Artificial neural network inference (ANNI): a study on gene-gene interaction for biomarkers in childhood sarcomas

Temporal gene signature of myofibroblast transformation in Peyronie's disease: first insights into the molecular mechanisms of irreversibility

BACKGROUND

Transformation of resident fibroblasts to profibrotic myofibroblasts in the tunica albuginea is a critical step in the pathophysiology of Peyronie's disease (PD). We have previously shown that myofibroblasts do not revert to the fibroblast phenotype and we suggested that there is a point of no return at 36 hours after induction of the transformation. However, the molecular mechanisms that drive this proposed irreversibility are not known.

AIM

Identify molecular pathways that drive the irreversibility of myofibroblast transformation by analyzing the expression of the genes involved in the process in a temporal fashion.

METHODS

Human primary fibroblasts obtained from tunica albuginea of patients with Peyronie's disease were transformed to myofibroblasts using transforming growth factor beta 1 (TGF-β1). The mRNA of the cells was collected at 0, 24, 36, 48, and 72 hours after stimulation with TGF-β1 and then analyzed using a Nanostring nCounter Fibrosis panel. The gene expression results were analyzed using Reactome pathway analysis database and ANNi, a deep learning-based inference algorithm based on a swarm approach.

OUTCOMES

The study outcome was the time course of changes in gene expression during transformation of PD-derived fibroblasts to myofibroblasts.

RESULTS

The temporal analysis of the gene expression revealed that the majority of the changes at the gene expression level happened within the first 24 hours and remained so throughout the 72-hour period. At 36 hours, significant changes were observed in genes involved in MAPK-Hedgehog signaling pathways.

CLINICAL TRANSLATION

This study highlights the importance of early intervention in clinical management of PD and the future potential of new drugs targeting the point of no return.

STRENGTHS AND LIMITATIONS

The use of human primary cells and confirmation of results with further RNA analysis are the strengths of this study. The study was limited to 760 genes rather than the whole transcriptome.

CONCLUSION

This study is to our knowledge the first analysis of temporal gene expression associated with the regulation of the transformation of resident fibroblasts to profibrotic myofibroblasts in PD. Further research is warranted to investigate the role of the MAPK-Hedgehog signaling pathways in reversibility of PD.

The impact of metabolic endotoxaemia on the browning process in human adipocytes

BACKGROUND

Dysfunctional adipose tissue (AT) is known to contribute to the pathophysiology of metabolic disease, including type 2 diabetes mellitus (T2DM). This dysfunction may occur, in part, as a consequence of gut-derived endotoxaemia inducing changes in adipocyte mitochondrial function and reducing the proportion of BRITE (brown-in-white) adipocytes. Therefore, the present study investigated whether endotoxin (lipopolysaccharide; LPS) directly contributes to impaired human adipocyte mitochondrial function and browning in human adipocytes, and the relevant impact of obesity status pre and post bariatric surgery.

METHODS

Human differentiated abdominal subcutaneous (AbdSc) adipocytes from participants with obesity and normal-weight participants were treated with endotoxin to assess in vitro changes in mitochondrial function and BRITE phenotype. Ex vivo human AbdSc AT from different groups of participants (normal-weight, obesity, pre- and 6 months post-bariatric surgery) were assessed for similar analyses including circulating endotoxin levels.

RESULTS

Ex vivo AT analysis (lean & obese, weight loss post-bariatric surgery) identified that systemic endotoxin negatively correlated with BAT gene expression (p < 0.05). In vitro endotoxin treatment of AbdSc adipocytes (lean & obese) reduced mitochondrial dynamics (74.6% reduction; p < 0.0001), biogenesis (81.2% reduction; p < 0.0001) and the BRITE phenotype (93.8% reduction; p < 0.0001). Lean AbdSc adipocytes were more responsive to adrenergic signalling than obese AbdSc adipocytes; although endotoxin mitigated this response (92.6% reduction; p < 0.0001).

