Exome Analysis Reveals Differentially Mutated Gene Signatures of Stage,Grade and Subtype in Breast Cancers

Breast cancers exhibit highly heterogeneous molecular profiles. Although gene expression profiles have been used to predict the risks and prognostic outcomes of breast cancers, the high variability of gene expression limits its clinical application. In contrast, genetic mutation profiles would be more advantageous than gene expression profiles because genetic mutations can be stably detected and the mutational heterogeneity widely exists in breast cancer genomes. We analyzed 98 breast cancer whole exome samples that were sorted into three subtypes, two grades and two stages. The sum deleterious effect of all mutations in each gene was scored to identify differentially mutated genes (DMGs) for this case-control study. DMGs were corroborated using extensive published knowledge. Functional consequences of deleterious SNVs on protein structure and function were also investigated. Genes such as ERBB2, ESP8, PPP2R4, KIAA0922, SP4, CENPJ, PRCP and SELP that have been experimentally or clinically verified to be tightly associated with breast cancer prognosis are among the DMGs identified in this study. We also identified some genes such as ARL6IP5, RAET1E, and ANO7 that could be crucial for breast cancer development and prognosis. Further, SNVs such as rs1058808, rs2480452, rs61751507, rs79167802, rs11540666, and rs2229437 that potentially influence protein functions are observed at significantly different frequencies in different comparison groups. Protein structure modeling revealed that many non-synonymous SNVs have a deleterious effect on protein stability, structure and function. Mutational profiling at gene- and SNV-level revealed differential patterns within each breast cancer comparison group, and the gene signatures correlate with expected prognostic characteristics of breast cancer classes. Some of the genes and SNVs identified in this study show high promise and are worthy of further investigation by experimental studies.     

Convergence of mutation and epigenetic alterations identifies common genes in cancer that predict for poor prognosis

Background

The identification and characterization of tumor suppressor genes has enhanced our understanding of the biology of cancer and enabled the development of new diagnostic and therapeutic modalities. Whereas in past decades, a handful of tumor suppressors have been slowly identified using techniques such as linkage analysis, large-scale sequencing of the cancer genome has enabled the rapid identification of a large number of genes that are mutated in cancer. However, determining which of these many genes play key roles in cancer development has proven challenging. Specifically, recent sequencing of human breast and colon cancers has revealed a large number of somatic gene mutations, but virtually all are heterozygous, occur at low frequency, and are tumor-type specific. We hypothesize that key tumor suppressor genes in cancer may be subject to mutation or hypermethylation.

Methods and Findings

Here, we show that combined genetic and epigenetic analysis of these genes reveals many with a higher putative tumor suppressor status than would otherwise be appreciated. At least 36 of the 189 genes newly recognized to be mutated are targets of promoter CpG island hypermethylation, often in both colon and breast cancer cell lines. Analyses of primary tumors show that 18 of these genes are hypermethylated strictly in primary cancers and often with an incidence that is much higher than for the mutations and which is not restricted to a single tumor-type. In the identical breast cancer cell lines in which the mutations were identified, hypermethylation is usually, but not always, mutually exclusive from genetic changes for a given tumor, and there is a high incidence of concomitant loss of expression. Sixteen out of 18 (89%) of these genes map to loci deleted in human cancers. Lastly, and most importantly, the reduced expression of a subset of these genes strongly correlates with poor clinical outcome.

Conclusions

Using an unbiased genome-wide approach, our analysis has enabled the discovery of a number of clinically significant genes targeted by multiple modes of inactivation in breast and colon cancer. Importantly, we demonstrate that a subset of these genes predict strongly for poor clinical outcome. Our data define a set of genes that are targeted by both genetic and epigenetic events, predict for clinical prognosis, and are likely fundamentally important for cancer initiation or progression.     

