High-resolution transcriptomic and epigenetic profiling identifies novel regulators of COPD.

Patients with chronic obstructive pulmonary disease (COPD) are still waiting for curative treatments. Considering its environmental cause, we hypothesized that COPD will be associated with altered epigenetic signaling in lung cells. We generated genome-wide DNA methylation maps at single CpG resolution of primary human lung fibroblasts (HLFs) across COPD stages. We show that the epigenetic landscape is changed early in COPD, with DNA methylation changes occurring predominantly in regulatory regions. RNA sequencing of matched fibroblasts demonstrated dysregulation of genes involved in proliferation, DNA repair, and extracellular matrix organization. Data integration identified 110 candidate regulators of disease phenotypes that were linked to fibroblast repair processes using phenotypic screens. Our study provides high-resolution multi-omic maps of HLFs across COPD stages. We reveal novel transcriptomic and epigenetic signatures associated with COPD onset and progression and identify new candidate regulators involved in the pathogenesis of chronic lung diseases. The presence of various epigenetic factors among the candidates demonstrates that epigenetic regulation in COPD is an exciting research field that holds promise for novel therapeutic avenues for patients.

Graph-theoretical prediction of biological modules in quaternary structures of large protein complexes.

MOTIVATION: The functional complexity of biochemical processes is strongly related to the interplay of proteins and their assembly into protein complexes. In recent years, the discovery and characterization of protein complexes have substantially progressed through advances in cryo-electron microscopy, proteomics, and computational structure prediction. This development results in a strong need for computational approaches to analyse the data of large protein complexes for structural and functional characterization. Here, we aim to provide a suitable approach, which processes the growing number of large protein complexes, to obtain biologically meaningful information on the hierarchical organization of the structures of protein complexes. RESULTS: We modelled the quaternary structure of protein complexes as undirected, labelled graphs called complex graphs. In complex graphs, the vertices represent protein chains and the edges spatial chain-chain contacts. We hypothesized that clusters based on the complex graph correspond to functional biological modules. To compute the clusters, we applied the Leiden clustering algorithm. To evaluate our approach, we chose the human respiratory complex I, which has been extensively investigated and exhibits a known biological module structure experimentally validated. Additionally, we characterized a eukaryotic group II chaperonin TRiC/CCT and the head of the bacteriophage Φ29. The analysis of the protein complexes correlated with experimental findings and indicated known functional, biological modules. Using our approach enables not only to predict functional biological modules in large protein complexes with characteristic features but also to investigate the flexibility of specific regions and coformational changes. The predicted modules can aid in the planning and analysis of experiments. AVAILABILITY AND IMPLEMENTATION: Jupyter notebooks to reproduce the examples are available on our public GitHub repository: https://github.com/MolBIFFM/PTGLtools/tree/main/PTGLmodulePrediction.

Does Dual-Energy Computed Tomography Material Decomposition Improve Radiomics Capability to Predict Survival in Head and Neck Squamous Cell Carcinoma Patients? A Preliminary Investigation.

OBJECTIVE: Our study objective was to explore the additional value of dual-energy CT (DECT) material decomposition for squamous cell carcinoma of the head and neck (SCCHN) survival prognostication. METHODS: A group of 50 SCCHN patients (male, 37; female, 13; mean age, 63.6 ± 10.82 years) with baseline head and neck DECT between September 2014 and August 2020 were retrospectively included. Primary tumors were segmented, radiomics features were extracted, and DECT material decomposition was performed. We used independent train and validation datasets with cross-validation and 100 independent iterations to identify prognostic signatures applying elastic net (EN) and random survival forest (RSF). Features were ranked and intercorrelated according to their prognostic importance. We benchmarked the models against clinical parameters. Intraclass correlation coefficients were used to analyze the interreader variation. RESULTS: The exclusively radiomics-trained models achieved similar ( P = 0.947) prognostic performance of area under the curve (AUC) = 0.784 (95% confidence interval [CI], 0.775-0.812) (EN) and AUC = 0.785 (95% CI, 0.759-0.812) (RSF). The additional application of DECT material decomposition did not improve the model's performance (EN, P = 0.594; RSF, P = 0.198). In the clinical benchmark, the top averaged AUC value of 0.643 (95% CI, 0.611-0.675) was inferior to the quantitative imaging-biomarker models ( P < 0.001). A combined imaging and clinical model did not improve the imaging-based models ( P > 0.101). Shape features revealed high prognostic importance. CONCLUSIONS: Radiomics AI applications may be used for SCCHN survival prognostication, but the spectral information of DECT material decomposition did not improve the model's performance in our preliminary investigation.

Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology.

