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RETRACTED ARTICLE: Mutation pattern is an influential factor on functional mutation rates in cancer
© Du et al. 2016
- Received: 11 June 2015
- Accepted: 3 February 2016
- Published: 9 February 2016
The Retraction Note to this article has been published in Cancer Cell International 2017 17:67
Mutation rates are consistently varied in cancer genome and play an important role in tumorigenesis, however, little has been known about their function potential and impact on the distribution of functional mutations. In this study, we investigated genomic features which affect mutation pattern and the function importance of mutation pattern in cancer.
Somatic mutations of clear-cell renal cell carcinoma, liver cancer, lung cancer and melanoma and single nucleotide polymorphisms (SNPs) were intersected with 54 distinct genomic features. Somatic mutation and SNP densities were then computed for each feature type. We constructed 2856 1-Mb windows, in which each row (1-Mb window) contains somatic mutation, SNP densities and 54 feature vectors. Correlation analyses were conducted between somatic mutation, SNP densities and each feature vector. We also built two random forest models, namely somatic mutation model (CSM) and SNP model to predict somatic mutation and SNP densities on a 1-Kb scale. The relation of CSM and SNP scores was further analyzed with the distributions of deleterious coding variants predicted by SIFT and Mutation Assessor, non-coding functional variants evaluated with FunSeq 2 and GWAVA and disease-causing variants from HGMD and ClinVar databases.
We observed a wide range of genomic features which affect local mutation rates, such as replication time, transcription levels, histone marks and regulatory elements. Repressive histone marks, replication time and promoter contributed most to the CSM models, while, recombination rate and chromatin organizations were most important for the SNP model. We showed low mutated regions preferentially have higher densities of deleterious coding mutations, higher average scores of non-coding variants, higher fraction of functional regions and higher enrichment of disease-causing variants as compared to high mutated regions.
Somatic mutation densities vary largely across cancer genome, mutation frequency is a major indication of function and influence on the distribution of functional mutations in cancer.
- Clear-cell renal cell carcinoma
- Liver cancer
- Lung cancer
- Mutation pattern
- Random forest
- Functional variants
Cancer is a malignant disease as the result of the accumulation of somatic mutations (base pair substitutions, insertions, deletions, rearrangements and copy number changes) and the disruption of functions of critical genes and pathways in normal cells. Over the past 10 years, the rapid development and wide application of sequencing technology have enabled a full detection of somatic mutations in cancer genome. The big projects, such as The Cancer Genome Atlas and International Cancer Genome Consortium projects, have sequenced more than 25 thousand cancer genomes and exomes and provided tremendous mutation data, which facilitates a broad evaluation of mutation patterns and their roles in cancer initiation and development [1, 2]. Studies have consistently shown that somatic mutation rates are not constant across cancer genome and a variety of genomic properties influence local mutation densities, for instance, mutation frequency is increased close to breakpoints of structural rearrangements . Mutagenesis is also highly affected by genomic features such as replication timing [4, 5], transcription levels  and chromatin organizations  in various cancer types.
It’s well accepted that somatic mutations play a pivotal role in carcinogenesis, however the extent to which mutation frequency affects cancer formation and development isn’t completely understood. For example, tumor cells with enhanced mutation frequency are prone to accumulation of driver mutations that confer a growth advantage to tumor cells and therefore are likely to develop cancer . Hypermutated cancer genome possesses the prevalent signatures in genes which are critical to cancer initiation and progression [9, 10]. Moreover, recurrently mutated genes in a cohort of patients are regarded as cancer-driving genes under positive selection in cancers [6, 11]. However, few studies have been conducted on the function potential of mutation spectrum and its relation with functional somatic mutations in cancer. In this study, we characterized mutation patterns of four cancer types, including ccRCC, liver cancer, lung adenocarcinoma and melanoma. We observed a wide range of genomic features which affect local mutation rates and showed the importance of mutation frequency with respect to functionalities of somatic mutations. Low mutated regions have higher densities of deleterious mutations, higher average scores of non-coding variants, higher fraction of functional regions, and higher enrichment of disease-causing variants from HGMD and ClinVar databases than high mutated regions, supporting that mutation frequency is an important indicator of function and exerts a great impact on the distribution of functional mutations in cancer genome.
