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Single nucleotide variant (SNV) plays an important role in cellular proliferation and tumorigenesis in various types of human cancer. Next-generation sequencing (NGS) has provided high-throughput data at an unprecedented resolution to predict SNVs. Currently, there exist many computational methods for either germline or somatic SNV discovery from NGS data, but very few of them are versatile enough to adapt to any situations. In the absence of matched normal samples, the prediction of somatic SNVs from single-tumor samples becomes considerably challenging, especially when the tumor purity is unknown. Here, we propose a new approach, STIC, to predict somatic SNVs and estimate tumor purity from NGS data without matched normal samples. The main features of STIC include: (1) extracting a set of SNV-relevant features on each site and training the BP neural network algorithm on the features to predict SNVs; (2) creating an iterative process to distinguish somatic SNVs from germline ones by disturbing allele frequency; and (3) establishing a reasonable relationship between tumor purity and allele frequencies of somatic SNVs to accurately estimate the purity. We quantitatively evaluate the performance of STIC on both simulation and real sequencing datasets, the results of which indicate that STIC outperforms competing methods.

Breast cancer is the most common malignancy in women, and because it has a high mortality rate, it is urgent to develop computational methods to increase the accuracy of breast cancer survival predictive models. Although multi-omics data such as gene expression have been extensively used in recent studies, the accurate prognosis of breast cancer remains a challenge. Somatic mutations are another important and promising data source for studying cancer development, and its effect on the prognosis of breast cancer remains to be further explored. Meanwhile, these omics datasets are high-dimensional and redundant. Therefore, we adopted multiple kernel learning (MKL) to efficiently integrate somatic mutation to currently molecular data including gene expression, copy number variation (CNV), methylation, and protein expression data for the prediction of breast cancer survival. Before integration, the maximum relevance minimum redundancy (mRMR) feature selection method was utilized to select features that present high relevance to survival and low redundancy among themselves for each type of data. The experimental results demonstrated that the proposed method achieved the most optimal performance and there was a remarkable improvement in the prediction performance when somatic mutations were included, indicating that somatic mutations are critical for improving breast cancer survival predictions. Moreover, mRMR was superior to other feature selection methods used in previous studies. Furthermore, MKL outperformed the other traditional classifiers in multi-omics data integration. Our analysis indicated that through employing promising omics data such as somatic mutations and harnessing the power of proper feature selection methods and effective integration frameworks, the breast cancer survival predictive accuracy can be further increased, thereby providing a more optimal clinical diagnosis and more effective treatment for breast cancer patients.

piwi-interacting RNAs (piRNAs) are valuable biomarkers, but functional studies are still very limited. Recent research shows that piRNA-mediated cleavage acts on Transposable Elements (TEs), messenger RNAs (mRNAs), and long non-coding RNAs (lncRNAs). This study aimed to predict cancer-associated piRNA-mRNA and piRNA-lncRNA interactions as well as piRNA regulatory functions. Four cancer types (BRCA, HNSC, KIRC, and LUAD) were investigated. Interactions were identified by integrated analysis of the expression and sequence data. For the expression analysis, only piRNA-mRNA and piRNA-lncRNA pairs with expression profiles that were significantly inversely correlated were retained to reduce false-positive rates during the prediction. For the sequence analysis, miRanda was used for the target prediction. We identified 198 piRNA-mRNA and 10 piRNA-lncRNA pairs. Unlike mRNA and lncRNA expressions, the piRNA expression was relatively consistent across the cancer types. Furthermore, the identified piRNAs were consistent with previously published cancer biomarkers, such as piRNA-36741, piR-21032, and piRNA-57125. More importantly, predicted piRNA functions were determined by constructing an interaction network, and piRNA targets were placed in gene ontology categories related to the cancer hallmarks “activating invasion and metastasis” and “sustained angiogenesis”.

Breast cancer is a heterogeneous disease. Although gene expression profiling has led to the definition of several subtypes of breast cancer, the precise discovery of the subtypes remains a challenge. Clinical data is another promising source. In this study, clinical variables are utilized and integrated to gene expressions for the stratification of breast cancer. We adopt two phases: gene selection and clustering, where the integration is in the gene selection phase, only genes whose expressions are most relevant to each clinical variable and least redundant among themselves are selected for further clustering. In practice, we simply utilize maximum relevance minimum redundancy (mRMR) for gene selection and k-means for clustering. We compare the results of our method with those of two commonly used only expression-based breast cancer stratification methods: prediction analysis of microarray 50 (PAM50) and highest variability (HV). The result is that our method outperforms them in identifying subtypes significantly associated with five-year survival and recurrence time. Specifically, our method identified recurrence-associated breast cancer subtypes that were not identified by PAM50 and HV. Additionally, our analysis discovered three survival-associated luminal-A subgroups and two survival-associated luminal-B subgroups. The study indicates that screening clinically relevant gene expressions yields improved breast cancer stratification.

