ABSTRACT Certain mutagens, including the APOBEC3 (A3) cytosine deaminase enzymes, can create multiple genetic changes in a single event. Activity of A3s results in striking ‘mutation

showers’ occurring near DNA breakpoints; however, less is known about the mechanisms underlying the majority of A3 mutations. We classified the diverse patterns of clustered mutagenesis in

tumor genomes, which identified a new A3 pattern: nonrecurrent, diffuse hypermutation (omikli). This mechanism occurs independently of the known focal hypermutation (kataegis), and is

associated with activity of the DNA mismatch-repair pathway, which can provide the single-stranded DNA substrate needed by A3, and contributes to a substantial proportion of A3 mutations

subvert DNA repair can be remarkably potent. Access through your institution Buy or subscribe This is a preview of subscription content, access via your institution ACCESS OPTIONS Access

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support SIMILAR CONTENT BEING VIEWED BY OTHERS MUTATIONAL SIGNATURE SBS8 PREDOMINANTLY ARISES DUE TO LATE REPLICATION ERRORS IN CANCER Article Open access 03 August 2020 MAPPING CLUSTERED

MUTATIONS IN CANCER REVEALS APOBEC3 MUTAGENESIS OF ECDNA Article Open access 09 February 2022 CELL CYCLE GENE ALTERATIONS ASSOCIATE WITH A REDISTRIBUTION OF MUTATION RISK ACROSS CHROMOSOMAL

DOMAINS IN HUMAN CANCERS Article 10 January 2024 DATA AVAILABILITY Whole-genome sequences from the TCGA project were available through the Cancer Genomics Hub repository (now superseded by

the NCI Genomic Data Commons; https://gdc.cancer.gov/). Corresponding SNP array data were downloaded from the GDC legacy portal (https://portal.gdc.cancer.gov/legacy-archive). WGS data from

the Hartwig Medical Foundation are available at https://www.hartwigmedicalfoundation.nl/en. The whole-exome sequencing data of TCGA cohort are available through the MC3 dataset at

https://gdc.cancer.gov/about-data/publications/mc3-2017. Data generated by the analyses in this study are available in the Supplementary Tables. CODE AVAILABILITY Code to generate clustered

mutation calls was implemented in Python (version 3.6) and R environments (version 3.6). Relevant packages are biopython (version 1.73) and numpy (version 1.15.4) for Python, and Biostrings

(2.52.0), VariantAnnotation (1.30.1) and GenomicRanges (1.36.0) for R. Code is available at https://github.com/davidmasp/hyperclust. Statistical analysis of the data was performed using

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ACKNOWLEDGEMENTS We thank the members of the Genome Data Science group and B. Lehner for comments and discussions. This work was funded by the ERC Starting Grant HYPER-INSIGHT (757700) and

the Spanish Ministry of Economy and Competitiveness (REGIOMUT, grant number BFU2017-89833-P). The results published here are in whole or part based on data generated by the TCGA Research

Network (https://www.cancer.gov/tcga). This publication and the underlying research are partly facilitated by the Hartwig Medical Foundation and Center for Personalized Cancer Treatment

(CPCT), which have generated, analyzed and made available data for this research. D.M.P. was funded by a Severo Ochoa FPI fellowship (MCIU/Fondo Social Europeo; BES-2017-079820). F.S. was

funded by the ICREA Research Professor program and is a member of the EMBO Young Investigator Program. The authors acknowledge support from the Severo Ochoa Centre of Excellence program to

IRB Barcelona. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain David

Mas-Ponte & Fran Supek * Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain Fran Supek Authors * David Mas-Ponte View author publications You can also search for

this author inPubMed Google Scholar * Fran Supek View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS F.S. and D.M.-P. conceptualized the study

and devised the methodology. D.M.-P. carried out the formal analysis and the investigation, operated the software and performed data visualization. D.M.-P. and F.S. wrote and edited the

draft manuscript. F.S. acquired the funding and supervised the study. CORRESPONDING AUTHOR Correspondence to Fran Supek. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no

competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED

DATA EXTENDED DATA FIG. 1 DETECTING CLUSTERED MUTATIONS AND SIMULATING PROCESSES THAT GENERATE CLUSTERED MUTATIONS. A, Method to determine significant mutation clustering using HyperClust. A

baseline distribution is generated by shuffling mutations within 1 Mbp windows multiple times (R1, R2, …, Rn) to loci with matching trinucleotide contexts. For every mutation, the observed

