Genomic landscape and chronological reconstruction of driver events in multiple myeloma Francesco Maura 1,2,3* , Niccoló Bolli 1,2,3* , Nicos Angelopoulos 3 , Kevin J. Dawson 3 , Daniel Leongamornlert 3 , Inigo Martincorena 3 , Thomas J. Mitchell 3 , Anthony Fullam 3 , Santiago Gonzalez 4 , Raphael Szalat 5 , Bernardo Rodriguez-Martin 6 , Mehmet Kemal Samur 5 , Dominik Glodzik 3 , Marco Roncador 3 , Mariateresa Fulciniti 5 , Yu Tzu Tai 5 , Stephane Minvielle 7 , Florence Magrangeas 7 , Philippe Moreau 7 , Paolo Corradini 1,2 , Kenneth C. Anderson 5 , Jose M. C. Tubio 3,6 , David C. Wedge 8 , Moritz Gerstung 4 , Herve Avet-Loiseau 9 , Nikhil Munshi 5,10 # and Peter J. Campbell 3 # *These authors contributed equally to this work; # Co-corresponding authors 1 Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy 2 Department of Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; 3 The Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridgeshire CB10 1SA, United Kingdom 4 European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL- EBI) 5 Jerome Lipper Multiple Myeloma Center, Dana–Farber Cancer Institute, Harvard Medical School, Boston, MA; 6 CIMUS - Molecular Medicine and Chronic Diseases Research Centre University of Santiago de Compostela, Spain 7 CRCINA, INSERM, CNRS, Université d’Angers, Université de Nantes, Nantes, France 8 University of Oxford, Big Data Institute 9 IUC-Oncopole, and CRCT INSERM U1037, 31100, Toulouse, France. 10 Veterans Administration Boston Healthcare System, West Roxbury, MA; Running Title: Multiple Myeloma Driver Events Key words: Multiple Myeloma, Whole Genome Sequencing, Driver Events, Structural Variations, Chromothripsis Corresponding Authors: Dr Peter J Campbell, Cancer Genome Project, Wellcome Sanger Institute, Hinxton CB10 1SA, United Kingdom. Phone: +44 1223 494745 e-mail: [email protected]Dr Nikhil C. Munshi Dana-Farber Cancer Institute 450 Brookline Avenue, Dana B106 Boston, MA 02215, USA Phone: +1-617-632-4218 Fax +1-617-582-8608 e-mail: [email protected]certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted August 12, 2018. ; https://doi.org/10.1101/388611 doi: bioRxiv preprint
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Genomic landscape and chronological reconstruction of driver events in multiple myeloma Francesco Maura1,2,3*, Niccoló Bolli1,2,3*, Nicos Angelopoulos3, Kevin J. Dawson3, Daniel Leongamornlert3, Inigo Martincorena3, Thomas J. Mitchell3, Anthony Fullam3, Santiago Gonzalez4, Raphael Szalat5, Bernardo Rodriguez-Martin6, Mehmet Kemal Samur5, Dominik Glodzik3, Marco Roncador3, Mariateresa Fulciniti5, Yu Tzu Tai5, Stephane Minvielle7, Florence Magrangeas7, Philippe Moreau7, Paolo Corradini1,2, Kenneth C. Anderson5, Jose M. C. Tubio3,6, David C. Wedge8, Moritz Gerstung4, Herve Avet-Loiseau9, Nikhil Munshi5,10 # and Peter J. Campbell3 #
*These authors contributed equally to this work; # Co-corresponding authors 1 Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy 2 Department of Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; 3 The Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridgeshire CB10 1SA, United Kingdom 4 European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI) 5 Jerome Lipper Multiple Myeloma Center, Dana–Farber Cancer Institute, Harvard Medical School, Boston, MA; 6 CIMUS - Molecular Medicine and Chronic Diseases Research Centre University of Santiago de Compostela, Spain 7 CRCINA, INSERM, CNRS, Université d’Angers, Université de Nantes, Nantes, France 8 University of Oxford, Big Data Institute 9 IUC-Oncopole, and CRCT INSERM U1037, 31100, Toulouse, France. 