Copy number variation (CNV) is an important form of genomic structural variation and is linked to dozens of human diseases. Using next-generation sequencing (NGS) data and developing computational methods to characterize such structural variants is significant for understanding the mechanisms of diseases. The objective of this study is to develop a new statistical method of detection recurrent CNVs across multiple samples from genomic sequences. A statistical method is carried out to detect recurrent CNVs, referred to as SM-RCNV. This method uses a statistic associated with each location by combining the frequency of variation at one location across whole samples and the correlation among consecutive locations. The weights of the frequency and correlation are trained using real datasets with known CNVs. P-value is assessed for each location on the genome by permutation testing. Compared with six peer methods, SM-RCNV outperforms the peer methods under receiver operating characteristic curves. SM-RCNV successfully identifies many consistent recurrent CNVs, most of which are known to be of biological significance and associated with diseased genes. The validation rate of SM-RCNV in the CEU call set and YRI call set with Database of Genomic Variants are 258/328 (79%) and (157/309) 51%, respectively. SM-RCNV is a well-grounded statistical framework for detecting recurrent CNVs from multiple genomic sequences, providing valuable information to study genomes in human diseases. The source code is freely available at https://sourceforge.net/projects/sm-rcnv/ .

MicroRNAs (miRNAs) play an indispensable role in cancer initiation and progression. Different cancers have some common hallmarks in general. Analyzing miRNAs that consistently contribute to different cancers can help us to discover the relationship between miRNAs and traits shared by cancers. Most previous works focus on analyzing single miRNA. However, dysregulation of a single miRNA is generally not sufficient to contribute to complex cancer processes. In this study, we put emphasis on analyzing cooperation of miRNAs across cancers. We assume that miRNAs can cooperatively regulate oncogenic pathways and contribute to cancer hallmarks. Such a cooperation is modeled by a miRNA module referred to as a pan-cancer conserved miRNA module. The module consists of miRNAs which simultaneously regulate cancers and are significantly intra-correlated. A novel computational workflow for the module discovery is presented. Multiple modules are discovered from miRNA expression profiles using the method. The function of top two ranked modules are analyzed using the mRNAs which correlate to all the miRNAs in a module across cancers, inferring that the two modules function in regulating the cell cycle which relates to cancer hallmarks as self sufficiency in growth signals and insensitivity to antigrowth signals. Additionally, two novel miRNAs mir-590 and mir-629 are found to cooperate with well-known onco-miRNAs in the modules to contribute to cancers. We also found that PTEN, which is a well known tumor suppressor that regulates the cell cycle, is a common target of miRNAs in the top-one module and cooperative control of PTEN can be a reason for the miRNAs' cooperation. We believe that analyzing the cooperative mechanism of the miRNAs in modules rather than focusing on only single miRNAs may help us know more about the complicated relationship between miRNAs and cancers and develop more effective treatment strategies for cancers.

Genome-wide association study (GWAS) is an important way to fully understand SNPs underlying complex diseases. The central difficulty is to exactly find out both strong and weak effective SNPs. In this paper we propose a new statistical method to identify risk SNPs from case-control samples. This method measures each SNP by evaluating its conditional probability of disease status. Those SNPs contributing the highest probability to disease status are regarded as putative risk loci. The conditional probabilities are computed through binomial probability density functions based on Monte Carlo sampling. We test the performance of our method using simulation datasets and validate its applicability using one real dataset. Experimental results show that our method performs well and generates stable results even if we change the size of input samples. In addition, our method can works well using genotype data without phase information.

