View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector ARTICLE High-resolution genomic profiles define distinct clinico- pathogenetic subgroups of multiple myeloma patients Daniel R. Carrasco,1,2,8 Giovanni Tonon,1,8 Yongsheng Huang,3,8 Yunyu Zhang,1 Raktim Sinha,1 Bin Feng,1 James P. Stewart,3 Fenghuang Zhan,3 Deepak Khatry,1 Marina Protopopova,5 Alexei Protopopov,5 Kumar Sukhdeo,1 Ichiro Hanamura,3 Owen Stephens,3 Bart Barlogie,3 Kenneth C. Anderson,1,4 Lynda Chin,1,7 John D. Shaughnessy, Jr.,3,9 Cameron Brennan,6,9 and Ronald A. DePinho1,5,9,* 1 Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115 2 Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts 02115 3 The Donna and Donald Lambert Laboratory of Myeloma Genetics, Myeloma Institute for Research and Therapy, University of Arkansas for Medical Sciences, Little Rock, Arkansas 72205 4 The Jerome Lipper Multiple Myeloma Center, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115 5 Center for Applied Cancer Science, Belfer Institute for Innovative Cancer Science, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts 6 Neurosurgery Service, Memorial Sloan-Kettering Cancer Center, Department of Neurosurgery, Weill Cornell Medical College, New York, New York 10021 7 Department of Dermatology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, 02115 8 These authors contributed equally to this work. 9 Cocorresponding authors: [email protected] (C.B.); [email protected] (J.D.S.); [email protected] (R.A.D.) *Correspondence: [email protected] Summary To identify genetic events underlying the genesis and progression of multiple myeloma (MM), we conducted a high-resolu- tion analysis of recurrent copy number alterations (CNAs) and expression profiles in a collection of MM cell lines and out- come-annotated clinical specimens. Attesting to the molecular heterogeneity of MM, unsupervised classification using non- negative 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. Introduction cell support. Novel agents such as thalidomide, the immunore- gulator Revlimid, and the proteasome inhibitor bortezomib can Multiple myeloma (MM) is characterized by clonal proliferation achieve responses in patients with relapsed and refractory of plasma cells in the bone marrow, usually with elevated serum MM; however, the median survival remains at 6 years, with and urine monoclonal paraprotein and associated end organ se- only 10% of patients surviving at 10 years (Barlogie et al., quelae. MM is the second most frequent hematological cancer 2004; Rajkumar and Kyle, 2005; Richardson et al., 2005). in the US after non-Hodgkin’s lymphoma. MM is typically pre- Significant effort has been directed toward the identification ceded by an age-progressive condition termed monoclonal of the molecular genetic events in this malignancy with the goals gammopathy of undetermined significance (MGUS), a condition of improving early detection and providing new therapeutic tar- present in 1% of adults over the age of 25 that progresses to gets. Unlike most hematological malignancies and more similar MM at a rate of 0.5%–3% per year (Bergsagel et al., 2005; to solid tissue neoplasms, MM genomes are typified by numer- Kyle and Rajkumar, 2004; Mitsiades et al., 2004). MM remains ous structural and numerical chromosomal aberrations (Kuehl largely incurable despite high-dose chemotherapy with stem and Bergsagel, 2002). Reflecting the increasing genomic SIGNIFICANCE MM is the second most common hematological malignancy and remains incurable. Effective targeted therapies require detailed knowledge of the spectrum of genetic lesions governing MM pathogenesis. This study provides a comprehensive and integrated view of recurrent amplifications and deletions and their associated gene expression alterations. This integration, coupled with this high-resolution aCGH platform, delimited a tractable number of candidate genes for enlistment into functional validation and ulti- mately drug discovery. Importantly, unsupervised NMF-based classification of the MM genome profiles stratified MM into specific sub- groups in which the classical hyperdiploid MM was further subdivided into two subgroups with distinct clinical outcomes. Thus, MM is a molecularly heterogeneous disease in which distinct loci can be linked to clinical behavior and prognosis. CANCER CELL 9, 313–325, APRIL 2006 ª2006 ELSEVIER INC. DOI 10.1016/j.ccr.2006.03.019 313 ARTICLE instability that characterizes