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A systematic CRISPR screen defines mutational mechanisms underpinning signatures caused by replication errors and endogenous DNA damage

Nature Cancer volume 2, pages 643–657 (2021)Cite this article

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Abstract

Mutational signatures are imprints of pathophysiological processes arising through tumorigenesis. We generated isogenic CRISPR–Cas9 knockouts (∆) of 43 genes in human induced pluripotent stem cells, cultured them in the absence of added DNA damage and performed whole-genome sequencing of 173 subclones. ∆OGG1, ∆UNG, ∆EXO1, ∆RNF168, ∆MLH1, ∆MSH2, ∆MSH6, ∆PMS1 and ∆PMS2 produced marked mutational signatures indicative of them being critical mitigators of endogenous DNA modifications. Detailed analyses revealed mutational mechanistic insights, including how 8-oxo-2′-deoxyguanosine elimination is sequence context specific while uracil clearance is sequence context independent. Mismatch repair (MMR) deficiency signatures are engendered by oxidative damage (C > A transversions) and differential misincorporation by replicative polymerases (T > C and C > T transitions), and we propose a reverse template slippage model for T > A transversions. ∆MLH1, ∆MSH6 and ∆MSH2 signatures were similar to each other but distinct from ∆PMS2. Finally, we developed a classifier, MMRDetect, where application to 7,695 whole-genome-sequenced cancers showed enhanced detection of MMR-deficient tumors, with implications for responsiveness to immunotherapies.

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Fig. 1: Mutational consequences of DNA replicative/repair pathway gene knockouts.
Fig. 2: Safeguarding the genome from oxidative damage and cytosine deamination.
Fig. 3: Multiple endogenous sources of DNA damage managed by MMR.
Fig. 4: Gene-specific features of signatures of MMRd are recapitulated in other model systems.
Fig. 5: Mutational signature-based MMRd classifier, MMRDetect.
Fig. 6: Impact of experimental validation of cancer-derived mutational signatures on biological understanding and the development of clinical applications.

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Data availability

Raw sequence files are deposited at the European Genome-Phenome Archive with accession numbers EGAS00001000800 and EGAS00001000874. Mutation calls have been deposited at Mendeley: https://doi.org/10.17632/ymn3ykkmyx. hiPSCs can be obtained directly from the authors. The curated data are available for general browsing from our reference mutational signatures website, Signal (https://signal.mutationalsignatures.com). Age information relating to human patient samples is not publicly available as this could compromise privacy and lead to identification of the individuals. Publicly available genomic datasets reanalyzed here to compare the performance of MMRDetect and MSIseq are available from the European Genome-Phenome Archive (EGAS0001001178)72, http://dcc.icgc.org/pcawg/ (ref. 73), https://data.mendeley.com/datasets/2mn4ctdpxp/1 (ref. 74), https://resources.hartwigmedicalfoundation.nl/ (ref. 75) and the Genomics England Research Environment (main program version 8) via https://re.extge.co.uk/ovd/. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The R code used to generate results presented in Figs. 1–5 and the R source code of MMRDetect can be obtained from https://github.com/Nik-Zainal-Group/COMSIG_KO.git.

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Acknowledgements

We thank the Wellcome Sanger Institute Cellular Genetics and Phenotyping Facility for assistance and the CASM IT team, J. Foreman and G. Ping for assistance in carrying out and completing this project. We thank the COMSIG Consortium spearheaded by S. Jackson. In Cambridge, this work was funded by the Cancer Research UK (CRUK) Advanced Clinician Scientist Award (C60100/A23916), the Dr. Josef Steiner Cancer Research Award 2019, a Medical Research Council (MRC) Grant-in-Aid to the MRC Cancer Unit, the CRUK Pioneer Award, a Wellcome Strategic Award (WT101126) and Wellcome Sanger Institute faculty funding and supported by the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC-1215-20014) and UK Regenerative Medicine Platform (MR/R015724/1). The work of T.I.R. and J.S.C. was funded by a CRUK Centre grant (reference number C309/A25144). Support for the MMRDetect classifier was enabled by access to data and findings generated by the 100,000 Genomes Project. The 100,000 Genomes Project is managed by Genomics England (a wholly owned company of the Department of Health and Social Care), funded by the NIHR and NHS England. The Wellcome Trust, CRUK and the MRC have also funded research infrastructure. The 100,000 Genomes Project uses data provided by patients and collected by the National Health Service as part of their care and support. The views expressed are those of the author(s) and not necessarily those of the NIHR or Department of Health and Social Care. This publication and the underlying research were facilitated by data that were generated by the Hartwig Medical Foundation (HMF) and Center for Personalized Cancer Treatment (CPCT) in the Netherlands and the International Cancer Genome Consortium.