CONCLUSIONS

Taken together, these data suggest that systemic gut-derived endotoxaemia contributes to both individual adipocyte dysfunction and reduced browning capacity of the adipocyte cell population, exacerbating metabolic consequences. As bariatric surgery reduces endotoxin levels and is associated with improving adipocyte functionality, this may provide further evidence regarding the metabolic benefits of such surgical interventions.

Troponin T1 Promotes the Proliferation of Ovarian Cancer by Regulating Cell Cycle and Apoptosis

Background

Troponin T1 (TNNT1) is implicated in human carcinogenesis. However, the role of TNNT1 in ovarian cancer (OC) remains unclear.

Objectives

To investigate the effect of TNNT1 on the progression of ovarian cancer.

Materials and Methods

The level of TNNT1 was evaluated in OC patients based on The Cancer Genome Atlas (TCGA). Knockdown or overexpression of TNNT1 using siRNA targeting TNNT1 or plasmid carrying TNNT1 was performed in the ovarian cancer SKOV3 cell, respectively. RT-qPCR was performed to detect mRNA expression. Western blotting was used to examine protein expression. Cell Counting Kit-8, colony formation, cell cycle, and transwell assays were performed to analyze the role of TNNT1 on the proliferation and migration of ovarian cancer. Besides, xenograft model was carried out to evaluate the in vivo effect of TNNT1 on OC progression.

Results

Based on available bioinformatics data in TCGA, we found that TNNT1 was overexpressed in ovarian cancer samples comparing to normal samples. Knocking down TNNT1 repressed the migration as well as the proliferation of SKOV3 cells, while overexpression of TNNT1 exhibited opposite effect. In addition, down-regulation of TNNT1 hampered the xenografted tumor growth of SKOV3 cells. Up-regulation of TNNT1 in SKOV3 cells induced the expression of Cyclin E1 and Cyclin D1, promoted cell cycle progression, and also suppressed the activity of Cas-3/Cas-7.

Conclusions

In conclusion, TNNT1 overexpression promotes SKOV3 cell growth and tumorigenesis by inhibiting cell apoptosis and accelerating cell-cycle progression. TNNT1 might be a potent biomarker for the treatment of ovarian cancer.

Identification of Gene Regulatory Networks Using Variational Bayesian Inference in the Presence of Missing Data

The identification of gene regulatory networks (GRN) from gene expression time series data is a challenge and open problem in system biology. This paper considers the structure inference of GRN from the incomplete and noisy gene expression data, which is a not well-studied issue for GRN inference. In this paper, the dynamical behavior of the gene expression process is described by a stochastic nonlinear state-space model with unknown noise information. A variational Bayesian (VB) framework are proposed to estimate the parameters and gene expression levels simultaneously. One of the advantages of this method is that it can easily handle the missing observations by generating the prediction values. Considering the sparsity of GRN, the smoothed gene data are modeled by the extreme gradient boosting tree, and the regulatory interactions among genes are identified by the importance scores based on the tree model. The proposed method is tested on the artificial DREAM4 datasets and one real gene expression dataset of yeast. The comparative results show that the proposed method can effectively recover the regulatory interactions of GRN in the presence of missing observations and outperforms the existing methods for GRN identification.

Ulcerated melanoma: Systems biology evidence of inflammatory imbalance towards pro-tumourigenicity

Microscopic ulceration is an independent predictor of melanoma death. Here, we used systems biology to query the role of host and tumour-specific processes in defining the phenotype. Albumin level as a measure of systemic inflammation was predictive of fewer tumour-infiltrating lymphocytes and poorer survival in the Leeds Melanoma Cohort. Ulcerated melanomas were thicker and more mitotically active (with corresponding transcriptomic upregulated cell cycle pathways). Sequencing identified tumoural p53 and APC mutations, and TUBB2B amplification as associated with the phenotype. Ulcerated tumours had perturbed expression of cytokine genes, consistent with protumourigenic inflammation and histological and transcriptomic evidence for reduced adaptive immune cell infiltration. Pathway/network analysis of multiomic data using neural networks highlighted a role for the β-catenin pathway in the ulceration, linking genomic changes in the tumour to immunosuppression and cell proliferation. In summary, the data suggest that ulceration is in part associated with genomic changes but that host factors also predict melanoma death with evidence of reduced immune responses to the tumour.

