Machine Learning Methods for Precision Dosing in Anticancer Drug Therapy: A Scoping Review.

INTRODUCTION: In the last decade, various Machine Learning techniques have been proposed aiming to individualise the dose of anticancer drugs mostly based on a presumed drug effect or measured effect biomarkers. The aim of this scoping review was to comprehensively summarise the research status on the use of Machine Learning for precision dosing in anticancer drug therapy. METHODS: This scoping review was conducted in accordance with the interim guidance by Cochrane and the Joanna Briggs Institute. We systematically searched the databases Medline (via PubMed), Embase and the Cochrane Library for research articles and reviews including results published after 2016. Results were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. RESULTS: A total of 17 relevant studies was identified. In 12 of the included studies, Reinforcement Learning methods were used, including Classical, Deep, Double Deep and Conservative Q-Learning and Fuzzy Reinforcement Learning. Furthermore, classical Machine Learning methods were compared in terms of their performance and an artificial intelligence platform based on parabolic equations was used to guide dosing prospectively and retrospectively, albeit only in a limited number of patients. Due to the significantly different algorithm structures, a meaningful comparison between the various Machine Learning approaches was not possible. CONCLUSION: Overall, this review emphasises the clinical relevance of Machine Learning methods for anticancer drug dose optimisation, as many algorithms have shown promising results enabling model-free predictions with the potential to maximise efficacy and minimise toxicity when compared to standard protocols.

Dataset of miRNA-disease relations extracted from textual data using transformer-based neural networks.

MicroRNAs (miRNAs) play important roles in post-transcriptional processes and regulate major cellular functions. The abnormal regulation of expression of miRNAs has been linked to numerous human diseases such as respiratory diseases, cancer, and neurodegenerative diseases. Latest miRNA-disease associations are predominantly found in unstructured biomedical literature. Retrieving these associations manually can be cumbersome and time-consuming due to the continuously expanding number of publications. We propose a deep learning-based text mining approach that extracts normalized miRNA-disease associations from biomedical literature. To train the deep learning models, we build a new training corpus that is extended by distant supervision utilizing multiple external databases. A quantitative evaluation shows that the workflow achieves an area under receiver operator characteristic curve of 98% on a holdout test set for the detection of miRNA-disease associations. We demonstrate the applicability of the approach by extracting new miRNA-disease associations from biomedical literature (PubMed and PubMed Central). We have shown through quantitative analysis and evaluation on three different neurodegenerative diseases that our approach can effectively extract miRNA-disease associations not yet available in public databases. Database URL: https://zenodo.org/records/10523046.

Bayesian network modeling of risk and prodromal markers of Parkinson's disease.

Parkinson's disease (PD) is characterized by a long prodromal phase with a multitude of markers indicating an increased PD risk prior to clinical diagnosis based on motor symptoms. Current PD prediction models do not consider interdependencies of single predictors, lack differentiation by subtypes of prodromal PD, and may be limited and potentially biased by confounding factors, unspecific assessment methods and restricted access to comprehensive marker data of prospective cohorts. We used prospective data of 18 established risk and prodromal markers of PD in 1178 healthy, PD-free individuals and 24 incident PD cases collected longitudinally in the Tübingen evaluation of Risk factors for Early detection of NeuroDegeneration (TREND) study at 4 visits over up to 10 years. We employed artificial intelligence (AI) to learn and quantify PD marker interdependencies via a Bayesian network (BN) with probabilistic confidence estimation using bootstrapping. The BN was employed to generate a synthetic cohort and individual marker profiles. Robust interdependencies were observed for BN edges from age to subthreshold parkinsonism and urinary dysfunction, sex to substantia nigra hyperechogenicity, depression, non-smoking and to constipation; depression to symptomatic hypotension and excessive daytime somnolence; solvent exposure to cognitive deficits and to physical inactivity; and non-smoking to physical inactivity. Conversion to PD was interdependent with prior subthreshold parkinsonism, sex and substantia nigra hyperechogenicity. Several additional interdependencies with lower probabilistic confidence were identified. Synthetic subjects generated via the BN based representation of the TREND study were realistic as assessed through multiple comparison approaches of real and synthetic data. Altogether our work demonstrates the potential of modern AI approaches (specifically BNs) both for modelling and understanding interdependencies between PD risk and prodromal markers, which are so far not accounted for in PD prediction models, as well as for generating realistic synthetic data.

A Sequential Targeting Strategy Interrupts AKT-Driven Subclone-Mediated Progression in Glioblastoma.