Background Gene expression profiling has led to the definition of breast cancer molecular subtypes: Basal-like, HER2-enriched, LuminalA, LuminalB and Normal-like. Different subtypes exhibit diverse responses to treatment. In the past years, several traditional clustering algorithms have been applied to analyze gene expression profiling. However, accurate identification of breast cancer subtypes, especially within highly variable LuminalA subtype, remains a challenge. Furthermore, the relationship between DNA methylation and expression level in different breast cancer subtypes is not clear. Results In this study, a modified ISA biclustering algorithm, termed AP-ISA, was proposed to identify breast cancer subtypes. Comparing with ISA, AP-ISA provides the optimized strategy to select seeds and thresholds in the circumstance that prior knowledge is absent. Experimental results on 574 breast cancer samples were evaluated using clinical ER/PR information, PAM50 subtypes and the results of five peer to peer methods. One remarkable point in the experiment is that, AP-ISA divided the expression profiles of the luminal samples into four distinct classes. Enrichment analysis and methylation analysis showed obvious distinction among the four subgroups. Tumor variability within the Luminal subtype is observed in the experiments, which could contribute to the development of novel directed therapies. Conclusions Aiming at breast cancer subtype classification, a novel biclustering algorithm AP-ISA is proposed in this paper. AP-ISA classifies breast cancer into seven subtypes and we argue that there are four subtypes in luminal samples. Comparison with other methods validates the effectiveness of AP-ISA. New genes that would be useful for targeted treatment of breast cancer were also obtained in this study. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1926-z) contains supplementary material, which is available to authorized users.

The stratification of cancer into subtypes that are significantly associated with clinical outcomes is beneficial for targeted prognosis and treatment. In this study, we integrated somatic mutation and gene expression data to identify clusters of patients. In contrast to previous studies, we constructed cancer-type-specific significant co-expression networks (SCNs) rather than using a fixed gene network across all cancers, such as the network-based stratification (NBS) method, which ignores cancer heterogeneity. For each type of cancer, the gene expression data were used to construct the SCN network, while the gene somatic mutation data were mapped onto the network, propagated, and used for further clustering. For the clustering, we adopted an improved network-regularized non-negative matrix factorization (netNMF) (netNMF_HC) for a more precise classification. We applied our method to various datasets, including ovarian cancer (OV), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC) cohorts derived from the TCGA (The Cancer Genome Atlas) project. Based on the results, we evaluated the performance of our method to identify survival-relevant subtypes and further compared it to the NBS method, which adopts priori networks and netNMF algorithm. The proposed algorithm outperformed the NBS method in identifying informative cancer subtypes that were significantly associated with clinical outcomes in most cancer types we studied. In particular, our method identified survival-associated UCEC subtypes that were not identified by the NBS method. Our analysis indicated valid subtyping of patient could be applied by mutation data with cancer-type-specific SCNs and netNMF_HC for individual cancers because of specific cancer co-expression patterns and more precise clustering.

MicroRNAs (miRNAs) are important post-transcriptional regulators of gene expression. In recent years, many novel microRNAs have been discovered at unprecedented depth after advent of deep sequencing technology, but accurate identification of miRNAs is still a challenge due to a large number of isoforms, dynamic range of miRNA expression and unobvious biological characteristics in short mature miRNA sequences. We present a pattern-based approach, mirPD, which uses a two-stage filtration to identify miRNAs from deep sequencing data. In the first filtration stage, patterns capturing conserved knowledge of real miRNAs are extracted from real (published) miRNAs to filter reads. The reads passing the pattern filtration are then mapped to the genome to get candidate precursors which are further filtered according to miRNA biological features in the second stage. Compared with the classic miRNA identification method miRDeep (v1 and v2) on a typical dataset, the experimental result indicates that the mirPD provides higher sensitivity and similar precision, accuracy and specificity.

Two main reasons for the difficulties to search for susceptibility single-nucleotide polymorphisms (SNPs) underlying genetic diseases are that the findings are not easy to be confirmed and the interactions between potential susceptibility SNPs are not clear. Many available association studies usually presented results with significance levels but did not illustrate the stability of the results. In some sense, their performances might be unclear in real practice. In this paper, we develop a novel method based on mutual information theory and linkage disequilibrium by grouping case-control. Mutual information (MI) is used to test multiple SNPs in combining with disease status. Those SNPs contributing the maximum MI are selected as potential susceptibility SNPs. Linkage disequilibrium (LD) analysis is used to extend MI detected result so that both direct and indirect factors can be included in the final result. The purpose of case-control grouping is to generate a number of data groups by randomly sampling from target samples. Each group is assumed to have almost the same number of individuals (cases and controls), and overlap is allowed among the groups. We apply the method to each data group, and then make comparisons and intersections between the results obtained from each of the groups so as to give the final result. We implement the method by continuously grouping until the final result reaches a stable state and a highly significance level. The experimental results indicate that our method to detect susceptibility SNPs in simulated and real data sets has shown remarkable success.