10 Veterans Administration Boston Healthcare System, West Roxbury, MA; Running Title: Multiple Myeloma Driver Events Key words: Multiple Myeloma, Whole Genome Sequencing, Driver Events, Structural Variations, Chromothripsis Corresponding Authors: Dr Peter J Campbell, Cancer Genome Project, Wellcome Sanger Institute, Hinxton CB10 1SA, United Kingdom. Phone: +44 1223 494745 e-mail: [email protected]
Dr Nikhil C. Munshi Dana-Farber Cancer Institute 450 Brookline Avenue, Dana B106 Boston, MA 02215, USA Phone: +1-617-632-4218 Fax +1-617-582-8608 e-mail: [email protected]
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Multiple myeloma (MM) has a heterogeneous genome, evolving through both
pre-clinical and post-diagnosis phases. Here, using sequences from 67 MM
genomes serially collected from 30 patients together with public datasets, we
establish a hierarchy of driver lesions. Point mutations, structural variants and copy
number aberrations define at least 7 genomic subgroups of MM, each with distinct
sets of co-operating driver mutations. Complex structural events are major drivers of
MM, including chromothripsis, chromoplexy and a replication-based mechanism of
templated insertions: these typically occur early. Hyperdiploidy also occurs early,
with individual chromosomes often gained in more than one chronological epoch of
MM evolution, showing a preferred order of acquisition. Positively selected point
mutations frequently occur in later phases of disease development, as do structural
variants involving MYC. Thus, initiating driver events of MM, drawn from a limited
repertoire of structural and numerical chromosomal changes, shape preferred
trajectories of subsequent evolution.
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The genome of multiple myeloma (MM) is complex and heterogeneous, with a
high frequency of structural variants (SVs) and copy-number abnormalities (CNAs)1-
3. Translocations between the immunoglobulin heavy chain (IGH) locus and
recurrent oncogenes are found in ~40% of patients. Cases without IGH
translocations often have a distinctive pattern of hyperdiploidy affecting odd-
numbered chromosomes, where the underlying target genes remain mysterious.
These SVs and recurrent CNAs are considered early drivers, being detectable also
in pre-malignant stages of the disease1,2,4. Cancer genes are also frequently altered
by driver point mutations, with MAPK and NF-κB signaling as major targets5-8.
Many blood cancers develop along preferred evolutionary trajectories. Early
driver events, drawn from a restricted set of possible events, differ in which
subsequent cancer genes confer clonal advantage, leading to considerable
substructures of co-operativity and mutual exclusivity among cancer genes. These
subtypes vary in chemosensitivity and survival, suggesting that although patients
share a common histological and clinical phenotype, the underlying biology is
distinctly heterogeneous. Preliminary studies have suggested that these patterns
exist in MM as well5,7,9-12, but have not yet been systematically defined in large
cohorts with broad sequencing coverage.