BACKGROUND: Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms. METHODS: Retrospectively, 106 prostate tissue samples from 48 patients (mean age, [Formula: see text] years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open-source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms. RESULTS: Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02-0.06 for ERG and PIN-4. CONCLUSIONS: Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.

Computational modeling of signal transduction networks without kinetic parameters: Petri net approaches.

More and more computational techniques have been applied to model biological systems, especially signaling pathways in medical systems. Due to the large number of experimental data driven by high-throughput technologies, new computational concepts have been developed. Nevertheless, often the necessary kinetic data cannot be determined in sufficient number and quality because of experimental complexity or ethical reasons. At the same time, the number of qualitative data drastically increased, for example, gene expression data, protein-protein interaction data, and imaging data. Especially for large-scale models, the application of kinetic modeling techniques can fail. On the other hand, many large-scale models have been constructed applying qualitative and semiquantitative techniques, for example, logical models or Petri net models. These techniques make it possible to explore system's dynamics without knowing kinetic parameters. Here, we summarize the work of the last 10 years for modeling signal transduction pathways in medical applications applying Petri net formalism. We focus on analysis techniques based on system's invariants without any kinetic parameters and show predictions of all signaling pathways of the system. We start with an intuitive introduction into Petri nets and system's invariants. We illustrate the main concepts using the tumor necrosis factor receptor 1 (TNFR1)-induced nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB) pathway as a case study. Summarizing recent models, we discuss the advantages and challenges of Petri net applications to medical signaling systems. In addition, we provide exemplarily interesting Petri net applications to model signaling in medical systems of the last years that use the well-known stochastic and kinetic concepts developed about 50 years ago.

Pathogenetic mechanisms and therapeutic approaches of acute-to-chronic liver failure.

Liver cirrhosis is the end stage of all chronic liver diseases and contributes significantly to overall mortality of 2% globally. The age-standardized mortality from liver cirrhosis in Europe is between 10 and 20% and can be explained by not only the development of liver cancer but also the acute deterioration in the patient's overall condition. The development of complications including accumulation of fluid in the abdomen (ascites), bleeding in the gastrointestinal tract (variceal bleeding), bacterial infections, or a decrease in brain function (hepatic encephalopathy) define an acute decompensation that requires therapy and often leads to acute-on-chronic liver failure (ACLF) by different precipitating events. However, due to its complexity and organ-spanning nature, the pathogenesis of ACLF is poorly understood, and the common underlying mechanisms leading to the development of organ dysfunction or failure in ACLF are still elusive. Apart from general intensive care interventions, there are no specific therapy options for ACLF. Liver transplantation is often not possible in these patients due to contraindications and a lack of prioritization. In this review, we describe the framework of the ACLF-I project consortium funded by the Hessian Ministry of Higher Education, Research and the Arts (HMWK) based on existing findings and will provide answers to these open questions.

Mathematical modeling of the molecular switch of TNFR1-mediated signaling pathways applying Petri net formalism and in silico knockout analysis.

The paper describes a mathematical model of the molecular switches of cell survival, apoptosis, and necroptosis in cellular signaling pathways initiated by tumor necrosis factor 1. Based on experimental findings in the literature, we constructed a Petri net model based on detailed molecular reactions of the molecular players, protein complexes, post-translational modifications, and cross talk. The model comprises 118 biochemical entities, 130 reactions, and 299 edges. We verified the model by evaluating invariant properties of the system at steady state and by in silico knockout analysis. Applying Petri net analysis techniques, we found 279 pathways, which describe signal flows from receptor activation to cellular response, representing the combinatorial diversity of functional pathways.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic and led to multiple possible outcomes. We investigated the in silico knockout behavior and identified important checkpoints of the TNFR1 signaling pathway in terms of ubiquitination within complex I and the gene expression dependent on NF-κB, which controls the caspase activity in complex II and apoptosis induction. Despite not knowing enough kinetic data of sufficient quality, we estimated system's dynamics using a discrete, semi-quantitative Petri net model.

Variation of butyrate production in the gut microbiome in type 2 diabetes patients.

