OVOL2 impairs RHO GTPase signaling to restrain mitosis and aggressiveness of Anaplastic Thyroid Cancer

Background: Anaplastic Thyroid Cancer (ATC) is an undifferentiated and aggressive tumor that often originates from well-Differentiated Thyroid Carcinoma (DTC) through a trans-differentiation process. Epithelial-to-Mesenchymal Transition (EMT) is recognized as one of the major players of this process. OVOL2 is a transcription factor (TF) that promotes epithelial differentiation and restrains EMT during embryonic development. OVOL2 loss in some types of cancers is linked to aggressiveness and poor prognosis. Here, we aim to clarify the unexplored role of OVOL2 in ATC. Methods: Gene expression analysis in thyroid cancer patients and cell lines showed that OVOL2 is mainly associated with epithelial features and its expression is deeply impaired in ATC. To assess OVOL2 function, we established an OVOL2-overexpression model in ATC cell lines and evaluated its effects by analyzing gene expression, proliferation, invasion and migration abilities, cell cycle, specific protein localization through immunofluorescence staining. RNA-seq profiling showed that OVOL2 controls a complex network of genes converging on cell cycle and mitosis regulation and Chromatin Immunoprecipitation identified new OVOL2 target genes. Results: Coherently with its reported function, OVOL2 re-expression restrained EMT and aggressiveness in ATC cells. Unexpectedly, we observed that it caused G2/M block, a consequent reduction in cell proliferation and an increase in cell death. This phenotype was associated to generalized abnormalities in the mitotic spindle structure and cytoskeletal organization. By RNA-seq experiments, we showed that many pathways related to cytoskeleton and migration, cell cycle and mitosis are profoundly affected by OVOL2 expression, in particular the RHO-GTPase pathway resulted as the most interesting. We demonstrated that RHO GTPase pathway is the central hub of OVOL2-mediated program in ATC and that OVOL2 transcriptionally inhibits RhoU and RhoJ. Silencing of RhoU recapitulated the OVOL2-driven phenotype pointing to this protein as a crucial target of OVOL2 in ATC. Conclusions: Collectively, these data describe the role of OVOL2 in ATC and uncover a novel function of this TF in inhibiting the RHO GTPase pathway interlacing its effects on EMT, cytoskeleton dynamics and mitosis

Adding pieces to the puzzle of differentiated-to-anaplastic thyroid cancer evolution: the oncogene E2F7

Anaplastic Thyroid Cancer (ATC) is the most aggressive and de-differentiated subtype of thyroid cancer. Many studies hypothesized that ATC derives from Differentiated Thyroid Carcinoma (DTC) through a de-differentiation process triggered by specific molecular events still largely unknown. E2F7 is an atypical member of the E2F family. Known as cell cycle inhibitor and keeper of genomic stability, in specific contexts its function is oncogenic, guiding cancer progression. We performed a meta-analysis on 279 gene expression profiles, from 8 Gene Expression Omnibus patient samples datasets, to explore the causal relationship between DTC and ATC. We defined 3 specific gene signatures describing the evolution from normal thyroid tissue to DTC and ATC and validated them in a cohort of human surgically resected ATCs collected in our Institution. We identified E2F7 as a key player in the DTC-ATC transition and showed in vitro that its down-regulation reduced ATC cells’ aggressiveness features. RNA-seq and ChIP-seq profiling allowed the identification of the E2F7 specific gene program, which is mainly related to cell cycle progression and DNA repair ability. Overall, this study identified a signature describing DTC de-differentiation toward ATC subtype and unveiled an E2F7-dependent transcriptional program supporting this process

Clinical and genomic-based decision support system to define the optimal timing of allogeneic hematopoietic stem-cell transplantation in patients with myelodysplastic syndromes

Statistical Scienc

Data fusion approach for learning transcriptional Bayesian networks

The complexity of gene expression regulation relies on the synergic nature underlying the molecular interplay among its principal actors, transcription factors (TFs). Exerting a spatiotemporal control on their target genes, they define transcriptional programs across the genome, which are strongly perturbed in a disease context. In order to gain a more comprehensive picture of these complex dynamics, a data fusion approach, aimed at performing the integration of heterogeneous -omics data is fundamental. Bayesian Networks provide a natural framework for integrating different sources of data and knowledge through the priors’ use. In this work, we developed an hybrid structure-learning algorithm with the aim of exploiting TF ChIP-seq and gene expression (GE) data to investigate disease-specific transcriptional regulations in a genome-wide perspective. TF ChIP seq profiles were firstly used for structure learning and then integrated in the model as a prior probability. GE panels were employed to learn the model parameters, trying to find the best heuristic transcriptional network. We applied our approach to a specific pathological case, the chronic myeloid leukemia (CML), a myeloproliferative disorder, whose transcriptional mechanisms have not yet been deeply elucidated. The proposed data-driven method allows to investigate transcriptional signatures, highlighting in the obtained probabilistic network a three-layered hierarchy, as a different TFs influence on gene expression cellular programs

A Bioinformatics Approach to Explore MicroRNAs as Tools to Bridge Pathways Between Plants and Animals. Is DNA Damage Response (DDR) a Potential Target Process?