We performed whole genome sequencing (WGS) of 67 tumor samples
collected at different time points from 30 MM patients, together with matched
germline controls (Supplementary Fig. 1, Supplementary Table 1, Methods). We
also included in our analyses published whole exome data from 804 patients13,14. To
discover significant cancer genes, we analyzed the ratio of non-synonymous to
synonymous mutations, correcting for mutational spectrum and covariates of
mutation density across the genome using a published algorithm15,16. Overall, 55
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genes were significantly mutated with a false discovery rate of 1% (Fig. 1a,
Supplementary Table 2). A significant fraction of these driver mutations was
detected at subclonal level, suggesting a major role in late phases of cancer
development (Supplementary Fig. 2a). Beyond well-known myeloma genes such as
KRAS, NRAS, DIS3 and FAM46C5-8, several other interesting candidate genes
emerged. The linker histones HIST1H1B, HIST1H1D, HIST1H1E and HIST1H2BK
all showed a distinctive pattern of missense mutations clustered in the highly
conserved globular domain (Supplementary Figure 2b-e), as reported in follicular
lymphoma17. Many of the mutations were nearby, or directly affected, conserved
positively charged residues critical for nucleosome binding, suggesting that they
disrupt the histones’ role in regulating higher order chromatin structure. FUBP1, an
important regulator of MYC transcription18, showed an excess of splice site and
nonsense mutations, suggesting it may be a tumor suppressor gene in MM
(Supplementary Figure 2f). MAX, a DNA-binding partner of MYC, showed an
interesting pattern of start-lost mutations, nonsense and splice site mutations,
together with hotspot missense mutations at residues Arg35, Arg36 and Arg60,
known to abrogate DNA binding7 (Supplementary Fig. 2g). Genes with rather more
mysterious function were also significant: the zinc finger ZNF292, recently described
as mutated in chronic lymphocytic leukemia and diffuse large B-cell lymphoma19,20,
showed an excess of protein-truncating variants (Supplementary Fig. 2h); the
uncharacterized TBC1D29 gene showed two hotspots of missense mutations21
(Supplementary Fig. 2i).
In pairwise comparisons, these cancer genes showed distinct patterns of co-
mutation and mutual exclusivity (Supplementary Fig. 2j). To define the logic rules
underpinning the conditional dependencies of driver events, we employed Bayesian
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networks and the hierarchical Dirichlet process (Fig. 1b-c, Supplementary Fig. 3).
This confirmed that the strongest determinants of genomic substructure in myeloma
are IGH translocations and recurrent CNAs. Some co-operating genetic lesions were
non-randomly distributed across these main groups: a significant fraction of patients
without IGH translocations were generally enriched for 1q gain and deletions on
1p13, 1p32, 13q, TRAF3 and CYLD (Cluster 1). RAS signaling mutations, especially
NRAS and KRAS, were associated with hyperdiploidy and MYC translocations
(Cluster 2). A significant fraction (33%) of patients with t(11;14) were characterized
by low genomic complexity and high prevalence of IRF4 and CCDN1 mutations
(Cluster 3). Patients harboring TP53 bi-allelic inactivation were clustered in an
independent sub group (Cluster 4). A significant fraction of MMSET (51%) and
CCND1 (19%) translocated patients were characterized by multiple cytogenetic
aberrations, with low prevalence of CYLD/FAM46C deletions and TRAF3 deletion
respectively (Cluster 5). A second fraction of patients with MMSET translocation
(46%) were grouped with deletion of 13q14, gain of 1q21, DIS3 and FGFR3
mutations (Cluster 6); finally, patients with either MAF/MAFB translocations or no
IGH translocations was characterized by a high driver mutation rate (Cluster 7).
Thus, there is evidence for at least 7 distinct genomic subtypes of myeloma, each
with distinct combinations of driver mutations and recurrent SVs (Figure 1c).
Straightforward reciprocal translocations such as the canonical IGH-oncogene
translocations only accounted for 6% (127/2113) of SVs in the 67 samples from 30
patients studied by WGS (Figure 2a, Supplementary Fig. 4a-b). Other structural
variants included many unbalanced translocations and complex events
(Supplementary Fig. 4c-f, Methods)22. Most (24/30; 80%) patients had at least one
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In 6/30 (20%) patients, we found a novel complex pattern characterized by
cycles of templated insertions. Here, several low-amplitude copy number gains on
different chromosomes were linked together through SVs demarcating the region of
duplication (Fig. 2b,d; Supplementary Fig. 5d-h). The most plausible explanation
for this pattern is that the templates are strung together into a single chain, hosted
within one of the chromosomes. We have observed such events in some solid
cancers22, where the copy number gains suggest a mutational process that is
replication-based, rather than the break-and-ligate processes generating
chromothripsis and chromoplexy23.
