Gaining insights into relevance across cancers based on mutation features of TP53 gene

The tumor suppressor gene TP53, one of the most frequently mutated genes, is recognized as the guardian of genome and can provide a significant barrier to neoplastic transformation and tumor progression. Traditional theory believes that TP53 mutations are equal among cancer types. However, to date, no study has explored the TP53 mutation profile from a holistic and systematic standpoint to discovery its relevance and feature with cancers. Mutation signature, an unbiased approach to identify the mutational processes, can be a potent indicator for exploring mutation-driven tumor occurrence and progression. In this research, several features such as hotspots, mutability and mutation signature of somatic TP53 mutations derived from 18 types of cancer tissues from cBioPortal were analyzed and manifested the organizational preference among cancers. Mutation signatures found in almost all cancer types were Signature 6 related to mismatch repair deficiency, and Signature 1 that reflects the natural decomposition of 5-methylcytosine into thymine associated with aging. Meanwhile, several signatures of TP53 mutations displayed tissue-selective. Mutations enriched in bladder, skin, lung cancer were associated with signatures of APOBEC activity (Signature 2 and 13), alkylating agents (Signature 11), and tobacco smoke (Signature 4), respectively. Moreover, Signature 4 and 29 associated with tobacco smoking or chewing found in lung, sarcoma, esophageal, and head and neck cancer may be related to their smoking history. In addition, several digestive cancers, including colorectal, stomach, pancreatic and esophageal cancers, showed the high correlation in context and mutation signature profiles. Our study suggests that the tissue-selective activity of mutational processes would reflect the tissue-specific enrichment of TP53 mutations and provides a new perspective to understand the relevance of diverse diseases based on the spectrum of TP53 mutations.     

AID expression in B-cell lymphomas causes accumulation of genomic uracil and a distinct AID mutational signature

The most common mutations in cancer are C to T transitions, but their origin has remained elusive. Recently, mutational signatures of APOBEC-family cytosine deaminases were identified in many common cancers, suggesting off-target deamination of cytosine to uracil as a common mutagenic mechanism. Here we present evidence from mass spectrometric quantitation of deoxyuridine in DNA that shows significantly higher genomic uracil content in B-cell lymphoma cell lines compared to non-lymphoma cancer cell lines and normal circulating lymphocytes. The genomic uracil levels were highly correlated with AID mRNA and protein expression, but not with expression of other APOBECs. Accordingly, AID knockdown significantly reduced genomic uracil content. B-cells stimulated to express endogenous AID and undergo class switch recombination displayed a several-fold increase in total genomic uracil, indicating that B cells may undergo widespread cytosine deamination after stimulation. In line with this, we found that clustered mutations (kataegis) in lymphoma and chronic lymphocytic leukemia predominantly carry AID-hotspot mutational signatures. Moreover, we observed an inverse correlation of genomic uracil with uracil excision activity and expression of the uracil-DNA glycosylases UNG and SMUG1. In conclusion, AID-induced mutagenic U:G mismatches in DNA may be a fundamental and common cause of mutations in B-cell malignancies.     

Interaction with the CCT chaperonin complex limits APOBEC3A cytidine deaminase cytotoxicity

The APOBEC3 cytidine deaminases are implicated as the cause of a prevalent somatic mutation pattern found in cancer genomes. The APOBEC3 enzymes act as viral restriction factors by mutating viral genomes. Mutation of the cellular genome is presumed to be an off‐target activity of the enzymes, although the regulatory measures for APOBEC3 expression and activity remain undefined. It is therefore difficult to predict circumstances that enable APOBEC3 interaction with cellular DNA that leads to mutagenesis. The APOBEC3A (A3A) enzyme is the most potent deaminase of the family. Using proteomics, we evaluate protein interactors of A3A to identify potential regulators. We find that A3A interacts with the chaperonin‐containing TCP‐1 (CCT) complex, a cellular machine that assists in protein folding and function. Importantly, depletion of CCT results in A3A‐induced DNA damage and cytotoxicity. Evaluation of cancer genomes demonstrates an enrichment of A3A mutational signatures in cancers with silencing mutations in CCT subunit genes. Together, these data suggest that the CCT complex interacts with A3A, and that disruption of CCT function results in increased A3A mutational activity.     