PURPOSE: Therapy resistance and fatal disease progression in glioblastoma are thought to result from the dynamics of intra-tumor heterogeneity. This study aimed at identifying and molecularly targeting tumor cells that can survive, adapt, and subclonally expand under primary therapy. EXPERIMENTAL DESIGN: To identify candidate markers and to experimentally access dynamics of subclonal progression in glioblastoma, we established a discovery cohort of paired vital cell samples obtained before and after primary therapy. We further used two independent validation cohorts of paired clinical tissues to test our findings. Follow-up preclinical treatment strategies were evaluated in patient-derived xenografts. RESULTS: We describe, in clinical samples, an archetype of rare ALDH1A1+ tumor cells that enrich and acquire AKT-mediated drug resistance in response to standard-of-care temozolomide (TMZ). Importantly, we observe that drug resistance of ALDH1A1+ cells is not intrinsic, but rather an adaptive mechanism emerging exclusively after TMZ treatment. In patient cells and xenograft models of disease, we recapitulate the enrichment of ALDH1A1+ cells under the influence of TMZ. We demonstrate that their subclonal progression is AKT-driven and can be interfered with by well-timed sequential rather than simultaneous antitumor combination strategy. CONCLUSIONS: Drug-resistant ALDH1A1+/pAKT+ subclones accumulate in patient tissues upon adaptation to TMZ therapy. These subclones may therefore represent a dynamic target in glioblastoma. Our study proposes the combination of TMZ and AKT inhibitors in a sequential treatment schedule as a rationale for future clinical investigation.

Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings.

Prediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coronavirus 2 and John Cunningham polyomavirus, to predict virus-host PPIs. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of NLP as well as explainable artificial intelligence methods for the analysis of biological sequences.

Epigenome-Wide Analysis of DNA Methylation in Parkinson's Disease Cortex.

Background: Epigenetic factors including DNA methylation contribute to specific patterns of gene expression. Gene−environment interactions can change the methylation status in the brain, and accumulation of these epigenetic changes over a lifespan may be co-responsible for a neurodegenerative disease like Parkinson's disease, which that is characterised by a late onset in life. Aims: To determine epigenetic modifications in the brains of Parkinson's disease patients. Patients and Methods: DNA methylation patterns were compared in the cortex tissue of 14 male PD patients and 10 male healthy individuals using the Illumina Methylation 450 K chip. Subsequently, DNA methylation of candidate genes was evaluated using bisulphite pyrosequencing, and DNA methylation of cytochrome P450 2E1 (CYP2E1) was characterized in DNA from blood mononuclear cells (259 PD patients and 182 healthy controls) and skin fibroblasts (10 PD patients and 5 healthy controls). Protein levels of CYP2E1 were analysed using Western blot in human cortex and knock-out mice brain samples. Results: We found 35 hypomethylated and 22 hypermethylated genes with a methylation M-value difference >0.5. Decreased methylation of cytochrome P450 2E1 (CYP2E1) was associated with increased protein levels in PD brains, but in peripheral tissues, i.e., in blood cells and skin fibroblasts, DNA methylation of CYP2E1 was unchanged. In CYP2E1 knock-out mice brain alpha-synuclein (SNCA) protein levels were down-regulated compared to wild-type mice, whereas treatment with trichloroethylene (TCE) up-regulated CYP2E1 protein in a dose-dependent manner in cultured cells. We further identified an interconnected group of genes associated with oxidative stress, such as Methionine sulfoxide reductase A (MSRA) and tumour protein 73 (TP73) in the brain, which again were not paralleled in other tissues and appeared to indicate brain-specific changes. Conclusions: Our study revealed surprisingly few dysmethylated genes in a brain region less affected in PD. We confirmed hypomethylation of CYP2E1.

Corrigendum to "Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases"[Artificial Intelligence in Life Sciences] 1(2021), 100020.

[This corrects the article DOI: 10.1016/j.ailsci.2021.100020.].

GuiltyTargets: Prioritization of Novel Therapeutic Targets With Network Representation Learning.