This complex landscape of SVs in myeloma often involved known driver
genes: MYC (14/30 cases; 46%), CCND1 (7/30; 23%) and MMSET (3/30; 10%) were
common targets (Supplementary Fig. 4b) of these non-canonical events. The
juxtaposition of CCND1 to the IGH locus was caused by either unbalanced
translocations or insertional events in 5/8 patients (Supplementary Fig. 6). Similarly,
MYC translocations showed unanticipated complexity, with four cases of templated
insertions involving MYC or its regulatory regions (Supplementary Fig. 7). Such
events are the structural basis of oncogene amplification observed by FISH in many
cases of t(11;14) and t(8;14)28,29. Interestingly, many of the MYC SVs involved the
immunoglobulin light chain loci, IGK or IGL, rather than the heavy chain IGH locus,
and were seen in patients with hyperdiploidy (Supplementary Fig. 4b). Although
sometimes occurring late, these events were under strong selective pressure: we
identified a striking case of convergent evolution where a subclone bearing an
IGL:MYC translocation was lost and one bearing an IGH:MYC was acquired at
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By pairwise comparison of the relative timings of copy number alterations we
reconstructed the preferred chronological order of CNAs acquisition (Methods).
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Gains of chromosomes 19, 11, 9 and 1q were amongst the earliest in our series
(Fig. 3c) and recurrent chromosome losses were generally acquired later than
trisomies, consistent with the proposition that hyperdiploidy is an early driver event in
MM.
Translocations involving CCND1 and MMSET were always fully clonal,
similarly confirming their early driver role in MM pathogenesis. Most chromothripsis
events were clonal and conserved during evolution (17/22; 77%), suggesting they
occurred early in MM pathogenesis. However, a small fraction of patients showed
some evidence of subclonal or late chromothripsis (5/22; 23%), implying a potential
involvement in drug resistance and late cancer progression (Supplementary Fig.
9h).
We integrated all extracted chronological data on SVs, hyperdiploidy and
point mutations to generate phylogenetic trees for each sample (Methods and
Supplementary Fig. 10)5,33. The methodology is worked through for one illustrative
patient carrying i) several chromosome gains, ii) 3 separate chromothripsis events
and iii) a whole genome duplication (Fig. 3d-f). One chromothripsis involved
chromosomes 8 and 15, duplicating the long arm of chromosome 15. Because few
mutations were present on chromosome 15 at the time it duplicated, this must have
occurred early in molecular time (Fig. 3e). Gain of chromosome 3 and copy-neutral
loss-of-heterozygosity of small arm of chromosome 1 (chr1p) occurred not long after,
and were followed by a second chromosomal crisis involving chromosomes 3, 5, 13
and 22. This chromothripsis must have occurred in one of the two duplicated alleles
of chromosome 3 (and therefore after the acquisition of a chromosome 3 trisomy)
because losses within the chromothripsis region had copy number 2 and SNPs were
heterozygous. Within the same time window, a separate chromothripsis event
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occurred on chr1p after copy-neutral loss-of-heterozygosity. Finally, this patient
underwent whole genome duplication after two therapy lines (Supplementary Fig.
9f).
Applying this approach to all patients, we observed that the trunks of the
phylogenetic trees of 29/30 (97%) patients were characterized by few genomic
events generally acquired during different time windows of the MM life history before
emergence of the most recent common ancestor (Fig. 4). Overall, chromothripsis,
cycles of templated insertions, chromosomal gains and other SVs accounted for
most of the earliest events, emerging as key early drivers of the disease and paving
the way for subsequent driver mutations that would confer further selective
advantage to the clone.
Taken together, these data suggest that MM development follows preferred
evolutionary trajectories, with stuttering accumulation of driver events in keeping with
its insidiously progressive but unpredictable clinical course. Critical early events
include immunoglobulin translocation with MMSET and CCND1; hyperdiploidy and
focal complex structural variation processes hitting key myeloma genes. These early
driver mutations shape the subsequent evolution of myeloma, each with preferred
sets of co-operating cancer genes.