Recurrent copy number alterations (CNAs) play an important role in cancer genesis. While a number of computational methods have been proposed for identifying such CNAs, their relative merits remain largely unknown in practice since very few efforts have been focused on comparative analysis of the methods. To facilitate studies of recurrent CNA identification in cancer genome, it is imperative to conduct a comprehensive comparison of performance and limitations among existing methods. In this paper, six representative methods proposed in the latest six years are compared. These include one-stage and two-stage approaches, working with raw intensity ratio data and discretized data respectively. They are based on various techniques such as kernel regression, correlation matrix diagonal segmentation, semi-parametric permutation and cyclic permutation schemes. We explore multiple criteria including type I error rate, detection power, Receiver Operating Characteristics (ROC) curve and the area under curve (AUC), and computational complexity, to evaluate performance of the methods under multiple simulation scenarios. We also characterize their abilities on applications to two real datasets obtained from cancers with lung adenocarcinoma and glioblastoma. This comparison study reveals general characteristics of the existing methods for identifying recurrent CNAs, and further provides new insights into their strengths and weaknesses. It is believed helpful to accelerate the development of novel and improved methods. Published version

Somatic Copy Number Alterations (CNAs) are quite common in human cancers. Identifying CNAs and Significant Copy number Aberrations (SCAs) in cancer genomes is a critical task in searching for cancer-associated genes. The advanced genomic technologies, such as SNP array technology, facilitate copy number study at a genome-wide scale with high resolution. However, in reality, due to normal tissue contamination, the observed intensity signals are actually the mixture of copy number signals contributed from both tumor cells and normal cells. This genetic heterogeneity could significantly affect the subsequent copy number analysis and SCAs detection. In order to accurately identify significant aberrations in contaminated cancer genome, we devise an approach including two major steps. We first use a statistical method, Bayesian Analysis of Copy number Mixtures (BACOM) to estimate the normal tissue contamination fraction and recover the “true” copy number profile. Then, based on the recovered profiles, we detect SCAs using Genome-wide Identification of Significant Aberrations in Cancer Genome (SAIC). We comprehensively evaluate the performance of the proposed algorithm on a large number of simulation data. The results show that the algorithm has higher detection power than peer methods including the most popular GISTIC. We then apply the method to the real copy number data of Glioblastoma Multiforme and successfully identified majority of SCAs reported by GISTIC, and some novel SCAs that contain some cancer-associated genes.

Uterine cancer is the fourth most common malignancy in women, and uterine serous carcinoma is the most aggressive subtype. However, the molecular pathogenesis of uterine serous carcinoma is largely unknown. We analyzed the genomes of uterine serous carcinoma samples to better understand the molecular genetic characteristics of this cancer.Whole-exome sequencing was performed on 10 uterine serous carcinomas and the matched normal blood or tissue samples. Somatically acquired sequence mutations were further verified by Sanger sequencing. The most frequent molecular genetic changes were further validated by Sanger sequencing in 66 additional uterine serous carcinomas and in nine serous endometrial intraepithelial carcinomas (the preinvasive precursor of uterine serous carcinoma) that were isolated by laser capture microdissection. In addition, gene copy number was characterized by single-nucleotide polymorphism (SNP) arrays in 23 uterine serous carcinomas, including 10 that were subjected to whole-exome sequencing.We found frequent somatic mutations in TP53 (81.6%), PIK3CA (23.7%), FBXW7 (19.7%), and PPP2R1A (18.4%) among the 76 uterine serous carcinomas examined. All nine serous carcinomas that had an associated serous endometrial intraepithelial carcinoma had concordant PIK3CA, PPP2R1A, and TP53 mutation status between uterine serous carcinoma and the concurrent serous endometrial intraepithelial carcinoma component. DNA copy number analysis revealed frequent genomic amplification of the CCNE1 locus (which encodes cyclin E, a known substrate of FBXW7) and deletion of the FBXW7 locus. Among 23 uterine serous carcinomas that were subjected to SNP array analysis, seven tumors with FBXW7 mutations (four tumors with point mutations, three tumors with hemizygous deletions) did not have CCNE1 amplification, and 13 (57%) tumors had either a molecular genetic alteration in FBXW7 or CCNE1 amplification. Nearly half of these uterine serous carcinomas (48%) harbored PIK3CA mutation and/or PIK3CA amplification.Molecular genetic aberrations involving the p53, cyclin E-FBXW7, and PI3K pathways represent major mechanisms in the development of uterine serous carcinoma.

