Computational methods for the discovery of molecular signatures from Omics Data

Molecular biomarkers, derived from high-throughput technologies, are the foundations of the "next-generation" precision medicine. Despite a decade of intense efforts and investments, the number of clinically valid biomarkers is modest. Indeed, the "big-data" nature of omics data provides new challenges that require an improvement in the strategies of data analysis and interpretation. In this thesis, two themes are proposed, both aimed at improving the statistical and computational methodology in the field of signatures discovery. The first work aim at identifying serum miRNAs to be used as diagnostic biomarkers associated with ovarian cancer. In particular, a guideline and an ad-hoc microarray normalization strategy for the analysis of circulating miRNAs is proposed. In the second work, a new approach for the identification of functional molecular signatures based on Gaussian graphical models is presented. The model can explore the topological information contained in the biological pathways and highlight the potential sources of differential behaviors in two experimental conditions

Leveraging three-dimensional chromatin architecture for effective reconstruction of enhancer-target gene regulatory interactions

A growing amount of evidence in literature suggests that germline sequence variants and somatic mutations in non-coding distal regulatory elements may be crucial for defining disease risk and prognostic stratification of patients, in genetic disorders as well as in cancer. Their functional interpretation is challenging because genome-wide enhancer-target gene (ETG) pairing is an open problem in genomics. The solutions proposed so far do not account for the hierarchy of structural domains which define chromatin three-dimensional (3D) architecture. Here we introduce a change of perspective based on the definition of multi-scale structural chromatin domains, integrated in a statistical framework to define ETG pairs. In this work (i) we develop a computational and statistical framework to reconstruct a comprehensive map of ETG pairs leveraging functional genomics data; (ii) we demonstrate that the incorporation of chromatin 3D architecture information improves ETG pairing accuracy and (iii) we use multiple experimental datasets to extensively benchmark our method against previous solutions for the genome-wide reconstruction of ETG pairs. This solution will facilitate the annotation and interpretation of sequence variants in distal non-coding regulatory elements. We expect this to be especially helpful in clinically oriented applications of whole genome sequencing in cancer and undiagnosed genetic diseases research.A growing amount of evidence in literature suggests that germline sequence variants and somatic mutations in non-coding distal regulatory elements may be crucial for defining disease risk and prognostic stratification of patients, in genetic disorders as well as in cancer. Their functional interpretation is challenging because genome-wide enhancer–target gene (ETG) pairing is an open problem in genomics. The solutions proposed so far do not account for the hierarchy of structural domains which define chromatin three-dimensional (3D) architecture. Here we introduce a change of perspective based on the definition of multi-scale structural chromatin domains, integrated in a statistical framework to define ETG pairs. In this work (i) we develop a computational and statistical framework to reconstruct a comprehensive map of ETG pairs leveraging functional genomics data; (ii) we demonstrate that the incorporation of chromatin 3D architecture information improves ETG pairing accuracy and (iii) we use multiple experimental datasets to extensively benchmark our method against previous solutions for the genome-wide reconstruction of ETG pairs. This solution will facilitate the annotation and interpretation of sequence variants in distal non-coding regulatory elements. We expect this to be especially helpful in clinically oriented applications of whole genome sequencing in cancer and undiagnosed genetic diseases research

Urinary proteomic profiles of prostate cancer with different risk of progression and correlation with histopathological features

Prostate cancer (PCa) is the most common tumor in men with extremely variable outcome, varying from latent or indolent form to very aggressive behavior. High grade tumors, expansions exceeding the prostatic capsule into the surrounding soft tissues and spreading through lymph vascular channels, represent the most consistent unfavorable prognostic factors. However, accuracy in the prediction of the disease progression is sometimes difficult. Along with new molecular diagnostic techniques and more accurate histopathological approaches, proteomic studies challenge to identify potential biomarkers predictive of PCa progression. In our study we analyzed the urinary proteomes of 42 patients affected by PCa through two-dimensional electrophoresis associated with mass spectrometry. Proteomic profiles were correlated to histopathological features including pTNM stage and tumor differentiation in order to provide new promising markers able to define more accurately the PCa aggressiveness and driving new therapeutic approaches