Acknowledgements:
FM is supported by A.I.L. (Associazione Italiana Contro le Leucemie-Linfomi e
Mieloma ONLUS) and by S.I.E.S. (Società Italiana di Ematologia Sperimentale).
NB is funded by AIRC (Associazione Italiana per la Ricerca sul Cancro) through a
MFAG (n.17658).
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Figure 1. a) Landscape of Driver Mutations in Multiple Myeloma (MM). Each bar
represents a distinct driver gene and each bar’s colour indicate its prevalence across
the main MM cytogenetic sub-groups. b) We built the optimal Bayesian network by
considering the recurrent SVs/ CNAs (n 14) and driver SNVs (n 55) across 724 MM
patients where the final list of 69 variables was assessed. To further investigate the
type of recurrence patterns we fitted logic gates between parent and child nodes in
the network. The gate combination with the highest Fisher exact test p value was
selected. The line width is proportional to the log hazard ratio of the test. Dashed
lines represent non-significant associations (p>0.05). CNAs and translocations were
coloured by red and light blue respectively. The width of the boundary line of each
drawn box is proportional to its prevalence across the entire series. c) Heat map
showing the main MM genomic subgroups across 724 MM patients. The genomic
profile of each cluster was generated by integrating the hierarchical Dirichlet process
and Bayesian network data. Rows in the graph represent individual genomic lesions,
and the columns represent patients.
Figure 2. a) SVs prevalence across the entire series. b) Heatmap representing the
distribution and prevalence of the main complex SVs: chromothripsis, chromoplexy
and templated insertion. c) Three examples of chromothripsis. d) Example of
templated insertion. In the middle, the genome plot of patient PD26422 represents all
main genomic events: mutations (external circle), indels (middle circle; dark green
and brown lines represent insertion and deletion respectively), copy number variants
(red = deletions, green = gain) and rearrangements (blue = inversion, red =
deletions, green = ITD, black = translocation). Externally, a copy
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number/rearrangement plot of each chromosome involved by the templated insertion
is provided, highlighting a focal CNA around each breakpoint. This case represents a
clear example of how templated insertion may involve critical driver oncogenes, like
CCND1 in this case. A schematic representation of this sample templated insertion is
reported on the right.
Figure 3. a-b) Molecular time estimation on all chromosomal gains observed in 2
hyperdiploid MMs. On the left the copy number profile is reported, where gold line =
total copy number, grey = copy number of the minor allele. The presence of more
than 1 cytogenetic segment is compatible with the existence of a subclonal CNA
whose CCF is proportional to the segment thickness [see for example the gain on 4q
in PD26410d (a)]. On the right, the molecular time (blue dots) estimated for each
clonal gain and copy neutral loss of heterozygosity (Methods). Red dots represent
the molecular time of a second extra gain occurred on a previous one. Dashed green
lines separate multi gain events occurring at different time windows c) Bradley Terry
model based on the integration between the CCF and molecular time of each
recurrent MM CNAs (gains and deletions). Segments were ordered from the earliest
(top) to the latest (bottom) occurring in relative time from sampling (X-axis). d)
Genome plot of patient PD26419a where the three chromothripsis events [(8;15),
(3;5;13;22) and 1p] were highlighted with different colored dashed lines connected to
specific rearrangements/copy number plots. In these plots, the red arch represents a
deletion, the green arch represents an ITD and the blue arch represents an
inversion. e) Molecular time of the main clonal gains and LOH in the PD26419a
sample. This data suggested the existence of at least 2 different and independent
time windows: the first involving alterations on chromosome 3, 15 and 1p and the
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second on chromosome 1q. f) The driver events of patient PD26419 are
reconstructed in chronological order.
