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Convergent somatic evolution commences in utero in a germline ribosomopathy

Machado, Heather E., Øbro, Nina F., Williams, Nicholas, Tan, Shengjiang, Boukerrou, Ahmed Z., Davies, Megan, Belmonte, Miriam, Mitchell, Emily, Baxter, E. Joanna, Mende, Nicole, Clay, Anna, Ancliff, Philip, Köglmeier, Jutta, Killick, Sally B., Kulasekararaj, Austin, Meyer, Stefan, Laurenti, Elisa, Campbell, Peter J., Kent, David G., Nangalia, Jyoti, Warren, Alan J.·2023·Nature Communications
DOI 10.1038/s41467-023-40896-5PMC PMC10444798
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Abstract

Clonal tracking of cells using somatic mutations permits exploration of clonal dynamics in human disease. Here, we perform whole genome sequencing of 323 haematopoietic colonies from 10 individuals with the inherited ribosomopathy Shwachman-Diamond syndrome to reconstruct haematopoietic phylogenies. In ~30% of colonies, we identify mutually exclusive mutations in TP53, EIF6, RPL5, RPL22, PRPF8, plus chromosome 7 and 15 aberrations that increase SBDS and EFL1 gene dosage, respectively. Target gene mutations commence in utero, resulting in a profusion of clonal expansions, with only a few haematopoietic stem cell lineages (mean 8, range 1-24) contributing ~50% of haematopoietic colonies across 8 individuals (range 4-100% clonality) by young adulthood. Rapid clonal expansion during disease transformation is associated with biallelic TP53 mutations and increased mutation burden. Our study highlights how convergent somatic mutation of the p53-dependent nucleolar surveillance pathway offsets the deleterious effects of germline ribosomopathy but increases opportunity for TP53-mutated cancer evolution.

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Figures

Fig. 1 Study design and cohort. a Schematic of experimental design. Single haematopoietic stem and progenitor cells (HSPC) and mononuclear cells (MNCs) from peripheral blood or bone marrow from individuals with SDS were expanded into colonies in vitro and each colony underwent whole-genome sequencing (WGS). Somatic mutations were used to reconstruct haematopoietic phylogenies. The timing of acquisition, clonal dynamics and functional consequences were investigated for driver mutations associated with SDS. Inkscape. b Age at sampling and SBDS genotype for each individual with SDS. The two bi-coloured columns represent the two parental alleles, with all individuals having biallelic germline mutations in SBDS. Highlighted samples (SDS2, SDS7, SDS9 and SDS10) were measured for frequency of CD34+ HSPCs, shown in (c). N the number of haematopoietic colonies analysed per individual. c Frequency of CD34+ HSPCs in bone marrow samples (expressed as a % of total viable MNCs) was analysed by flow cytometry in four of the individuals with SDS and three healthy individuals (black). Source data are provided as a Source Data file.
Fig. 1 Study design and cohort. a Schematic of experimental design. Single haematopoietic stem and progenitor cells (HSPC) and mononuclear cells (MNCs) from peripheral blood or bone marrow from individuals with SDS were expanded into colonies in vitro and each colony underwent whole-genome sequencing (WGS). Somatic mutations were used to reconstruct haematopoietic phylogenies. The timing of acquisition, clonal dynamics and functional consequences were investigated for driver mutations associated with SDS. Inkscape. b Age at sampling and SBDS genotype for each individual with SDS. The two bi-coloured columns represent the two parental alleles, with all individuals having biallelic germline mutations in SBDS. Highlighted samples (SDS2, SDS7, SDS9 and SDS10) were measured for frequency of CD34+ HSPCs, shown in (c). N the number of haematopoietic colonies analysed per individual. c Frequency of CD34+ HSPCs in bone marrow samples (expressed as a % of total viable MNCs) was analysed by flow cytometry in four of the individuals with SDS and three healthy individuals (black). Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 2 Recurrent mutations across SDS haematopoietic phylogenies. Phylogenetic trees of haematopoietic colonies for ten individuals with SDS. Each individual had between 10–100 colonies sequenced and included for phylogenetic analysis. Branches with somatic mutations in driver genes previously reported and/or under positive selection in this study are coloured. Branches with known driver mutations of clonal haematopoiesis are shown in black, and those associated with SDS in other colours. The branch harbouring the driver mutation is shown with a thicker coloured line. The Y-axis shows the total number of somatic mutations including driver mutations. Rows beneath phylogenetic trees show the specific driver mutations, with colonies harbouring that mutation more densely coloured. Of note, no known driver mutations were detected amongst the somatic mutations found in SDS1 and SDS3. *SDS8 was diagnosed with transformation to myelodysplasia with trilineage dysplasia 1 month before sampling. All SDS8 colonies had a complex karyotype with many chromosomal (Chr) copy number aberrations (CNA). CNAs confidently shared across SDS8 colonies are shown on the tree.
