Analytical methods for inferring functional effects of single base pair substitutions in human cancers

Mutation Space of Spatially Conserved Amino Acid Sites in Proteins

The mutation space of spatially conserved (MSSC) amino acid residues is a protein structural quantity developed and described in this work. The MSSC quantifies how many mutations and which different mutations, i.e., the mutation space, occur in each amino acid site in a protein. The MSSC calculates the mutation space of amino acids in a target protein from the spatially conserved residues in a group of multiple protein structures. Spatially conserved amino acid residues are identified based on their relative positions in the protein structure. The MSSC examines each residue in a target protein, compares it to the residues present in the same relative position in other protein structures, and uses physicochemical criteria of mutations found in each conserved spatial site to quantify the mutation space of each amino acid in the target protein. The MSSC is analogous to scoring each site in a multiple sequence alignment but in three-dimensional space considering the spatial location of residues instead of solely the order in which they appear in a protein sequence. MSSC analysis was performed on example cases, and it reproduces the well-known observation that, regardless of secondary structure, solvent-exposed residues are more likely to be mutated than internal ones. The MSSC code is available on GitHub: "https://github.com/Cantu-Research-Group/Mutation_Space".

YTHDC1 gene polymorphisms and Wilms tumor susceptibility in Chinese children: A five-center case-control study

Wilms tumor is a common pediatric tumor with abundant genetic drivers. YTHDC1 is an important reader of the N6-methyladenosine modification that widely regulates eukaryotic transcripts. YTHDC1 has been associated with the occurrence and development of some tumors. However, this is the first study on YTHDC1 gene polymorphisms and Wilms tumor susceptibility. In brief, we conducted a five-center case-control study to explore the associations between YTHDC1 polymorphisms (rs2293596 T > C, rs2293595 T > C, and rs3813832 T > C) and Wilms tumor susceptibility in Chinese children. A total of 404 cases and 1198 controls were successfully genotyped using TaqMan real-time PCR. Odds ratios (ORs) and 95% confidence intervals (CIs) were used as the evaluation indicators. We found that children with the 2-3 risk genotypes were more likely to develop Wilms tumor than those with the 0-1 risk genotypes (adjusted OR = 1.28, 95% CI = 1.01-1.62, P = 0.042). However, no other statistically significant results were found in this research study. The combined effect of YTHDC1 polymorphisms significantly increases Wilms tumor susceptibility. Our results need to be verified in different populations after increasing the sample size and controlling for confounding factors.

Role of genomic instability in human carcinogenesis

IMPACT STATEMENT

This review provides various genetic and cell line data previously published in a way to explain how cellular stress can lead into genetic instability.

Syntool: A Novel Region-Based Intolerance Score to Single Nucleotide Substitution for Synonymous Mutations Predictions Based on 123,136 Individuals

BACKGROUND

Synonymous mutation is the single nucleotide change that does not cause an amino acid change but can affect the rate and efficiency of translation. So recent increase in our knowledge has revealed a substantial contribution of synonymous mutations to human disease risk and other complex traits. Nevertheless, there are still rarely synonymous mutation prediction methods.

METHODS

Nonsynonymous and synonymous coding SNPs show similar likelihood and effect size of human disease association. Here we defined synonymous and missense variation as single nucleotide substitution variation. And then we evaluated the intolerance of genic transcripts to single nucleotide substitution variation based on gnomAD 123136 individuals. After regressing all variations on common variations, we defined residuals of regression model as every genomics region intolerance scores.

RESULTS

We constructed a total of 24799 nonoverlapped region-based intolerance score by their intolerance to single nucleotide substitution variation (Syntool). The results show that Syntool score can discriminate synonymous disease causing mutations in Human Gene Mutation Database (HGMD Professional) and ClinVar database much better than others. Taken together, this study provides a novel prediction system for synonymous mutations, called Syntool, which could be helpful in identifying candidate synonymous disease causing mutations.

