The Development and Characterization of a Next-Generation Oncolytic Virus Armed with an Anti-PD-1 sdAb for Osteosarcoma Treatment In Vitro.

Osteosarcoma (OS) is a primary bone malignancy characterized by an aggressive nature, limited treatment options, low survival rate, and poor patient prognosis. Conditionally replicative adenoviruses (CRAds) armed with immune checkpoint inhibitors hold great potential for enhanced therapeutic efficacy. The present study aims to investigate the anti-tumor efficacy of CAV2-AU-M2, a CAV2-based CRAd armed with an anti-PD-1 single-domain antibody (sdAb), against OS cell lines in vitro. The infection, conditional replication, cytopathic effects, and cytotoxicity of CAV2-AU-M2 were tested in four different OS cell lines in two-dimensional (2D) and three-dimensional (3D) cell cultures. CAV2-AU-M2 showed selective replication in the OS cells and induced efficient tumor cell lysis and death. Moreover, CAV2-AU-M2 produced an anti-PD-1 sdAb that demonstrated effective binding to the PD-1 receptors. This study demonstrated the first CRAd armed with an anti-PD-1 sdAb. This combined approach of two distinct immunotherapies is intended to enhance the anti-tumor immune response in the tumor microenvironment.

Honey bee colony loss linked to parasites, pesticides and extreme weather across the United States.

Honey bee (Apis mellifera) colony loss is a widespread phenomenon with important economic and biological implications, whose drivers are still an open matter of investigation. We contribute to this line of research through a large-scale, multi-variable study combining multiple publicly accessible data sources. Specifically, we analyzed quarterly data covering the contiguous United States for the years 2015-2021, and combined open data on honey bee colony status and stressors, weather data, and land use. The different spatio-temporal resolutions of these data are addressed through an up-scaling approach that generates additional statistical features which capture more complex distributional characteristics and significantly improve modeling performance. Treating this expanded feature set with state-of-the-art feature selection methods, we obtained findings that, nation-wide, are in line with the current knowledge on the aggravating roles of Varroa destructor and pesticides in colony loss. Moreover, we found that extreme temperature and precipitation events, even when controlling for other factors, significantly impact colony loss. Overall, our results reveal the complexity of biotic and abiotic factors affecting managed honey bee colonies across the United States.

Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks.

Non-coding micro RNAs (miRNAs) dysregulation seems to play an important role in the pathways involved in breast cancer occurrence and progression. In different studies, opposite functions may be assigned to the same miRNA, either promoting the disease or protecting from it. Our research tackles the following issues: (i) why aren't there any concordant findings in many research studies regarding the role of miRNAs in the progression of breast cancer? (ii) could a miRNA have either an activating effect or an inhibiting one in cancer progression according to the other miRNAs with which it interacts? For this purpose, we analyse the AHUS dataset made available on the ArrayExpress platform by Haakensen et al. The breast tissue specimens were collected over 7 years between 2003 and 2009. miRNA-expression profiling was obtained for 55 invasive carcinomas and 70 normal breast tissue samples. Our statistical analysis is based on a recently developed model and feature selection technique which, instead of selecting a single model (i.e. a unique combination of miRNAs), delivers a set of models with equivalent predictive capabilities that allows to interpret and visualize the interaction of these features. As a result, we discover a set of 112 indistinguishable models (in a predictive sense) each with 4 or 5 miRNAs. Within this set, by comparing the model coefficients, we are able to identify three classes of miRNA: (i) oncogenic miRNAs; (ii) protective miRNAs; (iii) undefined miRNAs which can play both an oncogenic and a protective role according to the network with which they interact. These results shed new light on the biological action of miRNAs in breast cancer and may contribute to explain why, in some cases, different studies attribute opposite functions to the same miRNA.

Influence of Community-Led Total Sanitation and Water Coverages in the Control of Cholera in Madarounfa, Niger (2018).

Every year, cholera affects 1.3-4.0 million people worldwide with a particularly high presence in Africa. Based on recent studies, effective targeting interventions in hotspots could eliminate up to 50% of cases in Sub-Saharan Africa. Those interventions include Water, Sanitation, and Hygiene (WASH) programs whose influence on cholera control, up to the present, has been poorly quantified. Among the few studies available, D'Mello-Guyett et al. underline how the distribution of hygiene kits is a promising form of intervention for cholera control and that the integration of a WASH intervention at the point of admission of suspected cases is new in cholera control efforts, particularly in outbreaks and complex emergencies. Considering the limited number of studies on Community-Led Total Sanitation (CLTS) and water coverages related to cholera control, the aim of our work is to determine whether these interventions in cholera hotspots (geographic areas vulnerable to disease transmission) have significant impact on cholera transmission. In this study, we consider data collected on 125 villages of the Madarounfa district (Niger) during the 2018 cholera outbreak. Using a hurdle model, our findings show that full access to improved sanitation significantly decreases the likelihood of cholera by 91% (P < 0.0001) compared to villages with no access to sanitation at all. Considering only the villages affected by cholera in the studied area, cholera cases decrease by a factor of 4.3 in those villages where there is partial access to at least quality water sources, while full access to improved water sources decreases the cholera cases by a factor of 6.3 when compared to villages without access to water (P < 0.001). In addition, villages without access to safe water and sanitation are 6.7 times (P < 0.0001) more likely to get cholera. Alternatively, villages with full sanitation and water coverage are 9.1 (P < 0.0001) less likely to get cholera. The findings of our study suggest that significant access to improved water and sanitation at the village level offer a strong barrier against cholera transmission. However, it requires full CLTS coverage of the village to observe a strong impact on cholera, as partial access only has a limited impact.

Granger-causal testing for irregularly sampled time series with application to nitrogen signalling in Arabidopsis.

MOTIVATION: Identification of system-wide causal relationships can contribute to our understanding of long-distance, intercellular signalling in biological organisms. Dynamic transcriptome analysis holds great potential to uncover coordinated biological processes between organs. However, many existing dynamic transcriptome studies are characterized by sparse and often unevenly spaced time points that make the identification of causal relationships across organs analytically challenging. Application of existing statistical models, designed for regular time series with abundant time points, to sparse data may fail to reveal biologically significant, causal relationships. With increasing research interest in biological time series data, there is a need for new statistical methods that are able to determine causality within and between time series data sets. Here, a statistical framework was developed to identify (Granger) causal gene-gene relationships of unevenly spaced, multivariate time series data from two different tissues of Arabidopsis thaliana in response to a nitrogen signal. RESULTS: This work delivers a statistical approach for modelling irregularly sampled bivariate signals which embeds functions from the domain of engineering that allow to adapt the model's dependence structure to the specific sampling time. Using maximum-likelihood to estimate the parameters of this model for each bivariate time series, it is then possible to use bootstrap procedures for small samples (or asymptotics for large samples) in order to test for Granger-Causality. When applied to the A.thaliana data, the proposed approach produced 3078 significant interactions, in which 2012 interactions have root causal genes and 1066 interactions have shoot causal genes. Many of the predicted causal and target genes are known players in local and long-distance nitrogen signalling, including genes encoding transcription factors, hormones and signalling peptides. Of the 1007 total causal genes (either organ), 384 are either known or predicted mobile transcripts, suggesting that the identified causal genes may be directly involved in long-distance nitrogen signalling through intercellular interactions. The model predictions and subsequent network analysis identified nitrogen-responsive genes that can be further tested for their specific roles in long-distance nitrogen signalling. AVAILABILITY AND IMPLEMENTATION: The method was developed with the R statistical software and is made available through the R package 'irg' hosted on the GitHub repository https://github.com/SMAC-Group/irg where also a running example vignette can be found (https://smac-group.github.io/irg/articles/vignette.html). A few signals from the original data set are made available in the package as an example to apply the method and the complete A.thaliana data can be found at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97500. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MiRNA Dysregulation in Childhood Hematological Cancer.

For decades, cancer biology focused largely on the protein-encoding genes that have clear roles in tumor development or progression: cell-cycle control, apoptotic evasion, genome instability, drug resistance, or signaling pathways that stimulate growth, angiogenesis, or metastasis. MicroRNAs (miRNAs), however, represent one of the more abundant classes of cell modulators in multicellular organisms and largely contribute to regulating gene expression. Many of the ~2500 miRNAs discovered to date in humans regulate vital biological processes, and their aberrant expression results in pathological and malignant outcomes. In this review, we highlight what has been learned about the roles of miRNAs in some of the most common human pediatric leukemias and lymphomas, along with their value as diagnostic/prognostic factors.

Is nonmetastatic cutaneous melanoma predictable through genomic biomarkers?

Cutaneous melanoma is a highly aggressive skin cancer whose treatment and prognosis are critically affected by the presence of metastasis. In this study, we address the following issue: which gene transcripts and what kind of interactions between them can allow to predict nonmetastatic from metastatic melanomas with a high level of accuracy? We carry out a meta-analysis on the first gene expression set of the Leeds melanoma cohort, as made available online on 11 May 2016 through the ArrayExpress platform with MicroArray Gene Expression number 4725. According to the authors, primary melanoma mRNA expression was measured in 204 tumours using an illumina DASL HT12 4 whole-genome array. The tumour transcripts were selected through a recently proposed predictive-based regression algorithm for gene-network selection. A set of 64 equivalent models, each including only two gene transcripts, were each sufficient to accurately classify primary tumours into metastatic and nonmetastatic melanomas. The sensitivity and specificity of the genomic-based models were, respectively, 4% (95% confidence interval: 0.11-21.95%) and 99% (95% confidence interval: 96.96-99.99%). The very high specificity coupled with a significantly large positive likelihood ratio leads to a conclusive increase in the likelihood of disease when these biomarkers are present in the primary tumour. In conjunction with other highly sensitive methods, this approach can aspire to be part of the future standard diagnosis methods for the screening of metastatic cutaneous melanoma. The small dimension of the selected transcripts models enables easy handling of large-scale genomic testing procedures. Moreover, some of the selected transcripts have an understandable link with what is known about cutaneous melanoma oncogenesis, opening a window on the molecular pathways underlying the metastatic process of this disease.

A Predictive Based Regression Algorithm for Gene Network Selection.

Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can still achieve good predictive performance. To do so, many of the recently proposed classification methods require some form of dimension-reduction of the problem which finally provide a single model as an output and, in most cases, rely on the likelihood function in order to achieve variable selection. We propose a new prediction-based objective function that can be tailored to the requirements of practitioners and can be used to assess and interpret a given problem. Based on cross-validation techniques and the idea of importance sampling, our proposal scans low-dimensional models under the assumption of sparsity and, for each of them, estimates their objective function to assess their predictive power in order to select. Two applications on cancer data sets and a simulation study show that the proposal compares favorably with competing alternatives such as, for example, Elastic Net and Support Vector Machine. Indeed, the proposed method not only selects smaller models for better, or at least comparable, classification errors but also provides a set of selected models instead of a single one, allowing to construct a network of possible models for a target prediction accuracy level.

Cancer of the nasopharynx.

Nasopharyngeal cancer (NPC) is quite rare throughout Europe, accounting for an annual incidence rate below 1 per 100.000, whereas the highest risk area is South East Asia. A predominant occurrence in males is to be noted. NPC is an etiologically multifactorial disease, most probably involving viral, genetic and environmental factors. Carcinomas of the nasopharynx can be divided into two major histotypes: keratinizing squamous cell carcinomas (WHO-type 1) and non-keratinizing carcinomas (WHO-type 2). The histological type is a prognostic factor and it has a clear impact on the outcome of treatment. Standard therapeutic option for early stages of NPC is radiation, while an integration of radiation therapy and chemotherapy is indicated in more advanced stages.

Major and minor salivary glands tumours.

Malignant salivary gland tumours are rare. The most common tumour site is the parotid. Aetiologic factors are not clear. Nutrition may be a risk factor, as well as irradiation or an histologically benign tumour occurred at a young age. Painless swelling of a salivary gland should always be considered as suspicious, especially if no sign of inflammation is present. Signs and symptoms related to major salivary gland tumours differ from those concerning minor salivary gland tumours, as they depend on the different location of the salivary gland. Surgical excision represents the standard option in the treatment of resectable tumours of both major and minor salivary glands. Neutron radiation may be a treatment option for inoperable locoregional disease. Surgery, irradiation or re-irradiation are treatment options for local relapse, whereas radical neck dissection is indicated for regional relapses. Metastastic disease may be either treated with radiotherapy or palliative chemotherapy, depending on the site of metastases.

Primary chemotherapy in resectable oral cavity squamous cell cancer: a randomized controlled trial.

PURPOSE: Prognosis of patients with advanced oral cavity cancer is worth improving. Chemotherapy has been reported to be especially active in oral cavity tumors. Here we repeat the results of a randomized, multicenter trial enrolling patients with a resectable, stage T2-T4 (> 3 cm), N0-N2, M0 untreated, squamous cell carcinoma of the oral cavity. PATIENTS AND METHODS: Patients were randomly assigned to three cycles of cisplatin and fluorouracil followed by surgery (chemotherapy arm) or surgery alone (control arm). In both arms, postoperative radiotherapy was reserved to high-risk patients, and surgery was modulated depending on the tumor's closeness to the mandible. Patients' accrual was opened in 1989 and closed in 1999. It included 195 patients. RESULTS: In the chemotherapy arm, three toxic deaths were recorded. No significant difference in overall survival was found. Five-year overall survival was, for both arms, 55%. Postoperative radiotherapy was administered in 33% of patients in the chemotherapy arm, versus 46% in the control arm. A mandible resection was performed in 52% of patients in the control arm, versus 31% in the chemotherapy arm. CONCLUSION: The addition of primary chemotherapy to standard surgery was unable to improve survival. However, in this study, primary chemotherapy seemed to play a role in reducing the number of patients who needed to undergo mandibulectomy and/or radiation therapy. Variations in the criteria used to select patients for these treatment options may make it difficult to generalize these results, but there appears to be room for using preoperative chemotherapy to spare destructive surgery or radiation therapy in patients with advanced, resectable oral cavity cancer.