Fig. 2 Recurrent mutations across SDS haematopoietic phylogenies. Phylogenetic trees of haematopoietic colonies for ten individuals with SDS. Each individual had between 10–100 colonies sequenced and included for phylogenetic analysis. Branches with somatic mutations in driver genes previously reported and/or under positive selection in this study are coloured. Branches with known driver mutations of clonal haematopoiesis are shown in black, and those associated with SDS in other colours. The branch harbouring the driver mutation is shown with a thicker coloured line. The Y-axis shows the total number of somatic mutations including driver mutations. Rows beneath phylogenetic trees show the specific driver mutations, with colonies harbouring that mutation more densely coloured. Of note, no known driver mutations were detected amongst the somatic mutations found in SDS1 and SDS3. *SDS8 was diagnosed with transformation to myelodysplasia with trilineage dysplasia 1 month before sampling. All SDS8 colonies had a complex karyotype with many chromosomal (Chr) copy number aberrations (CNA). CNAs confidently shared across SDS8 colonies are shown on the tree.CC BY · PubMed Central
Fig. 3 Detecting positive selection and timing of mutation acquisition. a Number of nonsynonymous mutations in the four genes under significant positive selection (ratio of normalised nonsynonymous mutations (dN) to normalised synonymous mutations (dS) dN:dS > 1, q < 0.01). b Proportion of cells per individual carrying driver mutations classified by gene and chromosomal abnormality. CH genes associated with clonal haematopoiesis, CNA copy number alteration. c Timing of division of the most recent common ancestor (MRCA) of clonal expansions (clade comprising 2 or more colonies) harbouring driver mutations. This gives the latest time point (together with 95% credibility interval) by which the driver mutation was acquired as represented by the inferred timing of the end of the shared branch, but it is possible that the driver mutation occurred at any time along the branch that harbours the driver mutation. The top panel illustrates the diversity of timings within a single individual (SDS5) and the bottom panel shows the timing of all the identified driver based expansions in the full cohort (including SDS5) with the exception of SDS8. A total of 14 branches harbouring driver mutations from the cohort are timed for their latest age of acquisition. The bars span the 95% credibility interval of the MRCAs. Source data are provided as a Source Data file.
Fig. 3 Detecting positive selection and timing of mutation acquisition. a Number of nonsynonymous mutations in the four genes under significant positive selection (ratio of normalised nonsynonymous mutations (dN) to normalised synonymous mutations (dS) dN:dS > 1, q < 0.01). b Proportion of cells per individual carrying driver mutations classified by gene and chromosomal abnormality. CH genes associated with clonal haematopoiesis, CNA copy number alteration. c Timing of division of the most recent common ancestor (MRCA) of clonal expansions (clade comprising 2 or more colonies) harbouring driver mutations. This gives the latest time point (together with 95% credibility interval) by which the driver mutation was acquired as represented by the inferred timing of the end of the shared branch, but it is possible that the driver mutation occurred at any time along the branch that harbours the driver mutation. The top panel illustrates the diversity of timings within a single individual (SDS5) and the bottom panel shows the timing of all the identified driver based expansions in the full cohort (including SDS5) with the exception of SDS8. A total of 14 branches harbouring driver mutations from the cohort are timed for their latest age of acquisition. The bars span the 95% credibility interval of the MRCAs. Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 4 Mutation burden, mutational processes and biallelic TP53 variants. a Mutation burden (number of SNVs) as a function of age for ten individuals with SDS. Each circle represents one colony genome, with the black horizontal bars representing the median burden per individual. Two timepoints from SDS5 are shown at different ages. Circles coloured black (normal) representing mutation burdens from three haematopoietically healthy (non-SDS) individuals (published data3) are shown for comparison. The blue line represents the regression line through the colonies from individuals with SDS. b Trinucleotide context of somatic mutations. The two mutational signatures were identified across all genomes. SBS142, 43 is characterised by spontaneous deamination of cytosines, and the second mutational signature, termed SBSBlood1, 2, 41 represents mutations typical of endogenous mutations in HSCs. c Number of SNVs attributable to the mutational signatures SBS1 (green) and SBSblood (blue) across each colony from each individual with SDS. Each bar represents the genome from one colony. Note SDS8 has a higher total mutation burden due to increased SBS1 mutations. d Copy number variation for two representative colony genomes with heterozygous/mono-allelic TP53 mutation (from different individuals) and two clonally related colonies from SDS8 with biallelic TP53 mutations. Ploidy is shown on the y-axis and genomic location on the x-axis, for the two parental alleles (green and red). CNA copy number aberration. Source data are provided as a Source Data file.
