (A) Patients with more complex karyotypes with a higher

NAPT (using 26 as a cut-off) have shorter survival,(median survival time of 22 months) compared to

those with less complex karyotypes (median of 40 months) This is independent of whether they are (B) newly diagnosed (14 vs 27 mths) or (C) relapsed (45 vs 59 mths) patients, (D) HRD (18 vs 40 mths) or

(E) NHRD (42 vs 39 mths) MM cases (Figure 2.5D and E) although the latter shows a more marginal

effect

2.2.2 Identification of survival critical regions and genes

We next sought to identify survival critical regions as defined by aCGH Since genetic complexity has been shown to have prognostic relevance, and it is known that some previously defined genetic abnormalities also have prognostic relevance, it

is likely that novel regions defined by aCGH may also have important prognostic relevance

For this analysis, we looked at two independent aCGH datasets One was from the Mayo Clinic as described above and the other, from UAMS (N = 67) For the UAMS dataset, aCGH hybridizations were performed using oligonucleotide expression profiling microarrays (Agilent Technologies, Human 1A v2) that contained 22,500 probes overall among which 16,097 uniquely mapped positions were defined

We assessed the association of each common (>10%) aCGH-defined abnormalities with survival in each dataset separately These regions were termed Survival Critical Genomic Abberations (SCGAs) Finally, the common SCGAs in the two datasets were identified These represented reproducible and robust SCGAs which were further validated

Clearly not all the frequent aberrations were associated with poor prognosis

(Figure 2.6) The number of SCGAs seemed to be influenced to a certain extent by

the resolution of the platform, with the higher resolution Mayo Clinic Dataset producing a much higher number of survival regions

A)

B)

Figure 2.6 Survival Critical Genomic Aberrations (SCGAs) from (A) Mayo dataset and (B) UAMS dataset The log-rank test p-values for all sliding windows are shown with their chromosomal locations

Chromosomal locations in which the genomic aberrations led to poor survival with log-rank test p-value

< 0.05, corresponding q-value < 0.05, and aberrations occurring in ≥ 10% of all samples are indicated in red Chromosomal locations with log-rank test p-value < 0.05, corresponding q-value < 0.05, but 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 3, 5, 7, 9, 11, 15 were prognostically neutral, which are consistent with the known prognosis of HRD MM

(Smadja, et al 1998, Chng, et al 2006c) In the UAMS dataset, gains of several

regions on 1q and losses of several regions on 1p were associated with shortened survival In addition, 19p+, 13q-, 14q-, 18p- and 20p- were also significantly

associated with survival (Appendix 2) For the Mayo Clinic Dataset, gains of several

regions on chromosomes 5q, 7q, 8q, 15q, and losses of several regions on 1p, 8p, 12q, 13q, 16p, 16q, 17p, 20p and 22q were also significantly associated with survival

(Appendix 3) Only the following regions are common to the two datasets: 1p33 gain, 1p31-32 loss, and 20p12 loss (2 regions) (Table 2.1)

We further assessed if the genes contained within these common SCGAs had significantly different mRNA expression by comparing their expression levels in samples with or without the SCGAs using parametric statistics (Student’s T-Test)

These are potentially the important genes gained or deleted on these genomic regions that may impact survival (Table 2.1)

Table 2.1 Survival hotspots common to the Mayo and UAMS datasets [This table 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.]