MicroRNAs, highly-conserved small RNAs, act as key regulators of many biological functions in both plants and animals by post-transcriptionally regulating gene expression through interactions with their target mRNAs. The microRNA research is a dynamic field, in which new and unconventional aspects are emerging alongside well-established roles in development and stress adaptation. A recent hypothesis states that miRNAs can be transferred from one species to another and potentially target genes across distant species. Here, we propose to look into the trans-kingdom potential of miRNAs as a tool to bridge conserved pathways between plant and human cells. To this aim, a novel multi-faceted bioinformatic analysis pipeline was developed, enabling the investigation of common biological processes and genes targeted in plant and human transcriptome by a set of publicly available Medicago truncatula miRNAs. Multiple datasets, including miRNA, gene, transcript and protein sequences, expression profiles and genetic interactions, were used. Three different strategies were employed, namely a network-based pipeline, an alignment-based pipeline, and a M. truncatula network reconstruction approach, to study functional modules and to evaluate gene/protein similarities among miRNA targets. The results were compared in order to find common features, e.g., microRNAs targeting similar processes. Biological processes like exocytosis and response to viruses were common denominators in the investigated species. Since the involvement of miRNAs in the regulation of DNA damage response (DDR)-associated pathways is barely explored, especially in the plant kingdom, a special attention is given to this aspect. Hereby, miRNAs predicted to target genes involved in DNA repair, recombination and replication, chromatin remodeling, cell cycle and cell death were identified in both plants and humans, paving the way for future interdisciplinary advancements

A continuous-time Markov model approach for modeling myelodysplastic syndromes progression from cross-sectional data

The integration of both genomics and clinical data to model disease progression is now possible, thanks to the increasing availability of molecular patients’ profiles. This may lead to the definition of novel decision support tools, able to tailor therapeutic interventions on the basis of a “precise” patients’ risk stratification, given their health status evolution. However, longitudinal analysis requires long-term data collection and curation, which can be time demanding, expensive and sometimes unfeasible. Here we present a clinical decision support framework that combines the simulation of disease progression from cross-sectional data with a Markov model that exploits continuous-time transition probabilities derived from Cox regression. Trajectories between patients at different disease stages are stochastically built according to a measure of patient similarity, computed with a matrix tri-factorization technique. Such trajectories are seen as realizations drawn from the stochastic process driving the transitions between the disease stages. Eventually, Markov models applied to the resulting longitudinal dataset highlight potentially relevant clinical information. We applied our method to cross-sectional genomic and clinical data from a cohort of Myelodysplastic syndromes (MDS) patients. MDS are heterogeneous clonal hematopoietic disorders whose patients are characterized by different risks of Acute Myeloid Leukemia (AML) development, defined by an international score. We computed patients’ trajectories across increasing and subsequent levels of risk of developing AML, and we applied a Cox model to the simulated longitudinal dataset to assess whether genomic characteristics could be associated with a higher or lower probability of disease progression. We then used the learned parameters of such Cox model to calculate the transition probabilities of a continuous-time Markov model that describes the patients’ evolution across stages. Our results are in most cases confirmed by previous studies, thus demonstrating that simulated longitudinal data represent a valuable resource to investigate disease progression of MDS patients

Linc00941 Is a Novel Transforming Growth Factor β Target That Primes Papillary Thyroid Cancer Metastatic Behavior by Regulating the Expression of Cadherin 6

Background: Papillary thyroid cancers (PTCs) are common, usually indolent malignancies. Still, a small but significant percentage of patients have aggressive tumors and develop distant metastases leading to death. Currently, it is not possible to discriminate aggressive lesions due to lack of prognostic markers. Long noncoding RNAs (lncRNAs), which are selectively expressed in a context-dependent manner, are expected to represent a new landscape to search for molecular discriminants. Transforming growth factor β (TGFβ) is a multifunctional cytokine that fosters epithelial-to-mesenchymal transition and metastatic spreading. In PTCs, it triggers the expression of the metastatic marker Cadherin 6 (CDH6). Here, we investigated the TGFβ-dependent lncRNAs that may cooperate to potentiate PTC aggressiveness. Methods: We used a genome-wide approach to map enhancer (ENH)-associated lncRNAs under TGFβ control. Linc00941 was selected and validated using functional in vitro assays. A combined approach using bioinformatic analyses of the thyroid cancer (THCA) - the cancer genome atlas (TCGA) dataset and RNA-seq analysis was used to identify the processes in which linc00941 was involved in and the genes under its regulation. Correlation with clinical data was performed to evaluate the potential of this lncRNA and its targets as prognostic markers in THCA. Results: Linc00941 was identified as transcribed starting from one of the TGFβ-induced ENHs. Linc00941 expression was significantly higher in aggressive cancer both in the TCGA dataset and in a separate validation cohort from our institution. Loss of function assays for linc00941 showed that it promotes response to stimuli and invasiveness while restraining proliferation in PTC cells, a typical phenotype of metastatic cells. From the integration of TCGA data and linc00941 knockdown RNA-seq profiling, we identified 77 genes under the regulation of this lncRNA. Among these, we found the prometastatic gene CDH6. Linc00941 knockdown partially recapitulates the effects observed upon CDH6 silencing, promoting cell cytoskeleton and membrane adhesions rearrangements and autophagy. The combined expression of CDH6 and linc00941 is a distinctive feature of highly aggressive PTC lesions. Conclusions: Our data provide new insights into the biology driving metastasis in PTCs and highlight how lncRNAs cooperate with coding transcripts to sustain these processes

An integrative functional genomics approach reveals EGLN1 as a novel therapeutic target in KRAS mutated lung adenocarcinoma

No Abstract available