Computational Screening and Molecular Dynamic Simulation of Breast Cancer Associated Deleterious Non-Synonymous Single Nucleotide Polymorphisms in TP53 Gene

Breast cancer is one of the most common cancers among the women around the world. Several genes are known to be responsible for conferring the susceptibility to breast cancer. Among them, TP53 is one of the major genetic risk factor which is known to be mutated in many of the breast tumor types. TP53 mutations in breast cancer are known to be related to a poor prognosis and chemo resistance. This renders them as a promising molecular target for the treatment of breast cancer. In this study, we present a computational based screening and molecular dynamic simulation of breast cancer associated deleterious non-synonymous single nucleotide polymorphisms in TP53. We have predicted three deleterious coding non-synonymous single nucleotide polymorphisms rs11540654 (R110P), rs17849781 (P278A) and rs28934874 (P151T) in TP53 with a phenotype in breast tumors using computational tools SIFT, Polyphen-2 and MutDB. We have performed molecular dynamics simulations to study the structural and dynamic effects of these TP53 mutations in comparison to the wild-type protein. Results from our simulations revealed a detailed consequence of the mutations on the p53 DNA-binding core domain that may provide insight for therapeutic approaches in breast cancer.     

Genetic Profiling of Breast Cancer with and Without Preexisting Metabolic Disease

Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death among women. Various mechanisms are involved in the initiation and progression of breast cancer. Metabolic dysregulation has been associated with increasing breast cancer incidence and mortality. However, little is known about how metabolic disease regulates the development and progression of breast cancer at the molecular level. Here, using a hybridization capture-based panel including 124 cancer-associated genes, we performed targeted next-generation sequencing of tumor tissues and matched blood samples from 20 postmenopausal patients with primary breast cancer, in which 6 cases suffered from preexisting metabolic disorders including hypertension, type 2 diabetes, and coronary heart disease. We took only the protein-altering variants and identified 170 somatic mutations of 59 genes. Among these, 40 mutated genes were found in the metabolic disease group, and 33 mutated genes were found in the non–metabolic disease group. Importantly, nonsynonymous mutations of 26 genes (MSH3, BRAF, MLH3, MTOR, DDR2, ALK, etc.) were uniquely present in the metabolic disease group. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed to investigate biological functions and key pathways of somatic mutations. TP53, PIK3CA, and PTEN were the top three commonly mutated genes at a higher frequency compared with the Cancer Genome Atlas (TCGA) data, and several novel but infrequent mutations in other genes were also found. Although further studies are required to validate these variants, our results are the first to suggest a specific molecular profile of breast cancer with preexisting metabolic disease.     

The ATM gene and breast cancer: is it really a risk factor?

The genetic determinants for most breast cancer cases remain elusive. Whilst mutations in BRCA1 and BRCA2 significantly contribute to familial breast cancer risk, their contribution to sporadic breast cancer is low. In such cases genes frequently altered in the general population, such as the gene mutated in Ataxia telangiectasia (AT), ATM may be important risk factors. The initial interest in studying ATM heterozygosity in breast cancer arose from the findings of epidemiological studies of AT families in which AT heterozygote women had an increased risk of breast cancer and estimations that 1% of the population are AT heterozygotes. One of the clinical features of AT patients is extreme cellular sensitivity to ionising radiation. This observation, together with the finding that a significant proportion of breast cancer patients show an exaggerated acute or late normal tissue reactions after radiotherapy, has lead to the suggestion that AT heterozygosity plays a role in radiosensitivity and breast cancer development. Loss of heterozygosity in the region of the ATM gene on chromosome 11, has been found in about 40% of sporadic breast tumours. However, screening for ATM mutations in sporadic breast cancer cases, showing or not adverse effects to radiotherapy, has not revealed the magnitude of involvement of the ATM gene expected. Their size and the use of the protein truncation test to identify mutations limit many of these studies. This latter parameter is critical as the profile of mutations in AT patients may not be representative of the ATM mutations in other diseases. The potential role of rare sequence variants within the ATM gene, sometimes reported as polymorphisms, also needs to be fully assessed in larger cohorts of breast cancer patients and controls in order to determine whether they represent cancer and/or radiation sensitivity predisposing mutations.     

