CORAL: Building up QSAR models for the chromosome aberration test

A high level of chromosomal aberrations in peripheral blood lymphocytes may be an early marker of cancer risk, but data on risk of specific cancers and types of chromosomal aberrations are limited. Consequently, the development of predictive models for chromosomal aberrations test is important task. Majority of models for chromosomal aberrations test are so-called knowledge-based rules system. The CORAL software (http://www.insilico.eu/coral, abbreviation of “CORrelation And Logic”) is an alternative for knowledge-based rules system. In contrast to knowledge-based rules system, the CORAL software gives possibility to estimate the influence upon the predictive potential of a model of different molecular alerts as well as different splits into the training set and validation set. This possibility is not available for the approaches based on the knowledge-based rules system. Quantitative Structure–Activity Relationships (QSAR) for chromosome aberration test are established for five random splits into the training, calibration, and validation sets. The QSAR approach is based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) without data on physicochemical and/or biochemical parameters. In spite of this limitation, the statistical quality of these models is quite good.     

Repetitive element-mediated recombination as a mechanism for new gene origination in Drosophila

Previous studies of repetitive elements (REs) have implicated a mechanistic role in generating new chimerical genes. Such examples are consistent with the classic model for exon shuffling, which relies on non-homologous recombination. However, recent data for chromosomal aberrations in model organisms suggest that ectopic homology-dependent recombination may also be important. Lack of a dataset comprising experimentally verified young duplicates has hampered an effective examination of these models as well as an investigation of sequence features that mediate the rearrangements. Here we use approximately 7,000 cDNA probes (approximately 112,000 primary images) to screen eight species within the Drosophila melanogaster subgroup and identify 17 duplicates that were generated through ectopic recombination within the last 12 mys. Most of these are functional and have evolved divergent expression patterns and novel chimeric structures. Examination of their flanking sequences revealed an excess of repetitive sequences, with the majority belonging to the transposable element DNAREP1 family, associated with the new genes. Our dataset strongly suggests an important role for REs in the generation of chimeric genes within these species.     

An integrated approach to uncover drivers of cancer

Systematic characterization of cancer genomes has revealed a staggering number of diverse aberrations that differ among individuals, such that the functional importance and physiological impact of most tumor genetic alterations remain poorly defined. We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma data set. Our analysis correctly identified known drivers of melanoma and predicted multiple tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify candidate drivers with biological, and possibly therapeutic, importance in cancer.     

Correlation of the sister chromatid exchange level with chromosome aberrations induced by chemical mutagens in vivo

The data on the dose dependencies of the induction of sister chromatid exchanges (SCE) and chromosomal aberrations during exposure of mouse bone marrow cells in vivo to 5 alkylating substances are provided. The efficacy of SCE induction was found to be higher than that of chromosomal aberrations. It was established that SCE induced by chemical mutagens in vivo and in vitro are more sensitive and stable tests than chromosomal aberrations.     

Towards a genetic-based classification of human lung cancer.

Lung cancer is a highly aggressive neoplasm which is reflected by a multitude of genetic aberrations being detectable on the chromosomal and molecular level. In order to understand this seemingly genetic chaos, we performed Comparative Genomic Hybridisation (CGH) in a large collective of human lung carcinomas investigating different tumor entities as well as multiple individual tumour specimens of single patients. Despite the considerable genetic instability being reflected by the well known morphological heterogeneity of lung cancer the comparison of different tumour groups using custom made computer software revealed recurrent aberration patterns and highlighted chromosomal imbalances that were significantly associated with morphological histotypes and biological phenotypes. Specifically we identified imbalances in NSCLC being associated with metastasis formation which are typically present in SCLC thus explaining why the latter is such an aggressive neoplasm characterized by widespread tumor dissemination. Based on the genetic data a new model for the development of SCLC is presented. It suggests that SCLC evolving from the same stem cell as NSCLC should be differentiated into primary and secondary tumors. Primary SCLC corresponding to the classical type evolved directly from an epithelial precursor cell. In contrast, secondary SCLC correlating with the combined SCLC develops via an NSCLC intermediate. In addition, we established libraries of differentially expressed genes from different human lung cancer types to identify new candidate genes for several of the chromosomal subregions identified by CGH. In this review, we summarise the status of our results aiming at a refined classification of lung cancer based on the pattern of genetic aberrations.     

