Rajkumar, S. V. Multiple myeloma: 2016 update on diagnosis, risk-stratification, and management. Am. J. Hematol. 91, 719–734 (2016).
Kyle, R. A. et al. Monoclonal gammopathy of undetermined significance (MGUS) and smoldering (asymptomatic) multiple myeloma: IMWG consensus perspectives risk factors for progression and guidelines for monitoring and management. Leukemia 24, 1121–1127 (2010).
Morgan, G. J., Walker, B. A. & Davies, F. E. The genetic architecture of multiple myeloma. Nat. Rev. Cancer 12, 335–348 (2012).
Dhodapkar, M. V. MGUS to myeloma: a mysterious gammopathy of underexplored significance. Blood 128, 2599–2606 (2016).
Bolli, N. et al. A DNA target-enrichment approach to detect mutations, copy number changes and immunoglobulin translocations in multiple myeloma. Blood Cancer J. 6, e467 (2016).
Chapman, M. et al. Initial genome sequencing and analysis of multiple myeloma. Nature 471, 467–472 (2011).
Egan, J. et al. Whole-genome sequencing of multiple myeloma from diagnosis to plasma cell leukemia reveals genomic initiating events, evolution, and clonal tides. Blood 120, 1060–1066 (2012).
Lohr, J. G. et al. Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell 25, 91–101 (2014).
Walker, B. et al. Intraclonal heterogeneity and distinct molecular mechanisms characterize the development of t(4; 14) and t(11; 14) myeloma. Blood 120, 1077–1086 (2012).
Laganà, A. et al. Integrative network analysis identifies novel drivers of pathogenesis and progression in newly diagnosed multiple myeloma. Leukemia 32, 120–130 (2018).
Shah, V. et al. Prediction of outcome in newly diagnosed myeloma: a meta-analysis of the molecular profiles of 1905 trial patients. Leukemia 32, 102–110 (2018).
Shaughnessy, J. D. et al. A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. Blood 109, 2276–2284 (2007).
Bolli, N. et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat. Commun. 5, 2997 (2014).
Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
Giladi, A. & Amit, I. Single-cell genomics: a stepping stone for future immunology discoveries. Cell 172, 14–21 (2018).
Paiva, B. et al. Differentiation stage of myeloma plasma cells: biological and clinical significance. Leukemia 31, 382–392 (2017).
Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).
Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).
Juneja, S., Viswanathan, S., Ganguly, M. & Veillette, C. A simplified method for the aspiration of bone marrow from patients undergoing hip and knee joint replacement for isolating mesenchymal stem cells and in vitro chondrogenesis. Bone Marrow Res. 2016, 1–18 (2016).
Halliley, J. et al. Long-lived plasma cells are contained within the CD19−CD38hiCD138+ subset in human bone marrow. Immunity 43, 132–145 (2015).
Chesi, M. et al. Frequent translocation t(4;14)(p16.3; q32.3) in multiple myeloma is associated with increased expression and activating mutations of fibroblast growth factor receptor 3. Nat. Genet. 16, 260–264 (1997).
Pawlyn, C. & Morgan, G. Evolutionary biology of high-risk multiple myeloma. Nat. Rev. Cancer 17, 543–556 (2017).
Combes, A. et al. BAD-LAMP controls TLR9 trafficking and signalling in human plasmacytoid dendritic cells. Nat. Commun. 8, 913 (2017).
Defays, A. et al. BAD-LAMP is a novel biomarker of nonactivated human plasmacytoid dendritic cells. Blood 118, 609–617 (2011).
Fathallah-Shaykh, H., Wolf, S., Wong, E., Posner, J. B. & Furneaux, H. M. Cloning of a leucine-zipper protein recognized by the sera of patients with antibody-associated paraneoplastic cerebellar degeneration. Proc. Natl Acad. Sci. USA 88, 3451–3454 (1991).
Hellström, I. et al. The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma. Cancer Res. 63, 3695–3700 (2003).
Nutt, S. L., Hodgkin, P. D., Tarlinton, D. M. & Corcoran, L. M. The generation of antibody-secreting plasma cells. Nat. Rev. Immunol. 15, 160–171 (2015).
Kumar, S. K. & Rajkumar, S. V. The multiple myelomas—current concepts in cytogenetic classification and therapy. Nat. Rev. Clin. Oncol. 15, 409–421 (2018).
Rajan, A. M. & Rajkumar, S. V. Interpretation of cytogenetic results in multiple myeloma for clinical practice. Blood Cancer J. 5, e365 (2015).
Puig, N. et al. The predominant myeloma clone at diagnosis, CDR3 defined, is constantly detectable across all stages of disease evolution. Leukemia 29, 1435–1437 (2015).
Lefranc, M.-P. et al. IMGT, the international ImMunoGeneTics information system 25 years on. Nucleic Acids Res. 43, D413–D422 (2015).
Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 (2018).
Tian, E. et al. The role of the Wnt-signaling antagonist DKK1 in the development of osteolytic lesions in multiple myeloma. N. Engl. J. Med. 349, 2483–2494 (2003).
Zhao, X.-Y. Y. et al. Long noncoding RNA licensing of obesity-linked hepatic lipogenesis and NAFLD pathogenesis. Nat. Commun. 9, 2986 (2018).
Leidi, M., Mariotti, M. & Maier, J. Transcriptional coactivator EDF-1 is required for PPARγ-stimulated adipogenesis. Cell. Mol. Life Sci. 66, 2733–2742 (2009).
Simaite, D. et al. Recessive mutations in PCBD1 cause a new type of early-onset diabetes. Diabetes 63, 3557–3564 (2014).
Chen, X. et al. Prognostic value of diametrically polarized tumor-associated macrophages in multiple myeloma. Oncotarget 8, 112685–112696 (2017).
Dubovsky, J. et al. Lymphocyte cytosolic protein 1 is a chronic lymphocytic leukemia membrane-associated antigen critical to niche homing. Blood 122, 3308–3316 (2013).
Mishima, Y. et al. The mutational landscape of circulating tumor cells in multiple myeloma. Cell Rep. 19, 218–224 (2017).
Lohr, J. et al. Genetic interrogation of circulating multiple myeloma cells at single-cell resolution. Sci. Transl. Med. 8, 363ra147 (2016).
Manier et al. Whole-exome sequencing of cell-free DNA and circulating tumor cells in multiple myeloma. Nat. Commun. 9, 1691 (2018).
Rasche et al. Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing. Nat. Commun. 8, 268 (2017).
Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).
Harris, P. A. et al. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 42, 377–381 (2009).
Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290 (2017).
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
Baran, Y. et al. MetaCell: analysis of single cell RNA-seq data using k-NN graph partitions. Preprint at bioRxiv https://doi.org/10.1101/437665 (2018).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Blecher-Gonen, R. et al. High-throughput chromatin immunoprecipitation for genome-wide mapping of in vivo protein-DNA interactions and epigenomic states. Nat. Protoc. 8, 539–554 (2013).
Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).
Flores-Montero, et al. Next generation flow for highly sensitive and standardized detection of minimal residual disease in multiple myeloma. Leukemia 31, 2094 (2017).