Fig. 4 Mutation burden, mutational processes and biallelic TP53 variants. a Mutation burden (number of SNVs) as a function of age for ten individuals with SDS. Each circle represents one colony genome, with the black horizontal bars representing the median burden per individual. Two timepoints from SDS5 are shown at different ages. Circles coloured black (normal) representing mutation burdens from three haematopoietically healthy (non-SDS) individuals (published data3) are shown for comparison. The blue line represents the regression line through the colonies from individuals with SDS. b Trinucleotide context of somatic mutations. The two mutational signatures were identified across all genomes. SBS142, 43 is characterised by spontaneous deamination of cytosines, and the second mutational signature, termed SBSBlood1, 2, 41 represents mutations typical of endogenous mutations in HSCs. c Number of SNVs attributable to the mutational signatures SBS1 (green) and SBSblood (blue) across each colony from each individual with SDS. Each bar represents the genome from one colony. Note SDS8 has a higher total mutation burden due to increased SBS1 mutations. d Copy number variation for two representative colony genomes with heterozygous/mono-allelic TP53 mutation (from different individuals) and two clonally related colonies from SDS8 with biallelic TP53 mutations. Ploidy is shown on the y-axis and genomic location on the x-axis, for the two parental alleles (green and red). CNA copy number aberration. Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 5 Functional consequences of EIF6 variants. a Atomic model of human eIF6 bound to the 60S ribosomal subunit (PDBID: 7OW7). CP central protuberance. Stabilising interactions formed by eIF6 residues N106 (b), I58 (c) and R96 (d) are predicted to be lost with somatic mutation. Figures were generated using Pymol v1.2. eL24 is coloured salmon; uL14, gold; eIF6, green. e Cell extracts from HEK293T cells expressing empty vector, human eIF6-WT-FLAG or eIF6-M1T-FLAG mutant for 24 h were immunoblotted to detect eIF6, FLAG and actin as loading control. f, g Overexpression of eIF6 variants in WT flies. Genotypes of fly samples are indicated in Supplementary Table 2. f Extracts from larvae with the stated genotypes were immunoblotted to detect the indicated proteins (minimum 3 replicates). Control, da-GAL4 line. g Proportion of indicated fly genotypes that eclosed (minimum 5 replicates, minimum n = 216; error bars represent mean ± SD). h Genetic complementation of Sbds-deficient Drosophila (Sbds P/P) with SDS-related eIF6 variants. Proportion of indicated genotypes developing to the pupal stage is shown (minimum 5 replicates, minimum n = 256; error bars represent mean ± SD; ***two tailed t-test, p(1) = 0.00060711; p(2) = 2.56426E−07; p(3) = 2.3141E−13. Source data are provided as a Source Data file.
