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BackgroundNon-small cell lung cancer (NSCLC) is the most common type of lung cancer, and the median overall survival is approximately 2-3 years among patients with stage III disease. Furthermore, it is one of the deadliest types of cancer globally due to non-specific symptoms and the lack of a biomarker for early detection. The most important decision that clinicians need to make after a lung cancer diagnosis is the selection of a treatment schedule. This decision is based on, among others factors, the risk of developing metastasis.MethodsA cohort of 115 NSCLC patients treated using chemotherapy and radiotherapy with curative intent was retrospectively collated and included patients for whom positron emission tomogra-phy/computed tomography (PET/CT) images, acquired before radiotherapy, were available. The PET/CT images were used to compute radiomic features extracted from a region of interest, the primary tumor. Radiomic and clinical features were then classified to stratify the patients into short and long time to metastasis, and regression analysis was used to predict the risk of metastasis.ResultsClassification based on binarized metastasis-free survival (MFS) was applied with moderate success. Indeed, an accuracy of 0.73 was obtained for the selection of features based on the Wilcoxon test and logistic regression model. However, the Cox regression model for metastasis risk prediction performed very well, with a concordance index (c-index) score equal to 0.84.ConclusionsIt is possible to accurately predict the risk of metastasis in NSCLC patients based on radiomic features. The results demonstrate the potential use of features extracted from cancer imaging in predicting the risk of metastasis.

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.

A synergy between two therapeutic agents occurs when the effect of using both is greater than the sum of the effects of using each of them separately. There are several methods of detecting synergies in the literature, but they do not take into account the relationship between the times in which the factors act. In the article, we propose and justify the use of second-order sensitivity analysis as a potential tool for the detection and visualization of this type of synergy. We test and illustrate the proposed approach using four exemplary models of combined radio-chemotherapy of cancer.

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 paper presents a novel approach to the prediction of the combined therapy outcome for non-small lung cancer patients. A hybrid model is proposed, consisting of two parts. The first one is a mathematical model of tumor response to therapy, whose parameters are expressed as linear function of data from massspectrometry of patient blood plasma samples. These linear functions constitute thesecond component of the hybrid model. A comparison of clinical and simulation-based survival curves is used to evaluate the quality of the model.

In this work, we consider a simple mathematical model of radiochemotherapy which includes a term responsible for radiosensitization. We focus on finding theoretically optimal controls which maximise tumour cure probability for a finite, fixed therapeutic horizon. We prove that the optimal controls for both therapies are of 0-bang type, a result which is not altered by the inclusion of the radiosensilization term. By means of numerical simulations, we show that optimal control offers a moderate increase in survival time over a sequential treatment. We then revisit in more detail a question of measuring the synergy between the therapies by means of isobolograms, a common experimental technique for measuring the additivity of two treatments.

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.

The work presents a gradient-based approach to estimation of initial functions of time delay elements appearing in models of dynamical systems. It is shown how to generate the gradient of the estimation objective function in the initial function space using adjoint sensitivity analysis. It is assumed that the system is continuous-time and described by ordinary differential equations with delays but the estimation is done based on discrete-time measurements of the signals appearing in the system. Results of gradient-based estimation of initial functions for exemplary models are presented and discussed.

There are many impediments to progress in cancer research. Insufficient or low quality data and computational tools that are dispersed among various sites are one of them. In this paper we present an integrated system that combines all stages of cancer studies, from gathering of clinical data, through elaborate patient questionnaires and bioinformatics tools, to data warehousing and preparation of analysis reports.