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Origins and functional consequences of somatic mitochondrial DNA mutations in human cancer

  1. Young Seok Ju
  2. Ludmil B Alexandrov
  3. Moritz Gerstung
  4. Inigo Martincorena
  5. Serena Nik-Zainal
  6. Manasa Ramakrishna
  7. Helen R Davies
  8. Elli Papaemmanuil
  9. Gunes Gundem
  10. Adam Shlien
  11. Niccolo Bolli
  12. Sam Behjati
  13. Patrick S Tarpey
  14. Jyoti Nangalia
  15. Charles E Massie
  16. Adam P Butler
  17. Jon W Teague
  18. George S Vassiliou
  19. Anthony R Green
  20. Ming-Qing Du
  21. Ashwin Unnikrishnan
  22. John E Pimanda
  23. Bin Tean Teh
  24. Nikhil Munshi
  25. Mel Greaves
  26. Paresh Vyas
  27. Adel K El-Naggar
  28. Tom Santarius
  29. V Peter Collins
  30. Richard Grundy
  31. Jack A Taylor
  32. D Neil Hayes
  33. David Malkin
  34. ICGC Breast Cancer Group
  35. ICGC Chronic Myeloid Disorders Group
  36. ICGC Prostate Cancer Group
  37. Christopher S Foster
  38. Anne Y Warren
  39. Hayley C Whitaker
  40. Daniel Brewer
  41. Rosalind Eeles
  42. Colin Cooper
  43. David Neal
  44. Tapio Visakorpi
  45. William B Isaacs
  46. G Steven Bova
  47. Adrienne M Flanagan
  48. P Andrew Futreal
  49. Andy G Lynch
  50. Patrick F Chinnery
  51. Ultan McDermott
  52. Michael R Stratton
  53. Peter J Campbell Is a corresponding author
  1. Wellcome Trust Sanger Institute, United Kingdom
  2. Cambridge University Hospitals NHS Foundation Trust, United Kingdom
  3. University of Cambridge, United Kingdom
  4. University of New South Wales, Australia
  5. National Cancer Centre, Singapore
  6. Duke-NUS Graduate Medical School, Singapore
  7. Dana-Farber Cancer Institute, United States
  8. Institute of Cancer Research, Sutton, United Kingdom
  9. University of Oxford, United Kingdom
  10. MD Anderson Cancer Center, United States
  11. University of Nottingham, United Kingdom
  12. National Institute of Health, United States
  13. University of North Carolina, United States
  14. University of Toronto, Canada
  15. University of Liverpool, United Kingdom
  16. HCA Pathology Laboratories, United Kingdom
  17. University of East Anglia, United Kingdom
  18. University of Tampere and Tampere University Hospital, Finland
  19. Johns Hopkins University, United States
  20. Royal National Orthopaedic Hospital, United Kingdom
  21. University College London, United Kingdom
  22. The University of Texas, MD Anderson Cancer Center, Houston, United States
  23. Newcastle University, United Kingdom
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Cite as: eLife 2014;3:e02935 doi: 10.7554/eLife.02935

Abstract

Recent sequencing studies have extensively explored the somatic alterations present in the nuclear genomes of cancers. Although mitochondria control energy metabolism and apoptosis, the origins and impact of cancer-associated mutations in mtDNA are unclear. In this study, we analyzed somatic alterations in mtDNA from 1675 tumors. We identified 1907 somatic substitutions, which exhibited dramatic replicative strand bias, predominantly C > T and A > G on the mitochondrial heavy strand. This strand-asymmetric signature differs from those found in nuclear cancer genomes but matches the inferred germline process shaping primate mtDNA sequence content. A number of mtDNA mutations showed considerable heterogeneity across tumor types. Missense mutations were selectively neutral and often gradually drifted towards homoplasmy over time. In contrast, mutations resulting in protein truncation undergo negative selection and were almost exclusively heteroplasmic. Our findings indicate that the endogenous mutational mechanism has far greater impact than any other external mutagens in mitochondria and is fundamentally linked to mtDNA replication.

https://doi.org/10.7554/eLife.02935.001

eLife digest

The DNA in a cell's nucleus must be copied faithfully, and divided equally, when a cell divides to produce two new cells. Mistakes—or mutations—are sometimes made during the copying process, and mutations can also be introduced by exposing DNA to damaging agents known as mutagens, such as UV light or cigarette smoke. These mutations are then maintained in all of the descendants of the cell. Most of these mutations have no impact on the cell's characteristics (‘passenger mutations’). However, ‘driver mutations’ that allow cells to divide uncontrollably and spread to other body sites can lead to cancer.

Mitochondria are cellular compartments that are responsible for generating the energy a cell needs to survive and are also responsible for initiating programmed cell death. Mitochondria contain their own DNA—entirely separate from that in the nucleus of the cell—that encodes the proteins most essential for energy production. Mitochondrial DNA molecules are frequently exposed to damaging molecules called reactive oxygen species that are produced by the mitochondria. Therefore, these reactive oxygen species have been thought to be one of the most important causes of mitochondrial DNA mutations. In addition, because cancer cells produce energy differently to normal cells, mutations in the mitochondrial DNA that change the ability of the mitochondria to produce energy have been conventionally thought to help normal cells to become cancerous. However, conclusive evidence for a link between cancer and mitochondrial DNA mutations is lacking.

Ju et al. examined the mitochondrial DNA sequences taken from 1675 cancer biopsies from over thirty different types of cancer and compared these to normal tissue from the same patients. This revealed 1907 mutations in the mitochondrial DNA taken from the cancer cells. The pattern of the mutations suggests that the majority of the mutations are not introduced from reactive oxygen species, but from the errors the mitochondria themselves make in the process of duplicating their DNA when a cell divides. Unexpectedly, known mutagens, such as cigarette smoke or UV light, had a negligible effect on mitochondrial DNA mutations.

Contrary to conventional wisdom, Ju et al. found no evidence that the mitochondrial DNA mutations help cancer to develop or spread. Instead, like passenger mutations found in the DNA in the cell nucleus, most mitochondrial genome mutations have no discernible effect. However, Ju et al. revealed that DNA mutations that damage normal mitochondrial activity are less likely to be maintained in cancer cells. Presumably, mitochondria containing these proteins produce less energy, and so a cell containing too many of these mutations will find it harder to survive. This shows that having enough correctly functioning mitochondria is essential for even cancer cells to thrive.

https://doi.org/10.7554/eLife.02935.002

Introduction

All cancers result from somatic mutations in their genomes. Beyond the ∼3200 Mb of nuclear genomic DNA, human cells have hundreds to thousands of mitochondria present in every cell, each carrying one or a few copies of the 16,569 bp circular mitochondrial genomes (Smeitink et al., 2001; Legros et al., 2004; Koppenol et al., 2011). In addition to their role in cellular energy balance through oxidative phosphorylation, mitochondria are involved in many essential cellular functions including modulation of oxidation–reduction status, contribution to cytosolic biosynthetic precursors, and initiation of apoptosis. Mitochondria in eukaryotic cells evolved by endosymbiosis from a free-living α-proteobacterium (Gray et al., 1999). Over 2 billion years of co-evolution, many ancestral mitochondrial genes have transferred to the nucleus (Falkenberg et al., 2007; Calvo and Mootha, 2010; Wallace, 2012). What remains in the mitochondrial genome is distinctive for the striking asymmetry between the two complementary mtDNA strands in terms of nucleotide content and gene distribution (Andrews et al., 1999). The heavy (H) strand is guanine-rich (C/G = 0.4) and is the template from which most mitochondrial proteins (12 out of 13) are transcribed, whereas only one protein-coding gene, MT-ND6, is transcribed from the correspondingly cytosine-rich light (L) strand.

Mutations in the mitochondrial genome cause inherited disease (Chinnery, 1993), with a maternal inheritance pattern because only eggs contribute mitochondria to the zygote. The penetrance of inherited mitochondrial disease is determined stochastically by both the random assortment of mutated vs wild-type mitochondrial genomes during meiosis and random drift during the early cell divisions after fertilization. In cancer, the role of somatically acquired mtDNA mutations is controversial. Although cancer-specific mutations have been previously reported (Polyak et al., 1998; Brandon et al., 2006; Chatterjee et al., 2006; He et al., 2010; Larman et al., 2012), the limited sample size or poor sensitivity of capillary sequencing for heteroplasmic mutations has not allowed a comprehensive analysis of the mutational signatures of mitochondrial mutations nor their likely functional significance. It has long been proposed that mitochondria might contribute to cancer development given their fundamental importance to cellular biology (Wallace, 2012). Previous reports suggested that mitochondrial somatic mutations might be under positive selection and thus contribute to cancer development, but the small number of reported mutations renders this conclusion uncertain (Brandon et al., 2006; Chatterjee et al., 2006; Larman et al., 2012; Schon et al., 2012). Nonetheless, the hypothesis of functionally relevant mitochondrial mutations is an appealing one because cancer cells have greatly increased energy demands over normal cells and demonstrate a switch from aerobic glycolysis in mitochondria to lactic acid fermentation in the cytosol (the Warburg effect) (Hanahan and Weinberg, 2011; Koppenol et al., 2011).

In each cell cycle, the replicating genome is at risk of de novo mutations, which can promote the development of cancer. These mutations may be generated by intrinsic cellular errors during DNA replication or repair or through exposure to mutagens, such as reactive oxygen species, tobacco smoke, and ultraviolet light (Pleasance et al., 2010a, 2010b). Recently, >20 mutational signatures operative in cancers have been identified in the nuclear genome (Alexandrov et al., 2013). Whether any of these mutational processes also affect the mitochondrial genome has not been studied. Furthermore, whether there are mtDNA-specific mutational processes in somatic cells remain unclear, although the many unique features of mtDNA replication and repair, coupled with the high concentration of reactive oxygen species generated by the electron transport chain, could be associated with distinctive mutation signatures.

In this study, we compare 1675 cancer and paired normal mtDNA sequences across 31 tumor types using massively parallel DNA sequencing technologies to obtain a systematic and unbiased catalog of somatic mitochondrial mutations. We find that mtDNA mutations are almost exclusively the product of a mutational process that is specific to mitochondria and probably linked to the unique mechanism of genome replication these organelles employ. We find no evidence for positive selection of mitochondrial mutations during oncogenesis, suggesting that they confer no clonal advantage on the nascent cancer cells.

Results

mtDNA sequencing and Mutation Calling

We extracted the mtDNA sequences from 704 whole-genome and 971 whole-exome sequencing data generated on primary cancers and compared them with mtDNA sequences from their matched normal samples. Given the abundance of mtDNA per cancer cell, a standard coverage of 30–40× in the nuclear genome provides significantly greater coverage of the mitochondrial genome (average read depth = 7901.0×), enabling accurate identification of somatic mutations including rare heteroplasmic variants. We also assessed whether whole-exome sequencing could be used to identify mtDNA mutations from off-target reads derived from the mitochondrial genome. We found an average read depth of 92.1× across the mitochondrial genome in exome studies. From 139 samples in which we had both exome and whole-genome sequencing data, the overall read depths correlated strongly (R2 = 0.59, Figure 1—figure supplement 1) as did variant allele fractions for mtDNA somatic mutations (R2 = 0.97, Figure 1—figure supplement 2). Validation experiments suggested the sensitivity of whole-exome sequencing for detection of mtDNA somatic mutations to be 71.4% compared to whole-genome sequencing (Figure 1—figure supplement 3 and ‘Materials and Methods’, ‘Off-target mtDNA reads in whole-exome sequencing’ and ‘DNA cross-contamination’).

To reduce potential false-positive calls of mtDNA somatic mutations, we only report variants called with an allele fraction of >3%. This eliminates the risk of miscalls due to mtDNA-derived pseudogenes in the nucleus (NuMTs) because mtDNA copy numbers are 100–1000 times higher than nuclear genomes in human somatic cells, and the sequence homology between mtDNA and NuMTs presented in the human reference genome is generally <95% (in 96 out of 101 NuMTs with length greater than 300 bp). Furthermore, pairwise comparison between cancer and matched normal mtDNAs from the same individual further minimizes the contamination of NuMTs in the mutation calling.

The catalog of mtDNA somatic mutations

In total, 1675 tumor–normal pairs across 31 tumor types were analyzed (Table 1 and Supplementary file 1). For 61 of these patients, we had sequencing data available from multiple sites of the primary cancer, several time points or matched primary cancers, and metastases (a total of 73 such cancer samples), allowing us to study the timing of mtDNA mutations in cancer evolution (Supplementary file 1). We identified 1907 somatic mtDNA substitutions (Figure 1 and Supplementary file 2). In contrast to inherited polymorphisms (n = 38,706, available at Supplementary file 2), which were almost always homoplasmic in both the cancer and counterpart normal, the variant allele fractions (VAFs) of these somatic substitutions were highly variable in the cancer, ranging from our detection threshold (3%) to homoplasmy (100%). Of these 1907 somatic substitutions, 1209 (63.4%) were not registered in the databases of mtDNA common polymorphism (Ingman and Gyllensten, 2006; Levin et al., 2013). In comparison, when we examined substitutions found in both the tumor and the normal samples from a patient, only 21 (0.05%) were not registered in the polymorphism databases, a significantly different fraction from the tumor-only variants (p < 10−10; Chi-squared test). We found 595 (31.2%) recurrent mutations that can be collapsed onto 246 mtDNA positions, which is a 6.9-fold higher level of recurrence than expected by chance (p < 10−10). This suggests that the generation or fixation of mtDNA mutations is not random, but influenced by factors such as the underlying mutational process or positive selection.