Integrative Analysis of Genome, 3D Genome,and Transcriptome Alterations of Clinical Lung Cancer Samples

Genomic studies of cancer cell alterations, such as mutations, copy number variations (CNVs), and translocations, greatly promote our understanding of the genesis and development of cancers. However, the 3D genome architecture of cancers remains less studied due to the complexity of cancer genomes and technical difficulties. To explore the 3D genome structure in clinical lung cancer, we performed Hi-C experiments using paired normal and tumor cells harvested from patients with lung cancer, combining with RNA sequenceing analysis. We demonstrated the feasibility of studying 3D genome of clinical lung cancer samples with a small number of cells (1 × 10⁴), compared the genome architecture between clinical samples and cell lines of lung cancer, and identified conserved and changed spatial chromatin structures between normal and cancer samples. We also showed that Hi-C data can be used to infer CNVs and point mutations in cancer. By integrating those different types of cancer alterations, we showed significant associations between CNVs, 3D genome, and gene expression. We propose that 3D genome mediates the effects of cancer genomic alterations on gene expression through altering regulatory chromatin structures. Our study highlights the importance of analyzing 3D genomes of clinical cancer samples in addition to cancer cell lines and provides an integrative genomic analysis pipeline for future larger-scale studies in lung cancer and other cancers.     

The origin and evolution of mutations in acute myeloid leukemia

Most mutations in cancer genomes are thought to be acquired after the initiating event, which may cause genomic instability and drive clonal evolution. However, for acute myeloid leukemia (AML), normal karyotypes are common, and genomic instability is unusual. To better understand clonal evolution in AML, we sequenced the genomes of M3-AML samples with a known initiating event (PML-RARA) versus the genomes of normal karyotype M1-AML samples and the exomes of hematopoietic stem/progenitor cells (HSPCs) from healthy people. Collectively, the data suggest that most of the mutations found in AML genomes are actually random events that occurred in HSPCs before they acquired the initiating mutation; the mutational history of that cell is "captured" as the clone expands. In many cases, only one or two additional, cooperating mutations are needed to generate the malignant founding clone. Cells from the founding clone can acquire additional cooperating mutations, yielding subclones that can contribute to disease progression and/or relapse.     

Diverse roles of RAD18 and Y-family DNA polymerases in tumorigenesis

Mutagenesis is a hallmark and enabling characteristic of cancer cells. The E3 ubiquitin ligase RAD18 and its downstream effectors, the ‘Y-family’ Trans-Lesion Synthesis (TLS) DNA polymerases, confer DNA damage tolerance at the expense of DNA replication fidelity. Thus, RAD18 and TLS polymerases are attractive candidate mediators of mutagenesis and carcinogenesis. The skin cancer-propensity disorder xeroderma pigmentosum-variant (XPV) is caused by defects in the Y-family DNA polymerase Pol eta (Polη). However it is unknown whether TLS dysfunction contributes more generally to other human cancers. Recent analyses of cancer genomes suggest that TLS polymerases generate many of the mutational signatures present in diverse cancers. Moreover biochemical studies suggest that the TLS pathway is often reprogrammed in cancer cells and that TLS facilitates tolerance of oncogene-induced DNA damage. Here we review recent evidence supporting widespread participation of RAD18 and the Y-family DNA polymerases in the different phases of multi-step carcinogenesis.     

A genome-wide view of mutation rate co-variation using multivariate analyses

Background  

While the abundance of available sequenced genomes has led to many studies of regional heterogeneity in mutation rates, the co-variation among rates of different mutation types remains largely unexplored, hindering a deeper understanding of mutagenesis and genome dynamics. Here, utilizing primate and rodent genomic alignments, we apply two multivariate analysis techniques (principal components and canonical correlations) to investigate the structure of rate co-variation for four mutation types and simultaneously explore the associations with multiple genomic features at different genomic scales and phylogenetic distances.     