BACKGROUND: Diabetes mellitus type 2 is a common disease that poses a challenge to the healthcare system. The disease is very often diagnosed late. A better understanding of the relationship between the gut microbiome and type 2 diabetes can support early detection and form an approach for therapies. Microbiome analysis offers a potential opportunity to find markers for this disease. Next-generation sequencing methods can be used to identify the bacteria present in the stool sample and to generate a microbiome profile through an analysis pipeline. Statistical analysis, e.g., using Student's t-test, allows the identification of significant differences. The investigations are not only focused on single bacteria, but on the determination of a comprehensive profile. Also, the consideration of the functional microbiome is included in the analyses. The dataset is not from a clinical survey, but very extensive. RESULTS: By examining 946 microbiome profiles of diabetes mellitus type 2 sufferers (272) and healthy control persons (674), a large number of significant genera (25) are revealed. It is possible to identify a large profile for type 2 diabetes disease. Furthermore, it is shown that the diversity of bacteria per taxonomic level in the group of persons with diabetes mellitus type 2 is significantly reduced compared to a healthy control group. In addition, six pathways are determined to be significant for type 2 diabetes describing the fermentation to butyrate. These parameters tend to have high potential for disease detection. CONCLUSIONS: With this investigation of the gut microbiome of persons with diabetes type 2 disease, we present significant bacteria and pathways characteristic of this disease.

Radiomics for therapy-specific head and neck squamous cell carcinoma survival prognostication (part I).

BACKGROUND: Treatment plans for squamous cell carcinoma of the head and neck (SCCHN) are individually decided in tumor board meetings but some treatment decision-steps lack objective prognostic estimates. Our purpose was to explore the potential of radiomics for SCCHN therapy-specific survival prognostication and to increase the models' interpretability by ranking the features based on their predictive importance. METHODS: We included 157 SCCHN patients (male, 119; female, 38; mean age, 64.39 ± 10.71 years) with baseline head and neck CT between 09/2014 and 08/2020 in this retrospective study. Patients were stratified according to their treatment. Using independent training and test datasets with cross-validation and 100 iterations, we identified, ranked and inter-correlated prognostic signatures using elastic net (EN) and random survival forest (RSF). We benchmarked the models against clinical parameters. Inter-reader variation was analyzed using intraclass-correlation coefficients (ICC). RESULTS: EN and RSF achieved top prognostication performances of AUC = 0.795 (95% CI 0.767-0.822) and AUC = 0.811 (95% CI 0.782-0.839). RSF prognostication slightly outperformed the EN for the complete (ΔAUC 0.035, p = 0.002) and radiochemotherapy (ΔAUC 0.092, p < 0.001) cohort. RSF was superior to most clinical benchmarking (p ≤ 0.006). The inter-reader correlation was moderate or high for all features classes (ICC ≥ 0.77 (± 0.19)). Shape features had the highest prognostic importance, followed by texture features. CONCLUSIONS: EN and RSF built on radiomics features may be used for survival prognostication. The prognostically leading features may vary between treatment subgroups. This warrants further validation to potentially aid clinical treatment decision making in the future.

Prognostic phenotypes of early-stage lung adenocarcinoma.

BACKGROUND: Survival after curative resection of early-stage lung adenocarcinoma (LUAD) varies and prognostic biomarkers are urgently needed. METHODS: Large-format tissue samples from a prospective cohort of 200 patients with resected LUAD were immunophenotyped for cancer hallmarks TP53, NF1, CD45, PD-1, PCNA, TUNEL and FVIII, and were followed for a median of 2.34 (95% CI 1.71-3.49) years. RESULTS: Unsupervised hierarchical clustering revealed two patient subgroups with similar clinicopathological features and genotype, but with markedly different survival: "proliferative" patients (60%) with elevated TP53, NF1, CD45 and PCNA expression had 50% 5-year overall survival, while "apoptotic" patients (40%) with high TUNEL had 70% 5-year survival (hazard ratio 2.23, 95% CI 1.33-3.80; p=0.0069). Cox regression and machine learning algorithms including random forests built clinically useful models: a score to predict overall survival and a formula and nomogram to predict tumour phenotype. The distinct LUAD phenotypes were validated in The Cancer Genome Atlas and KMplotter data, and showed prognostic power supplementary to International Association for the Study of Lung Cancer tumour-node-metastasis stage and World Health Organization histologic classification. CONCLUSIONS: Two molecular subtypes of LUAD exist and their identification provides important prognostic information.

PTGL: extension to graph-based topologies of cryo-EM data for large protein structures.

SUMMARY: We provide a software to describe the topology of large protein complexes based mainly on cryo-EM data and stored as macromolecular Crystallographic Information Files (mmCIFs) in the PDB. The software extends the Protein Topology Graph Library and implements an efficient file parser to analyze mmCIFs. The extended Protein Topology Graph Library includes a graph-based representation of the topology of protein complexes on the supersecondary and quaternary structure level. The library holds topology graphs of 151 837 PDB files; 921 of them are large structures. The abstraction of protein structure complexes to undirected labeled graphs enables classification and comparison of large protein complexes on quaternary structure level. AVAILABILITY AND IMPLEMENTATION: Online access at http://ptgl.uni-frankfurt.de. Source code in Java under GNU public license 2.0 at https://github.com/MolBIFFM/vplg. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission.