Table 1

Summary statistics of mtDNA sequence data

https://doi.org/10.7554/eLife.02935.003
WGSWXSAverage mt RD (WGS)Average mt RD (WXS)TotalWGSWXSAverage mt RD (WGS)Average mt RD (WXS)Total
Breast2849811594.352.7382Meningioma012-42.512
Colorectal17534916.9276.676Ependymoma1910323.752.710
Lung6002798.1-60
Prostate80017810.6-80MPD121381517.010.9150
Hepatocellular047-205.847MDS3755648.744.578
Melanoma1313513.9353.526ALL646886.635.970
Gastric013-184.113CLL605002.2-6
Cholangiocarcinoma08-143.98AML166783.627.47
Mesothelioma06-106.36Multiple myeloma069-43.269
Bladder540646.2-54AMKL09-24.29
Renal023-35.423Lymphoma04-99.54
Ovarian038-58.938
Uterine2723736.0149.550Osteosarcoma38909525.5119.2128
Cervical052-85.252Chondrosarcoma047-99.147
Adenoid cystic ca.160714.775.661Ewing sarcoma027-69.527
Head & Neck4331369.118.846Kaposi sarcoma09-181.09
Chordoma16111240.082.127
Total; 31 cancer types7049711675
  1. WGS, whole-genome sequencing; WXS, whole-exome sequencing; mt RD, mitochondrial read depth; MPD, myeloproliferative disease; MDS, myelodysplastic syndrome; ALL, acute lymphoblastic leukemia; CLL, chronic lymphoblastic leukemia; AML, acute myeloid leukemia; AMKL, acute megakaryoblastic leukemia.

Figure 1 with 5 supplements see all
Mitochondrial somatic substitutions identified from 1675 Tumor–Normal pairs.

mtDNA genes and intergenic regions are shown. The strand of genes is shown based on mtDNA strand containing equivalent sequences of transcribed RNA. Substitution categories (silent, non-silent (missense and nonsense), non-coding (tRNA and rRNA), and intergenic) are shown by the shapes of each substitution. Six classes of substitutions are presented color-coded. The substitutions on the H, and L strand (when six substitutional classes were considered) are shown outside and inside of mtDNA genes, respectively. Vertical axes for H and L strand substitutions represent the VAF of each variant.

https://doi.org/10.7554/eLife.02935.004

Of the 1675 cancer samples, 976 (58.3%) harbored at least one somatic substitution and 521 (31.1%) had multiple substitutions, ranging from 2 to 7 (Figure 2A). In those with multiple substitutions, 72 pairs of mutations were sufficiently close to phase (Nik-Zainal et al., 2012b) such that we could determine whether they were linked on the same mtDNA genome or were on different copies. We found that 45 (62.5%) pairs of mutations were linked on the same mtDNA genome (Supplementary file 3 and Figure 2—figure supplement 1). Furthermore, of these linked mutations, 33 showed a clear temporal order: that is, one mutation was demonstrably sub-clonal to the other. This is rather unexpected, since each somatic cell has 100–1000 copies of the mitochondrial genome, and we might anticipate that random mutations would, on average, affect different copies. That many pairs of mutations are phased on the same mtDNA genome and yet show a clear sub-clonal relationship suggests that they occur sufficiently separated in time to allow the mitochondrial genome carrying the earlier mutation to drift towards a substantial fraction of all genomes in that cell before the second mutation occurs, consistent with a previous report (De Alwis et al., 2009).

Figure 2 with 1 supplement see all
mtDNA somatic substitutions of human cancer.

(A) Number of somatic substitutions in a tumor sample. (B) Average number of somatic substitutions per sample across 31 tumor types. (C) Age of diagnosis and number of mtDNA somatic substitutions in breast cancers.

https://doi.org/10.7554/eLife.02935.010

The number of somatic mtDNA substitutions varied significantly according to tumor type (p = 4.4 × 10−52) after correcting for confounding variables such as sequencing coverage: gastric, hepatocellular, prostate, and colorectal cancers had the highest number of mtDNA substitutions (Figure 2B). In contrast, hematologic cancers (acute lymphoblastic leukemia, myeloproliferative disease, and myelodysplastic syndrome) had fewer mutations. Several possible explanations could underpin these differences across tumor types. It could be that the mutation rates differ across cell lineages; it could be that selection pressures shape the number of mutations; or the number of mtDNA genome generations could differ across cell lineages. Of these explanations, we believe that the second is unlikely because, as we shall see, positive selection is not a major component of mitochondrial mutations. Interestingly, we find a positive correlation between the number of mtDNA somatic mutations and age at diagnosis in breast cancers (p = 0.0004; Figure 2C), in keeping with the idea that the number of mitochondrial generations is linked to mutation burden. The mutational burden of an established cancer represents the accumulated variation acquired in the lineage of cell divisions from fertilized egg to transformed cell and will include events acquired in normal development and homeostasis as well as those acquired during tumorigenesis (Stratton et al., 2009). Interestingly, mtDNA mutations have been found at high rates in normal colonic crypt cells (Taylor et al., 2003; Ericson et al., 2012). Given that we find high burdens of mutations in colonic tumors as well, the differences we see across tumor types may arise from pre- or post-transformation differences in mtDNA burden across tissues.

Extracting mtDNA mutational signatures

With respect to signatures of somatic substitutions, C > T and T > C transitions constituted 90.9% of all the 1907 substitutions (Figure 1) among the six classes of possible base substitutions. To characterize this aggregated signature of mtDNA cancer specific mutations in more detail, we looked for the presence of mtDNA strand bias between the complementary H and L strands of mtDNA. The two main substitution classes showed an extreme level of mtDNA strand bias. 84.1% of the C > T transitions were on the H strand. This level of strand bias occurred despite the fact that cytosine is 2.4-fold less common on the H than the L strand, so the C > T substitution rate is 12.6-fold higher on the H strand. By contrast, 76.8% of the T > C transitions were on the L strand despite its lower thymine content (1.3-fold less than the H strand). This implies that the T > C mutation rate on the L strand is 4.2-fold higher than on the H strand.

We then examined the sequence context in which these mutations occurred by examining the bases immediately 5′ and 3′ to the mutated bases. This generates 96 possible mutation classes (the 6 substitution classes multiplied by the 16 combinations of immediate 5′ and 3′ nucleotides). Both C > T and T > C mutations showed highly distinctive sequence contexts. CH > TH substitutions (i.e. C > T mutations on the H strand) were enriched for the NpCpG trinucleotide context (8- to 15-fold more frequent than expected by chance; Figure 3A). By contrast, TL > CL substitutions (i.e. T > C mutations on the L strand) showed 5- to 8-fold enrichment in NpTpC. This strand-asymmetric mutational signature is not similar to any of the 21 cancer-associated mutational signatures recently identified from the nuclear DNA of 30 different cancer types (Alexandrov et al., 2013).

Figure 3 with 1 supplement see all
Replicative strand bias for mtDNA somatic substitutions.

(A) Replicative strand-specific substitution rate (# of observed/# of expected) by 96 trinucleotide context. Substitutions in a specific mtDNA segment (from Ori-b to OH) are not included, because they present a different substitutional signature. (B) Mutational signature across tumor types. Eighteen tumor types, which include at least 25 mtDNA mutations, were shown. (C) Inverted substitution signature in the Ori-b–OH.

https://doi.org/10.7554/eLife.02935.012

Of the 18 tumor types that presented at least 25 mtDNA somatic substitutions in this study, the mutational signatures were broadly consistent across tumor types (Figure 3B), with the exception that multiple myeloma had a somewhat higher rate of TH > CH changes than other histologies (p = 8.1 × 10−6). Thus, in contrast to the mutational signatures found in nuclear genomes, where there is striking heterogeneity both across tumor types and across individuals within a tumor type (Alexandrov et al., 2013), the mutational profile in the mitochondrial genome of somatic cells is remarkably homogeneous.

Replication-coupled mutational process in mitochondria

The major known cause of mutational strand bias in nuclear DNA is transcription-coupled nucleotide excision repair, where DNA lesions on the transcribed (non-coding) strand are more frequently repaired (Alexandrov et al., 2013). However, we find that the strand bias always favors CH > TH and TL > CL whether the gene is transcribed from the H strand or from the L strand (Figure 3—figure supplement 1). This is not compatible with transcription-coupled repair, for which the direction of strand bias is fundamentally dictated by which strand is transcribed.

Instead, the mtDNA mutational strand bias reported here appears to be driven by differences in replication between the two strands. mtDNA replication harbors substantial strand asymmetry between the H and L strands: mtDNA replication initiates from an origin of replication (OH) in the D-loop, with the nascent H and the L strand replicating as leading and lagging strand, respectively (Clayton, 1982; Falkenberg et al., 2007; Holt and Reyes, 2012). We observed that C > T substitutions were prevalent in the leading (heavy) strand, whereas T > C substitutions were found in the lagging (light) strand (Figure 1). Remarkably, this strand bias was reversed in the D-loop itself (Figures 1 and 3C), further suggesting that the mtDNA somatic mutations are replication-coupled: according to a recently proposed bidirectional model of mtDNA replication (Yasukawa et al., 2005, 2006; Holt and Reyes, 2012), mtDNA replication is also able to initiate from the so-called Ori-b site, typically located around genomic position 16,197 and proceeds on both strands away from the origin (Figure 1). Replication of the nascent H strand continues unimpeded like the traditional model, but the nascent L strand terminates at the so-called OH site, typically around mtDNA position 191 bp. Under this model, then, the leading and lagging strand are reversed in the few hundred base-pairs of the D-loop, which is consistent with the reversed mutational signature in this region (Figures 1 and 3C).

Equivalent mutational signature during human mtDNA Evolution

It is not entirely straightforward to infer the mutational signatures operating on the mitochondrial genome in the germline. De novo mutations are generally rare and often discovered because they cause disease; distinguishing the ancestral base and the derived base is challenging for single nucleotide polymorphisms; and comparative mtDNA genomics across species extends over considerable evolutionary time. In contrast, because ancestral and derived states are defined for tumor–normal pairs, a much clearer picture emerges of the somatic mtDNA mutation signature. We therefore assessed whether the signature that emerges for somatic mitochondrial mutations could extend to explain sequence composition of the human mtDNA genome.

It appears that the mutational mechanism which has generated the CH > TH and TL > CL signature in cancer mtDNA is equivalent to the one that has been operating during evolution of human germline mtDNA (Nikolaou and Almirantis, 2006). This manifests as the depletion of certain codons in the reference human mtDNA sequence through the action of the CH > TH and TL > CL mutational process over time (Figure 4A). For example, the GCG triplet codon (Alanine) appears to have been replaced by its synonymous GCA codon (due to CH > TH (GL > AL)), with the former being 15.8-fold less frequently observed in the 12 mtDNA protein-coding genes that are transcribed from the H strand (and encoded on the L strand). All 32 synonymous codon pairs present the same tendency. Consistent with this interpretation, the gene transcribed from the L strand (MT-ND6) demonstrates the opposite direction of skew. Further analyses of mtDNA codon usage from seven animal species suggest that the CH > TH and TL > CL mutational pressure may be characteristic of vertebrates, and primates in particular (Figure 4—figure supplement 1).

Figure 4 with 2 supplements see all
Mutational signature similar to processes shaping human mtDNA sequence over evolutionary time.

(A) Triplet codon depletion in human mtDNA by equivalent (CH > TH and TL > CL) mutational pressure. Relative frequency of each triplet codon within synonymous pairs (NNT–NNC or NNA–NNG) is shown by color. The arrows beside the box highlight the T > C (red) and G > A (blue) substitutional pressures on the L strand in germline mtDNA. (B) Correlation of triplet codon frequencies between from observed and from simulated evolutions of a random sequence mtDNA by the mtDNA somatic mutational signature with constraining mitochondrial protein sequences.

https://doi.org/10.7554/eLife.02935.014

To quantify whether the somatic mutational signature we have defined can fully explain the trinucleotide frequency of human mtDNA, we performed evolutionary simulations. First, we simulated the evolution of a random DNA sequence under the mutational signature described here. By mutational pressure alone, the random sequence starts losing certain hypermutable trinucleotides until eventually reaching a stationary sequence composition. The actual sequence composition of the human mitochondrial genome strongly resembles this stationary distribution (Pearson's r = 0.83; p < 0.0001; Figure 4—figure supplement 2). In a second simulation, a random sequence encoding the exact amino acid sequence of the reference mitochondrial genome was evolved by synonymous mutations under the observed mtDNA signature until reaching a stationary sequence composition (mutation–selection equilibrium). These simulations also eventually approximate the observed human mitochondrial genome (Pearson's r = 0.96, p < 0.0001; Figure 4B). These analyses strongly suggest that the mitochondrial mutation signature observed in cancer cells closely reflects the mutation signature active in the germline, which has continuously shaped the mitochondrial genome during human evolution.