The somatic mutation profile in cancer
Cancer somatic mutation (CSM) and SNP random forest models
Functionality and mutation frequency
In this study, we have characterized the mutation spectrum in four cancer types and observed a wide range of genomic features that contribute to somatic mutation variations across cancer genome. The most influential features are replication time, transcription levels, repressive, active histone marks and regulatory elements. In line with many studies [4–6, 16], we found late replicated genes are more mutable as compared to early replicated ones, the mechanisms underlying this phenomenon might be explained in two ways. First of all, exhaustion of dNTP in the late stages of DNA replication might cause increase of single strand DNA regions which are more susceptible to mutagenesis [17, 18]; Secondly, mutation repair systems might erode in the late replicated genes, leading to lack of efficient repair of mutation lesions . Another feature associated with elevated mutation rate is low expressed genes. High transcription might reduce number of mutations through transcription-coupled repair (TCR), which would repair more DNA lesions along with global genome repair (GGR) than GGR could operate alone in low transcribed regions . TCR also in part accounts for mutation frequency variations among repressive, active histone marks, exons, CDS, UTR and introns. Regions, such as repressive condensed chromatin and introns, are subject to increased mutation rates, which could be due to more active TCR in highly transcribed open chromatin, CDS, UTR and exons. Regulatory elements like promoters show reduced local densities of somatic mutations, probably due to the integrity of nucleotide excision repair pathway consisting of global genome repair and TCR, which guarantees the efficient removal of mutation lesions . Lastly, we found that recombination rates positively correlate with somatic mutation and SNP densities, which is in agreement with Lercher’s study  but contrasts with that obtained by Renjamin’s study . In particular, recombination rates are a major influential factor on the SNP density, which is mostly attributed to mutagenesis of recombination rate and faulty repair of the double-strand breaks that initiate recombination . Consistent with mutation pattern in cancers, we found that repressive histone marks, promoter and replication time contribute most to the CSM models, features like recombination rate and chromatin organizations are most important for the SNP model.
Next, we asked whether mutation rate variations are a byproduct of mutation repair systems or represent cancer mutation selection and function in cancer. Here we showed mutation frequencies are linked to the distribution of functional mutations in cancers. Low mutated regions tend to enrich functional mutations, including deleterious coding mutations, functional non-coding mutations and disease-causing mutations, suggesting their importance in the formation of functionalities of somatic mutations. Another evidence that further support this idea is low mutated regions possess strikingly higher enrichment of functional regions, such as CDS, exons, UTR, splicing sites of protein coding genes, cancer-related miRNAs and cancer driver genes as compared to high mutated regions, which explains why low mutated regions are prone to form functional mutations. Currently, many studies prefer to emphasize the importance of hypermutation in cancer initiation and development [8, 19], and hypermutation is an indicator of positive selection in cancer genes and multiple computational methods have been developed to detect them [6, 11, 22]. Our study show that hypomutated regions are mutation constraints and associated to functions in cancer genome, which should draw more attention and work in the future.
Taken together, somatic mutation densities vary largely across cancer genome, replication time, transcription levels, chromatin modifications and regulatory elements are among the features which most affect local mutation rates. To a large extent, mutation frequency is an indication of function and influence on the distribution of functional mutations in cancer.
Somatic variants were generated by whole genome sequencing of paired cancer and normal tissues and obtained from three studies: 2,011,261 variants from 25 melanoma patients , 1,845,976 variants from 24 lung adenocarcinoma patients, and 881,136 variants from 88 liver cancer patients  and 71,424 variants from 14 paired ccRCC and normal samples .Variants described as “substitution” or “indel” were both collected and are referred to collectively as mutations in the text. Germ line mutation data including 38,248,779 SNPs (single nucleotide polymorphism) were obtained from the 1000 genomes project  (http://www.1000genomes.org). Disease-associated variants data come from ClinVar (Version 2014/03/03, 55,689 variants)  and HGMD  (Version 2014/04/14, 166,768 variants) databases which are two common curations of variants related to human inherited diseases, coding variants were removed in this study, forming 6045 and 13,108 disease-implicated variants in the non-coding genome.