Fig. 5 Functional consequences of EIF6 variants. a Atomic model of human eIF6 bound to the 60S ribosomal subunit (PDBID: 7OW7). CP central protuberance. Stabilising interactions formed by eIF6 residues N106 (b), I58 (c) and R96 (d) are predicted to be lost with somatic mutation. Figures were generated using Pymol v1.2. eL24 is coloured salmon; uL14, gold; eIF6, green. e Cell extracts from HEK293T cells expressing empty vector, human eIF6-WT-FLAG or eIF6-M1T-FLAG mutant for 24 h were immunoblotted to detect eIF6, FLAG and actin as loading control. f, g Overexpression of eIF6 variants in WT flies. Genotypes of fly samples are indicated in Supplementary Table 2. f Extracts from larvae with the stated genotypes were immunoblotted to detect the indicated proteins (minimum 3 replicates). Control, da-GAL4 line. g Proportion of indicated fly genotypes that eclosed (minimum 5 replicates, minimum n = 216; error bars represent mean ± SD). h Genetic complementation of Sbds-deficient Drosophila (Sbds P/P) with SDS-related eIF6 variants. Proportion of indicated genotypes developing to the pupal stage is shown (minimum 5 replicates, minimum n = 256; error bars represent mean ± SD; ***two tailed t-test, p(1) = 0.00060711; p(2) = 2.56426E−07; p(3) = 2.3141E−13. Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 6 TP53 and the nucleolar surveillance pathway as targets for convergent somatic mutation. a Defective germline ribosome assembly in SDS promotes nucleolar stress through inhibitory binding of the 5S RNP complex (consisting of the 5S rRNA, uL5, encoded by RPL11 and uL18, encoded by RPL5) to the nuclear E3 ligase HDM2 (enhanced by eL22, encoded by RPL22), promoting p53 accumulation and apoptosis22. b Convergent evolution of somatic mutations restores ribosome homoeostasis, favouring HDM2-dependent p53 ubiquitination and degradation, through multiple independent somatic genetic rescue events including: (1) increased dose of SBDS or EFL1 proteins (2) reduced eIF6 dosage or eIF6 binding to the 60S subunit; (3) disrupted inhibitory binding of HDM2 to p53 through mutations in RPL5 and RPL22 50; (4) TP53 mutations. Ub ubiquitin.
Fig. 6 TP53 and the nucleolar surveillance pathway as targets for convergent somatic mutation. a Defective germline ribosome assembly in SDS promotes nucleolar stress through inhibitory binding of the 5S RNP complex (consisting of the 5S rRNA, uL5, encoded by RPL11 and uL18, encoded by RPL5) to the nuclear E3 ligase HDM2 (enhanced by eL22, encoded by RPL22), promoting p53 accumulation and apoptosis22. b Convergent evolution of somatic mutations restores ribosome homoeostasis, favouring HDM2-dependent p53 ubiquitination and degradation, through multiple independent somatic genetic rescue events including: (1) increased dose of SBDS or EFL1 proteins (2) reduced eIF6 dosage or eIF6 binding to the 60S subunit; (3) disrupted inhibitory binding of HDM2 to p53 through mutations in RPL5 and RPL22 50; (4) TP53 mutations. Ub ubiquitin.CC BY · PubMed Central

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Research works printed but not linked: 57

  1. Osorio, F. G. et al. Somatic mutations reveal lineage relationships and age-related mutagenesis in human hematopoiesis. Cell Rep. 25, 2308–2316.e4 (2018). · DOI · Paper page
  2. Mitchell, E. et al. Clonal dynamics of haematopoiesis across the human lifespan. Nature 606, 343–350 (2022). · DOI · Paper page
  3. Zink, F. et al. Clonal hematopoiesis, with and without candidate driver mutations, is common in the elderly. Blood 130, 742–752 (2017). · DOI · Paper page
  4. Boocock, G. R. B. et al. Mutations in SBDS are associated with Shwachman–Diamond syndrome. Nat. Genet. 33, 97–101 (2003). · DOI
  5. Menne, T. F. et al. The Shwachman-Bodian-Diamond syndrome protein mediates translational activation of ribosomes in yeast. Nat. Genet. 39, 486–495 (2007). · DOI
  6. Warren, A. J. Molecular basis of the human ribosomopathy Shwachman-Diamond syndrome. Adv. Biol. Regul. 67, 109–127 (2018). · DOI
  7. Finch, A. J. et al. Uncoupling of GTP hydrolysis from eIF6 release on the ribosome causes Shwachman-Diamond syndrome. Genes Dev. 25, 917–929 (2011). · DOI
