Signs of positive selection of somatic mutations in human cancers detected by EST sequence analysis

BACKGROUND: Carcinogenesis typically involves multiple somatic mutations in caretaker (DNA repair) and gatekeeper (tumor suppressors and oncogenes) genes. Analysis of mutation spectra of the tumor suppressor that is most commonly mutated in human cancers, p53, unexpectedly suggested that somatic evolution of the p53 gene during tumorigenesis is dominated by positive selection for gain of function. This conclusion is supported by accumulating experimental evidence of evolution of new functions of p53 in tumors. These findings prompted a genome-wide analysis of possible positive selection during tumor evolution. METHODS: A comprehensive analysis of probable somatic mutations in the sequences of Expressed Sequence Tags (ESTs) from malignant tumors and normal tissues was performed in order to access the prevalence of positive selection in cancer evolution. For each EST, the numbers of synonymous and non-synonymous substitutions were calculated. In order to identify genes with a signature of positive selection in cancers, these numbers were compared to: i) expected numbers and ii) the numbers for the respective genes in the ESTs from normal tissues. RESULTS: We identified 112 genes with a signature of positive selection in cancers, i.e., a significantly elevated ratio of non-synonymous to synonymous substitutions, in tumors as compared to 37 such genes in an approximately equal-sized EST collection from normal tissues. A substantial fraction of the tumor-specific positive-selection candidates have experimentally demonstrated or strongly predicted links to cancer. CONCLUSION: The results of EST analysis should be interpreted with extreme caution given the noise introduced by sequencing errors and undetected polymorphisms. Furthermore, an inherent limitation of EST analysis is that multiple mutations amenable to statistical analysis can be detected only in relatively highly expressed genes. Nevertheless, the present results suggest that positive selection might affect a substantial number of genes during tumorigenic somatic evolution

Computational methods and resources for the interpretation of genomic variants in cancer

The recent improvement of the high-throughput sequencing technologies is having a strong impact on the detection of genetic variations associated with cancer. Several institutions worldwide have been sequencing the whole exomes and or genomes of cancer patients in the thousands, thereby providing an invaluable collection of new somatic mutations in different cancer types. These initiatives promoted the development of methods and tools for the analysis of cancer genomes that are aimed at studying the relationship between genotype and phenotype in cancer. In this article we review the online resources and computational tools for the analysis of cancer genome. First, we describe the available repositories of cancer genome data. Next, we provide an overview of the methods for the detection of genetic variation and computational tools for the prioritization of cancer related genes and causative somatic variations. Finally, we discuss the future perspectives in cancer genomics focusing on the impact of computational methods and quantitative approaches for defining personalized strategies to improve the diagnosis and treatment of cancer

ContrastRank: A new method for ranking putative cancer driver genes and classification of tumor samples

Motivation: The recent advance in high-throughput sequencing technologies is generating a huge amount of data that are becoming an important resource for deciphering the genotype underlying a given phenotype. Genome sequencing has been extensively applied to the study of the cancer genomes. Although a few methods have been already proposed for the detection of cancer-related genes, their automatic identification is still a challenging task. Using the genomic data made available by The Cancer Genome Atlas Consortium (TCGA), we propose a new prioritization approach based on the analysis of the distribution of putative deleterious variants in a large cohort of cancer samples. Results: In this paper, we present ContastRank, a new method for the prioritization of putative impaired genes in cancer. The method is based on the comparison of the putative defective rate of each gene in tumor versus normal and 1000 genome samples. We show that the method is able to provide a ranked list of putative impaired genes for colon, lung and prostate adenocarcinomas. The list significantly overlaps with the list of known cancer driver genes previously published. More importantly, by using our scoring approach, we can successfully discriminate between TCGA normal and tumor samples. A binary classifier based on ContrastRank score reaches an overall accuracy490% and the area under the curve (AUC) of receiver operating characteristics (ROC)>0.95 for all the three types of adenocarcinoma analyzed in this paper. In addition, using ContrastRank score, we are able to discriminate the three tumor types with a minimum overall accuracy of 77% and AUC of 0.83. Conclusions: We describe ContrastRank, a method for prioritizing putative impaired genes in cancer. The method is based on the comparison of exome sequencing data from different cohorts and can detect putative cancer driver genes. ContrastRank can also be used to estimate a global score for an individual genome about the risk of adenocarcinoma based on the genetic variants information from a whole-exome VCF (Variant Calling Format) file. We believe that the application of ContrastRank can be an important step in genomic medicine to enable genome-based diagnosis. \ua9 The Author 2014. Published by Oxford University Press. All rights reserved

