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The data from independent gene expression sources may be integrated for the purpose of molecular diagnostics of cancer. So far, multiple approaches were described. Here, we investigated the impacts of different data fusion strategies on classification accuracy and feature selection stability, which allow the costs of diagnostic tests to be reduced. We used molecular features (gene expression) combined with a feature extracted from the independent clinical data describing a patient’s sample. We considered the dependencies between selected features in two data fusion strategies (early fusion and late fusion) compared to classification models based on molecular features only. We compared the best accuracy classification models in terms of the number of features, which is connected to the potential cost reduction of the diagnostic classifier. We show that for thyroid cancer, the extracted clinical feature is correlated with (but not redundant to) the molecular data. The usage of data fusion allows a model to be obtained with similar or even higher classification quality (with a statistically significant accuracy improvement, a p-value below 0.05) and with a reduction in molecular dimensionality of the feature space from 15 to 3–8 (depending on the feature selection method). Both strategies give comparable quality results, but the early fusion method provides better feature selection stability.

UVA radiation, which accounts for about 95% of the solar spectrum, contributes to and may be the etiological factor of skin cancers of which malignant melanoma is the most aggressive. UVA causes oxidative stress in various types of cells in the skin, keratinocyte, melanocytes, and fibroblasts, which is responsible for its cytotoxic effect. Here we used a transwell system to explore how the responses of melanoma cells to a low dose of UVA (20 kJ/m2, ~ 10% of the minimal erythema dose) are influenced by neighboring co-cultured melanoma cells or fibroblasts. This dose had a low toxicity for melanoma cells, but after irradiation, co-culture with non-irradiated melanoma cells caused a strong decline in their viability and an increased frequency of apoptosis, whereas co-culture with fibroblast exerted a protective effect on irradiated melanoma cells. At the same time, the presence of non-irradiated cells, especially fibroblasts, decreased the level of UVA-induced reactive oxygen and nitrogen species. Interleukins efficiently produced by fibroblasts seem to be main players in these effects. Our studies reveal that coexistence of fibroblasts with melanoma cells may strongly modulate the direct action and may change bystander effects exerted by UVA light. Similar modulation of the effect of UVA on melanoma cells in vivo by bystander-like signaling from neighboring cells would have consequences for the development of malignant melanoma.

The primary diagnosis of thyroid tumors based on histopathological patterns can be ambiguous in some cases, so proper classification of thyroid diseases might be improved if molecular biomarkers support cytological and histological assessment. In this work, tissue microarrays representative for major types of thyroid malignancies—papillary thyroid cancer (classical and follicular variant), follicular thyroid cancer, anaplastic thyroid cancer, and medullary thyroid cancer—and benign thyroid follicular adenoma and normal thyroid were analyzed by mass spectrometry imaging (MSI), and then different computation approaches were implemented to test the suitability of the registered profiles of tryptic peptides for tumor classification. Molecular similarity among all seven types of thyroid specimens was estimated, and multicomponent classifiers were built for sample classification using individual MSI spectra that corresponded to small clusters of cells. Moreover, MSI components showing the most significant differences in abundance between the compared types of tissues detected and their putative identity were established by annotation with fragments of proteins identified by liquid chromatography-tandem mass spectrometry in corresponding tissue lysates. In general, high accuracy of sample classification was associated with low inter-tissue similarity index and a high number of components with significant differences in abundance between the tissues. Particularly, high molecular similarity was noted between three types of tumors with follicular morphology (adenoma, follicular cancer, and follicular variant of papillary cancer), whose differentiation represented the major classification problem in our dataset. However, low level of the intra-tissue heterogeneity increased the accuracy of classification despite high inter-tissue similarity (which was exemplified by normal thyroid and benign adenoma). We compared classifiers based on all detected MSI components (n = 1536) and the subset of the most abundant components (n = 147). Despite relatively higher contribution of components with significantly different abundance and lower overall inter-tissue similarity in the latter case, the precision of classification was generally higher using all MSI components. Moreover, the classification model based on individual spectra (a single-pixel approach) outperformed the model based on mean spectra of tissue cores. Our result confirmed the high feasibility of MSI-based approaches to multi-class detection of cancer types and proved the good performance of sample classification based on individual spectra (molecular image pixels) that overcame problems related to small amounts of heterogeneous material, which limit the applicability of classical proteomics.