  8. Weis, F. et al. Mechanism of eIF6 release from the nascent 60S ribosomal subunit. Nat. Struct. Mol. Biol. 22, 914–919 (2015). · DOI
  9. Jaako, P. et al. eIF6 rebinding dynamically couples ribosome maturation and translation. Nat. Commun. 13, 1562 (2022). · DOI
  10. Donadieu, J. et al. Classification of and risk factors for hematologic complications in a French national cohort of 102 patients with Shwachman-Diamond syndrome. Haematologica 97, 1312–1319 (2012). · DOI
  11. Dror, Y. et al. Clonal evolution in marrows of patients with Shwachman-Diamond syndrome: a prospective 5-year follow-up study. Exp. Hematol. 30, 659–669 (2002). · DOI
  12. Parikh, S. et al. Acquired copy number neutral loss of heterozygosity of chromosome 7 associated with clonal haematopoiesis in a patient with Shwachman-Diamond syndrome. Br. J. Haematol. 159, 480–482 (2012). · DOI
  13. Minelli, A. et al. The isochromosome i(7)(q10) carrying c.258+2t>c mutation of the SBDS gene does not promote development of myeloid malignancies in patients with Shwachman syndrome. Leukemia 23, 708–711 (2009). · DOI
  14. Lee, S. et al. Somatic uniparental disomy mitigates the most damaging EFL1 allele combination in Shwachman-Diamond syndrome. Blood 138, 2117–2128 (2021). · DOI
  15. Kennedy, A. L. et al. Distinct genetic pathways define pre-malignant versus compensatory clonal hematopoiesis in Shwachman-Diamond syndrome. Nat. Commun. 12, 1334 (2021). · DOI · Paper page
  16. Pressato, B. et al. Deletion of chromosome 20 in bone marrow of patients with Shwachman-Diamond syndrome, loss of the EIF6 gene and benign prognosis. Br. J. Haematol. 157, 503–505 (2012). · DOI
  17. Valli, R. et al. Different loss of material in recurrent chromosome 20 interstitial deletions in Shwachman-Diamond syndrome and in myeloid neoplasms. Mol. Cytogenet. 6, 56 (2013). · DOI
  18. Tan, S. et al. Somatic genetic rescue of a germline ribosome assembly defect. Nat. Commun. 12, 5044 (2021).
  19. Sloan, K. E., Bohnsack, M. T. & Watkins, N. J. The 5S RNP couples p53 homeostasis to ribosome biogenesis and nucleolar stress. Cell Rep. 5, 237–247 (2013). · DOI
  20. Elghetany, M. T. & Alter, B. P. p53 protein overexpression in bone marrow biopsies of patients with Shwachman-Diamond syndrome has a prevalence similar to that of patients with refractory anemia. Arch. Pathol. Lab. Med. 126, 452–455 (2002). · DOI
  21. Tourlakis, M. E. et al. In vivo senescence in the Sbds-deficient murine pancreas: cell-type specific consequences of translation insufficiency. PLoS Genet. 11, e1005288 (2015). · DOI · Paper page
  22. Zambetti, N. A. et al. Deficiency of the ribosome biogenesis gene Sbds in hematopoietic stem and progenitor cells causes neutropenia in mice by attenuating lineage progression in myelocytes. Haematologica 100, 1285–1293 (2015). · DOI
  23. Xia, J. et al. Somatic mutations and clonal hematopoiesis in congenital neutropenia. Blood 131, 408–416 (2018). · DOI
  24. Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013). · DOI · Paper page
  25. Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020). · DOI · Paper page
  26. Kuhn, E. et al. TP53 mutations in serous tubal intraepithelial carcinoma and concurrent pelvic high-grade serous carcinoma–evidence supporting the clonal relationship of the two lesions. J. Pathol. 226, 421–426 (2012). · DOI
  27. Folkins, A. K. et al. A candidate precursor to pelvic serous cancer (p53 signature) and its prevalence in ovaries and fallopian tubes from women with BRCA mutations. Gynecol. Oncol. 109, 168–173 (2008). · DOI
  28. Fearon, E. R. & Vogelstein, B. A genetic model for colorectal tumorigenesis. Cell 61, 759–767 (1990). · DOI · Paper page
  29. Mercuri, A. et al. Immunophenotypic analysis of hematopoiesis in patients suffering from Shwachman–Bodian–Diamond syndrome. Eur. J. Haematol. 95, 308–315 (2015). · DOI
  30. Grinfeld, J. et al. Classification and personalized prognosis in myeloproliferative neoplasms. N. Engl. J. Med. 379, 1416–1430 (2018). · DOI
  31. Myers, K. C. et al. Myelodysplastic syndrome and acute myeloid leukemia in patients with Shwachman Diamond syndrome: a multicentre, retrospective, cohort study. Lancet Haematol. 7, e238–e246 (2020). · DOI