The majority of clinical trials fail due to low efficacy of investigated drugs, often resulting from a poor choice of target protein. Existing computational approaches aim to support target selection either via genetic evidence or by putting potential targets into the context of a disease specific network reconstruction. The purpose of this work was to investigate whether network representation learning techniques could be used to allow for a machine learning based prioritization of putative targets. We propose a novel target prioritization approach, GuiltyTargets, which relies on attributed network representation learning of a genome-wide protein-protein interaction network annotated with disease-specific differential gene expression and uses positive-unlabeled (PU) machine learning for candidate ranking. We evaluated our approach on 12 datasets from six diseases of different type (cancer, metabolic, neurodegenerative) within a 10 times repeated 5-fold stratified cross-validation and achieved AUROC values between 0.92 - 0.97, significantly outperforming previous approaches that relied on manually engineered topological features. Moreover, we showed that GuiltyTargets allows for target repositioning across related disease areas. An application of GuiltyTargets to Alzheimer's disease resulted in a number of highly ranked candidates that are currently discussed as targets in the literature. Interestingly, one (COMT) is also the target of an approved drug (Tolcapone) for Parkinson's disease, highlighting the potential for target repositioning with our method. The GuiltyTargets Python package is available on PyPI and all code used for analysis can be found under the MIT License at https://github.com/GuiltyTargets. Attributed network representation learning techniques provide an interesting approach to effectively leverage the existing knowledge about the molecular mechanisms in different diseases. In this work, the combination with positive-unlabeled learning for target prioritization demonstrated a clear superiority compared to classical feature engineering approaches. Our work highlights the potential of attributed network representation learning for target prioritization. Given the overarching relevance of networks in computational biology we believe that attributed network representation learning techniques could have a broader impact in the future.

Caution, "normal" BMI: health risks associated with potentially masked individual underweight-EPMA Position Paper 2021.

An increasing interest in a healthy lifestyle raises questions about optimal body weight. Evidently, it should be clearly discriminated between the standardised "normal" body weight and individually optimal weight. To this end, the basic principle of personalised medicine "one size does not fit all" has to be applied. Contextually, "normal" but e.g. borderline body mass index might be optimal for one person but apparently suboptimal for another one strongly depending on the individual genetic predisposition, geographic origin, cultural and nutritional habits and relevant lifestyle parameters-all included into comprehensive individual patient profile. Even if only slightly deviant, both overweight and underweight are acknowledged risk factors for a shifted metabolism which, if being not optimised, may strongly contribute to the development and progression of severe pathologies. Development of innovative screening programmes is essential to promote population health by application of health risks assessment, individualised patient profiling and multi-parametric analysis, further used for cost-effective targeted prevention and treatments tailored to the person. The following healthcare areas are considered to be potentially strongly benefiting from the above proposed measures: suboptimal health conditions, sports medicine, stress overload and associated complications, planned pregnancies, periodontal health and dentistry, sleep medicine, eye health and disorders, inflammatory disorders, healing and pain management, metabolic disorders, cardiovascular disease, cancers, psychiatric and neurologic disorders, stroke of known and unknown aetiology, improved individual and population outcomes under pandemic conditions such as COVID-19. In a long-term way, a significantly improved healthcare economy is one of benefits of the proposed paradigm shift from reactive to Predictive, Preventive and Personalised Medicine (PPPM/3PM). A tight collaboration between all stakeholders including scientific community, healthcare givers, patient organisations, policy-makers and educators is essential for the smooth implementation of 3PM concepts in daily practice.

A method for the rational selection of drug repurposing candidates from multimodal knowledge harmonization.

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community's massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases.

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center 'Lean European Open Survey on SARS-CoV-2-infected patients' (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.

Voice perturbations under the stress overload in young individuals: phenotyping and suboptimal health as predictors for cascading pathologies.

Verbal communication is one of the most sophisticated human motor skills reflecting both-the mental and physical health of an individual. Voice parameters and quality changes are usually secondary towards functional and/or structural laryngological alterations under specific systemic processes, syndrome and pathologies. These include but are not restricted to dry mouth and Sicca syndromes, body dehydration, hormonal alterations linked to pubertal, menopausal, and andropausal status, respiratory disorders, gastrointestinal reflux, autoimmune diseases, endocrinologic disorders, underweight versus overweight and obesity, and diabetes mellitus. On the other hand, it is well-established that stress overload is a significant risk factor of cascading pathologies, including but not restricted to neurodegenerative and psychiatric disorders, diabetes mellitus, cardiovascular disease, stroke, and cancers. Our current study revealed voice perturbations under the stress overload as a potentially useful biomarker to identify individuals in suboptimal health conditions who might be strongly predisposed to associated pathologies. Contextually, extended surveys applied in the population might be useful to identify, for example, persons at high risk for respiratory complications under pandemic conditions such as COVID-19. Symptoms of dry mouth syndrome, disturbed microcirculation, altered sense regulation, shifted circadian rhythm, and low BMI were positively associated with voice perturbations under the stress overload. Their functional interrelationships and relevance for cascading associated pathologies are presented in the article. Automated analysis of voice recordings via artificial intelligence (AI) has a potential to derive digital biomarkers. Further, predictive machine learning models should be developed that allows for detecting a suboptimal health condition based on voice recordings, ideally in an automated manner using derived digital biomarkers. Follow-up stratification and monitoring of individuals in suboptimal health conditions are recommended using disease-specific cell-free nucleic acids (ccfDNA, ctDNA, mtDNA, miRNA) combined with metabolic patterns detected in body fluids. Application of the cost-effective targeted prevention within the phase of reversible health damage is recommended based on the individualised patient profiling.

