The DNA-helicase HELLS drives ALK - ALCL proliferation by the transcriptional control of a cytokinesis-related program.

Deregulation of chromatin modifiers, including DNA helicases, is emerging as one of the mechanisms underlying the transformation of anaplastic lymphoma kinase negative (ALK-) anaplastic large cell lymphoma (ALCL). We recently identified the DNA-helicase HELLS as central for proficient ALK-ALCL proliferation and progression. Here we assessed in detail its function by performing RNA-sequencing profiling coupled with bioinformatic prediction to identify HELLS targets and transcriptional cooperators. We demonstrated that HELLS, together with the transcription factor YY1, contributes to an appropriate cytokinesis via the transcriptional regulation of genes involved in cleavage furrow regulation. Binding target promoters, HELLS primes YY1 recruitment and transcriptional activation of cytoskeleton genes including the small GTPases RhoA and RhoU and their effector kinase Pak2. Single or multiple knockdowns of these genes reveal that RhoA and RhoU mediate HELLS effects on cell proliferation and cell division of ALK-ALCLs. Collectively, our work demonstrates the transcriptional role of HELLS in orchestrating a complex transcriptional program sustaining neoplastic features of ALK-ALCL

Ex vivo mapping of enhancer networks that define the transcriptional program driving melanoma metastasis

: Mortality from vmelanoma is associated with metastatic disease, but the mechanisms leading to spreading of the cancer cells remain obscure. Spatial profiling revealed that melanoma is characterized by a high degree of heterogeneity, which is established by the ability of melanoma cells to switch between different phenotypical stages. This plasticity, likely a heritage from embryonic pathways, accounts for a relevant part of the metastatic potential of these lesions, and requires the rapid and efficient reorganization of the transcriptional landscape of melanoma cells. A large part of the non-coding genome cooperates to control gene expression, specifically through the activity of enhancers (ENHs). In this study, we aimed to identify ex vivo the network of active ENHs and to outline their cooperative interactions in supporting transcriptional adaptation during melanoma metastatic progression. We conducted a genome-wide analysis to map active ENHs distribution in a retrospective cohort of 39 melanoma patients, comparing the profiles obtained in primary (N = 19) and metastatic (N = 20) melanoma lesions. Unsupervised clustering showed that the profile for acetylated histone H3 at lysine 27 (H3K27ac) efficiently segregates lesions into three different clusters corresponding to progressive stages of the disease. We reconstructed the map of super-ENHs (SEs) and cooperative ENHs that associate with metastatic progression in melanoma, which showed that cooperation among regulatory elements is a mandatory requirement for transcriptional plasticity. We also showed that these elements carry out specialized and non-redundant functions, and indicated the existence of a hierarchical organization, with SEs on top as masterminds of the entire transcriptional program and classical ENHs as executors. By providing an innovative vision of how the chromatin landscape of melanoma works during metastatic spreading, our data also point out the need to integrate functional profiling in the analysis of cancer lesions to increase definition and improve interpretation of tumor heterogeneity

The transcription factor NF-Y participates to stem cell fate decision and regeneration in adult skeletal muscle

Satellite cells represent myogenic stem cells that allow the homeostasis and repair of adult skeletal muscle. Here the authors report that the transcription factor NF-Y is expressed in satellite cells and is important for their maintenance and proper myogenic differentiation

A sex-informed approach to improve the personalised decision making process in myelodysplastic syndromes: a multicentre, observational cohort study