  32. Steele, C. D. et al. Signatures of copy number alterations in human cancer. Nature 606, 984–991 (2022). · DOI
  33. Light, N. et al. Germline TP53 mutations undergo copy number gain years prior to tumor diagnosis. Nat. Commun. 14, 77 (2023). · DOI
  34. Abascal, F. et al. Somatic mutation landscapes at single-molecule resolution. Nature 593, 405–410 (2021). · DOI · Paper page
  35. Machado, H. E. et al. Diverse mutational landscapes in human lymphocytes. Nature 608, 724–732 (2022). · DOI · Paper page
  36. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013). · DOI · Paper page
  37. Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020). · DOI · Paper page
  38. Wong, C. C., Traynor, D., Basse, N., Kay, R. R. & Warren, A. J. Defective ribosome assembly in Shwachman-Diamond syndrome. Blood 118, 4305–4312 (2011). · DOI
  39. Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014). · DOI · Paper page
  40. van Zeventer, I. A. et al. Evolutionary landscape of clonal hematopoiesis in 3,359 individuals from the general population. Cancer Cell 41, 1017–1031.e4 (2023). · DOI · Paper page
  41. Hecker, J. S. et al. CHIP and hips: clonal hematopoiesis is common in patients undergoing hip arthroplasty and is associated with autoimmune disease. Blood 138, 1727–1732 (2021). · DOI · Paper page
  42. Mayerhofer, C. et al. Clonal hematopoiesis in older patients with breast cancer receiving chemotherapy. J. Natl Cancer Inst. 115, 981–988 (2023). · DOI
  43. Cao, B. et al. Cancer-mutated ribosome protein L22 (RPL22/eL22) suppresses cancer cell survival by blocking p53-MDM2 circuit. Oncotarget 8, 90651–90661 (2017). · DOI
  44. Arzalluz-Luque, Á. et al. Mutant PRPF8 causes widespread splicing changes in spliceosome components in retinitis pigmentosa patient iPSC-derived RPE cells. Front. Neurosci. 15, 636969 (2021). · DOI
  45. Zemp, I. et al. CK1δ and CK1ε are components of human 40S subunit precursors required for cytoplasmic 40S maturation. J. Cell Sci. 127, 1242–1253 (2014).
  46. Ameur, A., Kloosterman, W. P. & Hestand, M. S. Single-molecule sequencing: towards clinical applications. Trends Biotechnol. 37, 72–85 (2019). · DOI
  47. Papaemmanuil, E. et al. Genomic classification and prognosis in acute myeloid leukemia. N. Engl. J. Med. 374, 2209–2221 (2016). · DOI · Paper page
  48. Gerstung, M. et al. Precision oncology for acute myeloid leukemia using a knowledge bank approach. Nat. Genet. 49, 332–340 (2017). · DOI
  49. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. https://doi.org/10.48550/arXiv.1303.3997 (2013). · DOI
  50. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009). · DOI · Paper page
  51. Jones, D. et al. cgpCaVEManWrapper: simple execution of CaVEMan in order to detect somatic single nucleotide variants in NGS data. Curr. Protoc. Bioinforma. 56, 15.10.1–15.10.18 (2016). · DOI
  52. Ellis, P. et al. Reliable detection of somatic mutations in solid tissues by laser-capture microdissection and low-input DNA sequencing. Nat. Protoc. 16, 841–871 (2021). · DOI
  53. Ye, K., Schulz, M. H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009). · DOI
  54. Loo, P. V. et al. Allele-specific copy number analysis of tumors. PNAS 107, 16910–16915 (2010). · DOI
  55. Hoang, D. T. et al. MPBoot: fast phylogenetic maximum parsimony tree inference and bootstrap approximation. BMC Evolut. Biol. 18, 11 (2018). · DOI
  56. Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041.e21 (2017). · DOI · Paper page
  57. Gori, K. & Baez-Ortega, A. sigfit: flexible Bayesian inference of mutational signatures. bioRxiv 372896 https://doi.org/10.1101/372896 (2020). · DOI

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Metadata

Authors
Machado, H. E., Øbro, N. F., Williams, N., Tan, S., Boukerrou, A. Z., Davies, M., Belmonte, M., Mitchell, E., Baxter, E. J., Mende, N., Clay, A., Ancliff, P., Köglmeier, J., Killick, S. B., Kulasekararaj, A., Meyer, S., Laurenti, E., Campbell, P. J., Kent, D. G., Nangalia, J., & Warren, A. J.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2023-08-22
Volume
14
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-023-40896-5
PMC
PMC10444798
Cited by
26
References
12
Language
en
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