Negative selection on truncating mtDNA mutations and tRNA anticodons

Next, we assessed the functional impact of somatic mtDNA mutations. Of the 1907 substitutions, 1153 (60.5%) were in the 13 protein-coding genes. These include 63 nonsense, 4 stop-lost, 878 missense, and 208 silent substitutions (Supplementary file 2). In addition, out of 251 indels we observed, 110 occurred within protein-coding genes (Supplementary file 2). Of the missense substitutions, 245 (27.9%) were recurrent, affecting 107 distinct mtDNA sites. Although this very high level of mutation clustering could, at first sight, be interpreted as evidence for positive selection, we found that silent substitutions were also frequently recurrent (28 recurrent variants in 13 mtDNA sites), with no substantial difference in recurrence rates between silent and missense mutations (p = 0.19; Figure 5—figure supplement 1). We believe this recurrence to be the consequence of a high mtDNA mutation rate with restricted mutational signature (CH > TH and TL > CL). Independently recurring mutations in human germline mtDNA are well described across human evolution (Levin et al., 2013).

The ratio of somatic missense to silent substitutions (Rms:s) is apparently higher (4.2, 878/208) than that observed for cancer-associated somatic mutations in nuclear DNA (generally around 2:1 to 3:1 across tumor types) (Greenman et al., 2007; Nik-Zainal et al., 2012a). At face value, this again could be interpreted as evidence for positive selection. However, as described above, the somatic mtDNA mutational signature shows extreme strand asymmetry and the same mutational signature has been operative in the germline over evolutionary time. Thus, the dominant mutational signature has already acted on potentially synonymous sites in the mitochondrial genome (Figure 4A), meaning that any new somatic changes are much less likely to be silent. In keeping with this, a dN/dS ratio (See ‘Materials and Methods’) calculated taking into account both the mutational signature and the mtDNA codon usage revealed that missense mutations accumulate at a frequency very close to that expected under neutrality (dN/dS = 1.21; 95% confidence interval, 1.015–1.434; p = 0.031). This indicates that despite the apparent high ratio of missense to silent mutations, the vast majority of mtDNA mutations are passengers with no convincing evidence suggesting the existence of driver mitochondrial DNA mutations. Additional gene-by-gene analysis further revealed that no single gene had a higher than expected rate of missense or nonsense mutations (Supplementary file 4).

For nonsense substitutions and frameshift indels, we observe a somewhat different picture. Taking into account the mutation signature and amino acid composition of the mitochondrial genome, the overall ratio of nonsense mutations to silent mutations is exactly that expected by chance (dNonsense/dS = 1.004; 95% confidence interval, 0.699–1.443; p = 0.98). However, while missense and silent substitutions exhibited equivalent variant allele fractions (average VAFs; 40.1% and 40.9%, respectively; p = 0.8), nonsense substitutions presented significantly lower VAFs (average 26.4%; p = 6x10−5), as did frameshift indels (average 25.0%; p = 2 × 10−3; Figure 5A). Taken together, these data suggest that nonsense mutations occur at the expected rate given the underlying mutational process. However, while silent and missense substitutions frequently achieve high allele fractions in tumor cells due to the effects of random drift, there are significantly greater constraints on mitochondrial genomes carrying protein-inactivating mutations. The inference here is that cancer cells carrying such deleterious mutations at or near homoplasmy are at a selective disadvantage and hence do not contribute to clonal expansions, underlining the importance of functional mitochondria to cancer cells. The extent of such disadvantage may vary according to tumor type: for example colorectal cancers show less negative selection compared to breast cancers (p = 0.028; Figure 5—figure supplement 2).

Figure 5 with 3 supplements see all
Selection and mutational process for mtDNA somatic substitutions.

(A) Truncating mutations (nonsense substitutions and frame-shifting (FS) coding indels) present significantly lower VAF. (B) Change of VAF of mtDNA somatic mutation between primary and metastatic (or late) cancer tissues. (C) Mutational signature for mtDNA across various tumor types. None of the three highlighted mechanisms or nuclear DNA double-strand breaks repair mechanism (BRCA) match with the mtDNA mutational signature. * Only substitutions in protein-coding genes considered. (D) A proposed model of mtDNA mutational process.

https://doi.org/10.7554/eLife.02935.017

We found 171 mtDNA substitutions in mitochondrial tRNA sequences, which are very similar to the expected number (168.2, p = 0.82) from the mutational signature. Interestingly, none of the substitutions was located in the trinucleotide anticodon site of the tRNA (expected number = 7.6, p = 0.006). This suggests that mutations in tRNA anticodons confer a similar selective disadvantage as protein-truncating mutations, presumably because such mutations would lead to systematic erroneous aminoacylation of nascent proteins during translation of the relevant codon.

Next, we assessed whether any specific somatic mutations showed evidence of positive selection. Out of the 1907 somatic substitutions, 16 (0.8%) overlapped with known disease-associated mtDNA mutations, such as mutations frequently detected in MELAS (Mitochondrial Encephalomyopathy, lactic acidosis, and stroke-like episodes) and LHON (Leber hereditary optic neuropathy) (Supplementary file 2). In addition, ten mutations within mitochondrial protein-coding, tRNA and rRNA genes showed significantly higher recurrent rate than expected from background mutational signature (Supplementary file 5). However, it remains unclear whether this high recurrence reflects positive selection, because any factors not included in our background model of the mutational process, such as local mutation hotspots, could also explain a mild excess of mutations at a given nucleotide.

mtDNA mutations across tumor Evolution

We investigated whether somatic mtDNA mutations are more likely to become homoplasmic later in tumor evolution by assessing paired cancer samples, either primary and metastasis (breast, colorectal, and prostate) or primary and relapse (myeloma) (Figure 5B and Supplementary file 1). As mentioned earlier, 73 late (metastasis or relapse) cancer samples were sequenced in addition to the primary tissues. Among the mtDNA mutations identified in either of the paired cancer samples, a number of different patterns were observed. There were mutations at high VAF in the primary not found in the metastasis (n = 49); mutations in the metastasis not found in the primary (n = 49); and shared mutations (n = 71) at high or low VAF, sometimes with evidence for drift (VAF difference >0.2) between the two samples (n = 25). These data, particularly the mutations found in the metastasis only, suggest that mitochondrial mutations can occur throughout the time course of tumor evolution, and still drift to homoplasmy with appreciable frequency, as suggested previously (Coller et al., 2001). To assess the plausibility of this conclusion, we modeled the dynamics of mtDNA mutations based on a few simplifying assumptions (See ‘Materials and Methods’, Evolutionary dynamics of neutral mitochondrial mutations). We find that the expected number of neutral mitochondrial mutations drifting to homoplasmy increases linearly with mutation rate and number of cell divisions. Based on a mutation rate of 10−7/base-pair/generation (Coller et al., 2001; Hudson and Chinnery, 2006), this leads to an average ∼1 homoplasmic mutation for every 1000 cell generations.

Origins of mtDNA somatic mutations

We also explored whether the mutational forces that are so critical to shaping the nuclear genome during tumor evolution could affect the mitochondrial genome. In cancers associated with exogenous mutagens, such as tobacco-associated lung cancer and ultraviolet light-associated melanomas, we found no evidence of the mutational signatures characteristic of these carcinogens among the mtDNA mutations (Figure 5C, Figure 5—figure supplement 3). Moreover, BRCA1 and BRCA2 mutations showed no evident influence on mitochondrial genomes in breast cancer (Figure 5C), in contrast to their effects on nuclear genomes exhibiting an even distribution of mutations across all trinucleotide contexts (Nik-Zainal et al., 2012a; Alexandrov et al., 2013). Taken together, it appears that the primary mtDNA mutational process is endogenous to mitochondria and is very different to those operating in nuclear DNA. It is surprising that the endogenous mutational process has far greater impact than any external forces, as the physicochemical interactions of ultraviolet light or the chemicals in cigarette smoke with DNA should be similar in both genomes. The simulations described above suggest the major explanation to be that the endogenous mutation rate is several orders of magnitude greater than that expected for exogenous carcinogens, thus swamping any signal.

Discussion

In theory, there are two potential sources of the mtDNA variants we observed in cancer tissues: (1) somatically acquired, or de novo, mutations accumulated during the cancer clone's lineage of cell divisions from the fertilized egg or (2) low-level heteroplasmic mtDNA present in the oocyte (therefore maternally inherited) amplified in cancer but lost from normal tissue by random drift (He et al., 2010; Freyer et al., 2012; Payne et al., 2013). We believe the majority of the variants we find are genuinely acquired somatically. First, of the 45 pairs of somatic mutations phased together on the same copy of the mtDNA genome, at least 33 (73.3%) showed a clear sub-clonal relationship and therefore their occurrence is separated in time, or apparently somatic. Secondly, 63.4% of our substitutions were not previously reported as germline polymorphisms. This is a much higher rate than reported for equivalent analyses on heteroplasmic variants in non-cancer samples (8/37; 21.6%) (Li et al., 2010), although methodological differences may somewhat contribute to this apparent difference (Goto et al., 2011; Avital et al., 2012). Thirdly, if the variants were due to inherited low-level heteroplasmy, we would not expect to see such variation across tissue types, since all tissue types derive from the fertilized egg. It is difficult to distinguish whether the variants we observe occur before or after the initiating driver mutations that herald tumorigenesis, but our analysis of paired samples does suggest that they can occur both early and late. Given the homogeneity of the mutational signature across tumor types and its inferred resemblance to the germline mtDNA mutational process, we would hypothesize that new mutations occur at a fairly constant and high rate per mitochondrial genome replication throughout all cell divisions.

On the basis of the mutational signature observed here, somatic substitutions are unlikely to be attributable to reactive oxygen species (ROS), as previous reports have suggested (Polyak et al., 1998; Larman et al., 2012). Guanine oxidation by ROS predominantly causes G:C > T:A transversion (Thilly, 2003; Delaney et al., 2012), which constitute only 4.0% of the mutations in our data (Figure 5C). Instead, we propose three replication-coupled mechanisms that can explain the strand asymmetric CH > TH and TL > CL mutational signature and define a model of the mtDNA mutational process (Figure 5D). First, the parent H strand, displaced and single-stranded during mtDNA replication (Holt and Reyes, 2012), could be more prone to cytosine deamination (generating CH > TH) and/or adenine deamination (Lindahl, 1993; Saccone et al., 1999; Faith and Pollock, 2003) (generating TL > CL). Secondly, endogenous mtDNA polymerase (POLG) replication errors (Zheng et al., 2006) (which show the pattern of C > T and A > G substitutions) could be preferentially generated on the leading strand (Pavlov et al., 2002). Thirdly, there may be differences between the efficiency of repair between the leading and lagging strand (Pavlov et al., 2003). Further, the mutation pattern reported here is consistent with the hypothesized bidirectional initiation of mtDNA genome replication (Yasukawa et al., 2005, 2006; Holt and Reyes, 2012).

It appears that most of the mtDNA missense mutations we observe become fixed in tumor progenitor cells without distinct physiological advantage. All the statistical testing performed in this study—variant allele fraction comparison across different categories of somatic mutations, number of recurrent mutations, and dN/dS ratio—suggest that mtDNA somatic substitutions accumulate largely neutrally. This is not different from previous observations in nuclear genomes: of the thousands of somatic mutations found in a cancer genome, many fewer than a hundred are believed to confer a selective advantage to the cancer cell (Stratton et al., 2009). In contrast, protein-truncating mutations showed evidence of negative selection, at the level of constraints on the allele fraction achieved. The implication of this is that the inactivating mutations occur at an appreciable rate, but the fraction of mitochondrial genomes per cell carrying these variants cannot increase beyond a certain limit without impairing the selective fitness of that cell. Having a sizable number of mitochondria with fully intact proteome remains critical to the fitness of a cancer cell.

Materials and methods

Sequencing data

All the sequences were generated by Illumina platforms (either Genome Analyzer or HiSeq 2000). With respect to TCGA data, we downloaded aligned bam files through UCSC CGHub (http://cghub.ucsc.edu). Sequencing reads were aligned on the human reference genome build 37 (GRCh37) and human reference mtDNA sequence (revised Cambridge reference sequence, rCRS [Andrews et al., 1999]), mainly by BWA alignment tool. Samtools (Li and Durbin, 2009) and Varscan2 (Koboldt et al., 2012) were used for manipulating sequence reads and for calling somatic mutations, respectively. Sequence data have been deposited in the European Genome-phenome Archive (EGA; https://www.ebi.ac.uk/ega/home ; study accession # EGAS00001000968; dataset accession numbers EGAD00001001014 for primary samples and EGAD00001001015 for metastatic samples). Sample accession numbers are available in the Supplementary file 6.