Figure 4. The most likely phylogenetic trees generated from the Dirichlet process
analysis (Methods). The root (always colored in red) and branch length is
proportional to the (sub)clone mutational load. All main drivers (CNAs, SNVs and
SVs) were annotated according to their chronological occurrence. Early clonal
events (root), where it was possible to establish a specific time window, were
chronologically annotated on the right. All different “root” time windows were
separated by a green line; conversely, early drivers without a clear timing were
grouped together on the left of the root. All driver events that occurred in the root
were reported with larger font size. Patients were grouped according to the genomic
clustering showed in Figure 1c. Templated insertion is abbreviated with TI.
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1 Corre, J., Munshi, N. & Avet-Loiseau, H. Genetics of multiple myeloma: another heterogeneity level? Blood 125, 1870-1876, doi:10.1182/blood-2014-10-567370 (2015).
2 Manier, S. et al. Genomic complexity of multiple myeloma and its clinical implications. Nat Rev Clin Oncol 14, 100-113, doi:10.1038/nrclinonc.2016.122 (2017).
3 Morgan, G. J., Walker, B. A. & Davies, F. E. The genetic architecture of multiple myeloma. Nat Rev Cancer 12, 335-348, doi:10.1038/nrc3257 (2012).
4 Walker, B. A. et al. APOBEC family mutational signatures are associated with poor prognosis translocations in multiple myeloma. Nat Commun 6, 6997, doi:10.1038/ncomms7997 (2015).
5 Bolli, N. et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat Commun 5, 2997, doi:10.1038/ncomms3997 (2014).
6 Chapman, M. A. et al. Initial genome sequencing and analysis of multiple myeloma. Nature 471, 467-472, doi:10.1038/nature09837 (2011).
7 Lohr, J. G. et al. Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell 25, 91-101, doi:10.1016/j.ccr.2013.12.015 (2014).
8 Walker, B. A. et al. Mutational Spectrum, Copy Number Changes, and Outcome: Results of a Sequencing Study of Patients With Newly Diagnosed Myeloma. J Clin Oncol 33, 3911-3920, doi:10.1200/JCO.2014.59.1503 (2015).
9 Bolli, N. et al. Analysis of the genomic landscape of multiple myeloma highlights novel prognostic markers and disease subgroups. Leukemia, doi:10.1038/leu.2017.344 (2017).
10 Keats, J. J. et al. Clonal competition with alternating dominance in multiple myeloma. Blood 120, 1067-1076, doi:10.1182/blood-2012-01-405985 (2012).
11 Magrangeas, F. et al. Minor clone provides a reservoir for relapse in multiple myeloma. Leukemia 27, 473-481, doi:10.1038/leu.2012.226 (2013).
certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was notthis version posted August 12, 2018. ; https://doi.org/10.1101/388611doi: bioRxiv preprint
12 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, 384-390, doi:10.1038/leu.2013.199 (2014).
13 Maura, F. et al. Biological and prognostic impact of APOBEC-induced mutations in the spectrum of plasma cell dyscrasias and multiple myeloma cell lines. Leukemia, doi:10.1038/leu.2017.345 (2017).
14 Miller, A. et al. High somatic mutation and neoantigen burden are correlated with decreased progression-free survival in multiple myeloma. Blood Cancer J 7, e612, doi:10.1038/bcj.2017.94 (2017).
15 Martincorena, I. & Campbell, P. J. Somatic mutation in cancer and normal cells. Science 349, 1483-1489, doi:10.1126/science.aab4082 (2015).
16 Martincorena, I. et al. Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin. Science 348, 880-886, doi:10.1126/science.aaa6806 (2015).
17 Krysiak, K. et al. Recurrent somatic mutations affecting B-cell receptor signaling pathway genes in follicular lymphoma. Blood 129, 473-483, doi:10.1182/blood-2016-07-729954 (2017).
18 Seiler, M. et al. Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types. Cell Rep 23, 282-296 e284, doi:10.1016/j.celrep.2018.01.088 (2018).
19 Puente, X. S. et al. Non-coding recurrent mutations in chronic lymphocytic leukaemia. Nature 526, 519-524, doi:10.1038/nature14666 (2015).