Attribution of Cancer Origins to Endogenous,Exogenous, and Preventable Mutational Processes

Mutational processes in tumors create distinctive patterns of mutations, composed of neutral “passenger” mutations and oncogenic drivers that have quantifiable effects on the proliferation and survival of cancer cell lineages. Increases in proliferation and survival are mediated by natural selection, which can be quantified by comparing the frequency at which we detect substitutions to the frequency at which we expect to detect substitutions assuming neutrality. Most of the variants detectable with whole-exome sequencing in tumors are neutral or nearly neutral in effect, and thus the processes generating the majority of mutations may not be the primary sources of the tumorigenic mutations. Across 24 cancer types, we identify the contributions of mutational processes to each oncogenic variant and quantify the degree to which each process contributes to tumorigenesis. We demonstrate that the origination of variants driving melanomas and lung cancers is predominantly attributable to the preventable, exogenous mutational processes associated with ultraviolet light and tobacco exposure, respectively, whereas the origination of selected variants in gliomas and prostate adenocarcinomas is largely attributable to endogenous processes associated with aging. Preventable mutations associated with pathogen exposure and apolipoprotein B mRNA-editing enzyme activity account for a large proportion of the cancer effect within head-and-neck, bladder, cervical, and breast cancers. These attributions complement epidemiological approaches—revealing the burden of cancer driven by single-nucleotide variants caused by either endogenous or exogenous, nonpreventable, or preventable processes, and crucially inform public health strategies.     

Genetic progression and the waiting time to cancer

Cancer results from genetic alterations that disturb the normal cooperative behavior of cells. Recent high-throughput genomic studies of cancer cells have shown that the mutational landscape of cancer is complex and that individual cancers may evolve through mutations in as many as 20 different cancer-associated genes. We use data published by Sjöblom et al. (2006) to develop a new mathematical model for the somatic evolution of colorectal cancers. We employ the Wright-Fisher process for exploring the basic parameters of this evolutionary process and derive an analytical approximation for the expected waiting time to the cancer phenotype. Our results highlight the relative importance of selection over both the size of the cell population at risk and the mutation rate. The model predicts that the observed genetic diversity of cancer genomes can arise under a normal mutation rate if the average selective advantage per mutation is on the order of 1%. Increased mutation rates due to genetic instability would allow even smaller selective advantages during tumorigenesis. The complexity of cancer progression can be understood as the result of multiple sequential mutations, each of which has a relatively small but positive effect on net cell growth.     

Phosphatidylinositol-3-OH kinase or RAS pathway mutations in human breast cancer cell lines

Constitutive activation of the phosphatidylinositol-3-OH kinase (PI3K) and RAS signaling pathways are important events in tumor formation. This is illustrated by the frequent genetic alteration of several key players from these pathways in a wide variety of human cancers. Here, we report a detailed sequence analysis of the PTEN, PIK3CA, KRAS, HRAS, NRAS, and BRAF genes in a collection of 40 human breast cancer cell lines. We identified a surprisingly large proportion of cell lines with mutations in the PI3K or RAS pathways (54% and 25%, respectively), with mutants for each of the six genes. The PIK3CA, KRAS, and BRAF mutation spectra of the breast cancer cell lines were similar to those of colorectal cancers. Unlike in colorectal cancers, however, mutational activation of the PI3K pathway was mutually exclusive with mutational activation of the RAS pathway in all but 1 of 30 mutant breast cancer cell lines (P = 0.001). These results suggest that there is a fine distinction between the signaling activators and downstream effectors of the oncogenic PI3K and RAS pathways in breast epithelium and those in other tissues.     