Molecular mechanisms of distant metastases (M1) in papillary thyroid cancer (PTC) are poorly understood. We attempted to analyze the gene expression profile in PTC primary tumors to seek the genes associated with M1 status and characterize their molecular function. One hundred and twenty-three patients, including 36 M1 cases, were subjected to transcriptome oligonucleotide microarray analyses: (set A&mdash, U133, set B&mdash, HG 1.0 ST) at transcript and gene group level (limma, gene set enrichment analysis (GSEA)). An additional independent set of 63 PTCs, including 9 M1 cases, was used to validate results by qPCR. The analysis on dataset A detected eleven transcripts showing significant differences in expression between metastatic and non-metastatic PTC. These genes were validated on microarray dataset B. The differential expression was positively confirmed for only two genes: IGFBP3, (most significant) and ECM1. However, when analyzed on an independent dataset by qPCR, the IGFBP3 gene showed no differences in expression. Gene group analysis showed differences mainly among immune-related transcripts, indicating the potential influence of tumor immune infiltration or signal within the primary tumor. The differences in gene expression profile between metastatic and non-metastatic PTC, if they exist, are subtle and potentially detectable only in large datasets.

One of the important data mining method in biomedical research is classification task. Recent advances in biomedicine provide opportunities for molecular biology, such as measurement of activity of thousands of molecular tissue biomarkers. For example we can use data of gene expression measured by DNA microarrays or RNA-Seq technique, DNA methylation levels measured by DNA methylation microarrays or protein and phosphoprotein levels measured by reverse phase protein array. A big problem in applying large-scale genomic and proteomic data for classification problem is the dimension of these data. In this work, we propose novel multiclass feature selection and classification system for merged data from different molecular biomedical techniques. However, when we merge these data the biggest problem is the huge number of features with a limited number of samples. For that reason the feature selection step is crucial in high dimension data classification problem. Our results have shown that integrated analysis with proper feature selection and classification techniques used for large-scale meta-data can improve the classification accuracy and feature selection stability index. We have proofed, that for merged data we observe significantly higher classification accuracy for the same number of selected features as for single technique dataset.

Radiation-induced bystander effect, appearing as different biological changes in cells that are not directly exposed to ionizing radiation but are under the influence of molecular signals secreted by irradiated neighbors, have recently attracted considerable interest due to their possible implication for radiotherapy. However, various cells present diverse radiosensitivity and bystander responses that depend, inter alia, on genetic status including TP53, the gene controlling the cell cycle, DNA repair and apoptosis. Here we compared the ionizing radiation and bystander responses of human colorectal carcinoma HCT116 cells with wild type or knockout TP53 using a transwell co-culture system. The viability of exposed to X-rays (0–8 Gy) and bystander cells of both lines showed a roughly comparable decline with increasing dose. The frequency of micronuclei was also comparable at lower doses but at higher increased considerably, especially in bystander TP53-/- cells. Moreover, the TP53-/- cells showed a significantly elevated frequency of apoptosis, while TP53+/+ counterparts expressed high level of senescence. The cross-matched experiments where irradiated cells of one line were co-cultured with non-irradiated cells of opposite line show that both cell lines were also able to induce bystander effects in their counterparts, however different endpoints revealed with different strength. Potential mediators of bystander effects, IL-6 and IL-8, were also generated differently in both lines. The knockout cells secreted IL-6 at lower doses whereas wild type cells only at higher doses. Secretion of IL-8 by TP53−/− control cells was many times lower than that by TP53+/+ but increased significantly after irradiation. Transcription of the NFκBIA was induced in irradiated TP53+/+ mainly, but in bystanders a higher level was observed in TP53−/− cells, suggesting that TP53 is required for induction of NFκB pathway after irradiation but another mechanism of activation must operate in bystander cells.

This article presents a method for sensitivity analysis of non-linear continuous-time models with delays and its application to parameter estimation. The method is universal and may be used for sensitivity analysis of any system given as a block diagram with arbitrary structure and any number of delays. The method gives sensitivity functions of model trajectories with respect to all model parameters, including delay times, and both forward and adjoint sensitivity analysis may be performed. Two examples application of the method are presented: identification of a Wiener model with delay and identification of a model of JAK-STAT cell signal transduction mechanism.

Planning identification experiments for cell signaling pathways: An NFκB case studyMathematical modeling of cell signaling pathways has become a very important and challenging problem in recent years. The importance comes from possible applications of obtained models. It may help us to understand phenomena appearing in single cells and cell populations on a molecular level. Furthermore, it may help us with the discovery of new drug therapies. Mathematical models of cell signaling pathways take different forms. The most popular way of mathematical modeling is to use a set of nonlinear ordinary differential equations (ODEs). It is very difficult to obtain a proper model. There are many hypotheses about the structure of the model (sets of variables and phenomena) that should be verified. The next step, fitting the parameters of the model, is also very complicated because of the nature of measurements. The blotting technique usually gives only semi-quantitative observations, which are very noisy and collected only at a limited number of time moments. The accuracy of parameter estimation may be significantly improved by a proper experiment design. Recently, we have proposed a gradient-based algorithm for the optimization of a sampling schedule. In this paper we use the algorithm in order to optimize a sampling schedule for the identification of the mathematical model of the NFκB regulatory module, known from the literature. We propose a two-stage optimization approach: a gradient-based procedure to find all stationary points and then pair-wise replacement for finding optimal numbers of replicates of measurements. Convergence properties of the presented algorithm are examined.