intermutational distance to its nearest neighbour (nIMD) is compared with distributions of expected IMDs (from randomized data) to determine a local FDR (lfdr). Thresholding by lfdr yields

clustered mutation calls (blue). B, Overview of study. C, Precision-recall curves for models in Fig. 1a, derived from simulated data with spiked-in mutation clusters: kataegis (top; with

five mutations per cluster at an average 600 bp pairwise distance) or omikli_M (bottom; two mutations at 101 bp). Two examples of high mutation burden tumors (TCGA-AP-A0LD, TCGA-AP-A0LE)

were used to generate the background mutation distributions. D, E, Testing accuracy of mutation cluster calling methods using simulated data. Points represent randomized tumor samples into

which spiked-in mutation clusters were introduced. Samples are ordered according to total mutation burden (panel D). Columns show different performance metrics: F1 score, precision, and

recall, all at lfdr=20%. Rows represent different types of spiked-in mutation clusters (IMD distributions plotted in panel e, where kataegis have five mutations and omikli_K/M/O two

mutations. Boxplots compare cluster calling methods, including implementations of some previous methodologies (details in Methods). The “strand-clonality-lfdr” (blue) is the HyperClust

method used throughout our work. F, G, Poisson mixture modelling (related with Fig. 1d) of the number of mutations per cluster, showing relative likelihood (panel F) of models with

increasing number of components and the density functions (panel G) of a model with two Poisson components. solid line represents mean and dashed lines the 95% C.I. H, Number of mutation

events per tumor sample (_x_ axis, n) per local hypermutation type (rows), either the A3 context TCW>K mutations, or the remaining mutations (columns). EXTENDED DATA FIG. 2

TETRANUCLEOTIDE CONTEXT SUGGESTS A ROLE FOR THE A3A ENZYME IN GENERATING OMIKLI AND A3B IN KATAEGIS MUTATIONS. A, C, Ratios of the YTCA (A3A-like) and RTCA (A3B-like) mutation frequencies

suggest differential mutagenic activity of A3A versus A3B enzymes in cancer samples. The C>T and the C>G changes in the two A3 contexts are shown in a pan-cancer analysis (panel A) and

broken down by cancer type (panel C). At least 100 TCW mutations of a certain type across all tumor samples in a tissue were required to perform analyses on that tissue (number of mutations

in brackets). Error bars are the bootstrap 95% C.I. of the ratio. KICH and THCA cancer types are not shown due to low overall number of A3-context mutations. B, Across multiple cancer

types, omikli shows a tendency towards A3A-like, lower RTCA/YTCA-ratios than does kataegis. Difference tested by Fisher’s exact test (per tumor type), two-tailed; p-values were adjusted for

multiple testing. Dashed line is FDR=20%. Lower odds ratios (<1) denote relative enrichment of YTCA (A3A-like) mutations in omikli compared to kataegis; see schematic above plot. EXTENDED

DATA FIG. 3 ASSOCIATION OF CLUSTERED MUTATION RATES WITH REPLICATION TIME (RT). A, RT association per cancer type. Number of mutations per RT bin: A3 context (top row) and the non-A3

control context at C:G nucleotide pairs (bottom row). RT bins are ordered from the latest-replicating quartile to the earliest-replicating quartile; mutation rates are shown relative to the

latest RT bin. Enrichments are not shown when the mutation count was lower than 10. B, Trinucleotide composition of the human reference genome in four RT bins, normalized to the latest RT

quartile (leftmost point). The A3 trinucleotide contexts (TCW, green) are similarly abundant in the late and in the early-replicating regions of the genome. C, D, Enrichment of A3-context

kataegis clusters, considering only RT (C), or jointly considering RT, mRNA levels and the H3K36me3 histone mark levels (D); points are coefficients from negative binomial regression, and

error bars are 95% C.I. E, Mutation rates in genomic bins with different CpG density (determined per 10 kb segment), stratified by RT quartiles. _y_ axis shows mutation densities relative to

the first bin (‘t1’, lowest tertile by CpG content). F, Spearman correlation between mRNA expression of A3A, A3B and MMR genes, and the TCW context enrichment of clustered mutations in a

tumor. Error bars are 95% C.I. from the Fisher transformation of the correlation coefficient. G, Association of A3 mutation burden (clustered and unclustered) with copy number alterations of

MMR genes. Significance by a two-tailed Mann-Whitney test, comparing tumor samples with neutral (0) versus gain/amplification (+1 and +2) states (blue stars, showing p-values according to

legend), and independently, comparing samples with neutral (0) versus loss (−1 and −2) states (purple stars). P-values were not adjusted. EXTENDED DATA FIG. 4 SIMULATIONS ESTIMATE POWER TO