CRYZ, C1orf171, LHX8, SLC44A5, LOC646417, LOC646425, ACADM, DLSTP, RABGGTB,

SNORD45A, SNORD45B, MSH4

20

p12.3-12.1 6,717,170 14,530,401 pFUSIP1, HAO1, TXNDC13, PHKBP1, PLCB1,

LOC728357, PLCB4, C20orf103, PAK7, ANKRD5,

SNAP25, RPL23AP6, MKKS, C20orf94, JAG1, FAT1P1, HCG_2045828, RPS11P1, LOC441940, PGAM3P, LOC728573, LOC728450, BTBD3, PA2G4P2, C20orf38, C20orf82, LOC728600, TASP1, GAPDHP2, MRPS36P6, C20orf6, C20orf7, C20orf50, RPS3P1, SCYE1P, FLRT3, RNF11B

20

LOC728628, C20orf23, RPLP0P1, RPL7AL3,

SNRPB2, OTOR, PCSK2, BFSP1, RPS27AP2,

DSTN, RRBP1

Of the potentially important genes with correlated reduced expression on

1p31-32, DAB1 has been recently implicated as a potential tumor suppressor gene

on a large genomic region that is a fragile site commonly deleted in cancers

(McAvoy, et al 2008) MSH4 is a member of the MutH homolog family that has been implicated in DNA instability in cancer as it is involved in DNA repair (Her, et al 2007) On 20p12.3-12.1, PLCB1 deletion has been implicated in progression from myelodysplastic syndrome to acute myeloid leukemia (Lo Vasco, et al 2004) There are other genes such as RABGGTB on 1p31, and JAG1 on 20p12.1, which have

increased expression in diffuse large B cell lymphoma and acute myeloid leukemia,

respectively (Linderoth, et al 2008, Stirewalt, et al 2008) In fact, JAG1 expression is associated with poorer prognosis in breast cancer (Dickson, et al

over-2007) However, these genes are unlikely to be relevant in our setting as these

genetic loci are deleted and the genes under-expressed The exact role(s) that these candidates may play in myeloma biology will have to be further investigated and these works are currently ongoing

When these SCGAs were clustered, distinct patterns were seen in both datasets characterized by low, intermediate or high number of these SCGAs In both datasets, those patients with high number of SCGAs had significantly poorer survival

Of note, ∆13 was seen across all the clusters from low to high SCGAs in both

datasets In fact in some, it could be the only SCGA (Figure 2.7) This is consistent

with it being one of the earliest SCGAs manifested, whereas chromosome 1 abnormalities tend to occur in the setting of a high number of SCGAs, suggesting that it may be a late event This observation is also consistent with the association of chromosome 1 abnormalities with a higher NAPT identified earlier

A)

B)

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.]

2.2.3 Validation of common SCGAs

We next validated several of these SCGAs using FISH in a separate dataset

of 127 MM patients that had undergone HDT at the Mayo Clinic For chromosome

1p, we used a probe against MSH4 on 1p31.1 and DAB1 on 1p32 Of the 127

patients we had enough material for these assays in 122 patients In these patients,

the cases with loss of 1p31 overlapped with those with loss of 1p32 and so they were considered together as 1p31-32 loss These abnormalities were detected in 19.7% of patients in our validation cohort For 20p12.3-12.1, we used two probes targeting two

genes (PAK7 and PCSK2) within this region Material for FISH was available in 126

cases In all cases, both genes were deleted together 20p12.3-12.1 loss was detected in 11.9% of patients Loss of 1p31-32 and 20p12.3-12.1 occurred together

in three patients

1p31-32 loss had no significant correlation with other recurrent genetic aberrations such as 1q gain, t(4;14), t(11;14), 13 deletion or 17p13 deletion, although the number of cases studied was too small to draw any firm conclusions On the other hand, 20p12.3-12.1 loss was significant associated with 17p13 deletion and

showed a strong trend towards association with t(11;14) (Table 2.2)

Table 2.2 Association of 1p loss and 20p loss with other recurrent genetic aberration [This table 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.]

