Heterogeneity of genomic evolution and mutational profiles in multiple myeloma
By N. Bolli, H. Avet-Loiseau, David C. Wedge, P. van Loo, Ludmil B. Alexandrov, Iñigo Martincorena, Kevin J. Dawson, F. Iorio, S. Nik-Zainal, G. Bignell and 25 more. Nature Communications, 2014
862 citations recorded by the source corpus.
Multiple myeloma is an incurable plasma cell malignancy with a complex and incompletely understood molecular pathogenesis. Here we use whole-exome sequencing, copy-number profiling and cytogenetics to analyse 84 myeloma samples. Most cases have a complex subclonal structure and show clusters of subclonal variants, including subclonal driver mutations. Serial sampling reveals diverse patterns of clonal evolution, including linear evolution, differential clonal response and branching evolution. Diverse processes contribute to the mutational repertoire, including kataegis and somatic hypermutation, and their relative contribution changes over time. We find heterogeneity of mutational spectrum across samples, with few recurrent genes. We identify new candidate genes, including truncations of SP140, LTB, ROBO1 and clustered missense mutations in EGR1. The myeloma genome is heterogeneous across the cohort, and exhibits diversity in clonal admixture and in dynamics of evolution, which may impact prognostic stratification, therapeutic approaches and assessment of disease response to treatment. Multiple myeloma is a malignant plasma cell disorder with a complex molecular pathogenesis. Here, the authors perform whole-exome sequencing, copy-number profiling and cytogenetic analysis in 84 myeloma samples and highlight the diversity and evolution of the mutational profile underlying the disease.
Subfields: Cancer Research, Hematology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/8ca17f72c98932468b2b2c38e99e7654d396ccb0
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In Cancer Research
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Citations in the corpus
- SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution (2014, methodology): “Most existing methods [6,10,22,31] inferring clonality from copy number alterations (CNAs) avoid additional computational overhead by making the simplifying assumption that the tumor sample is ‘‘monogenomic’’ [31] and does not harbor subclonal copy-number events.”
- Universal Patterns of Selection in Cancer and Somatic Tissues (2018, neutral): “In early exome studies with small numbers of samples, methods to detect significant mutation recurrence at gene level often assumed that the substitution rate was uniform across genes (Bolli et al., 2014; Greenman et al., 2007; Lawrence et al., 2013).”
- Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. (2017, neutral): “…et al., 2015; Wang et al., 2016); breast cancer (Yates et al., 2015); clear cell renal cell carcinoma (Gerlinger et al., 2014a); multiple myeloma (Bolli et al., 2014); lung adenocarcinomas (de Bruin et al., 2014; Zhang et al., 2014); prostate (Gundem et al., 2015; Ju et al., 2014); bladder (Lamy…”
- Subclonal diversification of primary breast cancer revealed by multiregion sequencing (2015, neutral): “…(ii) Clustering of subclonal somatic substitutions in whole genome data using a Bayesian Dirichlet process in multiple dimensions across related samples as previously described 47 ; (iii) Hierachical clustering across multiple samples by applying the ‘pigeon hole principle’ (PHP).”