Off-target mtDNA reads in whole-exome sequencing

Most of the currently available whole-exome capture kits, including Agilent Technologies SureSelect Human All Exon 50 Mb (Agilent Technologies Inc., Santa Clara, CA) used mostly in this study, do not target mtDNA genes (Falk et al., 2012). However, because of the abundance of mtDNA in human cells (100–100,000 copies per cell), it is expected that a number of mtDNA fragments could be off-target captured. We checked whether the amount of off-target mtDNA reads was sufficient for mtDNA variant detection. Whole-exome sequencing (normal samples) generated by CGP (n = 855), WUGSC (Washington University Genome Sequencing Center; n = 140), and BCM (Baylor College of Medicine; n = 85) contained ∼100 off-target mtDNA reads per 1M autosomal reads (Figure 1—figure supplement 4). We concluded that these could be sufficient for the downstream analyses, because ordinary 10 Gb whole-exome data would provide ∼60× read depth for mtDNA here. However, whole-exome data sequenced by BI (Broad Institute; n = 436) included far less, ∼3 off-target mtDNA per 1 M autosomal reads, which would show ∼2× mtDNA read depth per 10 Gb exome sequencing (Figure 1—figure supplement 4). It may be due to ‘improved’ exome-capture protocols by BI to increase the DNA-capture efficiency and on-target rate (Fisher et al., 2011). Therefore, we did not include whole-exome data sequenced from BI for further analysis.

139 samples were sequenced by both whole-genome and whole-exome sequencing. From these, we compared the amount of off-target mtDNA reads from whole-exome sequencing with that of whole-genome sequencing. It showed clear positive linear correlation (Figure 1—figure supplement 1).

DNA cross-contamination

Given the abundance of mtDNA in the cancer cells, 1–214× coverage cancer whole nuclear genome sequencing provides extensive coverage of mtDNA (average read depth = 7901.0×; Supplementary file 2) enabling accurate identification of somatic mutations, even if heteroplasmic. Whole-exome sequencing data were also included because off-target reads provided sufficient coverage (average read depth = 92.1×) to analyze mtDNA mutations.

This high coverage of mtDNA, especially from whole-genome sequencing, permitted us to identify heteroplasmic variants (our detection threshold was 3%; see ‘Variant calling’ for more details). However, because sample swaps and/or DNA cross-contaminations would definitely generate false-positive somatic variants, we filtered out suspicious DNA samples as described below.

  • 1. Major sample swaps

    A subset of tumor and normal sequencing pairs, of which the nuclear genotypes were not matching with each other, were removed from further analyses. We randomly selected 320 common single-nucleotide polymorphism sites on the 22 human autosomes, of which the minor allele frequency is ∼50% (45–55%) according to The 1000 Genomes Project (1000 Genomes Project Consortium et al., 2010). Of the 320 sites, homozygous positions in normal tissues (which showed >90% variant allele fraction (VAF) with bases Q score >20) were compared with the corresponding genotypes in the counterpart cancer. Sample pairs were removed if the genotype mismatch rate was greater than 0.1 (Nhet+NwtNhom+Nhet+Nwt; Nhet, number of heterozygote positions; Nhom, number of homozygote positions; Nwt, number of wild-type positions) (Figure 1—figure supplement 5A). We note 0 is expected for the rate when genotyping is perfect and sample pairs are from the same individual. By contrast, 0.5 is expected when samples were from different individuals.

  • 2. Minor cross-contamination

    We estimated DNA cross-contamination levels with the VAF of autosomal homozygous SNPs genotyped from the common (population minor allele frequency ∼50%) SNP sites. Theoretically, if there is no sequencing (and mapping) error, all the homozygote SNP sites in pure samples should present 100% VAFs. However, when samples are contaminated, corresponding VAFs are reduced because the contaminant has only an ∼25% of chance of having homozygote SNPs on the same site. Therefore, minor contamination levels (C) of each cancer sequencing data were estimated as below:

    C=2×(RCwt)Ne(RDhom)Ne,

    where RDhom is sequencing read depth, RCwt is read-count of wild-type alleles, and Ne is number of sequencing errors on each autosomal homozygote SNP site. For high accuracy, we only counted base with sufficient quality score (Q > 20). In order to estimate Ne, we assumed a conservative rate (sequencing error rate = 0.001). We considered sites covered by at least 10 reads and 90% VAF (Figure 1—figure supplement 5B). 95% confidence intervals of cross-contamination levels were calculated using binomial distribution.

    In order to clear somatic variants, here we made the very conservative assumption that somatic variants present in excess of 5-times of the 95% upper limit of C levels were true somatic rather than false-positives by low-level of cross-contamination.

  • 3. Germline polymorphisms and back mutations

    We further checked samples for contamination using known mtDNA polymorphisms. Because human mtDNA is small (16,569 bp) and extensively explored previously, most of germline mtDNA polymorphisms are already known. For example, 97.7% of the 39,036 inherited substitutions were known polymorphisms in the mtDB database (Ingman and Gyllensten, 2006). Therefore, when a tumor sample is contaminated by other samples, many somatic-like mtDNA substitutions by contaminants are likely to be overlapped with known mtDNA polymorphisms.

    At the same time, low-level contamination would generate excessive back mutations, which appeared to reverse germline common polymorphisms into wild-type alleles. Taken together, both the number of somatic substitutions known in mtDB and number of back mutations can be good indicators for mtDNA cross-contamination. Therefore, we filtered out tumor tissues with ≥3 known potentially somatic mutations or with ≥2 back mutations from the further analyses (Figure 1—figure supplement 5A and B).

Variant calling

We extracted mtDNA reads using Samtools (Li and Durbin, 2009). We used VarScan2 (Koboldt et al., 2012) for initial variant calling with a few options (--strand-filter 1 (mismatches should be reported by both forward and reverse reads), --min-var-freq 0.03 (minimum VAF 3%), --min-avg-qual 20 (minimum base quality 20), --min-coverage 3 and --min-reads2 2). With respect to the --strand-filter, it generally removes variant when >90% of mismatches are reported from either of the H or the L mtDNA strand. However, where only reads with a specific orientation are could be aligned dominantly (i.e. in both extreme region of mitochondrial reference genome; only L strand reads could be aligned on the 5′ extreme of mtDNA), we compared strand bias between ‘perfect matches’ (# perfect matches from L strand reads / total # perfect matches) and mismatches (# mismatches from L strand reads / total # mismatches). If the difference between those two bias <0.1, the mutations were rescued. Of the 1907 mutations, 54 (2.8%) were rescued accordingly.

Putative somatic variants called by VarScan2 were further filtered using criteria shown below.

  1. At least 4 unique reads supporting variants and all variant reads at least 20 phred scale sequencing quality score (Q 20 = 1% sequencing error rate) and at least 3% variant allele fractions (VAFs).

    • A. Regardless of in WGS and in WES, the ≥4 mismatches and the ≥3% VAF criteria must be satisfied simultaneously.

    • B. However, in WGS, the minimum number of reads (n = 4) criterion is not essential, because the ≥3% VAF criterion is much more stringent (3% VAF request at least 240 mismatches (>>4) given mtDNA coverage is ∼8000 for WGS).

    • C. In WES, the ≥3% VAF criterion is relatively less important than in WGS, because the ≥4 mismatches criterion is more stringent. For example, 4 mismatches in 90x (WXS average) coverage region (VAF = 4.4%) automatically fulfill the ≥3% VAF criterion. For less covered regions (i.e. <40x coverage; n = 285 out of total 1907 substitutions), the VAF criterion becomes less important, because 4 mismatches would generate ≥10% VAF, much higher than the minimum threshold (i.e. 3%). As results, we are missing lower heteroplasmic variants (i.e. variants with 3–10% heteroplasmic levels) from low coverage samples (mostly by WXS). The lower sensitivity of WXS is also confirmed in our validation study (see “Validation of somatic variants” below).

  2. There is no minimum threshold for total coverage (# perfect matches + # mismatches).

  3. To increase sensitivity for detecting mutations, we rescued mutations with 3 unique variant reads (with at least 20 phred scale sequencing quality score) when VAFs is ≥ 20%. Of 1907 somatic substitutions, 32 (1.7%) were rescued accordingly.

  4. All somatic variants presenting with VAFs lower than our very conservative threshold for minor cross-contamination (5-times 95% upper limit of contamination levels for each tumor sample, see above “Minor cross-contamination of DNA samples”) were removed. When we could not estimate cross-contamination levels because of low sequencing depth of coverage (for nuclear genome), a conservative criterion (10% contamination level threshold) was explicitly used.

  5. Substitutions were further visually inspected using IGV (Thorvaldsdottir et al., 2013). Thirteen frequent false-positive variants (shown below) by misalignment due to extensive level of homopolymers in rCRS and due to sequencing error in the reference mtDNA genome (3107N, see Mitomap (http://www.mitomap.org/bin/view.pl/MITOMAP/CambridgeReanalysis) for more information) were explicitly removed:

    • 1. Misalignment due to ACCCCCCCTCCCCC (rCRS 302-315)

    • A302C, C309T, C311T, C312T, C313T, G316C

    • 2. Misalignment due to GCACACACACACC (rCRS 513-525)

    • C514A, A515G, A523C, C524G

    • 3. Misalignment due to 3107N in rCRS (ACNTT, rCRS 3105-3109)

    • C3106A, T3109C, C3110A

We compared our variant calls with common inherited mtDNA polymorphisms deposited in the mtDB database as of 24th July 2013 (Ingman and Gyllensten, 2006). Gene annotation of somatic variants was done using custom script based on human mtDNA gene information (Ruiz-Pesini et al., 2007).

Validation of somatic variants

To validate the sensitivity and specificity of variant calling in this study, 19 tumor and normal pairs (which were originally whole-genome sequenced) were whole-exome sequenced and mtDNA variants were assessed independently. Among the 28 somatic substitutions originally detected from the 19 tumor–normal whole-genome sequencing pairs, 20 (71.4%) were called as somatic (Figure 1—figure supplement 3). In addition, 5 (17.9%) presented evidence of variant reads in the validation set, although it was filtered out because of its low read depth of coverage in exome sequencings (showed 2–5 variant reads). Moreover, because 3 remaining sites were not sufficiently covered in the validation set to call somatic variants, these could not be evidence of the inaccuracy of whole-genome sequencing data, therefore not considered in the accuracy validation. Taken together, all the 25 somatic substitutions by whole-genome sequencing were highly likely to be true positives, therefore we concluded it provided ∼100% accuracy in the mtDNA somatic substitution assessment. Actually, the high accuracy of whole-genome sequencing is very likely and what we expect, because it provides extensive coverage of mtDNA (average read depth >7,500×), ∼3% heteroplasmic variants would present >200 variant reads.

By contrast, the validation set (whole-exome sequencing) is called 21 somatic substitutions. Of these, 20 were common with whole-genome sequencing, and one was incorrectly called as somatic though it was actually germline substitutions in the whole-genome sequencing data. In addition, as mentioned above, the validation set missed 8 somatic substitutions called by whole-genome sequencing. Six out of eight undercalls (75%) were low heteroplasmic substitutions in whole-genome sequencing, ranging from 3.36% to 8.68%. Based on these data, we suggest 71.4% sensitivity (20/28) and 95.2% specificity (20/21) for exome-sequencing in detecting upto 3% heteroplasmic somatic mtDNA substitutions in cancer.

We further checked the correlation of heteroplasmy level between the 20 mtDNA somatic mutations called both whole-genome and whole-exome sequencing. It showed great linear relationship (R2 = 0.97, Figure 1—figure supplement 2), further suggesting whole-exome sequencing data are appropriate for accurate detection of mtDNA somatic mutations.

Substitution phasing

We phased 72 somatic substitution pairs, which arose in a single cancer sample and which located sufficiently close (from 10 bp to ∼500 bp), therefore both sites could be sequenced by same sequence fragments (Supplementary file 3 and Figure 2—figure supplement 1). We classified them as ‘different strand’, ‘co-clonal’, and ‘sub-clonal’ using criteria as follows:

Different strand: the two somatic substitutions are obligate on different strands. Reads that report wild-type1(wt)-substitution2(subs) and subs1-wt2, but subs1-subs2, are observed.

Co-clonal: reads reporting wt1-wt2 and subs1-subs2 are only observed.

Sub-clonal: One substitution is sub-clonal to the other, but the two are definitely phased. Reads subs1-subs2 and either subs1-wt2 or wt1-subs2 are observed.

Tumor type and mtDNA somatic substitutions

To understand the relationship between tumor types and number of mtDNA mutations, Poisson regression and ANOVA were applied to our dataset using R software (http://www.r-project.org).

Fit1<-glm(NsubCovT+CovN, family=poisson())
Fit2<-glm(NsubCovT+CovN+t, family=poisson())
anova(Fit1, Fit2, test=“Chisq”),

where Nsub is number of mtDNA substitutions of each sample, CovT and CovN are coverage of tumor and normal mtDNA, respectively, (if Cov is >200, we replaced it by 200), t is tumor types.

Age and mtDNA somatic substitutions

Poisson regression was applied to our breast cancer dataset.