Author information

Authors and Affiliations

  1. Academic Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK

    Xueqing Zou, Gene Ching Chiek Koh, Arjun Scott Nanda, Andrea Degasperi, Cherif Badja, Sophie Momen, Jamie Young, Tauanne Dias Amarante, Rebecca Harris & Serena Nik-Zainal

  2. MRC Cancer Unit, University of Cambridge, Cambridge, UK

    Xueqing Zou, Gene Ching Chiek Koh, Arjun Scott Nanda, Andrea Degasperi, Cherif Badja, Sophie Momen, Tauanne Dias Amarante & Serena Nik-Zainal

  3. Wellcome Sanger Institute, Hinxton, UK

    Xueqing Zou, Gene Ching Chiek Koh, Andrea Degasperi, Katie Urgo, Chukwuma A. Agu, Cherif Badja, Celine Gomez, Wendy Bushell, Rebecca Harris, William C. Skarnes & Serena Nik-Zainal

  4. The Institute of Cancer Research, Chester Beatty Laboratories, London, UK

    Theodoros I. Roumeliotis & Jyoti S. Choudhary

  5. UCL Institute for Women’s Health, Great Ormond Street Hospital, London, UK

    Lucy Side

  6. Wessex Clinical Genetics Service, Princess Anne Hospital, Southampton, UK

    Lucy Side

  7. Southwest Thames Regional Genetics Service, St George’s University of London, London, UK

    Glen Brice

  8. Pediatrics Department, Doce de Octubre University Hospital, i+12 Research Institute, Madrid, Spain

    Vanesa Perez-Alonso

  9. Hereditary Cancer Laboratory, Doce de Octubre University Hospital, i+12 Research Institute, Madrid, Spain

    Daniel Rueda

  10. Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

    Josef Jiricny

  11. Institute of Biochemistry, ETH Zurich, Zurich, Switzerland

    Josef Jiricny

  12. William Harvey Research Institute, Queen Mary University of London, London, UK

    William C. Skarnes

  13. The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA

    John C. Ambrose, Prabhu Arumugam, Emma L. Baple, Marta Bleda, Freya Boardman-Pretty, Jeanne M. Boissiere, Christopher R. Boustred, Helen Brittain, Mark J. Caulfield, Georgia C. Chan, Clare E. H. Craig, Louise C. Daugherty, Anna de Burca, Andrew Devereau, Greg Elgar, Rebecca E. Foulger, Tom Fowler, Pedro Furió-Tarí, Adam Giess, Joanne M. Hackett, Dina Halai, Angela Hamblin, Shirley Henderson, James E. Holman, Tim J. P. Hubbard, Kristina ibáñez, Rob Jackson, Louise J. Jones, Dalia Kasperaviciute, Melis Kayikci, Athanasios Kousathanas, Lea Lahnstein, Kay Lawson, Sarah E. A. Leigh, Ivonne U. S. Leong, Javier F. Lopez, Fiona Maleady-Crowe, Joanne Mason, Ellen M. McDonagh, Loukas Moutsianas, Michael Mueller, Nirupa Murugaesu, Anna C. Need, Pter O’Donovan, Chris A. Odhams, Andrea Orioli, Christine Patch, Mariana Buongermino Pereira, Daniel Perez-Gil, Dimitris Polychronopoulos, John Pullinger, Tahrima Rahim, Augusto Rendon, Pablo Riesgo-Ferreiro, Tim Rogers, Mina Ryten, Kevin Savage, Kushmita Sawant, Richard H. Scott, Afshan Siddiq, Alexander Sieghart, Damian Smedley, Katherine R. Smith, Samuel C. Smith, Alona Sosinsky, William Spooner, Helen E. Stevens, Alexander Stuckey, Razvan Sultana, Mélanie Tanguy, Ellen R. A. Thomas, Simon R. Thompson, Carolyn Tregidgo, Arianna Tucci, Emma Walsh, Sarah A. Watters, Matthew J. Welland, Eleanor Williams, Katarzyna Witkowska, Suzanne M. Wood & Magdalena Zarowiecki