Sequence and Structure Signatures of Cancer Mutation Hotspots in Protein Kinases

Protein kinases are the most common protein domains implicated in cancer, where somatically acquired mutations are known to be functionally linked to a variety of cancers. Resequencing studies of protein kinase coding regions have emphasized the importance of sequence and structure determinants of cancer-causing kinase mutations in understanding of the mutation-dependent activation process. We have developed an integrated bioinformatics resource, which consolidated and mapped all currently available information on genetic modifications in protein kinase genes with sequence, structure and functional data. The integration of diverse data types provided a convenient framework for kinome-wide study of sequence-based and structure-based signatures of cancer mutations. The database-driven analysis has revealed a differential enrichment of SNPs categories in functional regions of the kinase domain, demonstrating that a significant number of cancer mutations could fall at structurally equivalent positions (mutational hotspots) within the catalytic core. We have also found that structurally conserved mutational hotspots can be shared by multiple kinase genes and are often enriched by cancer driver mutations with high oncogenic activity. Structural modeling and energetic analysis of the mutational hotspots have suggested a common molecular mechanism of kinase activation by cancer mutations, and have allowed to reconcile the experimental data. According to a proposed mechanism, structural effect of kinase mutations with a high oncogenic potential may manifest in a significant destabilization of the autoinhibited kinase form, which is likely to drive tumorigenesis at some level. Structure-based functional annotation and prediction of cancer mutation effects in protein kinases can facilitate an understanding of the mutation-dependent activation process and inform experimental studies exploring molecular pathology of tumorigenesis.     

Genome Destabilizing Mutator Alleles Drive Specific Mutational Trajectories in Saccharomyces cerevisiae

In addition to environmental factors and intrinsic variations in base substitution rates, specific genome-destabilizing mutations can shape the mutational trajectory of genomes. How specific alleles influence the nature and position of accumulated mutations in a genomic context is largely unknown. Understanding the impact of genome-destabilizing alleles is particularly relevant to cancer genomes where biased mutational signatures are identifiable. We first created a more complete picture of cellular pathways that impact mutation rate using a primary screen to identify essential Saccharomyces cerevisiae gene mutations that cause mutator phenotypes. Drawing primarily on new alleles identified in this resource, we measure the impact of diverse mutator alleles on mutation patterns directly by whole-genome sequencing of 68 mutation-accumulation strains derived from wild-type and 11 parental mutator genotypes. The accumulated mutations differ across mutator strains, displaying base-substitution biases, allele-specific mutation hotspots, and break-associated mutation clustering. For example, in mutants of POLα and the Cdc13–Stn1–Ten1 complex, we find a distinct subtelomeric bias for mutations that we show is independent of the target sequence. Together our data suggest that specific genome-instability mutations are sufficient to drive discrete mutational signatures, some of which share properties with mutation patterns seen in tumors. Thus, in a population of cells, genome-instability mutations could influence clonal evolution by establishing discrete mutational trajectories for genomes.     

Mutational spectra of human cancer

The purpose of this review is to summarize the evidence that can be used to reconstruct the etiology of human cancers from mutations found in tumors. Mutational spectra of the tumor suppressor gene p53 (TP53) are tumor specific. In several cases, these mutational spectra can be linked to exogenous carcinogens, most notably for sunlight-associated skin cancers, tobacco-associated lung cancers, and aristolochic acid-related urothelial tumors. In the TP53 gene, methylated CpG dinucleotides are sequences selectively targeted by endogenous and exogenous mutagenic processes. Recent high-throughput sequencing efforts analyzing a large number of genes in cancer genomes have so far, for the most part, produced mutational spectra similar to those in TP53 but have unveiled a previously unrecognized common G to C transversion mutation signature at GpA dinucleotides in breast cancers and several other cancers. Unraveling the origin of these G to C mutations will be of importance for understanding cancer etiology.     

A data-driven approach for constructing mutation categories for mutational signature analysis

Mutational processes shape the genomes of cancer patients and their understanding has important applications in diagnosis and treatment. Current modeling of mutational processes by identifying their characteristic signatures views each base substitution in a limited context of a single flanking base on each side. This context definition gives rise to 96 categories of mutations that have become the standard in the field, even though wider contexts have been shown to be informative in specific cases. Here we propose a data-driven approach for constructing a mutation categorization for mutational signature analysis. Our approach is based on the assumption that tumor cells that are exposed to similar mutational processes, show similar expression levels of DNA damage repair genes that are involved in these processes. We attempt to find a categorization that maximizes the agreement between mutation and gene expression data, and show that it outperforms the standard categorization over multiple quality measures. Moreover, we show that the categorization we identify generalizes to unseen data from different cancer types, suggesting that mutation context patterns extend beyond the immediate flanking bases.