Aligned Poly-l-lactic Acid Nanofibers Induce Self-Assembly of Primary Cortical Neurons into 3D Cell Clusters

Relative to two-dimensional (2D) culture, three-dimensional (3D) culture of primary neurons has yielded increasingly physiological responses from cells. Electrospun nanofiber scaffolds are frequently used as a 3D biomaterial support for primary neurons in neural tissue engineering, while hydrophobic surfaces typically induce aggregation of cells. Poly-l-lactic acid (PLLA) was electrospun as aligned PLLA nanofiber scaffolds to generate a structure with both qualities. Primary cortical neurons from E18 Sprague-Dawley rats cultured on aligned PLLA nanofibers generated 3D clusters of cells that extended highly aligned, fasciculated neurite bundles within 10 days. These clusters were viable for 28 days and responsive to AMPA and GABA. Relative to the 2D culture, the 3D cultures exhibited a more developed profile; mass spectrometry demonstrated an upregulation of proteins involved in cortical lamination, polarization, and axon fasciculation and a downregulation of immature neuronal markers. The use of artificial neural network inference suggests that the increased formation of synapses may drive the increase in development that is observed for the 3D cell clusters. This research suggests that aligned PLLA nanofibers may be highly useful for generating advanced 3D cell cultures for high-throughput systems.

What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics

Genetic data have become increasingly complex within the past decade, leading researchers to pursue increasingly complex questions, such as those involving epistatic interactions and protein prediction. Traditional methods are ill-suited to answer these questions, but machine learning (ML) techniques offer an alternative solution. ML algorithms are commonly used in genetics to predict or classify subjects, but some methods evaluate which features (variables) are responsible for creating a good prediction; this is called feature importance. This is critical in genetics, as researchers are often interested in which features (e.g., SNP genotype or environmental exposure) are responsible for a good prediction. This allows for the deeper analysis beyond simple prediction, including the determination of risk factors associated with a given phenotype. Feature importance further permits the researcher to peer inside the black box of many ML algorithms to see how they work and which features are critical in informing a good prediction. This review focuses on ML methods that provide feature importance metrics for the analysis of genetic data. Five major categories of ML algorithms: k nearest neighbors, artificial neural networks, deep learning, support vector machines, and random forests are described. The review ends with a discussion of how to choose the best machine for a data set. This review will be particularly useful for genetic researchers looking to use ML methods to answer questions beyond basic prediction and classification.

Predictive modeling of gene expression regulation

Background

In-depth analysis of regulation networks of genes aberrantly expressed in cancer is essential for better understanding tumors and identifying key genes that could be therapeutically targeted.

Results

We developed a quantitative analysis approach to investigate the main biological relationships among different regulatory elements and target genes; we applied it to Ovarian Serous Cystadenocarcinoma and 177 target genes belonging to three main pathways (DNA REPAIR, STEM CELLS and GLUCOSE METABOLISM) relevant for this tumor. Combining data from ENCODE and TCGA datasets, we built a predictive linear model for the regulation of each target gene, assessing the relationships between its expression, promoter methylation, expression of genes in the same or in the other pathways and of putative transcription factors. We proved the reliability and significance of our approach in a similar tumor type (basal-like Breast cancer) and using a different existing algorithm (ARACNe), and we obtained experimental confirmations on potentially interesting results.

Conclusions

The analysis of the proposed models allowed disclosing the relations between a gene and its related biological processes, the interconnections between the different gene sets, and the evaluation of the relevant regulatory elements at single gene level. This led to the identification of already known regulators and/or gene correlations and to unveil a set of still unknown and potentially interesting biological relationships for their pharmacological and clinical use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04481-1.