Breast cancer gene discovery

Many important advances have been made in the past decade in understanding breast cancer at the molecular level, and two important high-penetrance breast cancer genes--BRCA1 and BRCA2--have been identified. However, germline mutations in these two genes are responsible for only a minority (approximately 5%) of all breast carcinomas, and the genes responsible for the majority of breast cancer cases remain to be identified. There is evidence that there are additional high-to-moderate-penetrance breast cancer susceptibility genes but, given the high degree of molecular heterogeneity in breast carcinomas, it is likely that each of these genes is responsible for only a subset of cases. There are also many candidate low-penetrance breast cancer genes and many more are likely to be identified. In addition to germline, and somatic, sequence alterations, epigenetic changes in many genes are likely to play an important role in the pathobiology of breast cancer. Recently developed genomic technologies and the completion of the human genome sequence provide us with powerful tools to identify novel candidate breast cancer genes that could play an important role in breast tumourigenesis.     

Recognition of DNA double strand breaks by the BRCA1 tumor suppressor network

DNA double-strand breaks (DSBs) occur in response to both endogenous and exogenous genotoxic stress. Inappropriate repair of DSBs can lead to either loss of viability or to chromosomal alterations that increase the likelihood of cancer development. In strong support of this assertion, many cancer predisposition syndromes stem from germline mutations in genes involved in DNA DSB repair. Among the most prominent of such tumor suppressor genes are the Breast Cancer 1 and Breast Cancer 2 genes (BRCA1 and BRCA2), which are mutated in familial forms of breast and ovarian cancer. Recent findings implicate BRCA1 as a central component of several distinct macromolecular protein complexes, each dedicated to distinct elements of DNA DSB repair and tumor suppression. Emerging evidence has shed light on some of the molecular recognition processes that are responsible for targeting BRCA1 and its associated partners to DNA and chromatin directly flanking DSBs. These events are required for BRCA1-dependent DNA repair and tumor suppression. Thus, a detailed temporal and spatial knowledge of how breaks are recognized and repaired has profound implications for understanding processes related to the genesis of malignancy and to its treatment.     

The ATM gene and breast cancer: is it really a risk factor?

The genetic determinants for most breast cancer cases remain elusive. Whilst mutations in BRCA1 and BRCA2 significantly contribute to familial breast cancer risk, their contribution to sporadic breast cancer is low. In such cases genes frequently altered in the general population, such as the gene mutated in Ataxia telangiectasia (AT), ATM may be important risk factors. The initial interest in studying ATM heterozygosity in breast cancer arose from the findings of epidemiological studies of AT families in which AT heterozygote women had an increased risk of breast cancer and estimations that 1% of the population are AT heterozygotes. One of the clinical features of AT patients is extreme cellular sensitivity to ionising radiation. This observation, together with the finding that a significant proportion of breast cancer patients show an exaggerated acute or late normal tissue reactions after radiotherapy, has lead to the suggestion that AT heterozygosity plays a role in radiosensitivity and breast cancer development. Loss of heterozygosity in the region of the ATM gene on chromosome 11, has been found in about 40% of sporadic breast tumours. However, screening for ATM mutations in sporadic breast cancer cases, showing or not adverse effects to radiotherapy, has not revealed the magnitude of involvement of the ATM gene expected. Their size and the use of the protein truncation test to identify mutations limit many of these studies. This latter parameter is critical as the profile of mutations in AT patients may not be representative of the ATM mutations in other diseases. The potential role of rare sequence variants within the ATM gene, sometimes reported as polymorphisms, also needs to be fully assessed in larger cohorts of breast cancer patients and controls in order to determine whether they represent cancer and/or radiation sensitivity predisposing mutations.     

