Heterogeneity of genomic evolution and mutational profiles in multiple myeloma

Niccolo Bolli, Hervé Avet-Loiseau, David C Wedge, Peter Van Loo, Ludmil B Alexandrov, Inigo Martincorena, Kevin J Dawson, Francesco Iorio, Serena Nik-Zainal, Graham R Bignell, Jonathan W Hinton, Yilong Li, Jose M C Tubio, Stuart McLaren, Sarah O' Meara, Adam P Butler, Jon W Teague, Laura Mudie, Elizabeth Anderson, Naim Rashid, Yu-Tzu Tai, Masood A Shammas, Adam S Sperling, Mariateresa Fulciniti, Paul G Richardson, Giovanni Parmigiani, Florence Magrangeas, Stephane Minvielle, Philippe Moreau, Michel Attal, Thierry Facon, P Andrew Futreal, Kenneth C Anderson, Peter J Campbell, Nikhil C Munshi, Niccolo Bolli, Hervé Avet-Loiseau, David C Wedge, Peter Van Loo, Ludmil B Alexandrov, Inigo Martincorena, Kevin J Dawson, Francesco Iorio, Serena Nik-Zainal, Graham R Bignell, Jonathan W Hinton, Yilong Li, Jose M C Tubio, Stuart McLaren, Sarah O' Meara, Adam P Butler, Jon W Teague, Laura Mudie, Elizabeth Anderson, Naim Rashid, Yu-Tzu Tai, Masood A Shammas, Adam S Sperling, Mariateresa Fulciniti, Paul G Richardson, Giovanni Parmigiani, Florence Magrangeas, Stephane Minvielle, Philippe Moreau, Michel Attal, Thierry Facon, P Andrew Futreal, Kenneth C Anderson, Peter J Campbell, Nikhil C Munshi

Abstract

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.