  14. Genomics England, London, UK

    Freya Boardman-Pretty, Mark J. Caulfield, Greg Elgar, Shirley Henderson, Kristina ibáñez, Louise J. Jones, Dalia Kasperaviciute, Ellen M. McDonagh, Loukas Moutsianas, Michael Mueller, Anna C. Need, Christine Patch, Damian Smedley, Katherine R. Smith, Alona Sosinsky, Ellen R. A. Thomas, Arianna Tucci, Katarzyna Witkowska & Suzanne M. Wood

Authors
  1. Xueqing Zou
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  2. Gene Ching Chiek Koh
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  3. Arjun Scott Nanda
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  4. Andrea Degasperi
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  5. Katie Urgo
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  6. Theodoros I. Roumeliotis
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  7. Chukwuma A. Agu
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  8. Cherif Badja
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  9. Sophie Momen
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  10. Jamie Young
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  11. Tauanne Dias Amarante
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  12. Lucy Side
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  13. Glen Brice
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  14. Vanesa Perez-Alonso
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  15. Daniel Rueda
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  16. Celine Gomez
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  17. Wendy Bushell
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  18. Rebecca Harris
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  19. Jyoti S. Choudhary
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  20. Josef Jiricny
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  21. William C. Skarnes
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  22. Serena Nik-Zainal
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Consortia

Genomics England Research Consortium

  • John C. Ambrose
  • , Prabhu Arumugam
  • , Emma L. Baple
  • , Marta Bleda
  • , Freya Boardman-Pretty
  • , Jeanne M. Boissiere
  • , Christopher R. Boustred
  • , Helen Brittain
  • , Mark J. Caulfield
  • , Georgia C. Chan
  • , Clare E. H. Craig
  • , Louise C. Daugherty
  • , Anna de Burca
  • , Andrew Devereau
  • , Greg Elgar
  • , Rebecca E. Foulger
  • , Tom Fowler
  • , Pedro Furió-Tarí
  • , Adam Giess
  • , Joanne M. Hackett
  • , Dina Halai
  • , Angela Hamblin
  • , Shirley Henderson
  • , James E. Holman
  • , Tim J. P. Hubbard
  • , Kristina ibáñez
  • , Rob Jackson
  • , Louise J. Jones
  • , Dalia Kasperaviciute
  • , Melis Kayikci
  • , Athanasios Kousathanas
  • , Lea Lahnstein
  • , Kay Lawson
  • , Sarah E. A. Leigh
  • , Ivonne U. S. Leong
  • , Javier F. Lopez
  • , Fiona Maleady-Crowe
  • , Joanne Mason
  • , Ellen M. McDonagh
  • , Loukas Moutsianas
  • , Michael Mueller
  • , Nirupa Murugaesu
  • , Anna C. Need
  • , Pter O’Donovan
  • , Chris A. Odhams
  • , Andrea Orioli
  • , Christine Patch
  • , Mariana Buongermino Pereira
  • , Daniel Perez-Gil
  • , Dimitris Polychronopoulos
  • , John Pullinger
  • , Tahrima Rahim
  • , Augusto Rendon
  • , Pablo Riesgo-Ferreiro
  • , Tim Rogers
  • , Mina Ryten
  • , Kevin Savage
  • , Kushmita Sawant
  • , Richard H. Scott
  • , Afshan Siddiq
  • , Alexander Sieghart
  • , Damian Smedley
  • , Katherine R. Smith
  • , Samuel C. Smith
  • , Alona Sosinsky
  • , William Spooner
  • , Helen E. Stevens
  • , Alexander Stuckey
  • , Razvan Sultana
  • , Mélanie Tanguy
  • , Ellen R. A. Thomas
  • , Simon R. Thompson
  • , Carolyn Tregidgo
  • , Arianna Tucci
  • , Emma Walsh
  • , Sarah A. Watters
  • , Matthew J. Welland
  • , Eleanor Williams
  • , Katarzyna Witkowska
  • , Suzanne M. Wood
  •  & Magdalena Zarowiecki