disease progression, metaphase tumor types, correlation with survival, and presence of known chromosomal abnormalities can be detected in only one-third oncogenes and TSGs or their homologs not previously impli- of newly diagnosed patients but are evident in the majority of pa- cated in MM pathogenesis. The high-resolution view afforded tients with end-stage disease (Fonseca et al., 2004). Yet, apply- here also provides an efficient entry point for the discovery of ing DNA content or interphase fluorescence in situ hybridization new MM-relevant genes. (FISH) analyses, aneuploidy and translocations are detectable in virtually all subjects with MM and even MGUS (Bergsagel and Results Kuehl, 2001; Chng et al., 2005). Extensive molecular (Kuehl and Bergsagel, 2002; Shaughnessy and Barlogie, 2003), cyto- Unsupervised classification of MM genomic features genetic (Bergsagel and Kuehl, 2001; Debes-Marun et al., identifies distinct patient subgroups 2003; Sawyer et al., 1998), and chromosomal CGH (Avet-Loi- seau et al., 1997; Cigudosa et al., 1998) analyses have uncov- High-resolution aCGH methodology was used to catalog copy number alterations (CNAs) in the genomes of CD138+-enriched ered a number of recurrent genetic alterations in MM and its pre- + 2 cursor MGUS, some of which have been linked to disease plasma cells (>90% CD138 CD45 ) derived from 67 newly diag- pathogenesis and clinical behavior. nosed MM patients prior to treatment (Table S1 in the Supple- Chromosomal translocations involving the IgH locus are seen mental Data available with this article online). This data set re- in most MM cell lines, consistent with MM’s origin from antigen- vealed a highly rearranged MM genome, harboring large driven B cells in postgerminal centers (Kuehl and Bergsagel, numbers of distinct CNAs. Since genomic alterations hold the 2002). Five recurrent loci/genes are commonly juxtaposed to potential to identify genetic events playing direct roles in disease the powerful IgH enhancer locus elements, including 11q13 pathogenesis and progression (Aguirre et al., 2004; Pollack (CCND1), 4p16 (FGFR3/WHSC1), 6p21 (CCND3), 16q23 et al., 2002; Tonon et al., 2005), we sought to determine whether (MAF), and 20q11 (MAFB), resulting in deregulated expression MM genomic profiles could specify meaningful genetic and clin- of these target genes in neoplastic plasma cells (Bergsagel ical subgroups by unsupervised methodologies. To this end, we and Kuehl, 2005). Such translocations, present in MGUS, ap- developed an algorithm based on nonnegative matrix factoriza- pear central to MM genesis, whereas progression is associated tion (NMF) (Brunet et al., 2004)(Experimental Procedures; C.B. with mutational activation of NRAS or KRAS oncogenes and in- and L.C., unpublished data), designed to extract distinctive ge- activation of CDKN2A, CDKN2C, CDKN1B, and/or PTEN tumor nomic features from aCGH profiles, hereafter designated as suppressor genes (TSGs). Late mutational events involve inacti- gNMF. Briefly, gNMF was performed on aCGH data after trans- vation of TP53 and secondary translocations involving MYC forming to nonnegative values, and gNMF consensus matrices (Kuehl and Bergsagel, 2002). were generated (Figure S1). Ranks K = 2, 3, and 4 generated ma- Two oncogenic pathways have been hypothesized for MGUS/ trices showing stable cluster assignments suggesting the exis- MM pathogenesis. Hyperdiploid MM involves multiple trisomies tence of up to four distinct genomic patterns among the 67 of chromosomes 3, 5, 7, 9, 11, 15, 19, and 21, whereas nonhy- MM samples. perdiploid MM is associated with prevalent IgH translocations The rank K = 2 classification divided these 67 samples into (Bergsagel et al., 2005; Cremer et al., 2005; Fonseca et al., a ‘‘kA’’ subgroup (n = 38) characterized by odd-chromosome 2004). Ploidy level also impacts prognosis: nonhyperdiploidy gains and a ‘‘kB’’ subgroup (n = 29) characterized by loss of imparts short survival (Fonseca et al., 2004) that can be counter- chromosomes 1p, 8p, 13, and 16q and amplification of ch1q acted by the presence of trisomies involving chromosomes 6, 9, (Figure 1A). Thus, the K = 2 classification yields a grouping rem- 11, and 17. Deletion of chromosome 13, especially band 13q14, iniscent of the well-recognized hyperdiploid (e.g., odd-chromo- is commonly observed in nonhyperdiploid MM and confers high some gain pattern) and nonhyperdiploid subclasses. However,