20 Reddy, A. et al. Genetic and Functional Drivers of Diffuse Large B Cell Lymphoma. Cell 171, 481-494 e415, doi:10.1016/j.cell.2017.09.027 (2017).
21 Hoang, P. H. et al. Whole-genome sequencing of multiple myeloma reveals oncogenic pathways are targeted somatically through multiple mechanisms. Leukemia, doi:10.1038/s41375-018-0103-3 (2018).
22 Li Y, R. N., Weischenfeldt j, Wala JA, Shapira O, Schumacher SE, Khurana W, & Korbel J, I. M., Beroukhim R, Campbell PJon behalf of the PCAWG-Structural Variation Working Group ^ and the PCAWG Network. Patterns of structural variation in human cancer. bioRxiv, doi:http://dx.doi.org/10.1101/181339. (2017).
certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was notthis version posted August 12, 2018. ; https://doi.org/10.1101/388611doi: bioRxiv preprint
23 Korbel, J. O. & Campbell, P. J. Criteria for inference of chromothripsis in cancer genomes. Cell 152, 1226-1236, doi:10.1016/j.cell.2013.02.023 (2013).
24 Li, Y. et al. Constitutional and somatic rearrangement of chromosome 21 in acute lymphoblastic leukaemia. Nature 508, 98-102, doi:10.1038/nature13115 (2014).
25 Maciejowski, J., Li, Y., Bosco, N., Campbell, P. J. & de Lange, T. Chromothripsis and Kataegis Induced by Telomere Crisis. Cell 163, 1641-1654, doi:10.1016/j.cell.2015.11.054 (2015).
26 Magrangeas, F., Avet-Loiseau, H., Munshi, N. C. & Minvielle, S. Chromothripsis identifies a rare and aggressive entity among newly diagnosed multiple myeloma patients. Blood 118, 675-678, doi:10.1182/blood-2011-03-344069 (2011).
27 Korde, N. et al. Treatment With Carfilzomib-Lenalidomide-Dexamethasone With Lenalidomide Extension in Patients With Smoldering or Newly Diagnosed Multiple Myeloma. JAMA Oncol 1, 746-754, doi:10.1001/jamaoncol.2015.2010 (2015).
28 Affer, M. et al. Promiscuous MYC locus rearrangements hijack enhancers but mostly super-enhancers to dysregulate MYC expression in multiple myeloma. Leukemia 28, 1725-1735, doi:10.1038/leu.2014.70 (2014).
29 Fabris, S. et al. Characterization of oncogene dysregulation in multiple myeloma by combined FISH and DNA microarray analyses. Genes Chromosomes Cancer 42, 117-127, doi:10.1002/gcc.20123 (2005).
30 Chavan, S. S. et al. Bi-allelic inactivation is more prevalent at relapse in multiple myeloma, identifying RB1 as an independent prognostic marker. Blood Cancer J 7, e535, doi:10.1038/bcj.2017.12 (2017).
31 Gerstung, G. et al. The evolutionary history of 2,658 cancers. bioRxiv, doi:http://dx.doi.org/10.1101/161562. (2018).
32 Rasche, L. et al. Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing. Nat Commun 8, 268, doi:10.1038/s41467-017-00296-y (2017).
33 Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994-1007, doi:10.1016/j.cell.2012.04.023 (2012).
certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was notthis version posted August 12, 2018. ; https://doi.org/10.1101/388611doi: bioRxiv preprint
34 Walker, B. A. et al. Characterization of IGH locus breakpoints in multiple myeloma indicates a subset of translocations appear to occur in pregerminal center B cells. Blood 121, 3413-3419, doi:10.1182/blood-2012-12-471888 (2013).
certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was notthis version posted August 12, 2018. ; https://doi.org/10.1101/388611doi: bioRxiv preprint
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certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was notthis version posted August 12, 2018. ; https://doi.org/10.1101/388611doi: bioRxiv preprint