FAS rs2234767 and rs1800682 polymorphisms jointly contributed to risk of colorectal cancer by affecting SP1/STAT1 complex recruitment to chromatin

FAS rs2234767 (−1377 G>A), rs1800682 (−670 A>G) and FASLG rs763110 (−844 C>T) promoter polymorphisms can influence transcriptional activities of the genes and thus multiple tumors susceptibility. To investigate their association with risk of colorectal cancer (CRC), the three SNPs were genotyped in 878 cases and 884 controls and the results showed that the FAS rs2234767 and rs1800682 were in a high linkage disequilibrium (LD) with each other (D’ = 0.994) and jointly contributed to an increased risk of CRC (without vs. with rs2234767 GG/rs1800682 AA genotypes, adjusted OR = 1.30, 95% CI = 1.05 − 1.61). In vivo ChIP assays evaluated the effect of rs2234767 and rs1800682 on recruitment of SP1 and STAT1, respectively, to chromatin. The results showed SP1 interacting specifically with STAT1 recruited to their respective motifs for transcriptional activation. The mutant alleles rs2234767 A and rs1800682 G jointly affected coupled SP1 and STAT1 recruitment to chromatin. The interplay between SP1 and STAT1 was critical for the functional outcome of rs2234767 and rs1800682 in view of their high LD. In conclusion, the FAS rs2234767 and rs1800682 polymorphisms were in high LD with each other, and they jointly contributed to an increased risk of CRC by altering recruitment of SP1/STAT1 complex to the FAS promoter for transcriptional activation.

Dipeptide analysis of p53 mutations and evolution of p53 family proteins

p53 gain-of-function mutations are similar to driver mutations in cancer genes, with both promoting tumorigenesis. Most previous studies focused on residues lost by mutations, providing information related to a dominantly-negative effect. However, to understand gain-of-function mutations, it is also important to investigate what are the distributions of residues gained by mutations. We compile available p53/p63/p73 protein sequences and construct a non-redundant dataset. We analyze the amino acid and dipeptide composition of p53/p63/p73 proteins across evolution and compare them with the gain/loss of amino acids and dipeptides in human p53 following cancer-related somatic mutations. We find that the ratios of amino acids gained via somatic mutations during evolution to those lost through p53 cancer mutations correlate with the ratios found in single nucleotide polymorphisms in the human proteome. The dipeptide mutational gain/loss ratios are inversely correlated with those observed over p53 evolution but tend to follow the increasing p63/p73-like dipeptide propensities. We successfully simulated the p53 cancer mutation spectrum using the dipeptide composition across the p53 family accounting for the likelihood of mutations in p53 codons. The results revealed that the p53 mutation spectrum is dominated not only by p53 evolution but also by reversal of evolution to a certain degree. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

Integrative annotation of variants from 1092 humans: application to cancer genomics

Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, "motif-breakers"). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

Genic intolerance to functional variation and the interpretation of personal genomes

A central challenge in interpreting personal genomes is determining which mutations most likely influence disease. Although progress has been made in scoring the functional impact of individual mutations, the characteristics of the genes in which those mutations are found remain largely unexplored. For example, genes known to carry few common functional variants in healthy individuals may be judged more likely to cause certain kinds of disease than genes known to carry many such variants. Until now, however, it has not been possible to develop a quantitative assessment of how well genes tolerate functional genetic variation on a genome-wide scale. Here we describe an effort that uses sequence data from 6503 whole exome sequences made available by the NHLBI Exome Sequencing Project (ESP). Specifically, we develop an intolerance scoring system that assesses whether genes have relatively more or less functional genetic variation than expected based on the apparently neutral variation found in the gene. To illustrate the utility of this intolerance score, we show that genes responsible for Mendelian diseases are significantly more intolerant to functional genetic variation than genes that do not cause any known disease, but with striking variation in intolerance among genes causing different classes of genetic disease. We conclude by showing that use of an intolerance ranking system can aid in interpreting personal genomes and identifying pathogenic mutations.