Somatic copy number alteration (CNA) is a common phenomenon in cancer genome. Distinguishing significant consensus events (SCEs) from random background CNAs in a set of subjects has been proven to be a valuable tool to study cancer. In order to identify SCEs with an acceptable type I error rate, better computational approaches should be developed based on reasonable statistics and null distributions. In this article, we propose a new approach named TAGCNA for identifying SCEs in somatic CNAs that may encompass cancer driver genes. TAGCNA employs a peel-off permutation scheme to generate a reasonable null distribution based on a prior step of selecting tag CNA markers from the genome being considered. We demonstrate the statistical power of TAGCNA on simulated ground truth data, and validate its applicability using two publicly available cancer datasets: lung and prostate adenocarcinoma. TAGCNA identifies SCEs that are known to be involved with proto-oncogenes (e.g. EGFR, CDK4) and tumor suppressor genes (e.g. CDKN2A, CDKN2B), and provides many additional SCEs with potential biological relevance in these data. TAGCNA can be used to analyze the significance of CNAs in various cancers. It is implemented in R and is freely available at http://tagcna.sourceforge.net/. Published version

Background Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted. Results We report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding multiple sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate, are quite conservative, thereby limiting their power and making it difficult to fairly compare them. Third, as expected, power varies for different models and as a function of penetrance, minor allele frequency, linkage disequilibrium and marginal effects. Fourth, the analytical relationships between power and these factors are derived, aiding in the interpretation of the study results. Fifth, for these methods the magnitude of the main effect influences the power of the tests. Sixth, most methods can detect some ground-truth SNPs but have modest power to detect the whole set of interacting SNPs. Conclusion This comparison study provides new insights into the strengths and limitations of current methods for detecting interacting loci. This study, along with freely available simulation tools we provide, should help support development of improved methods. The simulation tools are available at: http://code.google.com/p/simulation-tool-bmc-ms9169818735220977/downloads/list.

Existing simulation methods usually simulate linkage disequilibrium (LD) structures starting with an initial population that is randomly generated according to specified allele frequencies. These at random based methods might be unstable because the LD level of the initial population is generally extremely low. This study presents a new algorithm, SIMLD, to simulate genome populations with real LD structures. SIMLD begins from an initial population with possibly the highest LD level, and then the LD decays to fit the desired level through processes of mating and recombination over generations. SIMLD can produce case–control samples according to various disease models. Using empirical SNP marker information from three populations of HapMap data, we implement the proposed algorithm and demonstrate a set of experimental results.

One of the most challenging points in studying human common complex diseases is to search for both strong and weak susceptibility single-nucleotide polymorphisms (SNPs) and identify forms of genetic disease models. Currently, a number of methods have been proposed for this purpose. Many of them have not been validated through applications into various genome datasets, so their abilities are not clear in real practice. In this paper, we present a novel SNP association study method based on probability theory, called ProbSNP. The method firstly detects SNPs by evaluating their joint probabilities in combining with disease status and selects those with the lowest joint probabilities as susceptibility ones, and then identifies some forms of genetic disease models through testing multiple-locus interactions among the selected SNPs. The joint probabilities of combined SNPs are estimated by establishing Gaussian distribution probability density functions, in which the related parameters (i.e., mean value and standard deviation) are evaluated based on allele and haplotype frequencies. Finally, we test and validate the method using various genome datasets. We find that ProbSNP has shown remarkable success in the applications to both simulated genome data and real genome-wide data.

Background Somatic Copy Number Alterations (CNAs) in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC), a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1) exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2) performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3) iteratively detecting Significant Copy Number Aberrations (SCAs) and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme. Results We test and compare the performance of SAIC against four peer methods (GISTIC, STAC, KC-SMART, CMDS) on a large number of simulation datasets. Experimental results show that SAIC outperforms peer methods in terms of larger area under the Receiver Operating Characteristics curve and increased detection power. We then apply SAIC to analyze structural genomic aberrations acquired in four real cancer genome-wide copy number data sets (ovarian cancer, metastatic prostate cancer, lung adenocarcinoma, glioblastoma). When compared with previously reported results, SAIC successfully identifies most SCAs known to be of biological significance and associated with oncogenes (e.g., KRAS, CCNE1, and MYC) or tumor suppressor genes (e.g., CDKN2A/B). Furthermore, SAIC identifies a number of novel SCAs in these copy number data that encompass tumor related genes and may warrant further studies. Conclusions Supported by a well-grounded theoretical framework, SAIC has been developed and used to identify SCAs in various cancer copy number data sets, providing useful information to study the landscape of cancer genomes. Open–source and platform-independent SAIC software is implemented using C++, together with R scripts for data formatting and Perl scripts for user interfacing, and it is easy to install and efficient to use. The source code and documentation are freely available at http://www.cbil.ece.vt.edu/software.htm.