Fit1<-glm(NsubCovT+CovN+a, family=poisson())

where Nsub is number of mtDNA substitutions of each sample, CovT and CovN are coverage of tumor and normal mtDNA, respectively, (if Cov is >200, we replaced it by 200), a is age at diagnosis. p-value in estimation of a was shown in the manuscript.

Mutational signature and strand bias

Different mutational processes generate different combinations of mutation types, termed ‘signatures’ (Nik-Zainal et al., 2012a). For example, ultraviolet (UV) light and tobacco smoking (polycyclic aromatic hydrocarbons) frequently generate C > T transitions and G > T transversions on non-transcribed (coding) strands in melanoma and lung cancers, respectively (Pleasance et al., 2010a, 2010b). To understand the mutational processes influencing cancer mtDNA, we correlated the 1907 mtDNA substitutions with 21 cancer specific mutational signatures in the nuclear DNA recently identified (Alexandrov et al., 2013). However, none of the signature could explain the highly unique mtDNA substitutions.

Mutational signature and strand bias were assessed as described in our previous reports (Alexandrov et al., 2013). Briefly, the immediate 5′ and 3′ sequence context was extracted from rCRS. Substitution rate for each trinucleotide context was calculated with the number of substitution normalized by the frequency of the trinucleotide context observed in the rCRS, in the L and H strand, respectively. For analyses of substitutions falling in the mtDNA genes (13 protein-coding and 22 tRNA genes), transcribed/non-transcribed strand was also considered for comparison.

In order to prove the strand bias is not transcription but replication-coupled, we checked strand biases of polymorphisms in the 12 L strand protein-coding genes, 1 H strand protein-coding gene (MT-ND6), and/or 22 tRNAs (Figure 3—figure supplement 1). For this specific purpose, we did not consider the sequence context (immediate 5′ and 3′ bases) because it over-classifies mutations (i.e. the number of mutation classes (n = 96) is larger than that of mutations). In other words, 12 classes of substitutions (six classes of possible base substitutions (C > A, C > G, C > T, T > A, T > C, T > G) × two strands (L and H strands)) were considered. Substitution rates are ratio between observed and expected numbers (H0 = same mutation rate for all substitution classes) for each substitution class. In order to understand which model (replicative or transcriptional strand) is appropriate to explain the strand-bias, Chi-square tests were used between the number of observed mutations for each class and expected ones under the background signature.

mtDNA codon usage

We counted the codon frequencies in 13 mtDNA protein-coding genes. Because 12 L strand protein-coding genes and 1 H strand gene (MT-ND6) are under opposite mutational pressure (T > C and G > A for L strand genes; A > G and C > T for MT-ND6), we separated L and H strand genes for this analysis. T > C skew and G > A skew were calculated as shown below, to understand the TL > CL and CH > TH (equivalent to GL > AL) substitutions during the evolution of human mtDNA:

T>Cskew=NCNTNC+NTandG>Askew=NANGNA+NG,

where NA, NC, NG, and NT are number of A, C, G, and T base in the 3rd position of triplet codons in mtDNA genes, respectively.

For the assessment of mtDNA codon usage of other animal species, we analyzed the mtDNA sequence of Caenorhabditis elegans (accession# NC_001328), Drosophila melanogaster (accession# NC_001709), D. rerio (accession# NC_002333), Xenopus laevis (accession# NC_001573), Mus musculus (accession# EU450583), Gallus domesticus (accession # NC_235570), and Pan troglodytes (NC_001643). We considered only L strand mtDNA genes in the cross-species analysis.

Recurrent substitutions

To compare the number of recurrent substitutions between silent and missense substitutions, we randomly selected 100 substitutions each from 198 silent substitutions in the third base of triplet codons, 440 missense substitutions in the first base of triplet codons, and 405 missense substitutions in the second base of triplet codons. We counted the number of recurrent substitutions in each group. This was iterated 300 times independently. ANOVA testing was applied to determine the difference between the three groups (Figure 5—figure supplement 1).

dN/dS ratio

To estimate dN/dS values for missense mutations (wmis), we used an adaptation of the method described previously (Greenman et al., 2006). Briefly, the rate of mutations is modeled as a Poisson process, with a rate given by a product of the mutation rate and the impact of selection. To obtain accurate estimates of dN/dS, we used two separate models, one using 12 single-nucleotide substitution rates and a more complex one accounting for any context dependence effect by 1-nucleotide upstream and downstream using 192 substitution rates. For example in the 12-rate model, the expected number of A > C mutations (λA>C) would be modeled as follows:

λsyn,A>C=rA>C*Lsyn,A>C
λmis,A>C=rA>C*wmis*Lmis,A>C

where Lsyn,A>C and Lmis,A>C are the number of sites that can suffer a synonymous and missense A > C mutation, respectively, which are calculated for any particular sequence. The likelihood of observing the number of missense A > C mutations (Nmis,A>C) given the expected λmis,A>C is then calculated as:

Lik=Poisson(Nmis,A>C | rA>C,wmis)

and the likelihood of the entire model is the product of all individual likelihoods. Wmis is fixed to be equal in all 12 (or 192) equations describing each substitution type, and a hill-climbing algorithm is used to find the maximum likelihood estimates for all rate and selection parameters. Likelihood Ratio Tests are then used to test deviations from neutrality (wmis = 1). The dN/dS ratio reported in the main text corresponds to the full context dependent model with 192 substitution rates. This method allows quantifying the strength of selection avoiding the confounding effect of gene length, sequence composition, different rates of each substitution type, and context-dependent mutagenesis.

Short indels

Along with the 1907 somatic mtDNA substitutions, we identified 109 and 142 somatic short insertions and deletions, respectively, from the 1675 cancer mtDNA sequences using Varscan2 (Supplementary file 2).

Evolutionary dynamics of neutral mitochondrial mutations

We model the evolutionary dynamics of mitochondrial mutations under random drift and derive a simple equation for the expected number of homoplasmic mutations. There exist multiple levels at which mitochondrial mutations evolve: within mitochondria, in the cytoplasm, and on the cellular level (Rand, 2011). In this study, we focus on the dynamics in a single cell, which represents the founder of the last clonal expansion in the tumor cell population. The cellular dynamics during a clonal expansion is difficult to describe analytically, but it is important to realize that mutations of a clonal expansion preserves the allele frequencies of neutral variants and that mutations that occur after the expansion are unlikely to contribute to measurable allele frequencies, as the population becomes large.

We model the evolutionary dynamics of mitochondrial mutations in the cytoplasm of a single cell by a Wright–Fisher process (Wright, 1931), in which the number of mitochondria in a subsequent generation is a binomial sample of the mitochondria in the previous generation. The number of mitochondria M is kept fixed. The marginal allele frequency X of a single site has two absorbing boundaries, X = 0 and X = M (homoplasmy), and the probability of fixation of an allele at frequency X by neutral drift is ρ = X/M (Wright, 1931). Note that this process leads, on the population level, to a dichotomization of heteroplasmic variants to either go extinct or become homoplasmic and fixate in a cell.

Mutations on any of L (= 16,569 nt) sites in the mitochondrial genome are assumed to occur at a uniform rate μ per nucleotide per cell division, which is of order 10−7, based on a human inter-generational comparison (Coller et al., 2001). Hence the rate of neutral evolution is simply μLM/M = μL (Kimura, 1984). Lastly, the expected time to fixation in the Wright–Fisher process is t = 2M. Putting these things together, the expected number of mutant alleles N in a cell initially without any mitochondrial mutations after T generation is

E[N]=μ L(T2M)

This equation predicts a linear accumulation of neutral mutations over time, with a delay imposed by number of mitochondrial copies. A similar behavior has been reported using numerical simulations (Coller et al., 2001). When also considering heteroplasmic mutations, the expected number of alterations may be slightly higher.

To check whether our model yields the correct behavior, we use the following numbers: the observed order of magnitude of mitochondrial mutations per patient was N = 1. The sequencing coverage on the mitochondrial genome indicates that there were of order M = 100 mitochondrial genome copies present per cancer cell. The expected number of mutations per cell division is μL = 1.6 × 10−3, it therefore requires around 1000 cell generations T to accumulate on average one homoplasmic mutation. This number of generations appears realistic for regenerating tissues. As expected, epithelial cancers had among the highest observed number of mitochondrial mutations, while hematopoietic cancers typically had lower numbers.

Statistical testing

Statistical testing was performed using R software. All p-values were calculated by two-tailed testing. Figures were generated using R and Microsoft Excel software.

References

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
    Mitochondrial Disorders Overview
    1. PF Chinnery
    (1993)
    GeneReviews, Seattle, Washington.
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12
  13. 13
    Likelihood analysis of asymmetrical mutation bias gradients in vertebrate mitochondrial genomes
    1. JJ Faith
    2. DD Pollock
    (2003)
    Genetics 165:735–745.
  14. 14
    Mitochondrial disease genetic diagnostics: optimized whole-exome analysis for all MitoCarta nuclear genes and the mitochondrial genome
    1. MJ Falk
    2. EA Pierce
    3. M Consugar
    4. MH Xie
    5. M Guadalupe
    6. O Hardy
    7. EF Rappaport
    8. DC Wallace
    9. E LeProust
    10. X Gai
    (2012)
    Discovery Medicine 14:389–399.
  15. 15
  16. 16
  17. 17
  18. 18
  19. 19
  20. 20
  21. 21
  22. 22
  23. 23
  24. 24
  25. 25
  26. 26
  27. 27
  28. 28
    The neutral theory of molecular evolution
    1. M Kimura
    (1984)
    Cambridge University Press.
  29. 29
  30. 30
  31. 31
  32. 32
  33. 33
  34. 34
  35. 35
  36. 36
  37. 37
  38. 38
  39. 39
  40. 40
  41. 41
  42. 42
  43. 43
  44. 44
  45. 45
  46. 46
  47. 47
  48. 48
  49. 49
  50. 50
  51. 51
  52. 52
  53. 53
  54. 54
  55. 55
  56. 56
    Evolution in Mendelian populations
    1. S Wright
    (1931)
    Genetics 16:97–159.
  57. 57
  58. 58
  59. 59

Decision letter

  1. Todd Golub
    Reviewing Editor; Broad Institute, United States

eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.

Thank you for sending your work entitled “Origins and functional consequences of somatic mitochondrial DNA mutations” for consideration at eLife. Your article has been favorably evaluated by Stylianos Antonarakis (Senior editor), a Reviewing editor, and 3 reviewers.

The editors and the reviewers discussed their comments before we reached this decision, and the Senior editor has assembled the following comments to help you prepare a revised submission.

The substantial concerns of the 3 reviewers are:

Reviewer 1:

1) Is the generated sequence data accessible through a public repository such as GEO? Are database accession numbers provided somewhere in the manuscript.

2) Figure 3–figure supplement 1 is an example of possible artifacts in the authors' results/interpretation. This figure compares the substitution rates between 12 coding genes on heavy strand vs 1 on light strand, but information on how the graph was generated for the 12 genes cannot be found. Does the graph show the sum or average of observed substitution rates? If it is based on average rates, should there not be error bars or p-values from t-tests. Also, why are the 12 genes shown for the L strand, and the 1 gene as the H strand?

3) In Figure 4, what are the authors trying to demonstrate with Figure 4A? What do arrows beside the “box” indicate? What do the different colors of the arrow (blue and red) mean? Why is the “box” designed for the light strand sequence when it has only 1 out of 13 protein-coding genes? What does skew mean here?

Reviewer 2:

1) Do the authors observe differences between tumor types in terms of the relative proportion of negatively selected somatic mutations that are hetero- vs. homoplasmic? This may indicate differential (negative) selective pressures in different tumor types (e.g. negative selection could be less pronounced in tumors with a low rate of oxidative phosphorylation).

2) The authors suggest that the major explanation for lack of visible exogenous mutation signatures (i.e. such related to tobacco smoke or UV light exposure) is that the endogenous mutation rate in mitochondria is several orders of magnitude greater than the exogenous mutation rate. Can the authors, based on the rate of nuclear genome somatic mutations in tumors with pronounced exogenous mutation signatures (i.e. using data from earlier works by the authors) provide an estimate for the relative difference in endogeneous vs. exogeneous mutation rate?

Reviewer 3:

1) Several studies have already used massive parallel sequencing data for the identification of heteroplasmic mutations in cancer (He et al Nature 2010) and in normal samples (Goto et al 2011; Avital et al 2012; Payne et al 2013). These studies already set the standards for such analyses, which to a large extent were overlooked in the current manuscript. As an example, the authors used a threshold of >3% of the reads to be considered as trustworthy heteroplasmic mutations but the minimum coverage that they used does not allow such a threshold (10X!). Other studies used at least 100X (a minimum) or even 1000X coverage (preferable) and a threshold of 1.5% of the reads. Apart from the threshold issue, while considering secondary reads in the mtDNA (heteroplasmy) one should apply several filters such as 'no strand bias', unique mapping (NuMT exclusion) and 'exclusion of mutations at the end of reads'. These should be used in the revised manuscript and the list of mutations should be updated accordingly. These filters are especially important, since the authors give much weight for their strand bias hypothesis and replication-associated mutations. However, nothing is mentioned as to how the authors account for strand bias (i.e. mutations present mostly in forward or reverse reads). Until they exclude sequencing artifacts, including those resulting from strand bias, they cannot interpret the data as actual biological strand bias.