Comprehensive proteomic analysis reveals distinct functional modules associated with skull base and supratentorial meningiomas and perturbations in collagen pathway components

Meningiomas are brain tumors that originate from the meninges and has been primarily classified into three grades by the current WHO guidelines. Although widely prevalent and can be managed by surgery there are instances when the tumors are located in difficult regions. This results in considerable challenges for complete surgical resection and further clinical management. While the genetic signature of the skull base tumors is now known to be different from the non-skull base tumors, there is a lack of information at the functional aspects of these tumors at the proteomic level. Thus, the current study thereby aims to obtain mechanistic insights between the two radiologically distinct groups of meningiomas, namely the skull base & supratentorial (non-skull base-NSB) regions. We have employed a comprehensive mass spectrometry-based label-free quantitative proteomic analysis in Skull base and supratentorial meningiomas. Further, we have used an Artificial Neural Networking employing a sparse Multilayer perceptron (MLP) architecture to predict protein concordance. A patient-derived spectral library has been employed for a novel peptide-level validation of proteins that are specific to the radiological regions using the SRM assay based targeted proteomics approach. The comprehensive proteomics enabled the identification of nearly 4000 proteins with high confidence (1%FDR ≥ 2 unique peptides) among which 170 proteins were differentially abundant in Skull base vs Supratentorial tumors (p-value ≤0.05). In silico analysis enabled mapping of the major alterations and hinted towards an overall perturbation of extracellular matrix and collagen biosynthesis components in the non-skull base meningiomas and a prominent perturbation of molecular trafficking in the skull base meningiomas. Therefore, this study has yielded novel insights into the functional association of the proteins that are differentially abundant in the two radiological subgroups. SIGNIFICANCE: In the current study, we have performed label-free proteomic analysis on fresh frozen tissue of 14 Supratentorial (NSB) and 7 Skull base meningiomas to assess perturbations in the global proteome, we have further employed an in-depth in silico analysis to map the pathways that have enabled functional mapping of the differentially abundant proteins in the Skull base and Supratentorial tumors. The findings from the above were also subjected to a machine learning-based neural networking to find out the proteins that have the most concordance of occurrence to determine the most influential proteins of the network. We further validated the differential abundance of identified protein markers in a larger patient cohort of Skull base and Supratentorial employing targeted proteomics approach to validate key protein candidates emerging from ours and other recent studies. The previous studies that have explored the skull base and convexity meningiomas have been able to reveal alterations in the genetic mutations in these tumor types. However, there are not many studies that have explored the functional aspects of these tumors, especially at the proteome level. We have attempted for the first time to map the functional modules associated with altered proteins in these tumors and have been able to identify that there is a possibility that the Skull base meningiomas to be considerably different from the Non-skull base (NSB) tumors in terms of the perturbed pathways. Our study employed global as well as targeted proteomics to examine the proteomic alterations in these two tumor groups. The study indicates that proteins that were more abundant in Skull base tumors were part of molecular transport components, non-skull base proteins majorly mapped to the components of extracellular matrix remodeling pathways. In conclusion, this study substantiates the distinction in the proteomic signatures in the skull base and supratentorial meningiomas paving way for further investigation of the identified markers for determining if some of these proteins can be used for therapeutic interventions for cases that pose considerable challenges for complete resection.

Comprehending Meningioma Signaling Cascades Using Multipronged Proteomics Approaches & Targeted Validation of Potential Markers