Drug repositioning of antiretroviral ritonavir for combinatorial therapy in glioblastoma.

BACKGROUND: The protease inhibitor ritonavir (RTV) is a clinical-stage inhibitor of the human immunodeficiency virus. In a drug repositioning approach, we here exhibit the additional potential of RTV to augment current treatment of glioblastoma, the most aggressive primary brain tumour of adulthood. METHODS: We explored the antitumour activity of RTV and mechanisms of action in a broad spectrum of short-term expanded clinical cell samples from primary and recurrent glioblastoma and in a cohort of conventional cell lines and non-tumour human neural controls in vitro. To validate RTV efficacy in monotherapeutic and in combinatorial settings, we used patient-derived xenograft models in a series of in vivo studies. RESULTS: RTV monotherapy induced a selective antineoplastic response and demonstrated cytostatic and anti-migratory activity at clinical plasma peak levels. Additional exposure to temozolomide or irradiation further enhanced the effects synergistically, fostered by mechanisms of autophagy and increased endoplasmic reticulum stress. In xenograft models, we consequently observed increasing overall survival under the combinatorial effect of RTV and temozolomide. CONCLUSIONS: Our data establish RTV as a valuable repositioning candidate for further exploration as an adjunct therapeutic in the clinical care of glioblastoma.

Optimal multiparametric set-up modelled for best survival outcomes in palliative treatment of liver malignancies: unsupervised machine learning and 3 PM recommendations.

Over the last decade, a rapid rise in deaths due to liver disease has been observed especially amongst young people. Nowadays liver disease accounts for approximately 2 million deaths per year worldwide: 1 million due to complications of cirrhosis and 1 million due to viral hepatitis and hepatocellular carcinoma. Besides primary liver malignancies, almost all solid tumours are capable to spread metastases to the liver, in particular, gastrointestinal cancers, breast and genitourinary cancers, lung cancer, melanomas and sarcomas. A big portion of liver malignancies undergo palliative care. To this end, the paradigm of the palliative care in the liver cancer management is evolving from "just end of the life" care to careful evaluation of all aspects relevant for the survivorship. In the presented study, an evidence-based approach has been taken to target molecular pathways and subcellular components for modelling most optimal conditions with the longest survival rates for patients diagnosed with advanced liver malignancies who underwent palliative treatments. We developed an unsupervised machine learning (UML) approach to robustly identify patient subgroups based on estimated survival curves for each individual patient and each individual potential biomarker. UML using consensus hierarchical clustering of biomarker derived risk profiles resulted into 3 stable patient subgroups. There were no significant differences in age, gender, therapy, diagnosis or comorbidities across clusters. Survival times across clusters differed significantly. Furthermore, several of the biomarkers demonstrated highly significant pairwise differences between clusters after correction for multiple testing, namely, "comet assay" patterns of classes I, III, IV and expression rates of calgranulin A (S100), SOD2 and profilin-all measured ex vivo in circulating leucocytes. Considering worst, intermediate and best survival curves with regard to identified clusters and corresponding patterns of parameters measured, clear differences were found for "comet assay" and S100 expression patterns. In conclusion, multi-faceted cancer control within the palliative care of liver malignancies is crucial for improved disease outcomes including individualised patient profiling, predictive models and implementation of corresponding cost-effective risks mitigating measures detailed in the paper. The "proof-of-principle" model is presented.

PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data.