Identification of candidate genes for lung cancer somatic mutation test kits

Over the past three decades, mortality from lung cancer has sharply and continuously increased in China, ascending to the first cause of death among all types of cancer. The ability to identify the actual sequence of gene mutations may help doctors determine which mutations lead to precancerous lesions and which produce invasive carcinomas, especially using next-generation sequencing (NGS) technology. In this study, we analyzed the latest lung cancer data in the COSMIC database, in order to find genomic “hotspots” that are frequently mutated in human lung cancer genomes. The results revealed that the most frequently mutated lung cancer genes are EGFR, KRAS and TP53. In recent years, EGFR and KRAS lung cancer test kits have been utilized for detecting lung cancer patients, but they presented many disadvantages, as they proved to be of low sensitivity, labor-intensive and time-consuming. In this study, we constructed a more complete catalogue of lung cancer mutation events including 145 mutated genes. With the genes of this list it may be feasible to develop a NGS kit for lung cancer mutation detection.     

VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data

A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.     

A Bayesian ensemble approach with a disease gene network predicts damaging effects of missense variants of human cancers

Large-scale sequencing of cancer genomes has revealed many novel mutations and inter-tumoral heterogeneity. Therefore, prioritizing variants according to their potential deleterious effects has become essential. We constructed a disease gene network and proposed a Bayesian ensemble approach that integrates diverse sources to predict the functional effects of missense variants. We analyzed 23,336 missense disease mutations and 36,232 neutral polymorphisms of 12,039 human proteins. The results showed successful improvement of prediction accuracy in both sensitivity and specificity, and we demonstrated the utility of the method by applying it to somatic mutations obtained from colorectal and breast cancer cell lines. The candidate genes with predicted deleterious mutations as well as known cancer genes were significantly enriched in many KEGG pathways related to carcinogenesis, supporting genetic homogeneity of cancer at the pathway level. The breast cancer-specific network increased the prediction accuracy for breast cancer mutations. This study provides a ranked list of deleterious mutations and candidate cancer genes and suggests that mutations affecting cancer may occur in important pathways and should be interpreted on the phenotype-related network or pathway. A disease gene network may be of value in predicting functional effects of novel disease-specific mutations.     

Statistical analysis of pathogenicity of somatic mutations in cancer

Recent large-scale sequencing studies have revealed that cancer genomes contain variable numbers of somatic point mutations distributed across many genes. These somatic mutations most likely include passenger mutations that are not cancer causing and pathogenic driver mutations in cancer genes. Establishing a significant presence of driver mutations in such data sets is of biological interest. Whereas current techniques from phylogeny are applicable to large data sets composed of singly mutated samples, recently exemplified with a p53 mutation database, methods for smaller data sets containing individual samples with multiple mutations need to be developed. By constructing distinct models of both the mutation process and selection pressure upon the cancer samples, exact statistical tests to examine this problem are devised. Tests to examine the significance of selection toward missense, nonsense, and splice site mutations are derived, along with tests assessing variation in selection between functional domains. Maximum-likelihood methods facilitate parameter estimation, including levels of selection pressure and minimum numbers of pathogenic mutations. These methods are illustrated with 25 breast cancers screened across the coding sequences of 518 kinase genes, revealing 90 base substitutions in 71 genes. Significant selection pressure upon truncating mutations was established. Furthermore, an estimated minimum of 29.8 mutations were pathogenic.     

Genetic epidemiology of multistage carcinogenesis

It is commonly believed that cancer is a multistage, polygenic disease. Even though conceptually appealing, the evidence supporting the multistage theory remains limited. Most known tumor suppresser genes are associated with monogenic dominant cancers following a two-hit pathway. We review results from a recent twin study on 90000 individuals that give support to the multistage theory. Statistically significant heritability estimates were shown for cancers of the colorectum (35%), breast (27%), and prostate (42%). These estimates are much higher than those obtained from family studies in which parents and offspring, or sibs are compared. The difference can be accounted for by the involvement of many genes. A polygenic cancer would show small effects in family studies but large effects in twin studies. We present calculations on the decrease in familial risks when the number of genes involved increases or when the penetrance decreases. We test the apparent number of stages involved in the main cancers from the Swedish Family-Cancer Database. The logarithms of the slopes suggest large differences in the apparent numbers of mutations involved in different cancers. The number of mutations required appears to be less in familial breast cancer compared to sporadic breast cancer. Study designs for gene identification should be revised to accommodate polygenic cancers.     