Genome-wide data resources
Human genome annotation were obtained from Gencode V21, including protein coding genes, exons, introns, UTR, non-coding exons (ncExon) ect . Evolutionarily conserved bases with phast Cons score greater than 117 were identified through alignment of 46 mammalian genomes with human . Evolutionarily conserved structures (ECS) are RNA secondary structures predicted with a nouvel pipeline based on RNAz and SISSIz in Smith MA’ study . Promoters generated by the Gerstein lab are regulatory regions 2.5 Kb from transcription start sites (TSS) . Genome-wide mapping of histone acetylation and methylation data of CD4+ T cell line were produced by ChIP-seq in Wang’s  and Barski’s study  respectively, all coordinates conversion from hg18 assembly to hg19 was performed with the UCSC Lift Over program . Conserved TFBS (conserved transcription factor binding sites, cTFBS) were generated through aligning mouse and rat genomes with human . Replication time data were obtained from Hepg2, Imr90, K562 and Bg02 cell lines for liver cancer, lung cancer, ccRCC and melanoma respectively (http://genome.ucsc.edu/) ENCODE, ‘Repli-seq track’ . Genome-wide replication timing was mapped to protein coding genes and lncRNAs, the (G1b + S1)/(S4 + G2) ratio, early-to-late ratio, was determined for each protein coding gene and lncRNA. Genes that have a ratio greater than 1 or less than 1 were defined as early or late replicated genes respectively. Recombination rates (RR) were obtained from International HapMap Project (http://hapmap.ncbi.nlm.nih.gov/) and averaged over successive 1-Kb windows in human genome . 1-Kb windows that have an average RR above 4.0 were regarded as high RR regions (RRH), while low RR regions are 1-Kb windows with recombination rate less than 0.5 (RRL). GC content denotes the percentage of G or C nucleotides per 1-Kb window. 1-Kb windows that have greater than 50 % or less than 30 % GC coverage are considered as high (GCH) or low GC regions (GCL) respectively .
RNA-seq data in sra format generated by sequencing 6 Hek293T cell lines were downloaded from NCBI (GSE55572)  for expression analysis in ccRCC. Read alignment was conducted with TopHat2 release 2.0.13 . As for other cancer types, RNA-seq data in bam format were acquired from Hepg2, A549 and Nhek cell lines for liver cancer, lung cancer and melanoma respectively . Read coverage was determined with bedtools v2.22.1 for lncRNAs and protein coding genes . The number of reads per Kilobase per million reads (RPKM) was computed and averaged from three cell samples for each protein coding gene and lncRNA. Genes whose RPKM >20 or <0.25 were defined as high and low expressed respectively.
Cancer micro RNAs are a manual curation of mammalian miRNAs that have been experimentally characterized and actively involved in various cancers . Cancer census genes are 547 cancer-driving genes annotated in COSMIC v71 (catalogue of somatic mutations in cancer) .