2) The authors claim that their analysis was unbiased and generated a catalogue of somatic mtDNA mutations. However, they excluded all candidate somatic mutations that recapitulated known mtDNA SNPs. The exclusion of SNPs is understood while considering the nuclear genome, however unlike the nuclear DNA the mtDNA molecule is in full linkage disequilibrium. This means that in order to avoid sample contamination the authors should exclude mutations in SNP positions ONLY when they phase along with other SNP mutations to form a haplotype.

3) The functional potential of mutations is not properly analyzed: dN/dS ratio calculated for genes does not reflect the functionality of specific mutations. Some estimation could be given by manipulations of this ratio using PAML or SELECTON. However the best way to assess the functional potential of variants is a comparison to disease causing mutations.

https://doi.org/10.7554/eLife.02935.027

Author response

Reviewer 1:

1) Is the generated sequence data accessible through a public repository such as GEO? Are database accession numbers provided somewhere in the manuscript.

We have submitted our sequencing data to EBI. Corresponding accession numbers are available at Supplementary file 1. It may be finally updated after acceptance.

2) Figure 3–figure supplement 1 is an example of possible artifacts in the authors' results/interpretation. This figure compares the substitution rates between 12 coding genes on heavy strand vs 1 on light strand, but information on how the graph was generated for the 12 genes cannot be found. Does the graph show the sum or average of observed substitution rates? If it is based on average rates, should there not be error bars or p-values from t-tests. Also, why are the 12 genes shown for the L strand, and the 1 gene as the H strand?

We have updated our manuscript. In Materials and methods, section “Mutational signature and strand bias”, we describe how we calculated the substitution rates in more detail. In addition, we addressed chi-square tests to quantify the extent of difference in mutational signatures (Figure legends for Figure 3–figure supplement 1). Of the 13 protein-coding genes in mtDNA, we obtained their strands from human reference mtDNA sequence (rCRS). Notably, only 1 gene (MT-ND6) has its coding sequence on the H strand.

3) In Figure 4, what are the authors trying to demonstrate with Figure 4A? What do arrows beside the “box” indicate? What do the different colors of the arrow (blue and red) mean? Why is the “box” designed for the light strand sequence when it has only 1 out of 13 protein-coding genes? What does skew mean here?

This figure explains how the somatic mutational signature is identical or very similar to the germline one, observed across evolutionary time. From this, we conclude the mutational processes that shaped human mitochondria over time generate de novo mtDNA mutations in somatic tissues. Thus, the germline signature of human mtDNA can be inferred from codon usage biases (T>C skew and G>A skew; Materials and methods, “mtDNA codon usage”), which present T>C and G>A (L strand manner) exactly as observed in somatic substitutions. The arrows beside the box highlight the T>C (red) and G>A (blue) substitutional pressures in germline mtDNA. We have updated these in the figure legends of Figure 4A. The numbers in the box are based on 12 “L strand genes (genetic codes on the L strand, or transcribed from the H strand)”, not from 1 “H strand gene”.

Reviewer 2:

1) Do the authors observe differences between tumor types in terms of the relative proportion of negatively selected somatic mutations that are hetero- vs. homoplasmic? This may indicate differential (negative) selective pressures in different tumor types (e.g. negative selection could be less pronounced in tumors with a low rate of oxidative phosphorylation).

This is an interesting suggestion. Using 166 protein-truncating mutations, we tested the difference in variant allele fractions across tumor types. We have shown our results in the Figure 5–figure supplement 2 and have updated our manuscript accordingly. Interestingly, colorectal cancers show less extent of such constraints on mtDNA protein-truncating mutations.

Amended to: The extent of such disadvantage may vary according to tumor type: for example colorectal cancers show less negative selection compared to breast cancers (p=0.028; Figure 5–figure supplement 2).

2) The authors suggest that the major explanation for lack of visible exogenous mutation signatures (i.e. such related to tobacco smoke or UV light exposure) is that the endogenous mutation rate in mitochondria is several orders of magnitude greater than the exogenous mutation rate. Can the authors, based on the rate of nuclear genome somatic mutations in tumors with pronounced exogenous mutation signatures (i.e. using data from earlier works by the authors) provide an estimate for the relative difference in endogeneous vs. exogeneous mutation rate?

We tried to estimate the impact of ultraviolet light (UV) and tobacco smoking in the somatic mtDNA mutations as the reviewer suggested. We did not find any evidence that there were any mutations that could be unambiguously attributed to these mutational processes. We have added Figure 5–figure supplement 3 to summarize our estimates. Thus, it is impossible to estimate the relative difference between endogenous and exogenous mutation rates. Potentially, with larger sample sizes of these tumour types, this will be feasible.

According to the mutational signatures in the nuclear genomes reported by Alexandrov et al., (2013), UV and tobacco smoking are the most important exogenous mutagens. The mutational signatures of UV and tobacco smoking are C>T substitution at a dipyrimidine context and C>A substitution, respectively.

1) UV:

From 26 samples of melanoma, of which the UV mutational signature is found in each sample and accounts for >90% of somatic mutation in each nuclear genomes, we found 15 C>T (or G>A) mtDNA mutations. Of these, 12 (80%) were at dipyrimidine contexts. As controls, breast cancers mostly free from UV, we found 289 C>T (or G>A) substitution, and 227 (78.5%) were from dipyrimidine context. Because the proportions of C>T at dipyrimidine context are the same (chi-square test p=0.54) between melanomas and breast cancers, it is highly unlikely that the UV signature generates the mtDNA mutations even in the melanoma samples.

2) Tobacco smoking:

Tobacco signature is dominant in lung cancer nuclear genomes (61% for lung adenocarcinoma; 93% for small cell lung cancer; 45% for squamous cell carcinoma). In contrast, the mutational signature of tobacco smoking has not been previously found in breast cancer nuclear genomes (Alexandrov et al., Nature 2013). Of the 83 mtDNA somatic mutations in lung cancers, 3.6% (n=3) are C>A (or G>T) substitutions. From breast cancers, 4.7% of somatic mtDNA substitutions are C>A (or G>T) mutations. Because we cannot identify any C>A substitution enrichment in lung cancers, the impact of tobacco smoking is also minimal, approximately 0%.

Reviewer 3:

1) Several studies have already used massive parallel sequencing data for the identification of heteroplasmic mutations in cancer (He et al Nature 2010) and in normal samples (Goto et al 2011; Avital et al 2012; Payne et al 2013). These studies already set the standards for such analyses, which to a large extent were overlooked in the current manuscript. As an example, the authors used a threshold of >3% of the reads to be considered as trustworthy heteroplasmic mutations but the minimum coverage that they used does not allow such a threshold (10X!). Other studies used at least 100X (a minimum) or even 1000X coverage (preferable) and a threshold of 1.5% of the reads.

The threshold (“10x”) mentioned by the reviewer is not for “mtDNA somatic mutations” but for “autosomal SNPs” to assess “minor cross-contamination levels” (see Materials and methods, “DNA cross-contamination” section). As we described in the manuscript, average read-depths for mitochondrial DNA from WGS and WXS are 7,901x and 92x. For 1,907 somatic polymorphisms, the average and median read-depths are 4,560x and 419x, respectively. Our findings are based on “high-coverage mtDNA sequencing” like the previous studies the reviewer mentioned. It is worthy of note that WES is not as sensitive as WGS for mtDNA somatic mutations, but is sufficiently sensitive (71.4%) to provide useful information. With respect to the threshold (>3%), we decided to be more conservative compared to previous studies (1.5%), because copy number amplifications are very frequent in cancer cells, thus the influence of NuMTs in this study could be higher than previous studies from normal samples. Finally, we would like to emphasize that the major conclusions of the manuscript (the mutational signature and strand bias in mtDNA, and the lack of positive selection on mutations) are independent of the thresholds used.

Apart from the threshold issue, while considering secondary reads in the mtDNA (heteroplasmy) one should apply several filters such as 'no strand bias', unique mapping (NuMT exclusion) and 'exclusion of mutations at the end of reads'. These should be used in the revised manuscript and the list of mutations should be updated accordingly. These filters are especially important, since the authors give much weight for their strand bias hypothesis and replication-associated mutations. However, nothing is mentioned as to how the authors account for strand bias (i.e. mutations present mostly in forward or reverse reads). Until they exclude sequencing artifacts, including those resulting from strand bias, they cannot interpret the data as actual biological strand bias.

Most of the filters suggested by the reviewer were already applied in the original manuscript. Most importantly, we used the “strand-filter” (see Materials and methods, “Variant calling” section (strand-filter 1) and Supplementary Table 2). Therefore, we are very confident that the strand bias which is observed in the somatic substitutions is not an artifact from detection, or “mutations present mostly in forward OR reverse reads”. Additionally, we used only uniquely mapped reads in the mutation calling algorithm. We also note that the alignment algorithm simultaneously maps against the nuclear and mitochondrial genomes, so that reads from the NuMT regions will map to the nucleus, and not be included in the analysis presented here. We did not apply a “read-end filter”. To evaluate whether this would have had an impact, we checked 130 randomly selected mtDNA mutations. Only 2 were called as mutations based on mismatches at the end of reads only (<4bp from read-end); this is no more than what we would have expected by chance, so we believe the read-end issue to be negligible.

2) The authors claim that their analysis was unbiased and generated a catalogue of somatic mtDNA mutations. However, they excluded all candidate somatic mutations that recapitulated known mtDNA SNPs. The exclusion of SNPs is understood while considering the nuclear genome, however unlike the nuclear DNA the mtDNA molecule is in full linkage disequilibrium. This means that in order to avoid sample contamination the authors should exclude mutations in SNP positions ONLY when they phase along with other SNP mutations to form a haplotype.

We did not exclude candidate somatic mutations which overlap with known mtDNA SNPs; instead our algorithm uses sequencing from the matched normal sample to exclude germline polymorphisms. For example, in our main manuscript, we state “Of these 1,907 somatic substitutions, 1,385 (72.6%) were not registered in the database of mtDNA common polymorphism (mtDB)”. Thus, 27.4% of our mutations do overlap with previously known germline polymorphisms. Instead, we removed entire samples from the analysis when more than 3 of their somatic SNP candidates overlapped with known polymorphisms because such samples would be suggestive of cross-contamination with another sample, and lead to false mutation calls (see Materials and methods, Variant calling section, (3) Germline polymorphisms and back mutations). We would like to disclose that 66 samples (3.6%) were removed for this cross-contamination, out of the 1,848 samples initially enrolled in this study.

3) The functional potential of mutations is not properly analyzed: dN/dS ratio calculated for genes does not reflect the functionality of specific mutations. Some estimation could be given by manipulations of this ratio using PAML or SELECTON. However the best way to assess the functional potential of variants is a comparison to disease causing mutations.

We compared our mutation list with known disease-causing mtDNA mutations as the reviewer suggested. The results can be found in the Supplementary file 2. In addition, mutations more recurrent than expected are shown in Supplementary file 5. We have updated our manuscript accordingly.

Next, we assessed whether any specific somatic mutations showed evidence of positive selection. Out of the 1,907 somatic substitutions, 16 (0.8%) overlapped with known disease-associated mtDNA mutations, such as mutations frequently detected in MELAS (Mitochondrial Encephalomyopathy, lactic acidosis, and stroke-like episodes) and LHON (Leber hereditary optic neuropathy) (Supplementary file 2). In addition, ten mutations within mitochondrial protein-coding, tRNA and rRNA genes showed significantly higher recurrent rate than expected from background mutational signature (Supplementary file 5). However, it remains unclear whether this high recurrence reflects positive selection, because any factors not included in our background model of the mutational process, such as local mutation hotspots, could also explain a mild excess of mutations at a given nucleotide.

However, we still think our approaches are appropriate. Below are our responses to the reviewer’s concerns.

Point 1: The fraction of passenger mutations can be estimated from global dN/dS ratios

With respect to dN/dS ratio in detection of cancer genes, we have already shown that dN/dS measured at gene level is a powerful approach to identify driver genes (e.g. see Behjati et al, 2014 -PMID:24633157-, Bolli et al, 2014 -PMID:24429703- and Wong et al, 2014 -PMID: 24316979-). Of course, very subtle signals of selection, with infrequent positive selection acting at a single site, may escape this approach when applied to small datasets and a site-specific or codon-specific approach may be more powerful (see below).

Regarding our claim that most mutations are likely passenger events, we would like to highlight that the fraction of passenger mutations can indeed be estimated from gene or genome-level dN/dS ratios as long as negative selection is sufficiently weak. Here we have shown that, at least missense mutations seem to accumulate largely neutrally (see Figure 5A). Assuming that negative selection is absent, the fraction of driver mutations can be estimated using (w-1)/w, where w is the dN/dS ratio. This was explained in [Greenman C., et al. 2006]. If negative selection is stronger, this approach will provide an underestimation of the fraction of driver mutations.