BACKGROUND: Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics data increase the chance to robustly identify molecular patient sub-groups and hence open the door towards a better personalized treatment of diseases. Several methods have been proposed for unsupervised clustering of multi-omics data. However, a number of challenges remain, such as the magnitude of features and the large difference in dimensionality across different omics data sources. RESULTS: We propose a multi-modal sparse denoising autoencoder framework coupled with sparse non-negative matrix factorization to robustly cluster patients based on multi-omics data. The proposed model specifically leverages pathway information to effectively reduce the dimensionality of omics data into a pathway and patient specific score profile. In consequence, our method allows us to understand, which pathway is a feature of which particular patient cluster. Moreover, recently proposed machine learning techniques allow us to disentangle the specific impact of each individual omics feature on a pathway score. We applied our method to cluster patients in several cancer datasets using gene expression, miRNA expression, DNA methylation and CNVs, demonstrating the possibility to obtain biologically plausible disease subtypes characterized by specific molecular features. Comparison against several competing methods showed a competitive clustering performance. In addition, post-hoc analysis of somatic mutations and clinical data provided supporting evidence and interpretation of the identified clusters. CONCLUSIONS: Our suggested multi-modal sparse denoising autoencoder approach allows for an effective and interpretable integration of multi-omics data on pathway level while addressing the high dimensional character of omics data. Patient specific pathway score profiles derived from our model allow for a robust identification of disease subgroups.

Comprehensive analysis of tumor necrosis factor receptor TNFRSF9 (4-1BB) DNA methylation with regard to molecular and clinicopathological features, immune infiltrates, and response prediction to immunotherapy in melanoma.

BACKGROUND: Immunotherapy, including checkpoint inhibition, has remarkably improved prognosis in advanced melanoma. Despite this success, acquired resistance is still a major challenge. The T cell costimulatory receptor TNFRSF9 (also known as 4-1BB and CD137) is a promising new target for immunotherapy and two agonistic antibodies are currently tested in clinical trials. However, little is known about epigenetic regulation of the encoding gene. In this study we investigate a possible correlation of TNFRSF9 DNA methylation with gene expression, clinicopathological parameters, molecular and immune correlates, and response to anti-PD-1 immunotherapy to assess the validity of TNFRSF9 methylation to serve as a biomarker. METHODS: We performed a correlation analyses of methylation at twelve CpG sites within TNFRSF9 with regard to transcriptional activity, immune cell infiltration, mutation status, and survival in a cohort of N = 470 melanoma patients obtained from The Cancer Genome Atlas. Furthermore, we used quantitative methylation-specific PCR to confirm correlations in a cohort of N = 115 melanoma patients' samples (UHB validation cohort). Finally, we tested the ability of TNFRSF9 methylation and expression to predict progression-free survival (PFS) and response to anti-PD-1 immunotherapy in a cohort comprised of N = 121 patients (mRNA transcription), (mRNA ICB cohort) and a case-control study including N = 48 patients (DNA methylation, UHB ICB cohort). FINDINGS: We found a significant inverse correlation between TNFRSF9 DNA methylation and mRNA expression levels at six of twelve analyzed CpG sites (P ≤ 0.005), predominately located in the promoter flank region. Consistent with its role as costimulatory receptor in immune cells, TNFRSF9 mRNA expression and hypomethylation positively correlated with immune cell infiltrates and an interferon-γ signature. Furthermore, elevated TNFRSF9 mRNA expression and TNFRSF9 hypomethylation correlated with superior overall survival. In patients receiving anti-PD-1 immunotherapy (mRNA ICB cohort), we found that TNFRSF9 hypermethylation and reduced mRNA expression correlated with poor PFS and response. INTERPRETATION: Our study suggests that TNFRSF9 mRNA expression is regulated via DNA methylation. The observed correlations between TNFRSF9 DNA methylation or mRNA expression with known features of response to immune checkpoint blockage suggest TNFRSF9 methylation could serve as a biomarker in the context of immunotherapies. Concordantly, we identified a correlation between TNFRSF9 DNA methylation and mRNA expression with disease progression in patients under immunotherapy. Our study provides rationale for further investigating TNFRSF9 DNA methylation as a predictive biomarker for response to immunotherapy. FUNDING: AF was partly funded by the Mildred Scheel Foundation. SF received funding from the University Hospital Bonn BONFOR program (O-105.0069). DN was funded in part by DFG Cluster of Excellence ImmunoSensation (EXC 1023). The funders had no role in study design, data collection and analysis, interpretation, decision to publish, or preparation of the manuscript; or any aspect pertinent to the study.

The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling.

Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.

Classic bladder exstrophy and adenocarcinoma of the bladder: Methylome analysis provide no evidence for underlying disease-mechanisms of this association.