The temporal order of genetic and pathway alterations in tumorigenesis

Cancer evolves through the accumulation of mutations, but the order in which mutations occur is poorly understood. Inference of a temporal ordering on the level of genes is challenging because clinically and histologically identical tumors often have few mutated genes in common. This heterogeneity may at least in part be due to mutations in different genes having similar phenotypic effects by acting in the same functional pathway. We estimate the constraints on the order in which alterations accumulate during cancer progression from cross-sectional mutation data using a probabilistic graphical model termed Hidden Conjunctive Bayesian Network (H-CBN). The possible orders are analyzed on the level of genes and, after mapping genes to functional pathways, also on the pathway level. We find stronger evidence for pathway order constraints than for gene order constraints, indicating that temporal ordering results from selective pressure acting at the pathway level. The accumulation of changes in core pathways differs among cancer types, yet a common feature is that progression appears to begin with mutations in genes that regulate apoptosis pathways and to conclude with mutations in genes involved in invasion pathways. H-CBN models provide a quantitative and intuitive model of tumorigenesis showing that the genetic events can be linked to the phenotypic progression on the level of pathways.     

Sequencing analysis of SLX4/FANCP gene in Italian familial breast cancer cases

Breast cancer can be caused by germline mutations in several genes that are responsible for different hereditary cancer syndromes. Some of the genes causing the Fanconi anemia (FA) syndrome, such as BRCA2, BRIP1, PALB2, and RAD51C, are associated with high or moderate risk of developing breast cancer. Very recently, SLX4 has been established as a new FA gene raising the question of its implication in breast cancer risk. This study aimed at answering this question sequencing the entire coding region of SLX4 in 526 familial breast cancer cases from Italy. We found 81 different germline variants and none of these were clearly pathogenic. The statistical power of our sample size allows concluding that in Italy the frequency of carriers of truncating mutations of SLX4 may not exceed 0.6%. Our results indicate that testing for SLX4 germline mutations is unlikely to be relevant for the identification of individuals at risk of breast cancer, at least in the Italian population.     

Hereditary breast cancer; Genetic penetrance and current status with BRCA

The most important cause of developing hereditary breast cancer is germline mutations occurring in breast cancer (BCs) susceptibility genes, for example, BRCA1, BRCA2, TP53, CHEK2, PTEN, ATM, and PPM1D. Many BC susceptibility genes can be grouped into two classes, high- and low-penetrance genes, each of which interact with multiple genes and environmental factors. However, the penetrance of genes can also be represented by a spectrum, which ranges between high and low. Two of the most common susceptibility genes are BRCA1 and BRCA2, which perform vital cellular functions for repair of homologous DNA. Loss of heterozygosity accompanied by hereditary mutations in BRCA1 or BRCA2 increases chromosomal instability and the likelihood of cancer, as well as playing a key role in stimulating malignant transformation. With regard to pathological features, familial breast cancers caused by BRCA1 mutations usually differ from those caused by BRCA2 mutations and nonfamilial BCs. It is essential to acquire an understanding of these pathological features along with the genetic history of the patient to offer an individualized treatment. Germline mutations in BRCA1 and BRCA2 genes are the main genetic and inherited factors for breast and ovarian cancer. In fact, these mutations are very important in developing early onset and increasing the risk of familial breast and ovarian cancer and responsible for 90% of hereditary BC cases. Therefore, according to the conducted studies, screening of BRCA1 and BRCA2 genes is recommended as an important marker for early detection of all patients with breast or ovarian cancer risk with family history of the disease. In this review, we summarize the role of hereditary genes, mainly BRCA1 and BRCA2, in BC.     