BrainGrab: Capturing Curator Expertise as Reusable Annotation Rules

Experienced biocurators can outperform automated systems on specific genes once they determine which pieces of evidence should drive annotation, and which annotations should be spread. The annotation logic may weigh both homology evidence (BLAST matches or HMM hits) and non-homology evidence (neighboring genes, metabolic context, taxonomic group). Unfortunately, the expertise developed to annotate each gene is short-lived, and is mostly lost if the logic driving the annotation is not captured. We report the development of BrainGrab, an interface added to the MANATEE manual annotation tool for prokaryotic genomes. The curator can specify evidence scenarios that should always lead to equivalent annotation for similar genes in similar contexts, and thus create new annotation rules while the expertise is fresh. No special knowledge of programming or protein family construction is required. BrainGrab rules can mix and match evidence types from the large array of existing protein family definitions such as Pfam families, sequence analyses such as SignalP, and contextual clues, that is, the same types of evidence already familiar to experienced biocurators. We have now created an infrastructure for collecting, distributing, interpreting, and applying BrainGrab rules for automated annotation. A rules interpreter combines queries of existing evidence with specified new searches to determine if a rule must fire. If so, the interpreter writes a new piece of rule-based evidence. Once deposited, BrainGrab/RuleBase evidence can provide automated annotation, pathway reconstruction, and even input data for other rules. We demonstrate the system with sets of rules for annotating proteins and pathways of siderophore biosynthesis in human pathogens, for annotating common fusion proteins, and for applying the proper nomenclature to bacterial ribosomal proteins. The chance to harness curatorial expertise for building rules creates a promising avenue for community contributions to improved annotation pipelines

Grammar of protein domain architectures

From an abstract, informational perspective, protein domains appear analogous to words in natural languages in which the rules of word association are dictated by linguistic rules, or grammar. Such rules exist for protein domains as well, because only a small fraction of all possible domain combinations is viable in evolution. We employ a popular linguistic technique, n-gram analysis, to probe the “proteome grammar”—that is, the rules of association of domains that generate various domain architectures of proteins. Comparison of the complexity measures of “protein languages” in major branches of life shows that the relative entropy difference (information gain) between the observed domain architectures and random domain combinations is highly conserved in evolution and is close to being a universal constant, at ∼1.2 bits. Substantial deviations from this constant are observed in only two major groups of organisms: a subset of Archaea that appears to be cells simplified to the limit, and animals that display extreme complexity. We also identify the n-grams that represent signatures of the major branches of cellular life. The results of this analysis bolster the analogy between genomes and natural language and show that a “quasi-universal grammar” underlies the evolution of domain architectures in all divisions of cellular life. The nearly universal value of information gain by the domain architectures could reflect the minimum complexity of signal processing that is required to maintain a functioning cell.ISSN:0027-8424ISSN:1091-649

HPV16 genome structure analysis in oropharyngeal cancer PDXs identifies tumors with integrated and episomal genomes

HPV + oropharyngeal squamous cell carcinoma (OPC) incidence recently surpassed cervical cancer and is the most common HPV-related cancer in the developed world. HPV16 is in ∼90 % of HPV + OPCs, with episomal genomes in the majority of cases. Most existing HPV16+ cancer cell lines derive from outside the oropharynx and harbor integrated HPV genomes. Thus, there is need for OPC preclinical models to evaluate standard and experimental therapeutics in the presence of episomal HPV16 oncogenic drivers. Here we characterize HPV genome structures in eight HPV16+ OPC patient-derived xenografts (PDXs), and evaluate their responses to standard chemotherapy. HPV genome state was investigated by combining Southern blot, T5 exonuclease assay, whole genome sequencing, and RNAseq data. This analysis revealed complexity and variation in integrated vs. episomal HPV forms across PDXs and demonstrated that four PDXs predominantly contain episomal HPV16. Episomal status did not ensure favorable in vivo responses to cisplatin therapy, despite the more favorable prognosis previously attributed to episomal HPV + tumors; this could be due to the small number present in the dataset. Our analysis establishes PDX models as test platforms for novel therapies designed to target maintenance of the episomal forms of HPV16 that commonly appear in OPC

Table1_Caspase-1 and the inflammasome promote polycystic kidney disease progression.DOCX

We and others have previously shown that the presence of renal innate immune cells can promote polycystic kidney disease (PKD) progression. In this study, we examined the influence of the inflammasome, a key part of the innate immune system, on PKD. The inflammasome is a system of molecular sensors, receptors, and scaffolds that responds to stimuli like cellular damage or microbes by activating Caspase-1, and generating critical mediators of the inflammatory milieu, including IL-1β and IL-18. We provide evidence that the inflammasome is primed in PKD, as multiple inflammasome sensors were upregulated in cystic kidneys from human ADPKD patients, as well as in kidneys from both orthologous (PKD1RC/RC or RC/RC) and non-orthologous (jck) mouse models of PKD. Further, we demonstrate that the inflammasome is activated in female RC/RC mice kidneys, and this activation occurs in renal leukocytes, primarily in CD11c+ cells. Knock-out of Casp1, the gene encoding Caspase-1, in the RC/RC mice significantly restrained cystic disease progression in female mice, implying sex-specific differences in the renal immune environment. RNAseq analysis implicated the promotion of MYC/YAP pathways as a mechanism underlying the pro-cystic effects of the Caspase-1/inflammasome in females. Finally, treatment of RC/RC mice with hydroxychloroquine, a widely used immunomodulatory drug that has been shown to inhibit the inflammasome, protected renal function specifically in females and restrained cyst enlargement in both male and female RC/RC mice. Collectively, these results provide evidence for the first time that the activated Caspase-1/inflammasome promotes cyst expansion and disease progression in PKD, particularly in females. Moreover, the data suggest that this innate immune pathway may be a relevant target for therapy in PKD.</p