Abstract
The remarkable genetic heterogeneity of multiple myeloma poses a substantial challenge for proper prognostication and clinical management of patients. Here, we introduce MM-PSN, the first multiomics patient similarity network of myeloma. MM-PSN enabled accurate dissection of the genetic and molecular landscape of the disease and determined 12 distinct subgroups defined by five data types generated from genomic and transcriptomic profiling of 655 patients. MM-PSN identified patient subgroups not previously described defined by specific patterns of alterations, enriched for specific gene vulnerabilities, and associated with potential therapeutic options. Our analysis revealed that co-occurrence of t(4;14) and 1q gain identified patients at significantly higher risk of relapse and shorter survival as compared to t(4;14) as a single lesion. Furthermore, our results show that 1q gain is the most important single lesion conferring high risk of relapse and that it can improve on the current International Staging Systems (ISS and R-ISS).
INTRODUCTION
Multiple myeloma (MM) is a mostly incurable malignancy of bone marrow terminally differentiated plasma cells, affecting more than 30,000 patients each year in the United States, with a median survival of approximately 6 years (1). While most patients initially respond to standard-of-care treatment, most relapse and become refractory as they undergo multiple lines of therapy. In particular, about 15% of patients fall in the high-risk category and typically relapse within 2 years from diagnosis (2). MM is characterized by remarkable clinical and genomic heterogeneity (3). Recent studies based on next-generation sequencing have revealed complex patterns of primary and secondary genetic alterations across patients (4, 5), and novel precision medicine approaches, where treatment is guided by the genomic profile of the individual patient, are being tested in trials (6, 7). Accurate classification of patients with MM into biologically homogeneous classes is thus essential for diagnosis, prognosis, and clinical management.Several classifications of MM based on gene expression have been proposed in the past two decades. The TC (translocation/cyclin D) classification included eight groups characterized by different chromosomal translocations and the up-regulation of the cyclin D genes CCND1 and CCND3 (8). The UAMS (University of Arkansas for Medical Sciences) classification, based on unsupervised clustering of gene expression data, proposed seven clusters in part overlapping the TC classes and enriched for clinically relevant features and differential response to therapy (9). A further refinement was proposed by the HOVON Dutch-Belgium Hemato-Oncology Cooperative Group study group, which consisted of six of the UAMS classes and four novel classes enriched for activation of specific genes such as NFκB (nuclear factor κB) and PRL3 (protein-tyrosine phosphatase of regenerating liver 3) and a myeloid signature (10).Our recent network model of newly diagnosed MM based on gene coexpression, MMNet, revealed a clear molecular separation between patients with immunoglobulin (Ig) translocations and hyperdiploidy and identified three novel subtypes characterized by cytokine signaling (CK), immune signatures (IMM), and MYC translocations (MYC) (11). Another study investigated novel MM subtypes based on a targeted DNA panel (12). The analysis revealed a large cluster comprising most hyperdiploidy (HD) and IgH-translocated patients and two smaller clusters, one enriched for IgH translocations and high number of copy number alterations (CNAs) and one mostly composed of patients with HD, with the fewest CNAs and mutations. Other recent works have described novel approaches to classification of patients with MM based on DNA alterations (13, 14). However, currently, no classification of MM accounts for both genomic and transcriptomic abnormalities, and the different results obtained from different data types, e.g., DNA versus RNA, suggest that a more holistic approach including different omics might further improve patient classification and reveal biologically and clinically informative subtypes of the disease.Recently, patient similarity networks (PSNs) have emerged as a powerful tool to capture and structure the complexity and diversity of clinical, genetic, and molecular information across a patient population (15). In a PSN, patients are represented as nodes, much like in a social network, and connected with one another based on how similar their genomic and transcriptomic profiles are. The network structure enables effective identification of communities of highly similar patients, allowing a more comprehensive classification than other approaches based on a single measurement. PSNs have been successfully used to dissect the genomic and molecular complexity of several cancers, including medulloblastoma, glioblastoma multiforme, pancreatic ductal adenocarcinoma, and metastatic colorectal cancer (16–19).In this study, we generated MM-PSN, the first PSN of newly diagnosed MM based on multiomics data from the MMRF (Multiple Myeloma Research Foundation) CoMMpass study (20). Clustering of MM-PSN identified 12 subgroups, revealing novel insights into the co-occurrence of primary translocation events such as t(4;14)-MMSET (multiple myeloma SET domain) and secondary adverse lesions such as gain of 1q and whole-arm deletions of 16q and 17p, which harbors the tumor suppressor TP53.
RESULTS
Multiomics PSN of newly diagnosed MM reveals greater genetic and molecular heterogeneity than current classifications
We generated MM-PSN, a multiomics PSN based on whole-exome sequencing (WES), whole-genome sequencing (WGS), and RNA sequencing (RNA-seq) data from 655 tumor samples from newly diagnosed patients with MM enrolled in the MMRF CoMMpass study, using the similarity network fusion (SNF) method (see Table 1 for summary patient characteristics) (17). In MM-PSN, each node represents a patient, and connecting edges represent similarity on the basis of multiple data types. In particular, for each sample, we used (i) gene expression and (ii) gene fusion data from RNA-seq, (iii) somatic single-nucleotide variations (SNVs) from WES, (iv) CNAs (focal and broad), and (v) translocation calls from WGS (Fig. 1, A and B). Translocations and CNAs provided the strongest contribution to MM-PSN, followed by gene expression, gene fusions, and SNVs (Fig. 1C). We then applied spectral clustering to determine groups of highly similar patients sharing features across the five data types. Our evaluation of the network using eigengap and rotation cost suggested 3 as the optimal number of clusters (Fig. 1D; see Methods for further details). Differential feature analysis revealed that the three clusters were enriched for (i) HD and the t(8;14) translocation of MYC (tMYC), (ii) translocations t(4;14) of MMSET/FGFR3 (tMMSET) and t(14;16) of MAF (tMAF), and (iii) translocation t(11;14) of CCND1 (tCCND1), respectively (Fig. 1, A and E). We labeled each group on the basis of these features. Group 1 (HD) included n = 357 patients (54.5%) and was further enriched for mutations in NRAS and an LSAMP:RPL18 gene fusion. Group 2 (tMMSET/tMAF) included n = 166 patients (25.3%) and was enriched for mutations in FGFR3, DIS3, and MAX. Group 3 (tCCND1) included n = 132 patients (20.15%) and was enriched for mutations in CCND1 and NRAS (see Methods and Supplementary Materials for details and statistics). To further dissect intragroup heterogeneity, we reapplied spectral clustering within each group, determining a total of 12 subgroups (Fig.1, B and E). Table 2 provides a summary of the most relevant features enriched in each group and subgroups. The complete data are given in tables S1 to S22…..