Figures

Figure 1. Sequencing metrics of the study.
Figure 1. Sequencing metrics of the study.
(a) Total number of validated somatic variants for each patient in the cohort. (b) Breakdown of variants by type. (c) Breakdown of variants by nucleotide change. Transitions (C>T, T>C) are in light blue, transversions (C>A, C>G, T>A, T>G) in dark blue.
Figure 2. Modelling clonal and subclonal mutation…
Figure 2. Modelling clonal and subclonal mutation clusters.
(a) Stacked bar chart showing the number of clonal mutations (present in all tumour cells, light blue) and subclonal mutations (dark blue) in each patient. Also shown is the percentage of subclonal variants (orange triangles). For patients with multiple samples, the fraction of tumour cells used was derived from the earliest sample where the mutation was found. (b–d) Statistical modelling by a Bayesian Dirichlet process of the distribution of clonal and subclonal mutations for three patients for which only one sample was available. For each plot, the faction of tumour cells carrying the variant is represented on the x axis (1=100% of tumour cells), and the probability density (on an arbitrary scale) on the y axis. Grey bars represent the histogram of mutations, with the fitted distribution as a dark purple line. The 95% posterior confidence intervals for the fitted distribution are represented by a pale blue area. (b) Patient showing a vast majority of clonal variants; (c) patient with a dominant set of clonal mutations and a minor subclone; (d) patient showing a dominant set of subclonal mutations, at two different proportions. (e) Adjusted fraction of tumour cells carrying KRAS, NRAS and BRAF substitutions found in the study. Error bars represent 95% confidence intervals, accounting for chromosomal copy number of the locus, percentage contaminating normal cells and depth of coverage. (Note that patient PD5883, carrying a BRAF indel, was not included in this panel due to the inaccurate estimation of the allelic fraction of indels). All mutations whose confidence interval includes 1 (red line) are considered to be present in all tumour cells with 95% confidence. Driver mutations can be found at the clonal or subclonal level, and sometimes coexist in the same patient (Patient IDs in red). For patients with multiple samples, the fraction of tumour cells used was derived from the earliest sample where the mutation was found. In three patients (PD5876, PD5885, PD5892), a fraction of ~1.5 was assigned, likely due to focal subclonal gains of the variant locus that went undetected by our algorithms, so that the estimated fraction of tumour cells was not adjusted.
Figure 3. Clonal evolution.
Figure 3. Clonal evolution.
(a–d) Chromosomal copy-number plots for the early and late sample; in each plot, purple=total copy number, blue=copy number of the minor allele. (ai–di) Two-dimensional density plots showing the clustering of the fraction of tumour cells carrying each mutation (black dots) at each time point; increasing intensity of red indicates the location of a high posterior probability of a cluster. (aii–dii) Phylograms representing the clonal composition of the tumour at each timepoint, where the length of each branch is proportional to the size of the clone (that is, the number of variants), and the width to its clonality (that is, the proportion of tumour cells bearing each variant). Grey circles represent the nodes of the phylograms, that is, the point at which a separate group of cells diverges in evolution from the parental clone. The distance between nodes relates to the evolution time from the fertilized egg down the trunk of the phylograms (that is, the main clone of the tumour), through the most recent common ancestor to the branches of the phylogram (that is, the different subclones of the tumour arisen later in evolution). For each branch, the number and average clonality of the variants has been annotated (black text), along with notable copy-number changes and mutations (dark blue text). (e) Pie chart breakdown of the proportional representation of the different kinds of clonal evolution observed in the cohort. (f) Histograms showing the number of cases and type of clonal evolution for each karyotypic subgroup.
Figure 4. Mutational processes operative in multiple…
Figure 4. Mutational processes operative in multiple myeloma.
(a,b) Fraction of contribution of each mutation type at each context for the two mutational signatures identified by NMF analysis. The major components contributing to each signature are highlighted with arrows. (c) Stacked bar chart showing the percentage contribution of the two mutational processes identified by NMF to the total number of variants present in each case. Dark blue bars=Signature B, light blue bars=Signature A. (d) In two examples of branching evolution, PD4283 and PD4301, the percentage contribution of the two mutational processes identified by NMF to the variants present in the early sample (dark purple box) was compared with that of the new variants in the late sample (orange box). A stacked bar chart shows that the contribution from Signature B increases significantly in the ‘late’ variants (χ2 test). Dark blue bars=Signature B, light blue bars=Signature A.
Figure 5. Landscape of genetic alterations and…
Figure 5. Landscape of genetic alterations and recurrently mutated genes in multiple myeloma.
Table highlighting relevant genetic alterations and recurrently mutated genes in the study. Patients are represented in columns. (a) Karyotypic features of each patient. (b) Recurrently mutated genes, color coded for missense (green), nonsense (red) and splice-site (blue) substitutions; indels are in light purple and homozygous deletions in ocra. For patients with serial samples, events occurring only in one sample only are highlighted with a diagonal bar. In case of multiple mutations in the same gene in a patient, only one is plotted. In case a mutation is associated with the deletion of the wild-type allele, the gene shows a black contour. For each gene, the number of patients harbouring at least one non-silent mutation is provided in the ‘TOTAL’ column. Asterisks mark genes mutated at a significant recurrence rate in the data set (P-value<0.02 and false discovery rate of <10%).
Figure 6. Novel gene mutations identified in…
Figure 6. Novel gene mutations identified in multiple myeloma.
Schematic representation of known or predicted functional domains (from NCBI) of the protein products of the main novel genes identified in the screen, and location of the observed variants, color coded by type: red=truncating variants (nonsense, out-of-frame indels, essential splice-site mutations), green=missense variants, and blue=in-frame indels. FAT3 shows a homozygous deletion spanning the whole locus in a patient and this is shown by a red bar.