Contributions

S.N.-Z. and W.C.S. conceived of the study idea. S.N.-Z., X.Z., G.C.C.K., J.J. and W.C.S. wrote the paper. L.S., G.B., V.P.-A., D.R. and S.N.-Z. collected the clinical samples. G.C.C.K., K.U., T.I.R., C.A.A., W.B. and C.G. performed the laboratory work. X.Z., G.C.C.K., A.S.N., A.D., C.B., S.M., T.D.A., T.I.R., J.S.C. and S.N.-Z. performed data curation and formal analysis. R.H., W.B. and J.Y. performed administrative tasks.

Corresponding author

Correspondence to Serena Nik-Zainal.

Ethics declarations

Competing interests

S.N.-Z. holds patents on clinical algorithms of mutational signatures and, during completion of this project, served advisory roles for AstraZeneca, Artios Pharma and the Scottish Genomes Project.

Additional information

Peer review information Nature Cancer thanks Daniel Durocher and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Results of pilot study.

Three genes were selected for knockout (∆): MSH6, UNG and ATP2B4 (negative control). Two genotypes per gene were obtained and grown in culture to gauge reproducibility of signatures between different genotypes of a gene-knockout. These lines were cultured under normoxic (20%) and hypoxic (3%) states, for defined culture times of ~15, 30 or 45 days. Two single-cell subclones were derived for whole genome sequencing for each parental line (equivalent to four subclones per gene edit). One of the UNG genotypes appeared to be heterozygous, which was excluded in downstream analysis. a, Substitution burden for knockouts of ATP2B4, UNG and MSH6 under hypoxic and normoxic conditions as well as different culturing time. b, The cosine similarities between the mutational profile of each subclone and background signature of culture. c, Indel burden for knockouts of ATP2B4, UNG and MSH6 under hypoxic and normoxic conditions as well as different culturing time. d, The cosine similarities between the mutational profile of each subclone with background signature of culture. Overall, the differences between normoxic and hypoxic conditions were not marked, although normoxic conditions produced slightly more mutations. Time in culture made only a marginal, non-linear difference to burden of mutagenesis. Given the results of the pilot, weighing up the costs and risks associated with prolonged culture time (risk of infection, risk of selection, marked increase in cost of experimental reagents) with the minimal return in terms of mutation number, and also intending to minimize transitions between hypoxic to normoxic conditions while handling cell cultures, we opted to proceed with the full-scale study under normoxic conditions and for 15 days for the rest of study.

Source data

Extended Data Fig. 2 Detecting mutational consequences of knockouts in the absence of added external DNA damage.

a,b, Schematic illustration of potential components of background signature (a) and possible mutational consequences of the DNA repair gene knockouts for proteins that are critical mitigators of mutagenesis (b). c-e, Mutation burden of whole-genome-sequenced subclones of gene knockouts. c, Substitution, (d) indel and (e) double substitution. Bars represent the mean. Individual data points are shown in orange dots. In all comparative analyses, all gene knockouts were cultured for 15 days and only daughter subclones that were fully clonal (that is, clearly derived from a single cell) were included. N = 2~4, which is the number of clonal knockout subclones cultured under normoxic condition for 15 days (see Supplementary Table 2). f, 96-channel substitution mutation profiles of 173 gene knockout subclones.