Stochastic cancer progression driven by non-clonal chromosome aberrations

Cancer research has previously focused on the identification of specific genes and pathways responsible for cancer initiation and progression based on the prevailing viewpoint that cancer is caused by a stepwise accumulation of genetic aberrations. This viewpoint, however, is not consistent with the clinical finding that tumors display high levels of genetic heterogeneity and distinctive karyotypes. We show that chromosomal instability primarily generates stochastic karyotypic changes leading to the random progression of cancer. This was accomplished by tracing karyotypic patterns of individual cells that contained either defective genes responsible for genome integrity or were challenged by onco-proteins or carcinogens that destabilized the genome. Analysis included the tracing of patterns of karyotypic evolution during different stages of cellular immortalization. This study revealed that non-clonal chromosomal aberrations (NCCAs) (both aneuploidy and structural aberrations) and not recurrent clonal chromosomal aberrations (CCAs) are directly linked to genomic instability and karyotypic evolution. Discovery of "transitional CCAs" during in vitro immortalization clearly demonstrates that karyotypic evolution in solid tumors is not a continuous process. NCCAs and their dynamic interplay with CCAs create infinite genomic combinations leading to clonal diversity necessary for cancer cell evolution. The karyotypic chaos observed within the cell crisis stage prior to establishment of the immortalization further supports the ultimate importance of genetic aberrations at the karyotypic or genome level. Therefore, genomic instability generated NCCAs are a key driving force in cancer progression. The dynamic relationship between NCCAs and CCAs provides a mechanism underlying chromosomal based cancer evolution and could have broad clinical applications.     

Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data

MOTIVATION: Array Comparative Genomic Hybridization (CGH) can reveal chromosomal aberrations in the genomic DNA. These amplifications and deletions at the DNA level are important in the pathogenesis of cancer and other diseases. While a large number of approaches have been proposed for analyzing the large array CGH datasets, the relative merits of these methods in practice are not clear. RESULTS: We compare 11 different algorithms for analyzing array CGH data. These include both segment detection methods and smoothing methods, based on diverse techniques such as mixture models, Hidden Markov Models, maximum likelihood, regression, wavelets and genetic algorithms. We compute the Receiver Operating Characteristic (ROC) curves using simulated data to quantify sensitivity and specificity for various levels of signal-to-noise ratio and different sizes of abnormalities. We also characterize their performance on chromosomal regions of interest in a real dataset obtained from patients with Glioblastoma Multiforme. While comparisons of this type are difficult due to possibly sub-optimal choice of parameters in the methods, they nevertheless reveal general characteristics that are helpful to the biological investigator.     

Cancer progression by non-clonal chromosome aberrations

The establishment of the correct conceptual framework is vital to any scientific discipline including cancer research. Influenced by hematologic cancer studies, the current cancer concept focuses on the stepwise patterns of progression as defined by specific recurrent genetic aberrations. This concept has faced a tough challenge as the majority of cancer cases follow non-linear patterns and display stochastic progression. In light of the recent discovery that genomic instability is directly linked to stochastic non-clonal chromosome aberrations (NCCAs), and that cancer progression can be characterized as a dynamic relationship between NCCAs and recurrent clonal chromosome aberrations (CCAs), we propose that the dynamics of NCCAs is a key element for karyotypic evolution in solid tumors. To support this viewpoint, we briefly discuss various basic elements responsible for cancer initiation and progression within an evolutionary context. We argue that even though stochastic changes can be detected at various levels of genetic organization, such as at the gene level and epigenetic level, it is primarily detected at the chromosomal or genome level. Thus, NCCA-mediated genomic variation plays a dominant role in cancer progression. To further illustrate the involvement of NCCA/CCA cycles in the pattern of cancer evolution, four cancer evolutionary models have been proposed based on the comparative analysis of karyotype patterns of various types of cancer.     

Stability analysis of mixtures of mutagenetic trees

Background  

Mixture models of mutagenetic trees are evolutionary models that capture several pathways of ordered accumulation of genetic events observed in different subsets of patients. They were used to model HIV progression by accumulation of resistance mutations in the viral genome under drug pressure and cancer progression by accumulation of chromosomal aberrations in tumor cells. From the mixture models a genetic progression score (GPS) can be derived that estimates the genetic status of single patients according to the corresponding progression along the tree models. GPS values were shown to have predictive power for estimating drug resistance in HIV or the survival time in cancer. Still, the reliability of the exact values of such complex markers derived from graphical models can be questioned.     

A model-based approach to study nearest-neighbor influences reveals complex substitution patterns in non-coding sequences

In this article, we present a likelihood-based framework for modeling site dependencies. Our approach builds upon standard evolutionary models but incorporates site dependencies across the entire tree by letting the evolutionary parameters in these models depend upon the ancestral states at the neighboring sites. It thus avoids the need for introducing new and high-dimensional evolutionary models for site-dependent evolution. We propose a Markov chain Monte Carlo approach with data augmentation to infer the evolutionary parameters under our model. Although our approach allows for wide-ranging site dependencies, we illustrate its use, in two non-coding datasets, in the case of nearest-neighbor dependencies (i.e., evolution directly depending only upon the immediate flanking sites). The results reveal that the general time-reversible model with nearest-neighbor dependencies substantially improves the fit to the data as compared to the corresponding model with site independence. Using the parameter estimates from our model, we elaborate on the importance of the 5-methylcytosine deamination process (i.e., the CpG effect) and show that this process also depends upon the 5' neighboring base identity. We hint at the possibility of a so-called TpA effect and show that the observed substitution behavior is very complex in the light of dinucleotide estimates. We also discuss the presence of CpG effects in a nuclear small subunit dataset and find significant evidence that evolutionary models incorporating context-dependent effects perform substantially better than independent-site models and in some cases even outperform models that incorporate varying rates across sites.     