BACKGROUND: Machine learning (ML) algorithms have been trained to early predict critical in-hospital events from COVID-19 using patient data at admission, but little is known on how their performance compares with each other and/or with statistical logistic regression (LR). This prospective multicentre cohort study compares the performance of a LR and five ML models on the contribution of influencing predictors and predictor-to-event relationships on prediction model´s performance. METHODS: We used 25 baseline variables of 490 COVID-19 patients admitted to 8 hospitals in Germany (March-November 2020) to develop and validate (75/25 random-split) 3 linear (L1 and L2 penalty, elastic net [EN]) and 2 non-linear (support vector machine [SVM] with radial kernel, random forest [RF]) ML approaches for predicting critical events defined by intensive care unit transfer, invasive ventilation and/or death (composite end-point: 181 patients). Models were compared for performance (area-under-the-receiver-operating characteristic-curve [AUC], Brier score) and predictor importance (performance-loss metrics, partial-dependence profiles). RESULTS: Models performed close with a small benefit for LR (utilizing restricted cubic splines for non-linearity) and RF (AUC means: 0.763-0.731 [RF-L1]); Brier scores: 0.184-0.197 [LR-L1]). Top ranked predictor variables (consistently highest importance: C-reactive protein) were largely identical across models, except creatinine, which exhibited marginal (L1, L2, EN, SVM) or high/non-linear effects (LR, RF) on events. CONCLUSIONS: Although the LR and ML models analysed showed no strong differences in performance and the most influencing predictors for COVID-19-related event prediction, our results indicate a predictive benefit from taking account for non-linear predictor-to-event relationships and effects. Future efforts should focus on leveraging data-driven ML technologies from static towards dynamic modelling solutions that continuously learn and adapt to changes in data environments during the evolving pandemic. TRIAL REGISTRATION NUMBER: NCT04659187.

Prediction and analysis of redox-sensitive cysteines using machine learning and statistical methods.

Reactive oxygen species are produced by a number of stimuli and can lead both to irreversible intracellular damage and signaling through reversible post-translational modification. It is unclear which factors contribute to the sensitivity of cysteines to redox modification. Here, we used statistical and machine learning methods to investigate the influence of different structural and sequence features on the modifiability of cysteines. We found several strong structural predictors for redox modification. Sensitive cysteines tend to be characterized by higher exposure, a lack of secondary structure elements, and a high number of positively charged amino acids in their close environment. Our results indicate that modified cysteines tend to occur close to other post-translational modifications, such as phosphorylated serines. We used these features to create models and predict the presence of redox-modifiable cysteines in human mitochondrial complex I as well as make novel predictions regarding redox-sensitive cysteines in proteins.

Detection of follicular regions in actin-stained whole slide images of the human lymph node by shock filter.

Human lymph nodes play a central part of immune defense against infection agents and tumor cells. Lymphoid follicles are compartments of the lymph node which are spherical, mainly filled with B cells. B cells are cellular components of the adaptive immune systems. In the course of a specific immune response, lymphoid follicles pass different morphological differentiation stages. The morphology and the spatial distribution of lymphoid follicles can be sometimes associated to a particular causative agent and development stage of a disease. We report our new approach for the automatic detection of follicular regions in histological whole slide images of tissue sections immuno-stained with actin. The method is divided in two phases: (1) shock filter-based detection of transition points and (2) segmentation of follicular regions. Follicular regions in 10 whole slide images were manually annotated by visual inspection, and sample surveys were conducted by an expert pathologist. The results of our method were validated by comparing with the manual annotation. On average, we could achieve a Zijbendos similarity index of 0.71, with a standard deviation of 0.07.

Bioinformatics analysis of whole slide images reveals significant neighborhood preferences of tumor cells in Hodgkin lymphoma.

In pathology, tissue images are evaluated using a light microscope, relying on the expertise and experience of pathologists. There is a great need for computational methods to quantify and standardize histological observations. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of tumor cells and their microenvironment are of special interest. Here, we systematically investigated tumor cell properties and their spatial neighborhood relations by a new application of statistical analysis to whole slide images of Hodgkin lymphoma, a tumor arising in lymph nodes, and inflammation of lymph nodes called lymphadenitis. We considered properties of more than 400, 000 immunohistochemically stained, CD30-positive cells in 35 whole slide images of tissue sections from subtypes of the classical Hodgkin lymphoma, nodular sclerosis and mixed cellularity, as well as from lymphadenitis. We found that cells of specific morphology exhibited significantly favored and unfavored spatial neighborhood relations of cells in dependence of their morphology. This information is important to evaluate differences between Hodgkin lymph nodes infiltrated by tumor cells (Hodgkin lymphoma) and inflamed lymph nodes, concerning the neighborhood relations of cells and the sizes of cells. The quantification of neighborhood relations revealed new insights of relations of CD30-positive cells in different diagnosis cases. The approach is general and can easily be applied to whole slide image analysis of other tumor types.