There were no significant differences in complete response (CR) rates amongst patients with and without 1p31-32 loss and those with or without 20p12.3-12.1 loss Among 24 patients with 1p31-32 loss, 7 (29%) had a complete remission

whilst 31 of 98 (32%) patients without the deletion had a CR (p-value 0.8) Among

the 15 patients with 20p12.3-12.1 loss, 5 (33%) achieved a CR whereas 37 of 111

(33%) without the deletion achieved CR (p-value 1.0) Patients with either of these

abnormalities remained in CR for shorter duration, with those having 1p31-32 loss remaining in CR for a median of 14.4 months compared to 32.2 months for those without, and those with 20p12.3-12.1 loss remaining in CR for a median of 19.9

months compared to 30 months for those without (p-value 0.37 and 0.35,

respectively) Consistent with this, both 1p31-32 loss and 20p12.3-12.1 loss had no impact on progression free survival (PFS) PFS of patients in the cohort with and

without 1p31-32 loss is 12.8 months versus 16.3 months (log-rank p-value 0.28)

whereas those with and without 20p12.3-12.1 loss was 10.4 months versus 16.9

months (log-rank p-value 0.1), respectively

On univariate analysis, 1p31-32 loss was associated with shorter survival,

with a median survival of 24.5 months compared to 40 months (log-rank p=0.01),

validating our findings from the aCGH analysis 20p12.3-121 loss, on the other hand, showed a strong trend towards shorter survival, with a median survival of 20.6

months versus 40 months (log-rank p=0.06), again consistent with the aCGH

findings, although this did not reach statistical significance (Figure 2.8) The

significantly shorter overall survival (OS) in patients with either of these genetic abnormalities was probably due to shorter post-relapse survival Survival from relapse was significantly shorter for patients with 1p31-32 loss (12 months versus

19.3 months, log-rank p-value 0.01) Consistent with OS results, survival from

relapse was shorter for those with 20p12.3-12.1 loss but this was not statistically

significant (13.5 months versus 17.2 months, log-rank p-value 0.19) On multivariate

analysis including other genetic factors such as t(4;14), 1q gain and 17p13 deletion, 1p loss remained an independent prognostic factor in addition to 17p13 loss and

t(4;14) (Table 2.3) 1p31-32 loss retained statistical significance (p-value 0.018) even

when beta-2 microglobulin was inserted into the model

A)

B)

Figure 2.8 Validation of the two observed SCGAs using FISH for MSH4 (1p31.1) and PAK7 (20p12.3-12.1) (A) In the Mayo BMT cohort, patients with PAK7 deletion on 20p12.3-12.1 has a trend

towards shorter survival (20.6 months versus 40 months, log-rank p-value = 0.06) whereas (B) those

with MSH4 deletion on 1p31.1 have a significantly shorter survival (24.5 months versus 40 months, rank p-value = 0.01) BMT, Bone marrow transplantation [This figure was originally published in

log-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.]

Table 2.3 Cox proportional hazard regression model for overall survival (N=122) [This table 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.]

Variable Hazard Ratio (95% CI) Chi-square p-value

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 One pathway involves telomeric dysfunction with chromosome breakage and the presence of dicentric chromosomes Another pathway involves gains and losses of intact whole chromosomes and is thought to be

due to continuous chromosome mis-segregation during mitosis (Lengauer, et al

1998, Pihan, et al 1998) This latter phenotype may result from genetic alterations in

genes that delay cell entry into anaphase if chromosomes are mis-aligned on the

bipolar mitotic spindle such as BUB1, MAD2 and BUBR1 (so called mitotic checkpoint genes) or centrosome amplification (Balmain, et al 2003, Pihan and

Doxsey 2003) Few examples of mutations in the mitotic checkpoint genes have been documented in human cancers, despite elegant mechanistic studies showing

their putative pathogenetic role (Cahill, et al 1998) In a screen in a panel of HMCLs and MM patients, these mutations were not detected (Rafael Fonseca, unpublished)