Meningiomas are one of the most prevalent primary brain tumors. Our study aims to obtain mechanistic insights of meningioma pathobiology using mass spectrometry-based label-free quantitative proteome analysis to identifying druggable targets and perturbed pathways for therapeutic intervention. Label-free based proteomics study was done from peptide samples of 21 patients and 8 non-tumor controls which were followed up with Phosphoproteomics to identify the kinases and phosphorylated components of the perturbed pathways. In silico approaches revealed perturbations in extracellular matrix remodeling and associated cascades. To assess the extent of influence of Integrin and PI3K-Akt pathways, we used an Integrin Linked Kinase inhibitor on patient-derived meningioma cell line and performed a transcriptomic analysis of the components. Furthermore, we designed a Targeted proteomics assay which to the best of our knowledge for very first-time enables identification of peptides from 54 meningioma patients via SRM assay to validate the key proteins emerging from our study. This resulted in the identification of peptides from CLIC1, ES8L2, and AHNK many of which are receptors and kinases and are difficult to be characterized using conventional approaches. Furthermore, we were also able to monitor transitions for proteins like NEK9 and CKAP4 which have been reported to be associated with meningioma pathobiology. We believe, this study can aid in designing peptide-based validation assays for meningioma patients as well as IHC studies for clinical applications.

The neonatal Fc receptor in cancer FcRn in cancer

Since the neonatal IgG Fc receptor (FcRn) was discovered, it was found to be involved in immunoglobulin recycling and biodistribution, immune complexes routing, antigen presentation, humoral immune response, and cancer immunosurveillance. The latest data show that FcRn plays a part in cancer pathophysiology. In various types of cancers, such as lung and colorectal cancer, FcRn has been described as an early marker for prognosis. Dysregulation of FcRn expression by cancer cells allows them to increase their metabolism, and this process could be exploited for passive targeting of cytotoxic drugs. However, the roles of this receptor depend on whether the studied cell population is the tumor tissue or the infiltrating cells, bringing forward the need for further studies.

Development of a Bioinformatics Framework for Identification and Validation of Genomic Biomarkers and Key Immunopathology Processes and Controllers in Infectious and Non-infectious Severe Inflammatory Response Syndrome

Sepsis is defined as dysregulated host response caused by systemic infection, leading to organ failure. It is a life-threatening condition, often requiring admission to an intensive care unit (ICU). The causative agents and processes involved are multifactorial but are characterized by an overarching inflammatory response, sharing elements in common with severe inflammatory response syndrome (SIRS) of non-infectious origin. Sepsis presents with a range of pathophysiological and genetic features which make clinical differentiation from SIRS very challenging. This may reflect a poor understanding of the key gene inter-activities and/or pathway associations underlying these disease processes. Improved understanding is critical for early differential recognition of sepsis and SIRS and to improve patient management and clinical outcomes. Judicious selection of gene biomarkers suitable for development of diagnostic tests/testing could make differentiation of sepsis and SIRS feasible. Here we describe a methodologic framework for the identification and validation of biomarkers in SIRS, sepsis and septic shock patients, using a 2-tier gene screening, artificial neural network (ANN) data mining technique, using previously published gene expression datasets. Eight key hub markers have been identified which may delineate distinct, core disease processes and which show potential for informing underlying immunological and pathological processes and thus patient stratification and treatment. These do not show sufficient fold change differences between the different disease states to be useful as primary diagnostic biomarkers, but are instrumental in identifying candidate pathways and other associated biomarkers for further exploration.

Machine Learning Neuroprotective Strategy Reveals a Unique Set of Parkinson Therapeutic Nicotine Analogs

AIMS

Present a novel machine learning computational strategy to predict the neuroprotection potential of nicotine analogs acting over the behavior of unpaired signaling pathways in Parkinson's disease.

BACKGROUND

Dopaminergic replacement has been used for Parkinson's Disease (PD) treatment with positive effects on motor symptomatology but low progression and prevention effects. Epidemiological studies have shown that nicotine consumption decreases PD prevalence through neuroprotective mechanisms activation associated with the overstimulation of signaling pathways (SP) such as PI3K/AKT through nicotinic acetylcholine receptors (e.g α7 nAChRs) and over-expression of anti-apoptotic genes such as Bcl-2. Nicotine analogs with similar neuroprotective activity but decreased secondary effects remain as a promissory field.

OBJECTIVE

The objective of this study is to develop an interdisciplinary computational strategy predicting the neuroprotective activity of a series of 8 novel nicotine analogs over Parkinson's disease.