The paper concerns the problem of fitting mathematical models of cell signaling pathways. Such models frequently take the form of sets of nonlinear ordinary differential equations. While the model is continuous in time, the performance index used in the fitting procedure involves measurements taken at discrete time moments. Adjoint sensitivity analysis is a tool which can be used for finding the gradient of a performance index in the space of parameters of the model. In the paper, a structural formulation of adjoint sensitivity analysis called the generalized backpropagation through time (GBPTT) is used. The method is especially suited for hybrid, continuous-discrete time systems. As an example, we use the mathematical model NF-kappaB of the regulatory module, which plays a major role in the innate immune response in animals.

DNA microarray technology is the latest and the most advanced tool for parallel measuring of the activity and interactions of thousands of genes. This modern technology promises new insight into mechanisms of living systems, for example only two high-density oligonucleotide microarrays are sufficient to inspect the whole human genome. However, it provides unprecedented amount of data that require application of advanced computational methods. The appropriate choice of data analysis technique depends both on data and on goals of an experiment. In this paper we focus on two promising methods: singular value decomposition and support vector machines. We discuss the possibility of application of these methods for different purposes; particularly for clustering, classification, feature selection and modeling of dynamics of gene expression. We use for testing presented approaches existing data sets, which are widely available via Internet, and one new tumor/normal thyroid microarray data set.

Microarrays provide a new technique of measuring gene expression that attracted a lot of research interest in recent years. It has been suggested that gene expression data from microarrays (biochips) can be utilized in many biomedical areas, for example in cancer classification. Whereas several, new and existing, methods of classification has been tested, a selection of proper (optimal) set of genes, which expression serves during classification, is still an open problem. In this paper we propose a heuristic method of choosing suboptimal set of genes by using support vector machines (SVM). Obtained set of genes optimizes leave-one-out cross-validation error. The method is tested on microarray gene expression data of samples of two cancer types: acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). The results show that quality of classification is much better than for sets obtained using other methods of feature selection. In addition, we demonstrate that maximum separation in a training data set may lead to deterioration of performance in an independent validation data set, a phenomenon akin to overfitting.

The detection of somatic mutations in indeterminate or follicular proliferation fine-needle aspiration cytologies (FNACs) is able to clarify only a subgroup of those FNACs. Therefore, further markers to differentiate this problematic FNAC category by the identification of mutation negative thyroid cancers and benign nodules are urgently needed. Our objective was to evaluate previously published miRNA markers and discover novel ones from all publicly available miRNA expression profiling data sets. By literature review and data repository search we gathered 3 data sets describing human miRNA expression profiles of follicular thyroid cancer (FTC) and follicular adenoma (FA) samples. Literature review summarized 27 previously published miRNAs, which were validated in the 3 available data sets. By means of uniform statistical analysis 6 further miRNAs were identified and tested in an independent, previously published microarray data set. Meta-analysis confirmed 7 out of 27 previously published, and 4 out of 6 de novo identified miRNAs. The low confirmation rate of previously published miRNA markers was induced by low numbers of samples in the analyzed studies and high false discovery rates that were higher than 0.2. Finally, miR-637, miR-181c-3p, miR-206, and miR-7-5p were discovered as de novo potential FTC markers and validated in at least one independent, previously published data set. Two out of these new identified miRNAs (miR-7-5p and miR-206) were validated by qPCR in an independent sample set of 32 FTC and 46 FA samples. Especially miR-7-5p was able to differentiate benign and malignant thyroid tumors in several datasets.

The objective of the studies described here was the development of a mathematical model which would fit experimental data for the repair of single and double strand breaks induced in DNA in living cells by exposure to ionizing radiation, and which would allow to better understand the processes of DNA repair. DNA breaks are believed to play the major role in radiation-induced lethality and formation of chromosome deletions, and are therefore crucial to the response of cells to radiotherapy. In an initial model which we reported on the basis of data for the repair of Epstein-Barr minichromosomes in irradiated Raji cells, we assumed that DNA breaks are induced only at the moment of irradiation and are later removed by repair systems. This work gives a development of that mathematical model which fits the experimental results more precisely and suggests strongly that DNA breaks are generated not only by direct irradiation but also later, probably by systems engaged in repair of oxidative damage.