References

    1. Fonseca R. et al. International Myeloma Working Group molecular classification of multiple myeloma: spotlight review. Leukemia 23, 2210–2221 (2009).
    1. Bergsagel P. L. & Kuehl W. M. Molecular pathogenesis and a consequent classification of multiple myeloma. J. Clin. Oncol. 23, 6333–6338 (2005).
    1. Hideshima T., Mitsiades C., Tonon G., Richardson P. G. & Anderson K. C. Understanding multiple myeloma pathogenesis in the bone marrow to identify new therapeutic targets. Nat. Rev. Cancer. 7, 585–598 (2007).
    1. Morgan G. J., Walker B. A. & Davies F. E. The genetic architecture of multiple myeloma. Nat. Rev. Cancer. 12, 335–348 (2012).
    1. Chapman M. A. et al. Initial genome sequencing and analysis of multiple myeloma. Nature 471, 467–472 (2011).
    1. Egan J. B. 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).
    1. Walker B. A. 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).
    1. Walker B. A. et al. Intraclonal heterogeneity is a critical early event in the development of myeloma and precedes the development of clinical symptoms. Leukemia 28, 335–341 (2013).
    1. Keats J. J. et al. Clonal competition with alternating dominance in multiple myeloma. Blood 120, 1067–1076 (2012).
    1. Magrangeas F. et al. Minor clone provides a reservoir for relapse in multiple myeloma. Leukemia 27, 473–481 (2012).
    1. Nik-Zainal S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).
    1. Aparicio S. & Caldas C. The implications of clonal genome evolution for cancer medicine. N. Engl. J. Med. 368, 842–851 (2013).
    1. Landau D. A. et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 152, 714–726 (2013).
    1. Alexandrov L. B., Nik-Zainal S., Wedge D. C., Campbell P. J. & Stratton M. R. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 3, 246–259 (2013).
    1. Alexandrov L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).
    1. Lawrence M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).
    1. Roberts S. A. et al. An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers. Nat. Genet. 45, 970–976 (2013).
    1. Anderson K. et al. Genetic variegation of clonal architecture and propagating cells in leukaemia. Nature 469, 356–361 (2011).
    1. Campbell P. J. et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 467, 1109–1113 (2010).
    1. Gerlinger M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).
    1. Greaves M. & Maley C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).
    1. Nik-Zainal S. et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).
    1. Papaemmanuil E. et al. Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N. Engl. J. Med. 365, 1384–1395 (2011).
    1. Welch J. S. et al. The origin and evolution of mutations in acute myeloid leukemia. Cell 150, 264–278 (2012).
    1. Hwang D. G. & Green P. Bayesian Markov chain Monte Carlo sequence analysis reveals varying neutral substitution patterns in mammalian evolution. Proc. Natl Acad. Sci. USA 101, 13994–14001 (2004).
    1. Stephens P. J. et al. The landscape of cancer genes and mutational processes in breast cancer. Nature 486, 400–404 (2012).
    1. Smith H. C., Bennett R. P., Kizilyer A., McDougall W. M. & Prohaska K. M. Functions and regulation of the APOBEC family of proteins. Semin. Cell Dev. Biol. 23, 258–268 (2012).
    1. Bergsagel P. L. & Kuehl W. M. Chromosome translocations in multiple myeloma. Oncogene 20, 5611–5622 (2001).
    1. Muramatsu M. et al. Class switch recombination and hypermutation require activation-induced cytidine deaminase (AID), a potential RNA editing enzyme. Cell 102, 553–563 (2000).
    1. Pasqualucci L. et al. Expression of the AID protein in normal and neoplastic B cells. Blood 104, 3318–3325 (2004).
    1. Kridel R. et al. Whole transcriptome sequencing reveals recurrent NOTCH1 mutations in mantle cell lymphoma. Blood 119, 1963–1971 (2012).