METHODS

We present a computational strategy integrating structural bioinformatics, SP manual reconstruction, and deep learning to predict the potential neuroprotective activity of 8 novel nicotine analogs over the behavior of PI3K/AKT. We performed a protein-ligand analysis between nicotine analogs and α7 nAChRs receptor using geometrical conformers, physicochemical characterization of the analogs and developed manually curated neuroprotective datasets to analyze their potential activity. Additionally, we developed a predictive machine-learning model for neuroprotection in PD through the integration of Markov Chain Monte-Carlo transition matrix for the 2 SP with synthetic training datasets of the physicochemical properties and structural dataset.

RESULTS

Our model was able to predict the potential neuroprotective activity of seven new nicotine analogs based on the binomial Bcl-2 response regulated by the activation of PI3K/AKT.

CONCLUSION

Hereby, we present a robust novel strategy to assess the neuroprotective potential of biomolecules based on SP architecture. Our theoretical strategy can be further applied to the study of new treatments related to SP deregulation and may ultimately offer new opportunities for therapeutic interventions in neurodegenerative diseases.

Slow skeletal muscle troponin T, titin and myosin light chain 3 are candidate prognostic biomarkers for Ewing's sarcoma

Ewing's sarcoma (ES) is a common malignant bone tumor in children and adolescents. Although great efforts have been made to understand the pathogenesis and development of ES, the underlying molecular mechanism remains unclear. The present study aimed to identify new key genes as potential biomarkers for the diagnosis, targeted therapy or prognosis of ES. mRNA expression profile chip data sets GSE17674, GSE17679 and GSE45544 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened using the R software limma package, and functional and pathway enrichment analyses were performed using the enrichplot package and GSEA software. The NetworkAnalyst online tool, as well as Cytoscape and its plug-ins cytoHubba and NetworkAnalyzer, were used to construct a protein-protein interaction network (PPI) and conduct module analysis to screen key (hub) genes. LABSO COX regression and overall survival (OS) analysis of the Hub genes were performed. A total of 211 DEGs were obtained by integrating and analyzing the three data sets. The functions and pathways of the DEGs were mainly associated with the regulation of small-molecule metabolic processes, cofactor-binding, amino acid, proteasome and ribosome biosynthesis in eukaryotes, as well as the Rac1, cell cycle and P53 signaling pathways. A total of one important module and 20 hub genes were screened from the PPI network using the Maximum Correlation Criteria algorithm of cytoHubba. LASSO COX regression results revealed that titin (TTN), fast skeletal muscle troponin T, skeletal muscle actin α-actin, nebulin, troponin C type 2 (fast), myosin light-chain 3 (MYL3), slow skeletal muscle troponin T (TNNT1), myosin-binding protein C1 slow-type, tropomyosin 3 and myosin heavy-chain 7 were associated with prognosis in patients with ES. The Kaplan-Meier curves demonstrated that high mRNA expression levels of TNNT1 (P<0.001), TTN (P=0.049), titin-cap (P=0.04), tropomodulin 1 (P=0.011), troponin I2 fast skeletal type (P=0.021) and MYL3 (P=0.017) were associated with poor OS in patients with ES. In conclusion, the DEGs identified in the present study may be key genes in the pathogenesis of ES, three of which, namely TNNT1, TTN and MYL3, may be potential prognostic biomarkers for ES.

A guide to gene regulatory network inference for obtaining predictive solutions: Underlying assumptions and fundamental biological and data constraints

The study of biological systems at a system level has become a reality due to the increasing powerful computational approaches able to handle increasingly larger datasets. Uncovering the dynamic nature of gene regulatory networks in order to attain a system level understanding and improve the predictive power of biological models is an important research field in systems biology. The task itself presents several challenges, since the problem is of combinatorial nature and highly depends on several biological constraints and also the intended application. Given the intrinsic interdisciplinary nature of gene regulatory network inference, we present a review on the currently available approaches, their challenges and limitations. We propose guidelines to select the most appropriate method considering the underlying assumptions and fundamental biological and data constraints.