Background Sex is a major source of diversity among patients and a sex-informed approach is becoming a new paradigm in precision medicine. We aimed to describe sex diversity in myelodysplastic syndromes in terms of disease genotype, phenotype, and clinical outcome. Moreover, we sought to incorporate sex information into the clinical decision-making process as a fundamental component of patient individuality. Methods In this multicentre, observational cohort study, we retrospectively analysed 13 284 patients aged 18 years or older with a diagnosis of myelodysplastic syndrome according to 2016 WHO criteria included in the EuroMDS network (n=2025), International Working Group for Prognosis in MDS (IWG-PM; n=2387), the Spanish Group of Myelodysplastic Syndromes registry (GESMD; n=7687), or the Dusseldorf MDS registry (n=1185). Recruitment periods for these cohorts were between 1990 and 2016. The correlation between sex and genomic features was analysed in the EuroMDS cohort and validated in the IWG-PM cohort. The effect of sex on clinical outcome, with overall survival as the main endpoint, was analysed in the EuroMDS population and validated in the other three cohorts. Finally, novel prognostic models incorporating sex and genomic information were built and validated, and compared to the widely used revised International Prognostic Scoring System (IPSS-R). This study is registered with ClinicalTrials.gov, NCT04889729. Findings The study included 7792 (58middot7%) men and 5492 (41middot3%) women. 10 906 (82middot1%) patients were White, and race was not reported for 2378 (17middot9%) patients. Sex biases were observed at the single-gene level with mutations in seven genes enriched in men (ASXL1, SRSF2, and ZRSR2 p<0middot0001 in both cohorts; DDX41 not available in the EuroMDS cohort vs p=0middot0062 in the IWG-PM cohort; IDH2 p<0middot0001 in EuroMDS vs p=0middot042 in IWG-PM; TET2 p=0middot031 vs p=0middot035; U2AF1 p=0middot033 vs p<0middot0001) and mutations in two genes were enriched in women (DNMT3A p<0middot0001 in EuroMDS vs p=0middot011 in IWG-PM; TP53 p=0middot030 vs p=0middot037). Additionally, sex biases were observed in co-mutational pathways of founding genomic lesions (splicing-related genes, predominantly in men, p<0middot0001 in both the EuroMDS and IWG-PM cohorts), in DNA methylation (predominantly in men, p=0middot046 in EuroMDS vs p<0middot0001 in IWG-PM), and TP53 mutational pathways (predominantly in women, p=0middot0073 in EuroMDS vs p<0middot0001 in IWG-PM). In the retrospective EuroMDS cohort, men had worse median overall survival (81middot3 months, 95% CI 70middot4-95middot0 in men vs 123middot5 months, 104middot5-127middot5 in women; hazard ratio [HR] 1middot40, 95% CI 1middot26-1middot52; p<0middot0001). This result was confirmed in the prospective validation cohorts (median overall survival was 54middot7 months, 95% CI 52middot4-59middot1 in men vs 74middot4 months, 69middot3-81middot2 in women; HR 1middot30, 95% CI 1middot23-1middot35; p<0middot0001 in the GEMSD MDS registry; 40middot0 months, 95% CI 33middot4-43middot7 in men vs 54middot2 months, 38middot6-63middot8 in women; HR 1middot23, 95% CI 1middot08-1middot36; p<0middot0001 in the Dusseldorf MDS registry). We developed new personalised prognostic tools that included sex information (the sex-informed prognostic scoring system and the sex-informed genomic scoring system). Sex maintained independent prognostic power in all prognostic systems; the highest performance was observed in the model that included both sex and genomic information. A five-to-five mapping between the IPSS-R and new score categories resulted in the re-stratification of 871 (43middot0%) of 2025 patients from the EuroMDS cohort and 1003 (42middot0%) of 2387 patients from the IWG-PM cohort by using the sex-informed prognostic scoring system, and of 1134 (56middot0%) patients from the EuroMDS cohort and 1265 (53middot0%) patients from the IWG-PM cohort by using the sex-informed genomic scoring system. We created a web portal that enables outcome predictions based on a sex-informed personalised approach. Interpretation Our results suggest that a sex-informed approach can improve the personalised decision making process in patients with myelodysplastic syndromes and should be considered in the design of clinical trials including low-risk patients. Copyright (c) 2022 Published by Elsevier Ltd. All rights reserved

CSNK1A1, KDM2A, and LTB4R2 Are New Druggable Vulnerabilities in Lung Cancer

Lung cancer is the leading cause of cancer-related human death. It is a heterogeneous disease, classified in two main histotypes, small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC), which is further subdivided into squamous-cell carcinoma (SCC) and adenocarcinoma (AD) subtypes. Despite the introduction of innovative therapeutics, mainly designed to specifically treat AD patients, the prognosis of lung cancer remains poor. In particular, available treatments for SCLC and SCC patients are currently limited to platinum-based chemotherapy and immune checkpoint inhibitors. In this work, we used an integrative approach to identify novel vulnerabilities in lung cancer. First, we compared the data from a CRISPR/Cas9 dependency screening performed in our laboratory with Cancer Dependency Map Project data, essentiality comprising information on 73 lung cancer cell lines. Next, to identify relevant therapeutic targets, we integrated dependency data with pharmacological data and TCGA gene expression information. Through this analysis, we identified CSNK1A1, KDM2A, and LTB4R2 as relevant druggable essentiality genes in lung cancer. We validated the antiproliferative effect of genetic or pharmacological inhibition of these genes in two lung cancer cell lines. Overall, our results identified new vulnerabilities associated with different lung cancer histotypes, laying the basis for the development of new therapeutic strategies

A data fusion approach for learning transcriptional Bayesian networks in chronic leukemia

La crescente disponibilità di dati omici ha determinato un importante cambiamento nel paradigma della ricerca scientifica, passando da uno studio “contesto specifico” focalizzato su un singolo aspetto biologico, ad un studio su larga scala guidato dai dati. L’analisi simultanea di diversi livelli omici potrebbe aiutare a chiarire la relazione tra caratteristiche o perturbazioni del sistema molecolare non rilevate in precedenza con un fenotipo specifico, specialmente nel caso di malattie complesse, come il cancro. A tal fine, un approccio computazionale integrativo in grado di gestire l'eterogeneità dei dati e la complessità biologica può consentire un'indagine approfondita di programmi di espressione genica disregolati responsabili dei meccanismi di insorgenza e di progressione della malattia. La ricostruzione dei pattern regolatori dei fattori determinanti della trascrizione (fattori di trascrizione, TF), che presiedono allo schema di espressione genica, potrebbe anche aiutare a ottenere informazioni sulle firme molecolari che guidano i fenotipi della malattia, offrendo così nuove ipotesi di ricerca. In questa tesi è stato sviluppato un approccio di “data fusion”, incentrato sull'integrazione a più livelli di dati omici per la modellizzazione di background trascrizionali su larga scala. La sua strategia di ricerca combina efficacemente un approccio network-centrico per ricostruire l'interattoma trascrizionale con la modellizzazione offerta dalla teoria Bayesiana, ed è in grado di indagare probabilisticamente, su scala genomica, le regolazioni trascrizionali e le sottostanti firme molecolari. Questo lavoro di ricerca fa parte del progetto "Rete Ematologica Lombarda (REL) cluster biotecnologico per l'implementazione dell'analisi genomica e lo sviluppo di trattamenti innovativi nelle neoplasie ematologiche", che mira a stabilire un centro di riferimento per lo studio delle neoplasie ematologiche, con particolare attenzione alle neoplasie mieloidi. La metodologia proposta è stata infatti applicata ad un tipo di patologia mieloide, la leucemia mieloide cronica (LMC), di cui è noto l’evento genetico causale, ma l’alterato ruolo trascrizionale alla base della progressione della malattia non è stato ancora approfondito a livello genomico.The increasing availability of omics data has caused an important paradigmatic shift in scientific research from case-based studies towards large scale data-driven research. The simultaneous interrogation of different omics levels, could help to elucidate the interrelation of previously-undetected system features or perturbations with a specific phenotype, especially in complex diseases, such as cancer. To this aim, an integrative computational approach able to deal with data heterogeneity and biological complexity may allow a deep investigation of dysregulated gene expression programs responsible of disease onset and progression mechanisms. The reconstruction of transcriptional determinants (transcription factors, TFs) regulatory patterns, which preside over the gene expression scheme could also help to gain insights into molecular signatures driving disease phenotypes, offering new research hypotheses. In this thesis, I have developed a data fusion approach focused on “multi-layered” omics data integration for modeling large-scale transcriptional background. Its framework efficiently combines a network-centric approach to reconstruct the transcriptional interactome to probabilistically inspect, on a genome-wide scale, the transcriptional regulations and the underlying regulative signatures. This work is part of the project “Rete Ematologica Lombarda (REL) biotechnology cluster for the implementation of genomic analysis and the development of innovative treatments in hematological malignancies”, which aims at establishing a reference center for the study of hematological malignancies, with focus on myeloid neoplasms. The proposed methodology has been applied to the case of a myeloid disorder, the Chronic Myeloid Leukemia (CML), whose causative genetic event is known but its emerging transcriptional altered role in disease progression has not yet been deeply investigated at a genomic level

A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks

Background Reverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex crosstalk among transcription factors (TFs) and their target genes, with a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information. Results In this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genome-wide perspective. Applying our method to high-throughput data, we verified its ability to deal with the complexity of a genomic TRN, providing a snapshot of the synergistic TFs regulatory activity. Given the noisy nature of data-driven prior knowledge, which potentially contains incorrect information, we also tested the method's robustness to false priors on a benchmark dataset, comparing the proposed approach to other regulatory network reconstruction algorithms. We demonstrated the effectiveness of our framework by evaluating structural commonalities of our learned genomic network with other existing networks inferred by different DNA binding information-based methods. Conclusions This Bayesian omics-data fusion based methodology allows to gain a genome-wide picture of the transcriptional interplay, helping to unravel key hierarchical transcriptional interactions, which could be subsequently investigated, and it represents a promising learning approach suitable for multi-layered genomic data integration, given its robustness to noisy sources and its tailored framework for handling high dimensional data.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Beyond Body Mass Index. Is the Body Cell Mass Index a useful prognostic factor to describe nutritional, inflammation and muscle mass status in hospitalized elderly?

BACKGROUND AND AIM. The aim of this study is to establish the effectiveness of Body CelL Mass Index (BCMI) as a prognostic index of (mal)nutrition, inflammation and muscle mass Status in the elderly. METHODS. A cross-sectional observational study has been conducted on 114 elderly patients (80 women and 34 men), with mean age equal to 81.07 ± 6.18 years. We performed a multivariate regression model by Structural Equation Modelling (SEM) framework. We detected the effects over a Mini Nutritional Assessment (MNA) stratification, by performing a multi-group multivariate regression model (via SEM) in two MNA nutritional strata, less and bigger (or equal) than 17. RESULTS. BCMI had a significant effect on albumin (=+0.062, P=0.001), adjusting for the Other predictors of the model as Body Mass Index (BMI), age, sex, fat mass and cognitive condition. An analogous result is maintained in MNA<17 stratum. BMI confirmed to be a solid prognostic factor, for both free fat mass (FFM) (=+0.480, P<0.001) and Relative Skeletal Muscle Mass (RSMM) (=+0.265, P<0.001). BCMI also returned suggestive evidences (0.05<P<0.10) for both the effect on FFM and on RSMM in overall sample. CONCLUSIONS. The main result of this study is that the BCMI, compared to BMI, proved to Be significantly related to such an important marker as albumin in geriatric population. Then, Assessing the BCMI could be a valuable, inexpensive, easy to perform tool to investigate the inflammation status of the elderly patient

Linc00941 is a novel TGFβ 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