    1. Lohr J. G. et al. Discovery and prioritization of somatic mutations in diffuse large B-cell lymphoma (DLBCL) by whole-exome sequencing. Proc. Natl Acad. Sci. USA 109, 3879–3884 (2012).
    1. Maekawa M., Nakamura S. & Hattori S. Purification of a novel ras GTPase-activating protein from rat brain. J. Biol. Chem. 268, 22948–22952 (1993).
    1. Garnett M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012).
    1. Prahallad A. et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 483, 100–103 (2012).
    1. Boyd K. D. et al. Mapping of chromosome 1p deletions in myeloma identifies FAM46C at 1p12 and CDKN2C at 1p32.3 as being genes in regions associated with adverse survival. Clin. Cancer Res. 17, 7776–7784 (2011).
    1. Vogelstein B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).
    1. Madani N. et al. Implication of the lymphocyte-specific nuclear body protein Sp140 in an innate response to human immunodeficiency virus type 1. J. Virol. 76, 11133–11138 (2002).
    1. Browning J. L. et al. Lymphotoxin beta, a novel member of the TNF family that forms a heteromeric complex with lymphotoxin on the cell surface. Cell 72, 847–856 (1993).
    1. Demchenko Y. N. et al. Classical and/or alternative NF-kappaB pathway activation in multiple myeloma. Blood 115, 3541–3552 (2010).
    1. Keats J. J. et al. Promiscuous mutations activate the noncanonical NF-kappaB pathway in multiple myeloma. Cancer cell 12, 131–144 (2007).
    1. Biankin A. V. et al. Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 491, 399–405 (2012).
    1. Katoh M. Function and cancer genomics of FAT family genes (review). Int. J. Oncol. 41, 1913–1918 (2012).
    1. Hegeman A. D., Gross J. W. & Frey P. A. Concerted and stepwise dehydration mechanisms observed in wild-type and mutated Escherichia coli dTDP-glucose 4,6-dehydratase. Biochemistry 41, 2797–2804 (2002).
    1. Worby C. A. & Dixon J. E. Sorting out the cellular functions of sorting nexins. Nat. Rev. Mol. Cell Biol. 3, 919–931 (2002).
    1. Kim J. D., Yu S., Choo J. H. & Kim J. Two evolutionarily conserved sequence elements for Peg3/Usp29 transcription. BMC Mol. Biol. 9, 108 (2008).
    1. Relaix F., Wei X. J., Wu X. & Sassoon D. A. Peg3/Pw1 is an imprinted gene involved in the TNF-NFkappaB signal transduction pathway. Nat. Genet. 18, 287–291 (1998).
    1. Liu J. et al. JTV1 co-activates FBP to induce USP29 transcription and stabilize p53 in response to oxidative stress. EMBO J. 30, 846–858 (2011).
    1. Robert C., Arnault J. P. & Mateus C. RAF inhibition and induction of cutaneous squamous cell carcinoma. Curr. Opin. Oncol. 23, 177–182 (2011).
    1. Callahan M. K. et al. Progression of RAS-mutant leukemia during RAF inhibitor treatment. N. Engl. J. Med. 367, 2316–2321 (2012).
    1. Di Bernardo M. C. et al. A genome-wide association study identifies six susceptibility loci for chronic lymphocytic leukemia. Nat. Genet. 40, 1204–1210 (2008).
    1. Chen L. et al. Identification of early growth response protein 1 (EGR-1) as a novel target for JUN-induced apoptosis in multiple myeloma. Blood 115, 61–70 (2010).
    1. Palumbo A. & Anderson K. Multiple myeloma. N. Engl. J. Med. 364, 1046–1060 (2011).
    1. Varela I. et al. Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 469, 539–542 (2011).
    1. Li H. & Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).
    1. Ye K., Schulz M. H., Long Q., Apweiler R. & Ning Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009).
    1. Stephens P. J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27–40 (2011).
    1. Van Loo P. et al. Allele-specific copy number analysis of tumors. Proc. Natl Acad. Sci. USA 107, 16910–16915 (2010).
    1. Tarpey P. S. et al. Frequent mutation of the major cartilage collagen gene COL2A1 in chondrosarcoma. Nat. Genet. 45, 923–926 (2013).
    1. Greenman C., Wooster R., Futreal P. A., Stratton M. R. & Easton D. F. Statistical analysis of pathogenicity of somatic mutations in cancer. Genetics 173, 2187–2198 (2006).

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe