New Approaches for the Molecular Profiling of Human Cancers through Omics Data Analysis

In this thesis, we present three studies in which we applied ad hoc computational methods for the molecular profiling of human cancers using omics data. In the first study our main goal was to develop a pipeline of analysis able to detect a wide range of single nucleotide mutations with high validation rates. We combined two standard tools to create the GATK-LODN method, and we applied our pipeline to exome sequencing data of hematological and solid tumors. We created simulated datasets and performed experimental validation to test the pipeline sensitivity and specificity. In the second study we characterized the gene expression profiles of 11 tumor types aiming the discovery of multi-tumor drug targets and new strategies of drug combination and repurposing. We clustered tumors and applied a network-based analysis to integrate gene expression and protein interaction information. We defined three multi-tumor gene signatures, characterized by the following categories: NF-KB signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis. We evaluated the gene signatures based on mutational, pharmacological and clinical evidences. Moreover, we defined new pharmacological strategies validated by in vitro experiments that showed inhibition of cell growth in two tumor cell lines. In the third study we evaluated thyroid gene expression profiles of normal, Papillary Thyroid Carcinoma (PTC) and Anaplastic Thyroid Carcinoma (ATC) samples. The samples grouped in a progressional trend according to tissue type and the main biological processes affected in the normal to PTC transition were related to extracellular matrix and cell morphology; and those affected in the PTC to ATC transition were related to the control of cell cycle. We defined signatures related to each step of tumor progression and mapped the signatures onto protein-protein interaction and transcriptomical regulatory networks to prioritize genes for following experimental validation

Optimized pipeline of MuTect and GATK tools to improve the detection of somatic single nucleotide polymorphisms in whole- exome sequencing data

Background: Detecting somatic mutations in whole exome sequencing data of cancer samples has become a popular approach for profiling cancer development, progression and chemotherapy resistance. Several studies have proposed software packages, filters and parametrizations. However, many research groups reported low concordance among different methods. We aimed to develop a pipeline which detects a wide range of single nucleotide mutations with high validation rates. We combined two standard tools – Genome Analysis Toolkit (GATK) and MuTect – to create the GATK-LODN method. As proof of principle, we applied our pipeline to exome sequencing data of hematological (Acute Myeloid and Acute Lymphoblastic Leukemias) and solid (Gastrointestinal Stromal Tumor and Lung Adenocarcinoma) tumors. We performed experiments on simulated data to test the sensitivity and specificity of our pipeline. Results: The software MuTect presented the highest validation rate (90 %) for mutation detection, but limited number of somatic mutations detected. The GATK detected a high number of mutations but with low specificity. The GATK-LODN increased the performance of the GATK variant detection (from 5 of 14 to 3 of 4 confirmed variants), while preserving mutations not detected by MuTect. However, GATK-LODN filtered more variants in the hematological samples than in the solid tumors. Experiments in simulated data demonstrated that GATK-LODN increased both specificity and sensitivity of GATK results. Conclusion: We presented a pipeline that detects a wide range of somatic single nucleotide variants, with good validation rates, from exome sequencing data of cancer samples. We also showed the advantage of combining standard algorithms to create the GATK-LODN method, that increased specificity and sensitivity of GATK results. This pipeline can be helpful in discovery studies aimed to profile the somatic mutational landscape of cancer genomes

Systems medicine of inflammaging

Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial. Despite the great technological progresses producing huge amount of data (Big Data) and impressive computational facilities, the Bio-Medical hypotheses are still of primary importance. A paradigmatic example of unifying Bio-Medical theory is the concept of Inflammaging. This complex phenotype is involved in a large number of pathologies and patho-physiological processes such as aging, age-related diseases and cancer, all sharing a common inflammatory pathogenesis. This Biomedical hypothesis can be mapped into an ecological perspective capable to describe by quantitative and predictive models some experimentally observed features, such as microenvironment, niche partitioning and phenotype propagation. In this article we show how this idea can be supported by computational methods useful to successfully integrate, analyze and model large data sets, combining cross-sectional and longitudinal information on clinical, environmental and omics data of healthy subjects and patients to provide new multidimensional biomarkers capable of distinguishing between different pathological conditions, e.g. healthy versus unhealthy state, physiological versus pathological aging

Where are we in the implementation of tissue-specific epigenetic clocks?

Introduction: DNA methylation clocks presents advantageous characteristics with respect to the ambitious goal of identifying very early markers of disease, based on the concept that accelerated ageing is a reliable predictor in this sense.Methods: Such tools, being epigenomic based, are expected to be conditioned by sex and tissue specificities, and this work is about quantifying this dependency as well as that from the regression model and the size of the training set.Results: Our quantitative results indicate that elastic-net penalization is the best performing strategy, and better so when—unsurprisingly—the data set is bigger; sex does not appear to condition clocks performances and tissue specific clocks appear to perform better than generic blood clocks. Finally, when considering all trained clocks, we identified a subset of genes that, to the best of our knowledge, have not been presented yet and might deserve further investigation: CPT1A, MMP15, SHROOM3, SLIT3, and SYNGR.Conclusion: These factual starting points can be useful for the future medical translation of clocks and in particular in the debate between multi-tissue clocks, generally trained on a large majority of blood samples, and tissue-specific clocks

RUNX2 expression in thyroid and breast cancer requires the cooperation of three non-redundant enhancers under the control of BRD4 and c-JUN

Aberrant reactivation of embryonic pathways is a common feature of cancer. RUNX2 is a transcription factor crucial during embryogenesis that is aberrantly reactivated in many tumors, including thyroid and breast cancer, where it promotes aggressiveness and metastatic spreading. Currently, the mechanisms driving RUNX2 expression in cancer are still largely unknown. Here we showed that RUNX2 transcription in thyroid and breast cancer requires the cooperation of three distantly located enhancers (ENHs) brought together by chromatin three-dimensional looping. We showed that BRD4 controls RUNX2 by binding to the newly identified ENHs and we demonstrated that the anti-proliferative effects of bromodomain inhibitors (BETi) is associated with RUNX2 transcriptional repression. We demonstrated that each RUNX2 ENH is potentially controlled by a distinct set of TFs and we identified c-JUN as the principal pivot of this regulatory platform. We also observed that accumulation of genetic mutations within these elements correlates with metastatic behavior in human thyroid tumors. Finally, we identified RAINs, a novel family of ENH-associated long non-coding RNAs, transcribed from the identified RUNX2 regulatory unit. Our data provide a new model to explain how RUNX2 expression is reactivated in thyroid and breast cancer and how cancer-driving signaling pathways converge on the regulation of this gene

KAP1 is a new non-genetic vulnerability of malignant pleural mesothelioma (MPM)

Malignant pleural mesothelioma (MPM) is a rare and incurable cancer, which incidence is increasing in many countries. MPM escapes the classical genetic model of cancer evolution, lacking a distinctive genetic fingerprint. Omics profiling revealed extensive heterogeneity failing to identify major vulnerabilities and restraining development of MPM-oriented therapies. Here, we performed a multilayered analysis based on a functional genome-wide CRISPR/Cas9 screening integrated with patients molecular and clinical data, to identify new non-genetic vulnerabilities of MPM. We identified a core of 18 functionally-related genes as essential for MPM cells. The chromatin reader KAP1 emerged as a dependency of MPM. We showed that KAP1 supports cell growth by orchestrating the expression of a G2/M-specific program, ensuring mitosis correct execution. Targeting KAP1 transcriptional function, by using CDK9 inhibitors resulted in a dramatic loss of MPM cells viability and shutdown of the KAP1-mediated program. Validation analysis on two independent MPM-patients sets, including a consecutive, retrospective cohort of 97 MPM, confirmed KAP1 as new non-genetic dependency of MPM and proved the association of its dependent gene program with reduced patients' survival probability. Overall these data: provided new insights into the biology of MPM delineating KAP1 and its target genes as building blocks of its clinical aggressiveness

Molecular and biological characterization of Trypanosoma cruzi strains isolated from children from Jequitinhonha Valley, State of Minas Gerais, Brazil

Introduction The biological diversity of Trypanosoma cruzi strains plays an important role in the clinical and epidemiological features of Chagas disease. Methods Eight T. cruzi strains isolated from children living in a Chagas disease vector-controlled area of Jequitinhonha Valley, State of Minas Gerais, Brazil, were genetically and biologically characterized. Results The characterizations demonstrated that all of the strains belonged to T. cruzi II, and showed high infectivity and a variable mean maximum peak of parasitemia. Six strains displayed low parasitemia, and two displayed moderate parasitemia. Later peaks of parasitemia and a predominance of intermediate and large trypomastigotes in all T. cruzi strains were observed. The mean pre-patent period was relatively short (4.2±0.25 to 13.7±3.08 days), whereas the patent period ranged from 3.3±1.08 to 34.5±3.52 days. Mortality was observed only in animals infected with strain 806 (62.5%). Histopathological analysis of the heart showed that strains 501 and 806 caused inflammation, but fibrosis was observed only in animals infected with strain 806. Conclusions The results indicate the presence of an association between the biological behavior in mice and the genetic characteristics of the parasites. The study also confirmed general data from Brazil where T. cruzi II lineage is the most prevalent in the domiciliary cycle and generally has low virulence, with some strains capable of inducing inflammatory processes and fibrosis