Data-Derived Modeling Characterizes Plasticity of MAPK Signaling in Melanoma

The majority of melanomas have been shown to harbor somatic mutations in the RAS-RAF-MEK-MAPK and PI3K-AKT pathways, which play a major role in regulation of proliferation and survival. The prevalence of these mutations makes these kinase signal transduction pathways an attractive target for cancer therapy. However, tumors have generally shown adaptive resistance to treatment. This adaptation is achieved in melanoma through its ability to undergo neovascularization, migration and rearrangement of signaling pathways. To understand the dynamic, nonlinear behavior of signaling pathways in cancer, several computational modeling approaches have been suggested. Most of those models require that the pathway topology remains constant over the entire observation period. However, changes in topology might underlie adaptive behavior to drug treatment. To study signaling rearrangements, here we present a new approach based on Fuzzy Logic (FL) that predicts changes in network architecture over time. This adaptive modeling approach was used to investigate pathway dynamics in a newly acquired experimental dataset describing total and phosphorylated protein signaling over four days in A375 melanoma cell line exposed to different kinase inhibitors. First, a generalized strategy was established to implement a parameter-reduced FL model encoding non-linear activity of a signaling network in response to perturbation. Next, a literature-based topology was generated and parameters of the FL model were derived from the full experimental dataset. Subsequently, the temporal evolution of model performance was evaluated by leaving time-defined data points out of training. Emerging discrepancies between model predictions and experimental data at specific time points allowed the characterization of potential network rearrangement. We demonstrate that this adaptive FL modeling approach helps to enhance our mechanistic understanding of the molecular plasticity of melanoma.     

Using genotyping‐by‐sequencing to predict gender in animals

Gender assignment errors are common in some animal species and lead to inaccuracies in downstream analyses. Procedures for detecting gender misassignment are available for array‐based SNP data but are still being developed for genotyping‐by‐sequencing (GBS) data. In this study, we describe a method for using GBS data to predict gender using X and Y chromosomal SNPs. From a set of 1286 X chromosomal and 23 Y chromosomal deer (Cervus sp.) SNPs discovered from GBS sequence reads, a prediction model was built using a training dataset of 422 Red deer and validated using a test dataset of 868 Red deer and Wapiti deer. Prediction was based on the proportion of heterozygous genotypes on the X chromosome and the proportion of non‐missing genotypes on the Y chromosome observed in each individual. The concordance between recorded gender and predicted gender was 98.6% in the training dataset and 99.3% in the test dataset. The model identified five individuals across both datasets with incorrect recorded gender and was unable to predict gender for another five individuals. Overall, our method predicted gender with a high degree of accuracy and could be used for quality control in gender assignment datasets or for assigning gender when unrecorded, provided a suitable reference genome is available.     

Increased risk of cancer in radon-exposed miners with elevated frequency of chromosomal aberrations

In spite of the extensive use of cytogenetic analysis of human peripheral blood lymphocytes in the biomonitoring of exposure to various mutagens and carcinogens, the long-term effects of an increased frequency of chromosomal aberrations in individuals are still uncertain. Few epidemiologic studies have addressed this issue, and a moderate risk of cancer in individuals with an elevated frequency of chromosomal aberrations has been observed.In the present study, we analyzed data on 1323 cytogenetic assays and 225 subjects examined because of occupational exposures to radon (range of exposure from 1.7 to 662.3 working level month (WLM)). Seventy-five subjects were non-smokers. We found 36 cases of cancer in this cohort.Chromatid breaks were the most frequently observed type of aberrations (mean frequency 1.2 per 100 cells), which statistically significantly correlated with radon exposure (Spearman's correlation coefficient R=0.22, P<0.001). Also, the frequency of aberrant cells (median of 2.5%) correlated with radon exposure (Spearman's correlation coefficient R=0.16, P<0.02). Smoking and silicosis were not associated with results of cytogenetic analyses.The Cox regression models, which accounted for the age at time of first cytogenetic assay, radon exposure, and smoking showed strong and statistically significant associations between cancer incidence and frequency of chromatid breaks and frequency of aberrant cells, respectively. A 1% increase in the frequency of aberrant cells was paralleled by a 62% increase in risk of cancer (P<0.000). An increase in frequency of chromatid breaks by 1 per 100 cells was followed by a 99% increase in risk of cancer (P<0.000). We obtained similar results when we analyzed the incidence of lung cancer and the incidence other than lung cancer separately.Contrary to frequency of chromatid breaks and frequency of aberrant cells, the frequency of chromatid exchanges, and chromosome-type aberrations were not predictive of cancer.     

Clonal and non-clonal chromosome aberrations and genome variation and aberration.

The theoretical view that genome aberrations rather than gene mutations cause a majority of cancers has gained increasing support from recent experimental data. Genetic aberration at the chromosome level is a key aspect of genome aberration and the systematic definition of chromosomal aberrations with their impact on genome variation and cancer genome evolution is of great importance. However, traditionally, efforts have focused on recurrent clonal chromosome aberrations (CCAs). The significance of stochastic non-clonal chromosome aberrations (NCCAs) is discussed in this paper with emphasis on the simple types of NCCAs that have until recently been considered "non-significant background". Comparison of various subtypes of transitional and late-stage CCAs with simple and complex types of NCCAs has uncovered a dynamic relationship among NCCAs, CCAs, overall genomic instability, and karyotypic evolution, as well as the stochastic nature of cancer evolution. Here, we review concepts and methodologies to measure NCCAs and discuss the possible causative mechanism and consequences of NCCAs. This study raises challenging questions regarding the concept of cancer evolution driven by stochastic chromosomal aberration mediated genome irregularities that could have repercussions reaching far beyond cancer and organismal genomes.     

Preeclampsia: a bioinformatics approach through protein-protein interaction networks analysis

ABSTRACT: BACKGROUND: In this study we explored preeclampsia through a bioinformatics approach. We create a comprehensive genes/proteins dataset by the analysis of both public proteomic data and text mining of public scientific literature. From this dataset the associated protein-protein interaction network has been obtained. Several indexes of centrality have been explored for hubs detection as well as the enrichment statistical analysis of metabolic pathway and disease. RESULTS: We confirmed the well known relationship between preeclampsia and cardiovascular diseases but also identified statistically significant relationships with respect to cancer and aging. Moreover, significant metabolic pathways such as apoptosis, cancer and cytokine-cytokine receptor interaction have also been identified by enrichment analysis. We obtained FLT1, VEGFA, FN1, F2 and PGF genes with the highest scores by hubs analysis; however, we also found other genes as PDIA3, LYN, SH2B2 and NDRG1 with high scores. CONCLUSIONS: The applied methodology not only led to the identification of well known genes related to preeclampsia but also to propose new candidates poorly explored or completely unknown in the pathogenesis of preeclampsia, which eventually need to be validated experimentally. Moreover, new possible connections were detected between preeclampsia and other diseases that could open new areas of research. More must be done in this area to resolve the identification of unknown interactions of proteins/genes and also for a better integration of metabolic pathways and diseases.     

Elevated Tolerance to Aneuploidy in Cancer Cells: Estimating the Fitness Effects of Chromosome Number Alterations by In Silico Modelling of Somatic Genome Evolution

An unbalanced chromosome number (aneuploidy) is present in most malignant tumours and has been attributed to mitotic mis-segregation of chromosomes. However, recent studies have shown a relatively high rate of chromosomal mis-segregation also in non-neoplastic human cells, while the frequency of aneuploid cells remains low throughout life in most normal tissues. This implies that newly formed aneuploid cells are subject to negative selection in healthy tissues and that attenuation of this selection could contribute to aneuploidy in cancer. To test this, we modelled cellular growth as discrete time branching processes, during which chromosome gains and losses were generated and their host cells subjected to selection pressures of various magnitudes. We then assessed experimentally the frequency of chromosomal mis-segregation as well as the prevalence of aneuploid cells in human non-neoplastic cells and in cancer cells. Integrating these data into our models allowed estimation of the fitness reduction resulting from a single chromosome copy number change to an average of ≈30% in normal cells. In comparison, cancer cells showed an average fitness reduction of only 6% (p = 0.0008), indicative of aneuploidy tolerance. Simulations based on the combined presence of chromosomal mis-segregation and aneuploidy tolerance reproduced distributions of chromosome aberrations in >400 cancer cases with higher fidelity than models based on chromosomal mis-segregation alone. Reverse engineering of aneuploid cancer cell development in silico predicted that aneuploidy intolerance is a stronger limiting factor for clonal expansion of aneuploid cells than chromosomal mis-segregation rate. In conclusion, our findings indicate that not only an elevated chromosomal mis-segregation rate, but also a generalised tolerance to novel chromosomal imbalances contribute to the genomic landscape of human tumours.