DETECT MUTATION CLUSTERS AND DECONVOLUTE THEIR IMD DISTRIBUTIONS. A, B, An analysis of somatic hypermutation (SHM) events in lymphoid cancers suggests length of MMR excision tracts in human

cells. The distance from the initiating AID mutation (here, WNCYN>N context) to the flanking mutation introduced by error-prone MMR (here, any mutation at a A:T pair) is plotted, in known

SHM off-target regions (blue) and, as a control, in intergenic regions (red) (panel A). A statistically significant enrichment is seen in the bins of the distance to central AID mutation

(_x_ axis) between 400–1000 nt (panel B). Numbers above/below bars are p-values by Chi-square test on the standardized residuals. C, Gamma mixture modelling of the IMD distributions.

Log-likelihood values for different number of components when modelling IMD of the A3 kataegis and omikli mutations. D, The alpha and beta parameters of the three fitted gamma distributions

(‘comp.1’, ‘comp.2’ and ‘comp. 3’) approximately match the alpha and beta parameters expected from simulated distributions with IMD at 30 bp, 800 bp and 200 bp, respectively. E, F,

Simulations using spiked-in clustered mutations into genomes obtained by randomizing and subsampling mutations from MSI-H hypermutated tumors (panel E) and other hypermutators (panel F),

with the goal of determining the recall (or sensitivity; _y_ axis) of recovering mutation clusters at various global mutation burdens (_x_ axis). Dashed line is a loess fit and shaded area

is its 95% C.I. Vertical lines are residuals of the fit. G, Difference between MSI and MSS tumor samples in the absolute burden of clustered A3 _omikli_ mutations; significance by

Mann-Whitney test (two-tailed). EXTENDED DATA FIG. 5 VALIDATION ANALYSES USING INDEPENDENT GENOMIC DATA SETS. A–C, Fitting a Poisson distribution mixture to the number of mutations per

cluster in the Hartwig Medical Foundation (HMF) dataset. The near-maximum log likelihood (LL) is obtained with two components (panel C) and the increase to three components is not

statistically supported; p-values are from a two-sided bootstrap test. D, E, The relative density of A3 context (left) clustered mutations is higher in MSS (MMR-proficient) than in MSI

(MMR-deficient) samples of the same tumor type (left column) in the HMF data. The difference is smaller for the non-A3, control context (right). Significance by Mann-Whitney (two-tailed), n

is the number of samples, *** is p < 0.001. Numbers show fold-difference between MSS and MSI samples. The ‘other A3 tissues’ are lung, head-and-neck, skin, pancreas and bladder cancer. F,

In HMF data, the A3-context _omikli_ clustered mutations are enriched in tumors with amplified MMR genes; significance by Mann-Whitney test (two-tailed) comparing the neutral (0) versus the

gain states (+1 and +2, considered jointly); n is the number of samples. G, In HMF data, A3-context _omikli_ are enriched in early replicating, H3K36me3-marked genomic regions; error bars

are 95% C.I. H, Intermutational distance distributions for kataegis (top) and omikli (bottom) A3 context mutations in the HMF data. Dashed lines show peaks of the simulated distributions

(Fig. 2) with segment lengths of 25 bp (green), 200 bp (purple) and 800 bp (orange). I, J, Whole-exome sequences in the TCGA data show an excess of A3 context (TCW) mutation fraction in MSS

compared to MSI cancers (panel I), and an excess of TCW mutations at distances <1000 bp, normalized to longer distances, in MSS over MSI samples (panel J). ‘MSI-exp’ (_n_ = 152) denotes

the experimentally established MSI-H status while ‘MSI-pred’ (_n_ = 18) is the MSI status predicted using machine learning (ref. 61), ‘nonMSI’ (_n_ = 5,661) is neither of these cases.

EXTENDED DATA FIG. 6 CONTRIBUTION OF THE _OMIKLI_ AND THE _KATAEGIS_ MECHANISMS TO THE UNCLUSTERED A3 MUTATION BURDEN IN VARIOUS TISSUES. A, The omikli mechanism generates many unclustered

mutations (‘A3-O’) in various cancer types. B, The kataegis mechanism generates comparatively few unclustered mutations (‘A3-K’). Panels show the fit (red line) of the unclustered A3 burden

(_y_ axis) to the clustered A3 burden (_x_ axis), (see Methods). Error bars are 95% prediction intervals at x=0, and at x = mean burden of A3 clustered mutations for that cancer type.