Construction of 1-Mb windows and correlation analyses
We used 1-Mb window strategy to construct 1-Mb windows for correlation analyses between SNP, cancer somatic mutation densities and genetic features as well as fitting Random forest models. Non-overlapping 1-Mb windows were formed across human genome, cancer mutations and SNPs were mapped into them and the number of somatic mutations and SNPs were counted for each 1-Mb window. Genome-wide replication timing was mapped into 1-Mb windows and the (G1b+S1)/(S4+G2) ratio was computed for each 1-Mb window. Read coverage was determined with bedtools v2.22.1 for each 1-Mb window, exons from Gencode V21 were intersected with 1-Mb windows and the length of exons was then calculated for each 1-Mb window. The number of reads per Kilobase per million reads (RPKM) was computed and averaged from three cell samples for each 1-Mb window. Recombination rates (RR) were averaged over successive 1-Mb windows in the human genome. Regarding other features, the number of bases covered by each feature was calculated for each 1-Mb window. As a number of 1-Mb windows are lack of coverage of features and mutation information, partial 1-Mb windows were discarded, including 1-Mb windows defined as telomere, centromere, stalk, pericentromere, 1-Mb windows which are all undefined bases. The whole chromosome Y was ruled out from this study too, due to its consistently low mutation rates caused by gender bias. In total, 224.3 Mb regions were abandoned in this study, forming 2856 1-Mb windows and 56 columns corresponding to cancer somatic mutation density, SNP density and 54 features ranging from conserved regions, promoters to histone modifications. Correlation analyses between SNP, cancer somatic mutation densities and each feature were performed in R.
Random forest model
The SNP and cancer somatic mutation (CSM) random forest (RF) models were constructed with the R random Forest package . In the RF models, we used 2856 1-Mb windows constructed above and cancer mutation density (CSM model), SNP density (SNP model) as response variables and 54 genomic features as predictor variables to build two RF models, CSM and SNP models. All predictor values were plus one and log scaled to reduce the great variation of vectors. The number of trees was set to 500, mtry was set to 20 for CSM model and 15 for SNP model, all other parameters were set to default values. Model calibration and validation were described in the Additional file 2.
For CSM and SNP score prediction, we used the same 1-Mb window strategy as in the model building, however, the 1 Mb-window was slided across the human genome with a step size of 1 Kb. 1 Mb windows overlapping telomere, centromere, stalk or pericentromere regions, 1 Mb windows which are all undefined bases and chromosome Y were removed from the annotation data, resulting in 2,832,687 row annotations. The CSM and SNP scores were predicted using the two RF models for each 1 Mb window and averaged on a 1-Kb window scale.
Correlation analyses between CSM, SNP scores and deleterious coding mutations, GWAVA and FunSeq 2 non-coding scores, disease-causing variants
Coding mutations mainly came from two sources for ccRCC: whole genome sequencing of 14 paired ccRCC samples and exome sequencing of 325 paired ccRCC samples from TCGA . The coding mutations of other cancer types were obtained from the same sources as described in the section of “Mutation data”. Their functional impacts were predicted by SIFT , and Mutation assessor  respectively, variants were regarded as deleterious based on the following criteria: SIFT score <0.05, and Mutation Assessor socre >1.9. 70,659 ccRCC, 881,130 liver cancer, 1,623,242 lung cancer and 2,011,261 melanoma non-coding variants were scored with FunSeq 2  (http://funseq2.gersteinlab.org/) and GWAVA  (https://www.sanger.ac.uk/sanger/StatGen_Gwava) respectively, all the parameters were set to default. Deleterious coding mutations, non-coding variants with GWAVA and FunSeq 2 scores and disease-causing variants from HGMD and ClinVar databases were mapped into 1-Kb windows which have average CSM and SNP scores. 1-Kb windows were then sorted based on SNP and CSM scores and divided into non-overlapping 200-Mb intervals respectively. For each 200-Mb interval, the following values were computed, including the average densities of deleterious coding mutations, disease-causing variants, average GWAVA and FunSeq 2 scores, average CSM and SNP scores. Correlation analyses were conducted in R between densities of deleterious coding mutations, disease-causing variants and average CSM and SNP scores.
Data were presented as mean, statistical differences between groups were computed with the Chi squared test (chisq.test) or Wilcoxon rank sum test (wilcox.test), correlation analysis (cor.test) was conducted in R, P < 0.05 was regarded statistically significant and the null hypothesis was rejected.
JL was in charge of building random forest models, model validation, prediction of deleterious coding mutations with SIFT and Mutation assessor and scoring of noncoding variants with FunSeq 2 and GWAVA. Chuance Du analyzed and interpreted the results, Xiaoyuan WU collected cancer mutations and genomic features from different sources. CD and JL drafted the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
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