Predicting the functional impact of protein mutations: application to cancer genomics

As large-scale re-sequencing of genomes reveals many protein mutations, especially in human cancer tissues, prediction of their likely functional impact becomes important practical goal. Here, we introduce a new functional impact score (FIS) for amino acid residue changes using evolutionary conservation patterns. The information in these patterns is derived from aligned families and sub-families of sequence homologs within and between species using combinatorial entropy formalism. The score performs well on a large set of human protein mutations in separating disease-associated variants (∼19 200), assumed to be strongly functional, from common polymorphisms (∼35 600), assumed to be weakly functional (area under the receiver operating characteristic curve of ∼0.86). In cancer, using recurrence, multiplicity and annotation for ∼10 000 mutations in the COSMIC database, the method does well in assigning higher scores to more likely functional mutations ('drivers'). To guide experimental prioritization, we report a list of about 1000 top human cancer genes frequently mutated in one or more cancer types ranked by likely functional impact; and, an additional 1000 candidate cancer genes with rare but likely functional mutations. In addition, we estimate that at least 5% of cancer-relevant mutations involve switch of function, rather than simply loss or gain of function.

Germline fitness-based scoring of cancer mutations

A key goal in cancer research is to find the genomic alterations that underlie malignant cells. Genomics has proved successful in identifying somatic variants at a large scale. However, it has become evident that a typical cancer exhibits a heterogenous mutation pattern across samples. Cases where the same alteration is observed repeatedly seem to be the exception rather than the norm. Thus, pinpointing the key alterations (driver mutations) from a background of variations with no direct causal link to cancer (passenger mutations) is difficult. Here we analyze somatic missense mutations from cancer samples and their healthy tissue counterparts (germline mutations) from the viewpoint of germline fitness. We calibrate a scoring system from protein domain alignments to score mutations and their target loci. We show first that this score predicts to a good degree the rate of polymorphism of the observed germline variation. The scoring is then applied to somatic mutations. We show that candidate cancer genes prone to copy number loss harbor mutations with germline fitness effects that are significantly more deleterious than expected by chance. This suggests that missense mutations play a driving role in tumor suppressor genes. Furthermore, these mutations fall preferably onto loci in sequence neighborhoods that are high scoring in terms of germline fitness. In contrast, for somatic mutations in candidate onco genes we do not observe a statistically significant effect. These results help to inform how to exploit germline fitness predictions in discovering new genes and mutations responsible for cancer.

Predicting disease-associated substitution of a single amino acid by analyzing residue interactions

BACKGROUND

The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.

RESULTS

We found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively.

CONCLUSIONS

The satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.

Genomic instability--an evolving hallmark of cancer

Genomic instability is a characteristic of most cancers. In hereditary cancers, genomic instability results from mutations in DNA repair genes and drives cancer development, as predicted by the mutator hypothesis. In sporadic (non-hereditary) cancers the molecular basis of genomic instability remains unclear, but recent high-throughput sequencing studies suggest that mutations in DNA repair genes are infrequent before therapy, arguing against the mutator hypothesis for these cancers. Instead, the mutation patterns of the tumour suppressor TP53 (which encodes p53), ataxia telangiectasia mutated (ATM) and cyclin-dependent kinase inhibitor 2A (CDKN2A; which encodes p16INK4A and p14ARF) support the oncogene-induced DNA replication stress model, which attributes genomic instability and TP53 and ATM mutations to oncogene-induced DNA damage.

Loss of post-translational modification sites in disease

Understanding and predicting molecular cause of disease is one of the major challenges for biology and medicine. One particular area of interest continues to be computational analyses of disease-associated amino acid substitutions. To this end, various studies have been performed to identify molecular functions disrupted by disease-causing mutations. Here, we investigate the influence of disease-associated mutations on post-translational modifications. In particular, we study the loss of modification target sites as a consequence of disease mutation. We find that about 5% of disease-associated mutations may affect known modification sites, either partially (4%) of fully (1%), compared to about 2% of putatively neutral polymorphisms. Most of the fifteen post-translational modification types analyzed were found to be disrupted at levels higher than expected by chance. Molecular functions and physiochemical properties at sites of disease mutation were also compared to those of neutral polymorphisms involved in the process of post-translational modification site disruption. Disease-associated mutations in the neighborhood of post-translationally modified sites were found to be enriched in mutations that change polarity, charge, and hydrophobicity of the wild-type amino acids. Overall, these results further suggest that disruption of modification sites is an important but not the major cause of human genetic disease.