Influence of tobacco smoking on urinary excretion of trans,trans-muconic acid

O hábito de fumar é considerado a maior fonte individual de benzeno para indivíduos não expostos ocupacionalmente e, em vista de sua comprovada carcinogenicidade, este solvente representa sério risco, tanto para a população em geral quanto para os trabalhadores. Um dos bioindicadores mais utilizados para avaliar a exposição benzênica é o ácido trans,trans-mucônico (AttM) em urina, o qual apresenta sensibilidade suficiente para a monitorização biológica da exposição a baixos níveis de benzeno. O objetivo deste trabalho foi verificar a influência do hábito de fumar na excreção urinária do AttM. A identificação e quantificação do analito foram realizadas por meio de cromatografia líquida de alta eficiência, utilizando-se coluna de fase reversa e detecção a 264 nm, após prévia extração em fase sólida. O método foi previamente validado demonstrando linearidade, no intervalo de 0,006 a 10,0 µg mL-1 (r= 0,996), limites de detecção e de quantificação de, respectivamente, 1,41 x 10-3 µg mL-1 e 6,0 x 10-3 µg mL-1 e coeficientes de variação de 1,79 a 2,91% (precisão intracorridas) e, de 3,53 a 18,37% (precisão intercorridas). Foi observada correlação positiva entre a excreção do AttM e o hábito de fumar, comparando o grupo de fumantes e o de não-fumantes (p= 0,005388). Contudo, o número de cigarros fumados por dia não influenciou significativamente a excreção deste produto de biotransformação do benzeno.Tobacco smoking is the major individual benzene source for no occupationally exposed subjects. This solvent is a volatile carcinogen and can induce serious health problems. Several biomarkers for benzene have been proposed, the most used is its metabolite trans,trans-muconic acid (ttMA) in urine, a suitable indicator since it reflects the internal dose of low benzene exposure. The present study aimed to evaluate the influence of smoking on urinary ttMA excretion. The analysis was performed by High Pressure Liquid Chromatography in a reversed-phase column and UV detection, after extraction of ttAM from urine using SAX cartridges. The method was validated, showing linearity between 0.006 to 10 µg mL-1 (r= 0.996), detection and quantification limits of 1.41 x 10-3 µg mL-1 and 6.0 x 10-3 µg mL-1 respectively, CVs from 1.79 to 2.91% (intra assay precision) and 3.53 to 18.37% (interassay precision). A significant positive correlation was observed between ttMA excretion and smoking (p= 0.005388). However, the cigarette number smoked by day seems have no influence on the urinary ttMA excretion

CHD8 suppression impacts on histone H3 lysine 36 trimethylation and alters RNA alternative splicing

Disruptive mutations in the chromodomain helicase DNA-binding protein 8 gene (CHD8) have been recurrently associated with autism spectrum disorders (ASDs). Here we investigated how chromatin reacts to CHD8 suppression by analyzing a panel of histone modifications in induced pluripotent stem cell-derived neural progenitors. CHD8 suppression led to significant reduction (47.82%) in histone H3K36me3 peaks at gene bodies, particularly impacting on transcriptional elongation chromatin states. H3K36me3 reduction specifically affects highly expressed, CHD8-bound genes and correlates with altered alternative splicing patterns of 462 genes implicated in ‘regulation of RNA splicing’ and ‘mRNA catabolic process’. Mass spectrometry analysis uncovered a novel interaction between CHD8 and the splicing regulator heterogeneous nuclear ribonucleoprotein L (hnRNPL), providing the first mechanistic insights to explain the CHD8 suppression-derived splicing phenotype, partly implicating SETD2, a H3K36me3 methyltransferase. In summary, our results point toward broad molecular consequences of CHD8 suppression, entailing altered histone deposition/maintenance and RNA processing regulation as important regulatory processes in ASD

Computational methods for the discovery of molecular signatures from Omics Data

Molecular biomarkers, derived from high-throughput technologies, are the foundations of the "next-generation" precision medicine. Despite a decade of intense efforts and investments, the number of clinically valid biomarkers is modest. Indeed, the "big-data" nature of omics data provides new challenges that require an improvement in the strategies of data analysis and interpretation. In this thesis, two themes are proposed, both aimed at improving the statistical and computational methodology in the field of signatures discovery. The first work aim at identifying serum miRNAs to be used as diagnostic biomarkers associated with ovarian cancer. In particular, a guideline and an ad-hoc microarray normalization strategy for the analysis of circulating miRNAs is proposed. In the second work, a new approach for the identification of functional molecular signatures based on Gaussian graphical models is presented. The model can explore the topological information contained in the biological pathways and highlight the potential sources of differential behaviors in two experimental conditions.I biomarcatori molecolari, ottenuti attraverso l'utilizzo di piattaforme high-throughput sequencing, costituiscono le basi della medicina personalizzata di nuova generazione. Nonostante un decennio di sforzi e di investimenti, il numero di biomarcatori validi a livello clinico rimane modesto. La natura di "big-data" dei dati omici infatti ha introdotto nuove sfide che richiedono un miglioramento sia degli strumenti di analisi che di quelli di esplorazione dei risultati. In questa tesi vengono proposti due temi centrali, entrambi volti al miglioramento delle metodologie statistiche e computazionali nell'ambito dell'individuazione di firme molecolari. Il primo lavoro si sviluppa attorno all'identificazione di miRNA su siero in pazienti affetti da carcinoma ovarico impiegabili a livello diagnostico. In particolare si propongono delle linee guida per il processo di analisi e una normalizzazione ad-hoc per dati di microarray da utilizzarsi nel contesto di molecole circolanti. Nel secondo lavoro si presenta un nuovo approccio basato sui modelli grafici Gaussiani per l'identificazione di firme molecolari funzionali. Il metodo proposto è in grado di esplorare le informazioni contenute nei pathway biologici e di evidenziare la potenziale origine del comportamento differenziale tra due condizioni sperimentali

simPATHy: a new method for simulating data from perturbed biological PATHways

In the omic era, one of the main aims is to discover groups of functionally related genes that drive the difference between different conditions. To this end, a plethora of potentially useful multivariate statistical approaches has been proposed, but their evaluation is hindered by the absence of a gold standard. Here, we propose a method for simulating biological data-gene expression, RPKM/FPKM or protein abundances-from two conditions, namely, a reference condition and a perturbation of it. Our approach is built upon probabilistic graphical models and is thus especially suited for testing topological wapproaches

Technical results, clinical efficacy and predictors of outcome of intercostal arteries embolization for hemothorax: a two-institutions’ experience

Background: To evaluate the clinical efficacy and identify the predictors of outcome of intercostal arterial embolization for hemothorax caused by intercostal artery (ICA) injuries. Methods: A retrospective multi-institutional study was conducted. Outcomes were analyzed in 30 consecutive patients presenting with hemothorax caused by active ICA hemorrhage undergoing transcatheter arterial embolization (TAE). Clinical and procedural parameters were compared between outcomes groups. Results: Overall technical success rate was 87% (n=26). Among the 4 failed cases, 2 underwent repeated TAE and 2 underwent additional surgery. Overall 30-day mortality rate was 23%. Low haemoglobin levels and haematocrit, hepatic comorbidities and more than one artery undergoing embolization increased technical failure rate significantly. Survival was poorer in patients with massive bleeding. Conclusions: ICA embolization was found to be a safe and effective method in treating hemothorax caused by active ICA haemorrhage. Careful pre-embolization evaluation may be required for patient with low haemoglobin levels and haematocrit, hepatic comorbidities and active haemorrhage from more than one artery