Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification

Motivation: Prediction of phenotypes from high-dimensional data is a crucial task in precision biology and medicine. Many technologies employ genomic biomarkers to characterize phenotypes. However, such elements are not sufficient to explain the underlying biology. To improve this, pathway analysis techniques have been proposed. Nevertheless, such methods have shown lack of accuracy in phenotypes classification. Results: Here we propose a novel methodology called MITHrIL (Mirna enrIched paTHway Impact anaLysis) for the analysis of signaling pathways, which has built on top of the work of Tarca et al., 2009. MITHrIL extends pathways by adding missing regulatory elements, such as microRNAs, and their interactions with genes. The method takes as input the expression values of genes and/or microRNAs and returns a list of pathways sorted according to their deregulation degree, together with the corresponding statistical significance (p-values). Our analysis shows that MITHrIL outperforms its competitors even in the worst case. In addition, our method is able to correctly classify sets of tumor samples drawn from TCGA. Availability: MITHrIL is freely available at the following URL: http://alpha.dmi.unict.it/mithril

Methods of single-molecule energy landscape reconstruction with optical traps

Optical traps facilitate measurement of force and position as single molecules of DNA, RNA, or protein are unfolded and refolded. The effective energy landscape of a biomolecule can be reconstructed from the force and position data, providing insight into its structure and regulatory functions. We have developed new experimental and analytical methods to reconstruct energy landscapes by taking advantage of the harmonic constraint of an optical trap. We demonstrate the effectiveness of these methods using a model DNA hairpin and then apply these methods to study problems of practical biophysical interest. CCR5 mRNA has been demonstrated to stimulate -1 programmed ribosomal frameshifting and we measure its structural properties. We measure the binding energy of a GA/AG tandem mismatch, one of many mismatches with unusual properties. We use our single-molecule methods to reproduce bulk measurements of the nearest-neighbor DNA base-pair free energy parameters and we consider possible refinements to the model. We also study an alternative method of measuring energy landscapes, Dynamic Force Spectroscopy (DFS), and conduct experiments on DNA quadruplexes to demonstrate the effectiveness of DFS with optical traps. Finally, we develop theory to elucidate the role of noise in optical trap measurements of energy landscapes

Urological Cancer 2021

Cancer of the urological sphere is a disease continuously increasing in numbers in the statistics of tumor malignancies in Western countries. Although this fact is mainly due to the contemporary increase of life expectancy of the people in these geographic areas, many other factors do contribute as well to this growth. Urological cancer is a complex and varied disease of different organs and mainly affects the male population. In fact, kidney, prostate, and bladder cancer are regularly included in the top-ten list of the most frequent neoplasms in males in most statistics. The female population, however, has also increasingly found itself affected by renal and bladder cancer in the last decade. Considering these altogether, urological cancer is a problem of major concern in developed societies. This Topic Issue of Cancers intends to shed some light into the complexity of this field and will consider all useful and appropriate contributions that scientists and clinicians may provide to improve urological cancer knowledge for patients’ benefit. The precise identification of the molecular routes involved, the diagnostic pathological criteria in the grey zones, the dilemma of T1G3 management, and the possible treatment options between superficial, nonmuscle-invasive and muscle-invasive diseases will be particularly welcomed in this Issue

Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates

ABC efflux transporters are polyspecific members of the ABC superfamily that, acting as drug and metabolite carriers, provide a biochemical barrier against drug penetration and contribute to detoxification. Their overexpression is linked tomultidrug resistance issues in a diversity of diseases. Breast cancer resistance protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting oral absorption and brain bioavailability of its substrates. Early recognition of BCRP substrates is thus essential to optimize oral drug absorption, design of novel therapeutics for central nervous systemconditions, and overcome BCRP-mediated cross-resistance issues. We present the development of an ensemble of ligand-based machine learning algorithms for the early recognition of BCRP substrates, from a database of 262 substrates and nonsubstrates compiled from the literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models were developed through application of linear discriminant analysis to randomsubsamples ofDragonmolecular descriptors. Simple data fusion and statistical comparison of partial areas under the curve of ROC curves were applied to obtain the best 2-model combination, which presented 82% and 74.5% of overall accuracy in the training and test set, respectively.Facultad de Ciencias Exacta

Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates

ABC efflux transporters are polyspecific members of the ABC superfamily that, acting as drug and metabolite carriers, provide a biochemical barrier against drug penetration and contribute to detoxification. Their overexpression is linked tomultidrug resistance issues in a diversity of diseases. Breast cancer resistance protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting oral absorption and brain bioavailability of its substrates. Early recognition of BCRP substrates is thus essential to optimize oral drug absorption, design of novel therapeutics for central nervous systemconditions, and overcome BCRP-mediated cross-resistance issues. We present the development of an ensemble of ligand-based machine learning algorithms for the early recognition of BCRP substrates, from a database of 262 substrates and nonsubstrates compiled from the literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models were developed through application of linear discriminant analysis to randomsubsamples ofDragonmolecular descriptors. Simple data fusion and statistical comparison of partial areas under the curve of ROC curves were applied to obtain the best 2-model combination, which presented 82% and 74.5% of overall accuracy in the training and test set, respectively.Facultad de Ciencias Exacta

Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates

ABC efflux transporters are polyspecific members of the ABC superfamily that, acting as drug and metabolite carriers, provide a biochemical barrier against drug penetration and contribute to detoxification. Their overexpression is linked tomultidrug resistance issues in a diversity of diseases. Breast cancer resistance protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting oral absorption and brain bioavailability of its substrates. Early recognition of BCRP substrates is thus essential to optimize oral drug absorption, design of novel therapeutics for central nervous systemconditions, and overcome BCRP-mediated cross-resistance issues. We present the development of an ensemble of ligand-based machine learning algorithms for the early recognition of BCRP substrates, from a database of 262 substrates and nonsubstrates compiled from the literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models were developed through application of linear discriminant analysis to randomsubsamples ofDragonmolecular descriptors. Simple data fusion and statistical comparison of partial areas under the curve of ROC curves were applied to obtain the best 2-model combination, which presented 82% and 74.5% of overall accuracy in the training and test set, respectively.Facultad de Ciencias Exacta

A study of the predictive value of morphometric assessments in clinical outcome in ovarian epithelial malignancy

Quantitative pathology as a tool in gynaecological pathology is fairly new. Such techniques allow greater objectivity than histological grading, typing, and residual tumour estimation. This study aims to determine: whether basic morphometry data can predict outcome and chemotherapeutic response, whether newer semi-automated methods of tumour morphometry provide similar results to older methods, and whether advanced image analysis methods can offer further tumour outcome data in ovarian carcinoma. The study was performed on a well-selected group of serous ovarian carcinomas. Tumour outcome, survival and chemotherapeutic response, were investigated in 132 patients treated with the same platinum containing regimes. Traditional clinicopathologic parameters, p53 & Bcl2, mitotic activity index (MAn and angiogenesis determinants were initially investigated. Semi-automated analysis, using immunohistochemically based techniques, were applied to estimate volume percentage epithelium (VPE) and nuclear morphometric parameters. Syntactic structure analysis including, minimum spanning tree, and neighbourhood features, was also investigated. Multivariate analysis revealed residual disease status, FIGO stage, MAl, VPE, equivalent nuclear diameter, and angiogenesis parameters to be strong prognosticators for overall and disease free survival. Residual disease status, VPE, nuclear length and angiogenesis parameters were found significant predictors of chemotherapy response. Angiogenesis parameters, as determined by semi-automated image analysis techniques, were found overall to be the strongest prognosticators. Morphometric data can predict outcome and chemotherapeutic response in ovarian serous carcinoma. Semi-automated morphometry techniques provide similar results to ()lder methods, and advanced image analysis can offer further outcome data. The rationale for the application of semi-automated and automated detection is that it may provide an unbiased sampling of a lesion and possibly a more representative estimate of areas that a human expert might label. Such determined, quantitative pathological findings were found to have important value in predicting prognosis in ovarian carcinoma and, if not to supersede, certainly to add to classical prognostic factors

O papel de fibroblastos associados ao câncer no carcinoma epidermóide oral : marcador prognóstico, secreção de vesículas extracelulares promovendo a invasão tumoral e associação com a expressão de ROCK2