Two Maize Genes Are Each Targeted Predominantly by Distinct Classes of Mu Elements

The Mutator transposable element system of maize has been used to isolate mutations at many different genes. Six different classes of Mu transposable elements have been identified. An important question is whether particular classes of Mu elements insert into different genes at equivalent frequencies. To begin to address this question, we used a small number of closely related Mutator plants to generate multiple independent mutations at two different genes. The overall mutation frequency was similar for the two genes. We then determined what types of Mu elements inserted into the genes. We found that each of the genes was preferentially targeted by a different class of Mu element, even when the two genes were mutated in the same plant. Possible explanations for these findings are discussed. These results have important implications for cloning Mu-tagged genes as other genes may also be resistant or susceptible to the insertion of particular classes of Mu elements.     

Moderate frequency of BRCA1 and BRCA2 germ-line mutations in Scandinavian familial breast cancer.

Previous studies of high-risk breast cancer families have proposed that two major breast cancer-susceptibility genes, BRCA1 and BRCA2, may account for at least two-thirds of all hereditary breast cancer. We have screened index cases from 106 Scandinavian (mainly southern Swedish) breast cancer and breast-ovarian cancer families for germ-line mutations in all coding exons of the BRCA1 and BRCA2 genes, using the protein-truncation test, SSCP analysis, or direct sequencing. A total of 24 families exhibited 11 different BRCA1 mutations, whereas 11 different BRCA2 mutations were detected in 12 families, of which 3 contained cases of male breast cancer. One BRCA2 mutation, 4486delG, was found in two families of the present study and, in a separate study, also in breast tumors from three unrelated males with unknown family history, suggesting that at least one BRCA2 founder mutation exists in the Scandinavian population. We report 1 novel BRCA1 mutation, eight additional cases of 4 BRCA1 mutations described elsewhere, and 11 novel BRCA2 mutations (9 frameshift deletions and 2 nonsense mutations), of which all are predicted to cause premature truncation of the translated products. The relatively low frequency of BRCA1 and BRCA2 mutations in the present study could be explained by insufficient screening sensitivity to the location of mutations in uncharacterized regulatory regions, the analysis of phenocopies, or, most likely, within predisposed families, additional uncharacterized BRCA genes.     

Identification of collaborative driver pathways in breast cancer

Background

An important challenge in cancer biology is to computationally screen mutations in cancer cells, separating those that might drive cancer initiation and progression, from the much larger number of bystanders. Since mutations are large in number and diverse in type, the frequency of any particular mutation pattern across a set of samples is low. This makes statistical distinctions and reproducibility across different populations difficult to establish.

Results

In this paper we develop a novel method that promises to partially ameliorate these problems. The basic idea is although mutations are highly heterogeneous and vary from one sample to another, the processes that are disrupted when cells undergo transformation tend to be invariant across a population for a particular cancer or cancer subtype. Specifically, we focus on finding mutated pathway-groups that are invariant across samples of breast cancer subtypes. The identification of informative pathway-groups consists of two steps. The first is identification of pathways significantly enriched in genes containing non-synonymous mutations; the second uses pathways so identified to find groups that are functionally related in the largest number of samples. An application to 4 subtypes of breast cancer identified pathway-groups that can highly explicate a particular subtype and rich in processes associated with transformation.

Conclusions

In contrast to previous methods that identify pathways across a set of samples without any further validation, we show that mutated pathway-groups can be found in each breast cancer subtype and that such groups are invariant across the majority of samples. The algorithm is available at http://www.visantnet.org/misi/MUDPAC.zip.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-605) contains supplementary material, which is available to authorized users.     

Genetik des Prostatakarzinoms

Prostate cancer is the most frequent malignancy in males and its etiology is strongly influenced by genetic factors. Nevertheless, no mutated genes which could be used for diagnosis have been identified in a major proportion of familial cases. Three genes with germline mutations have been identified after linkage analysis (ELAC2, RNASEL, MSR1), but these mutations are very rare and their penetrance is not well defined. The association of most genes with genetic variants is weak, and only BRCA2/familial breast cancer is of clinical relevance. As a consequence of the extreme genetic heterogeneity, diagnostic tools are not available and genetic counseling has to rely on risk estimates from pedigree data in which a single affected first degree relative indicates a relevant risk.