Source data

Extended Data Fig. 3 Results of contrastive principal component analysis and t-SNE.

a, Contrastive principal component analysis (cPCA) was employed to discriminate knockout profiles from control profiles (∆ATP2B4). Each figure contains six different genes. Nine gene knockouts separate from the controls. Using this method, ∆ADH5 did not separate clearly from ∆ATP2B4, indicative of either having no signature or a weak signature. Dot colours indicate the repair/replicative pathway that each gene is involved: in black - control; green - MMR; orange – BER; dark purple – HR and HR regulation; light purple - checkpoint. Each dot represents a subclone. The number of subclones for each gene knockout (N = 2~4) can be found in Supplementary Table 2. b, The t-SNE algorithm was applied to discriminate the mutational profiles of gene knockouts from those of control knockouts. Gene knockouts that produce mutational signatures separate clearly from control subclones and other knockouts which do not have signatures. Subclones of the gene knockouts which produce signatures are clustered together, indicating consistency between subclones.

Source data

Extended Data Fig. 4 Oxidative damage-associated mutational signatures.

a, Relative mutation frequency of G>T/C>A in 256 possible channels which take two adjacent bases 5’ and 3’ of each mutated base (4×4×4×4=256) for ∆ATP2B4, ∆OGG1, a head and neck cancer with strong SBS18 and SBS18. b, Left: tSNE plot of tissue-specific mutational signature 18. Two groups are featured with predominant peaks at TGC>TTC/GCA>GAA (highlighted in green) and AGA>ATA/TCT>TAT (highlighted in purple), respectively. Right: heatmap of 21 tissue-specific mutational signatures at C>A. We compared experimental signatures to previously published cancer-derived signatures, focusing on 21 tissue-specific variations of Signature 18. Interestingly, we found two distinct groups of Signature 18. Signatures of ∆OGG1, cellular models and signatures derived from head and neck tumors, pancreas, myeloid, bladder, uterus, cervix, lymphoid tumors were most similar to each other, with the predominant G>T/C>A peak at TGC>TTC/GCA>GAA. By contrast, an alternative version of this signature with a predominant G>T/C>A peak at AGA>ATA/TCT>TAT was noted in colorectal, esophagus, stomach, bone, lung, CNS, breast, skin, prostate, liver, head and neck tumors (Signature Head_neck_G), ovary, biliary and kidney cancers. Indeed, there are many types of oxidative species which could fluctuate between tissues, variably affecting trinucleotides resulting in the variation observed in Signature 18.

Source data

Extended Data Fig. 5 Indel signatures and double substitution signatures.

a, 15-channel Indel signatures. b, 186-channel Indel signatures. c, Aggregated double substitution profile of ∆RNF168 and ∆EXO1.

Source data

Extended Data Fig. 6 Similarities between ∆EXO1, ∆RNF168 signatures and RefSig5 and results of analysis on transcriptional strand bias and distribution of mutations on replication timing domains.

a, Hierarchical clustering of cancer-derived reference signatures (RefSig) with ∆EXO1 and ∆RNF168 signatures. b, Hierarchical clustering of tissue-specific signature 5 with ∆EXO1 and ∆RNF168 signatures. c, Transcriptional strand bias in 9 gene knockouts. Pearson’s Chi-Squared test (chisq.test()) was used to calculate the p-value. P-value was corrected using p.adjust(). Unlike mutational signatures of environmental mutagens, we do not observe striking transcriptional strand bias in signatures generated by DNA repair gene knockouts, except for T>C generated by ∆EXO1 and ∆RNF168. Since transcriptional strand bias is largely induced by NER repairing DNA bulky adducts, lack of it indicates that most of the endogenous DNA damage is not particularly bulky or DNA-deforming. d, Distribution of mutation density across replication timing domains (separated into deciles) for signatures associated with different gene knockouts. Green bars indicate observed distribution. Blue lines indicate expected distribution with correction of trinucleotide density of each domain. Bars and error bars represent mean ± SD of bootstrapping replicates (n=100).