Centrosomes are cellular microtubule-organizing centers whose normal function is crucial for chromosome segregation and cytokinesis during mitosis (Bornens 2002) The centrosome consists of two centrioles that are surrounded by amorphous pericentriolar material (PCM) The centrosome is duplicated only once during the cell cycle to give rise to two centrosomes that function as spindle poles of

the dividing cell Extra copies (aberrants) of the centrosome frequently result in formation of multi-polar spindles and inevitably chromosome mis-segregation and

aneuploidy (D'Assoro, et al 2002, Kramer, et al 2002, Nigg 2002, Sluder and

tumors, the centrosome abnormalities are associated with advancing stages of the

disease, aneuploidy and an aggressive clinical course (Lingle, et al 1998, Pihan, et al

1998, Lingle and Salisbury 1999, Gustafson, et al 2000, Pihan, et al 2001) The

existence of centrosome abnormalities in pre-invasive carcinomas suggests they are

early events in cellular transformation (Lingle, et al 2002, Pihan, et al 2003)

As multiple trisomies and monosomies can be observed in the clonal cells, and aneuploidy evolution is evident at all stages of the disease since there is heterogeneity in the number of cells harboring any specific aneuploid chromosomes, CIN may be more important than MIN in MM We therefore investigated the role of centrosome amplification in MM

2.3.1 Centrosome amplification is common in all stages of PC neoplasms since MGUS

To detect centrosomes, we used immunofluorescence (IF) staining for centrin, an integral protein component of the centrosome The pattern of centrin staining was different between PCs from normal donors and patients with monoclonal

gammopathies (The IF cohort is designated group 1 patients) (Figure 2.9a-h) The

majority of PCs derived from normal controls (mean ± SD, 74.9% ± 18.8%) did not

have any centrin signals detectable by IF (Figure 2.9a) In 8 of the 10 normal donors,

no PCs with >4 centrin signals were detected The other two cases had 5% and 8%

of PCs with >4 centrin signals This was in sharp contrast to PCs from patients with

PC neoplasms in which the majority expressed 1-4 or >4 centrin signals (51.5% ± 19.2% and 15.7% ± 14.5%, respectively) (Figure 2.10a) In addition, structurally abnormal centrosomes, where the shape and configuration of the centrosome were

abnormal (Figure 2.9d-h), were predominantly seen in MM

Figure 2.9 Different patterns of centrin staining The isotypic plasma cells (PCs) were identified by

cytoplasmic kappa or lambda light-chain antibody conjugated with AMCA (cIg, blue) and centrin was

stained with anti-centrin2 conjugated with Alexa 488 (arrowed; green) (A) Most PCs from normal donors had no signals (B and C) Cells with 1-4 signals were considered to have normal centrosome (D

and E) Centrosome amplification was seen in typical clonal PCs as well as the rare multinucleated PCs

in patients Various centrosome abnormalities were seen: (D and E) increased number of signals in a cluster; (F) Increased signals that were scattered; (G) Coalescence of centrins into string-like structures;

(H) Centrins staining up as ring-like structure Abnormal centrosome structures as seen in F-H were

predominantly seen in MM [This figure was originally published in Blood Chng WJ, et al Clinical implication of centrosome amplification in plasma cell neoplasm Blood 2006;107:3669-75 © the American Society of Hematology.]

Centrosome amplification was present in all stages of monoclonal gammopathies Using pre-defined criteria, 16 of 24 (67%) patients (2 of 3 MGUS (67%), 5 of 7 SMM (71%) and 9 of 14 MM (64%)) had centrosome amplification In addition, the percentage of PCs with centrosome amplification increased

progressively from MGUS to MM (Figure 2.10b)

Figure 2.10 Centrin staining and centrosome amplification in normal donors and patients with monoclonal gammopathies (A) Majority of normal plasma cells (PCs) had no centrin signals

compared to clonal PCs (p < 0.00002) while centrosome amplification was almost exclusively seen in

clonal PCs (p < 0.00008) (B) Percentage of cIg positive PCs with centrosome amplification increased

with more advanced stages of PC proliferation [This figure was originally published in Blood Chng WJ,

et al Clinical implication of centrosome amplification in plasma cell neoplasm Blood 2006;107:3669-75

© the American Society of Hematology.]