Orientador: Ricardo Della ColettaTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Odontologia de PiracicabaResumo: Uma importante característica dos carcinomas epidermóides orais (CEO) é a aquisição de fibroblastos que expressam a isoforma alfa da actina de músculo liso (alfa-SMA) no estroma adjacente, denominados miofibroblastos ou fibroblastos associados ao câncer (CAF, do inglês cancer-associated fibroblasts). Os objetivos deste estudo foram: 1) avaliar por meio de uma metanálise o potencial de CAF como marcador prognóstico para os CEO, 2) determinar o papel das vesículas extracelulares (VE) secretadas por CAF na indução da invasão celular em CEO, 3) avaliar o valor prognóstico da expressão tumoral de ROCK2, que é também secretada por CAF, em pacientes com CEO. A metanálise selecionou 12 estudos elegíveis, abrangendo um total de 1328 pacientes, e revelou que a elevada presença de CAF no estroma dos CEO pode significativamente predizer uma diminuição no tempo para a recidiva (HR: 3,32, 95% CI: 2,09-5,26, p<0,00001) e diminuição na sobrevida global do paciente (HR: 2,16, 95% CI: 1,60-2,92, p<0,00001). Em comparação com VE derivadas de fibroblastos orais normais (FON), CAF-VE promoveram um padrão disseminado de invasão tumoral,o qual é associado com um aumento significativo na migração e invasão das células tumorais.As análises interactômicas de FON- e CAF-VE e de células de CEO tratadas com VE demonstraram um enriquecimento para diferentes vias metabólicas e de adesão focal. No tocante à expressão de ROCK2, nossos resultados mostraram que o estadiamento clínico avançado (p=0,002) e o aumento na densidade de CAF (p=0,002) estão significativamente associados com o aumento na expressão tumoral de ROCK2 e alta expressão de ROCK2 está relacionada à diminuição da sobrevida específica (HR: 2,22, 95% CI: 1,15-4,38, p=0,04). Os resultados deste estudo mostram que a presença de CAF é um indicador de pior prognóstico para CEO, e CAF são relevantes no suporte da invasão das células de CEO. Elevada expressão de ROCK2 em CEO está associada com doença avançada e acompanha o aumento da densidade de CAF, sugerindo a organização de um microambiente permissivo à invasão, facilitando a progressão tumoralAbstract: An important feature of oral squamous cell carcinomas (OSCC) is the acquisition of alfa-smooth muscle actin (?-SMA)-positive fibroblasts, termed myofibroblasts or cancer-associated fibroblasts (CAF), into the adjacent stroma. The aims of this study were: 1) to assess through meta-analysis the potential of CAF as prognostic marker for OSCC, 2) to determine the role of extracellular vesicles (EV) released by CAF in invasion of OSCC cells, and 3) to evaluate the prognostic relevance of ROCK2 tumor expression, which is secreted by CAF, in patients with OSCC. In the meta-analysis, pooling data from 12 eligible studies comprising 1328 patients revealed that high presence of CAF in the stroma of OSCC significantly predicted shortened time to relapse (HR= 3.32, 95% CI: 2.09-5.26, p<0.00001) and an overall decrease in survival (HR: 2.16, 95% CI: 1.60-2.92, p<0.00001). In comparison with EV isolated from normal oral fibroblasts (NOF), CAF-EV promoted a disseminated pattern of invasion associated with significant increase in migration and invasion of the OSCC cells. Interactome network analysis of NOF- and CAF-EV and OSCC-treated cells with EV showed enrichment for metabolic and focal adhesion pathways. Regarding ROCK2 expression, our results showed that advanced clinical stage (p=0.002) and increased density of CAF (p=0.002) are significantly associated with high ROCK2 expression, and high expression of ROCK2 is related to shortened disease-specific survival (HR: 2.22, 95% CI: 1.15-4.38, p=0.04). Our results show that the presence of CAF is a marker of poor OSCC prognosis, and CAF are relevant in supporting OSCC cell invasion. High expression of ROCK2 in OSCC is associated with advanced disease and follows the increase in CAF density, suggesting the organization of a more invasion-permissive microenvironment, facilitating tumor progressionDoutoradoEstomatologiaCAPE