MTSS1 and SCAMP1 cooperate to prevent invasion in breast cancer

Cell-cell adhesions constitute the structural "glue" that retains cells together and contributes to tissue organisation and physiological function. The integrity of these structures is regulated by extracellular and intracellular signals and pathways that act on the functional units of cell adhesion such as the cell adhesion molecules/adhesion receptors, the extracellular matrix (ECM) proteins and the cytoplasmic plaque/peripheral membrane proteins. In advanced cancer, these regulatory pathways are dysregulated and lead to cell-cell adhesion disassembly, increased invasion and metastasis. The Metastasis suppressor protein 1 (MTSS1) plays a key role in the maintenance of cell-cell adhesions and its loss correlates with tumour progression in a variety of cancers. However, the mechanisms that regulate its function are not well-known. Using a system biology approach, we unravelled potential interacting partners of MTSS1. We found that the secretory carrier-associated membrane protein 1 (SCAMP1), a molecule involved in post-Golgi recycling pathways and in endosome cell membrane recycling, enhances Mtss1 anti-invasive function in HER2+/ER-/PR- breast cancer, by promoting its protein trafficking leading to elevated levels of RAC1-GTP and increased cell-cell adhesions. This was clinically tested in HER2 breast cancer tissue and shown that loss of MTSS1 and SCAMP1 correlates with reduced disease-specific survival. In summary, we provide evidence of the cooperative roles of MTSS1 and SCAMP1 in preventing HER2+/ER-/PR- breast cancer invasion and we show that the loss of Mtss1 and Scamp1 results in a more aggressive cancer cell phenotype.

An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study

The field of machine learning has allowed researchers to generate and analyse vast amounts of data using a wide variety of methodologies. Artificial Neural Networks (ANN) are some of the most commonly used statistical models and have been successful in biomarker discovery studies in multiple disease types. This review seeks to explore and evaluate an integrated ANN pipeline for biomarker discovery and validation in Alzheimer's disease, the most common form of dementia worldwide with no proven cause and no available cure. The proposed pipeline consists of analysing public data with a categorical and continuous stepwise algorithm and further examination through network inference to predict gene interactions. This methodology can reliably generate novel markers and further examine known ones and can be used to guide future research in Alzheimer's disease.

Lurbinectedin Inactivates the Ewing Sarcoma Oncoprotein EWS-FLI1 by Redistributing It within the Nucleus

There is a great need to develop novel approaches to target oncogenic transcription factors with small molecules. Ewing sarcoma is emblematic of this need, as it depends on the continued activity of the EWS-FLI1 transcription factor to maintain the malignant phenotype. We have previously shown that the small molecule trabectedin interferes with EWS-FLI1. Here, we report important mechanistic advances and a second-generation inhibitor to provide insight into the therapeutic targeting of EWS-FLI1. We discovered that trabectedin functionally inactivated EWS-FLI1 by redistributing the protein within the nucleus to the nucleolus. This effect was rooted in the wild-type functions of the EWSR1, compromising the N-terminal half of the chimeric oncoprotein, which is known to be similarly redistributed within the nucleus in the presence of UV light damage. A second-generation trabectedin analogue lurbinectedin (PM01183) caused the same nuclear redistribution of EWS-FLI1, leading to a loss of activity at the promoter, mRNA, and protein levels of expression. Tumor xenograft studies confirmed this effect, and it was increased in combination with irinotecan, leading to tumor regression and replacement of Ewing sarcoma cells with benign fat cells. The net result of combined lurbinectedin and irinotecan treatment was a complete reversal of EWS-FLI1 activity and elimination of established tumors in 30% to 70% of mice after only 11 days of therapy. Our results illustrate the preclinical safety and efficacy of a disease-specific therapy targeting the central oncogenic driver in Ewing sarcoma. Cancer Res; 76(22); 6657-68. ©2016 AACR.