2.3.2 Gene expression analysis of centrosome protein genes

Together with centrin, pericentrin and γ-tubulin are also major centrosome

proteins and components of the pericentriolar material (PCM) (Nigg 2002, Wang, et

al 2004) We assessed the expression levels of genes encoding these proteins in a

cohort of myeloma patients with gene expression data (group 2 patients, see section 7.1.7) The expression of these genes were significantly correlated and increased

progressively from MGUS to MM (Figure 2.11, Table 2.4), similar to results from

other studies which showed concordant results when these different proteins are

stained (Lingle, et al 1998, Giehl, et al 2005)

Figure 2.11 Expression levels of centrosome proteins are closely correlated Expression levels of

centrin was correlated with (A) pericentrin (r = 0.16, p = 0.046) and (B) γ-tubulin (r = 0.47, p<0.0001)

(C) Pericentrin was also correlated with γ-tubulin (r = 0.35, p<0.0001) [This figure was originally published in Blood Chng WJ, et al Clinical implication of centrosome amplification in plasma cell neoplasm Blood 2006;107:3669-75 © the American Society of Hematology.]

Table 2.4 Comparison of centrin, pericentrin and γ-tubulin gene expression

between MGUS/SMM and MM [This table was originally published in Blood Chng WJ, et al Clinical implication of centrosome amplification in plasma cell neoplasm Blood 2006;107:3669-75 © the American Society of Hematology.]

Centrin Expression 0.93 (0.43 – 1.69) 1.06 (0.25 - 2.37) 0.004 Pericentrin Expression 0.87 (0.40 – 1.77) 1.06 (0.37 – 2.32) 0.003 γ-tubulin Expression 0.78 (0.14 – 1.78) 1.16 (0.07 – 3.16) <0.0001

We then calculated a gene expression-based centrosome index (CI) by adding the normalized expression value of each of these 3 genes The CI increased

significantly from MGUS to MM (Figure 2.12a), consistent with our IF results CI>4

(mean+2SD CI of normal PCs) was highly correlated with centrosome amplification

by IF In 20 MM patients (10 CI>4 and 10 CI<4), 100% of patients with CI>4 had centrosome amplification whereas only 20% with CI<4 had centrosome amplification

(p=0.0007) Furthermore, the CI was strongly correlated with the percentage of clonal

cells with centrosome amplification in each patient (Figure 2.12b) This suggests that

the CI can be used as a surrogate for expression of centrosome proteins Overall, the data suggest that centrosome amplification occurs early in myelomagenesis and increases with disease progression

Figure 2.12 Gene expression-based centrosome index (CI) in plasma cell (PC) neoplasms (A) CI

increased progressively from MGUS (n = 23, median CI (range) = 2.27 (1.56 – 3.90)) to SMM (n = 25, median CI (range) = 2.91 (1.58 – 3.91)) to MM (n = 97, median CI (range) = 3.36 (1.29 – 6.97)) The

pairs represented by the brackets were significantly different (* p<0.05, *** p<0.001) (B) In the 20 group

2 MM patients where IF was also performed, the CI was highly correlated with the percentage of clonal

PCs with abnormal (>4) centrin signals (Spearman correlation coefficient r = 0.97, p < 0.0001) [This

figure was originally published in Blood Chng WJ, et al Clinical implication of centrosome amplification

in plasma cell neoplasm Blood 2006;107:3669-75 © the American Society of Hematology.]

2.3.3 Centrosome amplification and ploidy category (HRD versus NHRD)

No relation between ploidy categories of PC neoplasms and centrosome amplification was found In group 1 patients, neither the frequency of cases with centrosome amplification (67% v 75%) nor the percentage of cells with centrosome amplification was different between the 2 ploidy subtypes of MM (32.8% ± 22.1% v