The bladder exstrophy-epispadias complex (BEEC) represents the severe end of uro-rectal malformation spectrum involving aberrant embryonic morphogenesis of the cloacal membrane and the urorectal septum. The most common form of BEEC is isolated classic bladder exstrophy (CBE). Long-term complications in CBE are malignancies of the bladder with 95% of them being adenocarcinomas. Since CBE and adenocarcinoma of the bladder are rare entities, their frequent co-occurrence suggests a common etiology. Recent studies suggest that promoter methylation of various genes play a crucial role during the phenotypical morphogenesis of adenocarcinomas of urinary bladder. To examine, whether epigenetic processes such as DNA methylation patterns are potentially associated with CBE, we performed Illumina 450 K methylation arrays in blood (n = 10) and tissue samples (n = 2) of CBE patients and healthy matched controls (n = 12). In our analysis, we found total lack of methylation in the blood and methylation differences were restricted to 10 CpG sites in the tissue samples. In comparison to other bladder anomalies, CBE tissue methylation profiles differ from those of adenocarcinoma, adenocarcinoma with CBE, urothelial carcinoma and urachal carcinoma. In this preliminary study, we did not provide any strong evidence of major DNA methylation alterations which would be suggestive for strong underlying epigenetic mechanism. However, larger studies are required to provide more robust statistical evidence to exclude smaller effects in the tissues.

Towards a Central Role of ISL1 in the Bladder Exstrophy⁻Epispadias Complex (BEEC): Computational Characterization of Genetic Variants and Structural Modelling.

Genetic factors play a critical role in the development of human diseases. Recently, several molecular genetic studies have provided multiple lines of evidence for a critical role of genetic factors in the expression of human bladder exstrophy-epispadias complex (BEEC). At this point, ISL1 (ISL LIM homeobox 1) has emerged as the major susceptibility gene for classic bladder exstrophy (CBE), in a multifactorial disease model. Here, GWAS (Genome wide association studies) discovery and replication studies, as well as the re-sequencing of ISL1, identified sequence variants (rs9291768, rs6874700, c.137C > G (p.Ala46Gly)) associated with CBE. Here, we aimed to determine the molecular and functional consequences of these sequence variants and estimate the dependence of ISL1 protein on other predicted candidates. We used: (i) computational analysis of conserved sequence motifs to perform an evolutionary conservation analysis, based on a Bayesian algorithm, and (ii) computational 3D structural modeling. Furthermore, we looked into long non-coding RNAs (lncRNAs) residing within the ISL1 region, aiming to predict their targets. Our analysis suggests that the ISL1 protein specific N-terminal LIM domain (which harbors the variant c.137C>G), limits its transcriptional ability, and might interfere with ISL1-estrogen receptor α interactions. In conclusion, our analysis provides further useful insights about the ISL1 gene, which is involved in the formation of the BEEC, and in the development of the urinary bladder.

Molecular signatures in IASLC/ATS/ERS classified growth patterns of lung adenocarcinoma.

BACKGROUND: The current classification of human lung adenocarcinoma defines five different histological growth patterns within the group of conventional invasive adenocarcinomas. The five growth patterns are characterised by their typical architecture, but also by variable tumor biological behaviour. AIMS: The aim of this study was to identify specific gene signatures of the five adenocarcinoma growth patterns defined by the joint IASLC/ATS/ERS working group. METHODS: Total RNA from microdissected adenocarcinoma tissue samples of ten lepidic, ten acinar, ten solid, nine papillary, and nine micropapillary tumor portions was isolated and prepared for gene expression analysis. Differential expression of genes was determined using the R package "LIMMA". The overall significance of each signature was assessed via global test. Gene ontology statistics were analysed using GOstat. For immunohistochemical validation, tissue specimens from 20 tumors with solid and 20 tumors with lepidic growth pattern were used. RESULTS: Microarray analyses between the growth patterns resulted in numerous differentially expressed genes between the solid architecture and other patterns. The comparison of transcriptomic activity in the solid and lepidic patterns revealed 705 up- and 110 downregulated non-redundant genes. The pattern-specific protein expression of Inositol-1,4,5-trisphosphate-kinase-A (ITPKA) and angiogenin by immunohistochemistry confirmed the RNA levels. The strongest differences in protein expression between the two patterns were shown for ITPKA (p = 0.02) and angiogenin (p = 0.113). CONCLUSIONS: In this study growth pattern-specific gene signatures in pulmonary adenocarcinoma were identified and distinct transcriptomic differences between lung adenocarcinoma growth patterns were defined. The study provides valuable new information about pulmonary adenocarcinoma and allows a better assessment of the five adenocarcinoma subgroups.