Horizontal dashed lines are the predicted numbers of unclustered A3 mutations at those two points (for clarity also shown in blue/green bars next to each plot). Fits use robust regression

(rlm function in R). For visual clarity, only the part of the plot up to the mean of unclustered mutation burden plus a margin is shown, however the fit uses all data points (that is tumor

samples) including ones not visualized. EXTENDED DATA FIG. 7 MECHANISMS UNDERLYING A3 CLUSTERED MUTATIONS GENERATE MANY IMPACTFUL CHANGES, AFFECTING DISEASE GENES. A, Coding regions in the

human genome are enriched for CpG dinucleotides (NCG), but not with the A3-context TCW trinucleotides, compared to random expectation. B, Enrichment of mutations in exons _versus_ introns

(estimate of selection strength, _x_ axis) and the enrichment in intergenic regions versus introns (estimate of redistribution of mutations towards regions containing genic DNA, _y_ axis;

flipped). The comparison of mutagenic agents against APOBEC was performed for selected tissues, matching the relevant tissue with the particular mutagen (tumor samples listed in

Supplementary Table 7). Error bars are 95% C.I. from negative binomial regression; numbers in parenthesis are the tally of mutations. C, The differential functional impact of the tested

mutagens across replication time (RT) bins. Left: total length of coding sequences (CDS) in the late and early RT bins, shaded by the RT sextiles that were merged to create the two bins

(where 1 is the latest and 6 is the earliest RT). Middle: expected number of cancer gene CDS-affecting mutations in an average tumor sample (same sets of samples, genes and mutations as in

Fig. 5a; _y_ axis) for the late versus early RT bin (_x_ axis), for various mutagens (colors); error bars are s.e.m. Right: fold-difference between the functional impact at the late versus

early bin, for various mutagen types. D, E, The functional impact density (FID) of various mutational processes in a set of cell-essential genes (panel D) and neurodegenerative

disease-associated genes (panel E). Slope shows the fraction of impactful genetic changes i.e. those affecting the CDS of at least one gene in the set. Points show the expected number of

impactful changes resulting from a mutational process, on average, in a tumor genome affected by that mutational process. Error bars are s.e.m. ‘APOBEC-O4’ is A3 mutagenesis in omikli-rich

tumors. ‘APOBEC-K2’ is A3 mutagenesis in kataegis-rich tumors. EXTENDED DATA FIG. 8 ASSOCIATIONS BETWEEN GENIC MUTATIONS AND GLOBAL BURDEN OF CLUSTERED MUTATIONS. A, Associations between

A3-context TCW>K mutations in coding regions of each cancer gene, and the global burden of A3 kataegis (top left) or omikli (middle left) and their interaction term (bottom left). Right

panel is same as middle-left panel, but showing only the significant genes, with labels. Volcano plots show logistic regression coefficients (transformed to odds ratio) on the _x_ axis and

the log FDR on the _y_ axis. Genes that bore coding mutations in at least three tumor samples were tested. B, Number of TCW sites in a gene coding sequence (CDS; _x_ axis) predicts the

association of cancer gene mutations (_y_ axis) with A3 omikli burden (bottom) but not with A3 kataegis burden (top). Error bands are 95% C.I. of the linear fit. C, Same association analysis

as panel A but for the control, non-A3 context VCN>K mutations in the gene CDS. D, Early RT cancer genes are more affected by A3 mutagenesis. Cancer genes were stratified into RT

quartiles (_x_ axis) and logistic regression coefficient (log odds ratio, _y_ axis) linking A3 _omikli_ burden with the presence of a mutation in the CDS of any cancer gene in that RT bin

was determined. Error bars are 95% C.I. from logistic regression (on n=593 tumor samples). SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary Note REPORTING SUMMARY

SUPPLEMENTARY TABLES Supplementary Tables 1–10 RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Mas-Ponte, D., Supek, F. DNA mismatch repair promotes

APOBEC3-mediated diffuse hypermutation in human cancers. _Nat Genet_ 52, 958–968 (2020). https://doi.org/10.1038/s41588-020-0674-6 Download citation * Received: 22 August 2019 * Accepted: 30

June 2020 * Published: 03 August 2020 * Issue Date: September 2020 * DOI: https://doi.org/10.1038/s41588-020-0674-6 SHARE THIS ARTICLE Anyone you share the following link with will be able

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