Point 2: Use of PAML

We thank the referee for this interesting suggestion. We indeed considered and tried PAML. However, the assumptions of PAML are severely violated by this dataset leading to gross inaccuracies in the estimation of dN/dS. Critical assumptions that are violated are the simplistic mutation model (with a single rate parameter, the transition/transversion ratio), the assumption of strand symmetry (both strands mutate at very different rates), the reversibility of the mutation model (here we have an ancestral and a derived sequence, rather than two derived sequences) and potentially the stationarity of the sequence composition. To quantify the impact of these violations, we have performed simulations of neutral (randomly occurring) substitutions along the mitochondrial genome using the mutation spectrum described here (see Figure 3A or Author response image 1 below). In particular, we simulated 600 random mutations per genome, allowing for multiple mutations occurring in the same site (which is a much higher density of mutations than we observe, and so situation is more favourable for PAML than for our Poisson-based method).

Author response image 1

Somatic mutational signature of mitochondrial DNA

The resulting dN/dS estimates from 100 simulations are shown in Author response image 2 below. This clearly shows that the simplistic mutation model used in PAML leads to severe and systematic inaccuracies in dN/dS (3rd box plot). A similar bias is seen in the traditional Nei-Gojobori model (Nei & Gojobori, 1986) (4th box plot). In contrast, accounting for the different rates of each trinucleotide and for the extreme strand asymmetry in mitochondrial mutations, our model leads to unbiased dN/dS estimates.

Author response image 2

dN/dS ratio with 600 somatic substitutions randomly generated

Also, note that PAML cannot provide a dNon/dS ratio for nonsense mutation (nonsense mutations are excluded and a 61x61 codon substitution matrix is used).

Point 3: Selection at single-site level

Based on the simulations above, it is clear that PAML is not well suited to analyse this data reliably. The identification of highly recurrent sites within a protein should ideally take into consideration the exact sequence context of each site and the different mutation rates of each trinucleotide.

To address the referee's comments, we have extended our dN/dS approach to detect selection at single sites. Assuming a constant mutation spectrum across genes, we can test whether the level of site recurrence observed at each site violates the expected number of mutations given the local trinucleotide. A P-value can be computed for each site of a gene or a genome using a cumulative Poisson distribution (with lambda being the rate of the local trinucleotide), then correcting for multiple testing appropriately. This approach, which yields a p-value per site, is thus different from the Bayesian approach in PAML, being more similar to the method by Massingham and Goldman (2005, PMID: 15654091). Also note that this approach can be applied to non-coding sites as well as coding sites as it does not rely on a codon substitution matrix.

This analysis applied to our dataset revealed 10 sites within 37 mitochondrial genes (coding and non-coding) with a higher number of mutations than expected by neutral evolution assuming a constant mutation spectrum along the genome (shown in Supplementary file 4). It is, however, unclear whether the higher recurrence than expected reflects positive selection. Potential factors not considered in our statistical model, such as local mutation hotspots, may also explain these recurrences. It is worth noting that four of the recurrent sites are either synonymous or non-protein coding substitutions where positive selection is highly unlikely.

The analyses in this study suggest that most mutations in mitochondria are passenger events. We also did not find convincing evidence of driver events in any mitochondrial genes, using methods that have succeeded in finding known and novel cancer nuclear genes (see Behjati et al, 2014 -PMID:24633157-, Bolli et al, 2014 -PMID:24429703- and Wong et al, 2014 -PMID: 24316979-).

However, this does not rule out the possibility that a small fraction of mutations may be positively selected, too infrequently to be detected in the current dataset. We, however, found a list of recurrent hotspots whose origin and impact is unclear, that deserve further investigation.

https://doi.org/10.7554/eLife.02935.028

Article and author information

Author details

  1. Young Seok Ju

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    YSJ, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  2. Ludmil B Alexandrov

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    LBA, Analyzed mutational signature
    Competing interests
    The authors declare that no competing interests exist.
  3. Moritz Gerstung

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    MG, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  4. Inigo Martincorena

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    IM, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  5. Serena Nik-Zainal

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    SN-Z, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  6. Manasa Ramakrishna

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    MR, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  7. Helen R Davies

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    HRD, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  8. Elli Papaemmanuil

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    EP, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  9. Gunes Gundem

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    GG, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  10. Adam Shlien

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    AS, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  11. Niccolo Bolli

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    NB, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  12. Sam Behjati

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    SB, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  13. Patrick S Tarpey

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    PST, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  14. Jyoti Nangalia

    1. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    2. Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
    3. Department of Haematology, University of Cambridge, Cambridge, United Kingdom
    Contribution
    JN, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  15. Charles E Massie

    1. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    2. Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
    3. Department of Haematology, University of Cambridge, Cambridge, United Kingdom
    Contribution
    CEM, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  16. Adam P Butler

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    APB, Provided bioinformatics support for sequencing data acquisition
    Competing interests
    The authors declare that no competing interests exist.
  17. Jon W Teague

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    JWT, Provided bioinformatics support for sequencing data acquisition
    Competing interests
    The authors declare that no competing interests exist.
  18. George S Vassiliou

    1. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    2. Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
    3. Department of Haematology, University of Cambridge, Cambridge, United Kingdom
    Contribution
    GSV, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  19. Anthony R Green

    1. Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
    2. Department of Haematology, University of Cambridge, Cambridge, United Kingdom
    Contribution
    ARG, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  20. Ming-Qing Du

    Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
    Contribution
    M-QD, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  21. Ashwin Unnikrishnan

    Lowy Cancer Research Centre, University of New South Wales, Sydney, Australia
    Contribution
    AU, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  22. John E Pimanda

    Lowy Cancer Research Centre, University of New South Wales, Sydney, Australia
    Contribution
    JEP, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  23. Bin Tean Teh

    1. Laboratory of Cancer Epigenome, National Cancer Centre, Singapore, Singapore
    2. Duke-NUS Graduate Medical School, Singapore, Singapore
    Contribution
    BTT, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  24. Nikhil Munshi

    Department of Hematologic Oncology, Dana-Farber Cancer Institute, Boston, United States
    Contribution
    NM, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  25. Mel Greaves

    Institute of Cancer Research, Sutton, London, United Kingdom
    Contribution
    MG, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  26. Paresh Vyas

    Weatherall Institute for Molecular Medicine, University of Oxford, Oxford, United Kingdom
    Contribution
    PV, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  27. Adel K El-Naggar

    Department of Pathology, MD Anderson Cancer Center, Houston, United States
    Contribution
    AKE-N, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  28. Tom Santarius

    Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
    Contribution
    TS, Contributed samples and scientific advice
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    The authors declare that no competing interests exist.
  29. V Peter Collins

    Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
    Contribution
    VPC, Contributed samples and scientific advice
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    The authors declare that no competing interests exist.
  30. Richard Grundy

    Children's Brain Tumour Research Centre, University of Nottingham, Nottingham, United Kingdom
    Contribution
    RG, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  31. Jack A Taylor

    National Institute of Environmental Health Sciences, National Institute of Health, Triangle, North Carolina, United States
    Contribution
    JAT, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  32. D Neil Hayes

    Department of Internal Medicine, University of North Carolina, Chapel Hill, United States
    Contribution
    DNH, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  33. David Malkin

    Hospital for Sick Children, University of Toronto, Toronto, Canada
    Contribution
    DM, Contributed samples and scientific advice
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    The authors declare that no competing interests exist.
  34. ICGC Breast Cancer Group

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    1. Elena Provenzano, Cambridge Breast Unit, Addenbrooke’s Hospital, Cambridge University Hospital NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge CB2 2QQ, UK
    2. Marc van de Vijver, Department of Pathology, Academic Medical Center, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
    3. Andrea L Richardson, Department of Cancer Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, Massachusetts 02215, USA
    4. Colin Purdie, East of Scotland Breast Service, Ninewells Hospital, Dundee, United Kingdom
    5. Sarah Pinder, Department of Research Oncology, Guy’s Hospital, King’s Health Partners AHSC, King’s College London School of Medicine, London SE1 9RT, UK
    6. Gaetan MacGrogan, Institut Bergonié, 229 cours de l’Argone, 33076, Bordeaux, France
    7. Anne Vincent-Salomon, Institut Curie, Department of Tumor Biology, 26 rue d’Ulm, 75248 Paris cédex 05, France
    8. Denis Larsimont, Department of Pathology, Jules Bordet Institute, Brussels 1000, Belgium
    9. Dorthe Grabau, Department of Pathology, Skåne University Hospital, Lund University, SE-221 85 Lund, Sweden
    10. Torill Sauer, Department of Pathology, Oslo University Hospital Ulleval and University of Oslo, Faculty of Medicine and Institute of Clinical Medicine, Oslo, Norway
    11. Øystein Garred, Department of Pathology, Oslo University Hospital Ulleval and University of Oslo, Faculty of Medicine and Institute of Clinical Medicine, Oslo, Norway
    12. Anna Ehinger, Department of Gynecology & Obstetrics, Department of Clinical Sciences, Lund University, Skåne University Hospital Lund, SE-221 85 Lund, Sweden
    13. Gert G Van den Eynden, Translational Cancer Research Unit, GZA Hospitals St.-Augustinus, Antwerp, Belgium
    14. C.H.M. van Deurzen, Department of Pathology, Erasmus Medical Center, Rotterdam, the Netherlands
    15. Roberto Salgado, Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
    16. Jane E Brock, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston, Massachusetts 02115, USA
    17. Sunil R Lakhani, The University of Queensland, School of Medicine, Herston, Brisbane, QLD 4006, Australia
    18. Dilip D Giri, Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, USA
    19. Laurent Arnould, Centre Georges-François Leclerc, Dijon, France
    20. Jocelyne Jacquemier, biopathology department, 0nstitut Paoli Calmettes, Marseille, France
    21. Isabelle Treilleux, Centre Léon Bérard, Lyon, France
    22. Carlos Caldas, Cambridge Breast Unit, Addenbrooke’s Hospital, Cambridge University Hospital NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
    23. Suet-Feung Chin, Department of Oncology, University of Cambridge and Cancer Research UK Cambridge Research Institute, Li Ka Shin Centre, Cambridge
    24. Aquila Fatima, Department of Cancer Biology, Dana-Farber Cancer Institute, Massachusetts, USA
    25. Alastair M Thompson, Dundee Cancer Centre, Ninewells Hospital, Dundee, UK
    26. Alasdair Stenhouse, Dundee Cancer Centre, Ninewells Hospital, Dundee, UK
    27. John Foekens, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands
    28. John Martens, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands
    29. Anieta Sieuwerts, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, The Netherlands
    30. Arjen Brinkman, Department of Molecular Biology, Faculty of Science, Nijmegen Centre for Molecular Life Sciences, Radboud University, Nijmegen, The Netherlands
    31. Henk Stunnenberg, Department of Molecular Biology, Faculty of Science, Nijmegen Centre for Molecular Life Sciences, Radboud University, Nijmegen, The Netherlands
    32. Paul N. Span, Department of Radiation Oncology, Radboud University Medical Centre, Nijmegen, The Netherlands
    33. Fred Sweep, Department of Laboratory Medicine, Radboud University Medical Centre, Nijmegen, The Netherlands
    34. Christine Desmedt, Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
    35. Christos Sotiriou, Breast Cancer Translational Research Laboratory, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
    36. Gilles Thomas, Universite Lyon1, INCa-Synergie, Centre Leon Berard, Lyon Cedex 08, France
    37. Annegein Broeks, Department Experimental Therapy, The Netherlands Cancer Institute, Amsterdam, The Netherlands
    38. Anita Langerod, Department of Genetics, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
    39. Samuel Aparicio, Department of Molecular Oncology, BC Cancer Agency, Vancouver
    40. Peter Simpson, The University of Queensland, UQ Centre for Clinical Research, Herston, Brisbane, Australia
    41. Laura van 't Veer, Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands
    42. Jórunn Erla Eyfjörd, Cancer Research Laboratory, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
    43. Holmfridur Hilmarsdottir, Cancer Research Laboratory, Faculty of Medicine, University of Iceland, Reykjavik, Iceland
    44. Jon G Jonasson, Department of Pathology, University Hospital, Reykjavik, Iceland
    45. Anne-Lise Børresen-Dale, Department of Genetics, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
    46. Ming Ta Michael Lee, National Genotyping Center, Institute of Biomedical Sciences, Nankang, ROC
    47. Bernice Huimin Wong, NCCS-VARI Translational Research Laboratory, National Cancer Centre Singapore, Singapore
    48. Benita Kiat Tee Tan, Department of General Surgery, Singapore General Hospital, Singapore
    49. Gerrit K.J. Hooijer, Department of Pathology, Academic Medical Center, Amsterdam, The Netherlands
  35. ICGC Chronic Myeloid Disorders Group