Source data

Extended Data Fig. 7 Putative outcomes of all possible base-base mismatches.

Outcomes from 12 possible base-base mismatches. The red and black strands represent lagging and leading strands, respectively. The arrowed strand is the nascent strand. The highlighted pathways are the ones that generate C>A (blue), C>T (red) and T>C mutations (green) in the ∆MSH2 mutational signature.

Extended Data Fig. 8 Distribution of G>T/C>A mutations in polyG tracts of ∆MSH2, ∆MSH6 and ∆MLH1.

a, Relative frequency of occurrence of G>T/C>A in polyG tracts. b, Occurrence of G>T/C>A in polyG tracts.

Source data

Extended Data Fig. 9 Gene-specific mutational signatures in MMR-deficiency.

Proportion of different mutation types of substitution (a) and indel (b) signatures for four MMR gene knockouts. c, The ratio of substitution and indel burden. d, Schematic interpretation of the relative mutation burdens of ∆MSH2 and ∆MSH6.

Source data

Extended Data Fig. 10 Development of MMRDetect.

(a)-(e) Distribution of the five parameters across IHC-determined MMR gene abnormal (orange) and MMR gene normal (green) samples. black dots and error bars represent mean ± SD of the paramenters. NAbnormal=79 samples (yellow); NNormal= 257 samples (green). a, Exposure of MMRd signatures. b, Cosine similarity between the substitution profile of cancer samples and that of MMR gene knockouts. c, Number of indels in repetitive regions. d, Cosine similarity between the profile of repeat-mediated deletions of cancer sample and that of knockout generated indel signatures, (e) the cosine similarity between the profile of repeat-mediated insertion of cancer sample and that of knockout generated indel signatures. P-values were calculated through two-sided Mann-Whitney test. f, Distribution of coefficients from 10-fold cross validation using training data set. Box plots denote median (horizontal line) and 25th to 75th percentiles (boxes). The lower and upper whiskers extend to 1.5× the inter-quartile range. N = 10 iterations. g, MMRDetect-calculated probabilities for 336 colorectal cancers. With cut-off of 0.7, 77 out of 336 were predicted to be MMR-deficient samples (probability < 0.7). Colour bars represent the MSI status determined by IHC staining: red – abnormal; blue – normal. Four samples with abnormal IHC staining have probabilities > 0.7, whilst 2 samples with normal IHC staining have probabilities < 0.7. The four samples were revealed to be false positive cases and the two samples were false negative ones for IHC staining through validation using MSIseq and seeking coding mutations in MMR genes. h, Distribution of the mutation number of repeat-mediated indels, MMRd signatures and non-MMRd signatures across four groups of samples: MMR-deficient samples determined by only MMRDetect (yellow), MMR-deficient samples determined by only MSIseq (purple), MMR-deficient samples determined by both MMRDetect and MSIseq (blue) and non-MMR-deficient samples determined by both MMRDetect and MSIseq (pink). P-values were calculated through two-sided Mann-Whitney test. Numbers of MMR-deficient samples determined by MMRDetect only (blue), MSIseq only (pink), both (yellow) and none (purple) are 34, 20, 587 and 6,718, respectively.

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Supplementary information

Supplementary Information

Supplementary Table 6.

Reporting Summary

Supplementary Tables

Supplementary Tables 1–5 and 7–15.

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Zou, X., Koh, G.C.C., Nanda, A.S. et al. A systematic CRISPR screen defines mutational mechanisms underpinning signatures caused by replication errors and endogenous DNA damage. Nat Cancer 2, 643–657 (2021). https://doi.org/10.1038/s43018-021-00200-0

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