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    1. Luca Malcovati
    2. Sudhir Tauro
    3. Jacqueline Boultwood, Nuffield Department of Clinical Laboratory Sciences, University of Oxford, UK
    4. Andrea Pellagatti, Nuffield Department of Clinical Laboratory Sciences, University of Oxford, UK
    5. Michael Groves, Division of Medial Sciences, University of Dundee, Dundee, UK
    6. Alex Sternberg, Weatherall Institute of Molecular Medicine, University of Oxford, UK
    7. Carlo Gambacorti-Passerini, Department of Haematology, University of Milan Bicocca, Milan, Italy
    8. Paresh Vyas, Weatherall Institute of Molecular Medicine, University of Oxford, UK
    9. Eva Hellstrom-Lindberg, Department of Haematology, Karolinska Institute, Stockholm, Sweden
    10. David Bowen, St James Institute of Oncology, St James Hospital, Leeds, UK
    11. Nicholas CP Cross, School of Medicine, University of Southampton, Southampton, UK
    12. Anthony R Green, Department of Haematology, University of Cambridge, Cambridge, UK
    13. Mario Cazzola, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy
  36. ICGC Prostate Cancer Group

    1. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    2. Institute of Cancer Research, Sutton, London, United Kingdom
    3. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    1. Colin Cooper, Division of Genetics and Epidemiology, The Institute Of Cancer Research, Sutton, UK
    2. Rosalind Eeles, Division of Genetics and Epidemiology, The Institute Of Cancer Research, Sutton, UK
    3. David Wedge, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    4. Peter Van Loo, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    5. Gunes Gundem, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    6. Ludmil Alexandrov, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    7. Barbara Kremeyer, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    8. Adam Butler, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    9. Andrew Lynch, Statistics and Computational Biology Laboratory, Cancer Research UK Cambridge Research Institute, Cambridge, UK
    10. Sandra Edwards, Division of Genetics and Epidemiology, The Institute Of Cancer Research, Sutton, UK
    11. Niedzica Camacho, Division of Genetics and Epidemiology, The Institute Of Cancer Research, Sutton, UK
    12. Charlie Massie, Urological Research Laboratory, Cancer Research UK Cambridge Research Institute, Cambridge, UK
    13. ZSofia Kote-Jarai, Division of Genetics and Epidemiology, The Institute Of Cancer Research, Sutton, UK
    14. Nening Dennis, Royal Marsden NHS Foundation Trust, London and Sutton, UK
    15. Sue Merson, Division of Genetics and Epidemiology, The Institute Of Cancer Research, Sutton, UK
    16. Jorge Zamora, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    17. Jonathan Kay, Urological Research Laboratory, Cancer Research UK Cambridge Research Institute, Cambridge, UK
    18. Cathy Corbishley, Department of Histopathology, St Georges Hospital, London, UK
    19. Sarah Thomas, Royal Marsden NHS Foundation Trust, London and Sutton, UK
    20. Serena Nik-Zainai, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    21. Sarah O'Meara, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    22. Lucy Matthews, Division of Genetics and Epidemiology, The Institute Of Cancer Research, Sutton, UK
    23. Jeremy Clark, Department of Biological Sciences and School of Medicine, University of East Anglia, Norwich, UK
    24. Rachel Hurst, Department of Biological Sciences and School of Medicine, University of East Anglia, Norwich, UK
    25. Richard Mithen, Institute of Food Research, Norwich Research Park, Norwich, UK
    26. Susanna Cooke, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    27. Keiran Raine, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    28. David Jones, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    29. Andrew Menzies, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    30. Lucy Stebbings, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    31. Jon Hinton, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    32. Jon Teague, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    33. Stuart McLaren, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    34. Laura Mudie, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    35. Claire Hardy, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    36. Elizabeth Anderson, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    37. Olivia Joseph, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    38. Victoria Goody, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    39. Ben Robinson, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    40. Mark Maddison, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    41. Stephen Gamble, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    42. Christopher Greenman, School of Computing Sciences, University of East Anglia, Norwich, UK
    43. Dan Berney, Department of Molecular Oncology, Barts Cancer Centre, Barts and the London School of Medicine and Dentistry, London, UK
    44. Steven Hazell, Royal Marsden NHS Foundation Trust, London and Sutton, UK
    45. Naomi Livni, Royal Marsden NHS Foundation Trust, London and Sutton, UK
    46. Cyril Fisher, Royal Marsden NHS Foundation Trust, London and Sutton, UK
    47. Christopher Ogden, Royal Marsden NHS Foundation Trust, London and Sutton, UK
    48. Pardeep Kumar, Royal Marsden NHS Foundation Trust, London and Sutton, UK
    49. Alan Thompson, Royal Marsden NHS Foundation Trust, London and Sutton, UK
    50. Christopher Woodhouse, Royal Marsden NHS Foundation Trust, London and Sutton, UK
    51. David Nicol, Royal Marsden NHS Foundation Trust, London and Sutton, UK
    52. Erik Mayer, Royal Marsden NHS Foundation Trust, London and Sutton, UK
    53. Tim Dudderidge, Royal Marsden NHS Foundation Trust, London and Sutton, UK
    54. Nimish Shah, Urological Research Laboratory, Cancer Research UK Cambridge Research Institute, Cambridge, UK
    55. Vincent Gnanapragasam, Urological Research Laboratory, Cancer Research UK Cambridge Research Institute, Cambridge, UK
    56. Peter Campbell, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    57. Andrew Futreal, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    58. Douglas Easton, Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
    59. Anne Y Warren, Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
    60. Christopher Foster, Bostwick Laboratories, London, UK
    61. Michael Stratton, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    62. Hayley Whitaker, Urological Research Laboratory, Cancer Research UK Cambridge Research Institute, Cambridge, UK
    63. Ultan McDermott, Cancer Genome Project,, Wellcome Trust Sanger Institute, Hinxton, UK
    64. Daniel Brewer, Division of Genetics and Epidemiology, The Institute Of Cancer Research, Sutton, UK
    65. David Neal, Urological Research Laboratory, Cancer Research UK Cambridge Research Institute, Cambridge, UK
  37. Christopher S Foster

    1. Department of Molecular and Clinical Cancer Medicine, University of Liverpool, London, United Kingdom
    2. HCA Pathology Laboratories, London, United Kingdom
    Contribution
    CSF, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  38. Anne Y Warren

    Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
    Contribution
    AYW, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  39. Hayley C Whitaker

    Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
    Contribution
    HCW, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  40. Daniel Brewer

    1. Institute of Cancer Research, Sutton, London, United Kingdom
    2. School of Biological Sciences, University of East Anglia, Norwich, United Kingdom
    Contribution
    DB, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  41. Rosalind Eeles

    Institute of Cancer Research, Sutton, London, United Kingdom
    Contribution
    RE, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  42. Colin Cooper

    1. Institute of Cancer Research, Sutton, London, United Kingdom
    2. School of Biological Sciences, University of East Anglia, Norwich, United Kingdom
    Contribution
    CC, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  43. David Neal

    Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
    Contribution
    DN, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  44. Tapio Visakorpi

    Institute of Biosciences and Medical Technology - BioMediTech and Fimlab Laboratories, University of Tampere and Tampere University Hospital, Tampere, Finland
    Contribution
    TV, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  45. William B Isaacs

    Department of Oncology, Johns Hopkins University, Baltimore, United States
    Contribution
    WBI, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  46. G Steven Bova

    Institute of Biosciences and Medical Technology - BioMediTech and Fimlab Laboratories, University of Tampere and Tampere University Hospital, Tampere, Finland
    Contribution
    GSB, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  47. Adrienne M Flanagan

    1. Department of Histopathology, Royal National Orthopaedic Hospital, Middlesex, United Kingdom
    2. University College London Cancer Institute, University College London, London, United Kingdom
    Contribution
    AMF, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  48. P Andrew Futreal

    1. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    2. Department of Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, Texas, United States
    Contribution
    PAF, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  49. Andy G Lynch

    Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
    Contribution
    AGL, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  50. Patrick F Chinnery

    Wellcome Trust Centre for Mitochondrial Research, Institute of Genetic Medicine, Newcastle University, Newcastle-upon-tyne, United Kingdom
    Contribution
    PFC, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  51. Ultan McDermott

    1. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    2. Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
    Contribution
    UMD, Contributed samples and scientific advice
    Competing interests
    The authors declare that no competing interests exist.
  52. Michael R Stratton

    Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    Contribution
    MRS, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    Competing interests
    The authors declare that no competing interests exist.
  53. Peter J Campbell

    1. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, United Kingdom
    2. Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
    3. Department of Haematology, University of Cambridge, Cambridge, United Kingdom
    Contribution
    PJC, Conception and design, Analysis and interpretation of data, Drafting or revising the article
    For correspondence
    pc8@sanger.ac.uk
    Competing interests
    The authors declare that no competing interests exist.

Funding

Wellcome Trust

  • Ultan McDermott
  • Michael R Stratton
  • Peter J Campbell

Wellcome Trust (Health Innovation Challenge Fund (HICF))

  • Peter J Campbell

Kay Kendall Leukaemia Fund

  • Anthony R Green
  • Mel Greaves
  • Peter J Campbell

Chordoma Foundation

  • P Andrew Futreal

Adenoid Cystic Carcinoma Research Foundation

  • P Andrew Futreal

European Molecular Biology Organization (ALTF 1203_2012)

  • Young Seok Ju

National Institute for Health Research (Biomedical Research Center at University College London Hospitals)

  • Adrienne M Flanagan

Leukaemia and Lymphoma Research

  • Anthony R Green

Cancer Research UK

  • Anthony R Green

Leukemia and Lymphoma Society

  • Anthony R Green

European Union (Breast Cancer Somatic Genetics Study (BASIS))

  • Michael R Stratton

National Cancer Research Institute (PROMPT: G0500966/75466)

  • Rosalind Eeles
  • Colin Cooper
  • David Neal

National Institute of Environmental Health Sciences (Intramural Research Program of the NIH)

  • Jack A Taylor

National Institute for Health Research (Cambridge Biomedical Research Center)

  • Anthony R Green
  • David Neal

European Molecular Biology Organization (ALTF 1287-2012)

  • Inigo Martincorena

Department of Health (Health Innovation Challenge Fund (HICF))

  • Peter J Campbell

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

Data used in this manuscript are described in the supplementary materials (Supplementary file 1). We thank Thomas Bleazard at Faculty of Medical and Human Sciences, University of Manchester for discussion and assistance with manuscript preparation. We would like to thank The Cancer Genome Atlas (TCGA) Project Team and their specimen donors for providing sequencing data. This work was supported by the Wellcome Trust, the British Lung Foundation, the Health Innovation Challenge Fund, the Kay Kendall Leukaemia Fund, the Chordoma Foundation, and the Adenoid Cystic Carcinoma Research Foundation. Y.S.J and I.M. are supported by EMBO long-term fellowship (ALTF 1203-2012 and ALTF 1287-2012, respectively). PJC. is a Wellcome Trust Senior Clinical Fellow. Support was provided to AMF by the National Institute for Health Research (NIHR) UCLH Biomedical Research Centre. ARG. receives support from Leukaemia Lymphoma Research, Cancer Research UK, and the Leukemia Lymphoma Society. Samples from Addenbrooke's Hospital were collected with support from the NIHR Cambridge Biomedical Resource Centre. The ICGC Breast Cancer Consortium was supported by a grant from the European Union (BASIS) and the Wellcome Trust. The ICGC Prostate Cancer Consortium was funded by Cancer Research UK. We would also like to acknowledge the support of the National Cancer Research Prostate Cancer: Mechanisms of Progression and Treatment (PROMPT) collaborative (grant code G0500966/75466) which has funded tissue and urine collections in Cambridge. This research was supported in part by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (JAT.). We obtained informed consent and consent to publish from participants enrolled.

Ethics

Human subjects: We obtained informed consent and consent to publish from participants enrolled in this study, Ethical approval references: Genome Analysis of myeloid and lymphoid malignancies (10/H0306/40), Genomic Analysis of Mesothelioma (11/EE/0444), Myeloid and lymphoid cancer genome analysis (07/S1402/90), The Treatment of Down Syndrome Children with Acute Myeloid Leukemia and Myelodysplastic Syndrome(AAML0431), CLL (chronic lymphocytic leukaemia) genome analysis (07/Q0104/3), CGP-Exome sequencing of Down syndrome associated acute myeloid leukemia samples (IRB 13-010133), Cancer Genome Project - Global approaches to characterizing the molecular basis of paediatric ependymoma (05/MRE04/70), PREDICT-Cohort (09/H0801/96), ICGC Prostate (Evaluation of biomarkers in urological diseases) (LREC 03/018), ICGC Prostate (779) (Prostate Complex CRUK Sample Cohort) (MREC/0¼/061), ICGC Prostate (Tissue collection at radical prostatectomy) (CRE-2011.373), Somatic molecular genetics of human cancers, melanoma and myeloma (Dana Farber Cancer Institute)(08/H0308/303), Breast Cancer Genome Analysis for the International Cancer Genome Consortium Working Group (09/H0306/36), Genome analysis of tumours of the bone (09/H0308/165).

Reviewing Editor

  1. Todd Golub, Reviewing Editor, Broad Institute, United States

Publication history

  1. Received: March 28, 2014
  2. Accepted: September 26, 2014
  3. Accepted Manuscript published: October 1, 2014 (version 1)
  4. Version of Record published: November 4, 2014 (version 2)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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