Potential of high dimensional radiomic features to assess blood components in intraaortic vessels in non-contrast CT scans.

BACKGROUND: To assess the potential of radiomic features to quantify components of blood in intraaortic vessels to non-invasively predict moderate-to-severe anemia in non-contrast enhanced CT scans. METHODS: One hundred patients (median age, 69 years; range, 19-94 years) who received CT scans of the thoracolumbar spine and blood-testing for hemoglobin and hematocrit levels ± 24 h between 08/2018 and 11/2019 were retrospectively included. Intraaortic blood was segmented using a spherical volume of interest of 1 cm diameter with consecutive radiomic analysis applying PyRadiomics software. Feature selection was performed applying analysis of correlation and collinearity. The final feature set was obtained to differentiate moderate-to-severe anemia. Random forest machine learning was applied and predictive performance was assessed. A decision-tree was obtained to propose a cut-off value of CT Hounsfield units (HU). RESULTS: High correlation with hemoglobin and hematocrit levels was shown for first-order radiomic features (p < 0.001 to p = 0.032). The top 3 features showed high correlation to hemoglobin values (p) and minimal collinearity (r) to the top ranked feature Median (p < 0.001), Energy (p = 0.002, r = 0.387), Minimum (p = 0.032, r = 0.437). Median (p < 0.001) and Minimum (p = 0.003) differed in moderate-to-severe anemia compared to non-anemic state. Median yielded superiority to the combination of Median and Minimum (p(AUC) = 0.015, p(precision) = 0.017, p(accuracy) = 0.612) in the predictive performance employing random forest analysis. A Median HU value ≤ 36.5 indicated moderate-to-severe anemia (accuracy = 0.90, precision = 0.80). CONCLUSIONS: First-order radiomic features correlate with hemoglobin levels and may be feasible for the prediction of moderate-to-severe anemia. High dimensional radiomic features did not aid augmenting the data in our exemplary use case of intraluminal blood component assessment. Trial registration Retrospectively registered.

Prevention of COVID-19 in Thoracic Surgery Patients: Lessons Learned during the First Pandemic Wave.

BACKGROUND: The aim of this retrospective study was to investigate the implementation of measures to prevent perioperative COVID-19 in thoracic surgery during the first wave of the COVID-19 pandemic 2020 allowing a continued surgical treatment of patients. METHODS: The implemented preventive measures in patient management of the thoracic surgery department of the Asklepios Lung Clinic Munich-Gauting, Germany were retrospectively analyzed. Postoperative COVID-19 incidence before and after implementation of preventive measures was investigated. Patients admitted for thoracic surgical procedures between March and May 2020 were included in the study. Patient characteristics were analyzed. For the early detection of putative postoperative COVID-19 symptoms, typical post-discharge symptomatology of thoracic surgery patients was compared to non-surgical patients hospitalized for COVID-19. RESULTS: Thirty-five surgical procedures and fifty-seven surgical procedures were performed before and after implementation of the preventive measures, respectively. Three patients undergoing thoracic surgery before implementation of preventive measures developed a COVID-19 pneumonia post-discharge. After implementation of preventive measures, no postoperative COVID-19 cases were identified. Fever, dyspnea, dry cough and diarrhea were significantly more prevalent in COVID-19 patients compared to normally recovering thoracic surgery patients, while anosmia, phlegm, low energy levels, body ache and nausea were similarly frequent in both groups. CONCLUSIONS: Based on the lessons learned during the first pandemic wave, we here provide a blueprint for successful easily implementable preventive measures minimizing SARS-CoV-2 transmission to thoracic surgery patients perioperatively. While symptoms of COVID-19 and the normal postoperative course of thoracic surgery patients substantially overlap, we found dyspnea, fever, cough, and diarrhea significantly more prevalent in COVID-19 patients than in normally recovering thoracic surgery patients. These symptoms should trigger further diagnostic testing for postoperative COVID-19 in thoracic surgery patients.

isiKnock: in silico knockouts in signaling pathways.

SUMMARY: isiKnock is a new software that automatically conducts in silico knockouts for mathematical models of signaling pathways. The software allows for the prediction of the behavior of biological systems after single or multiple knockout. The implemented algorithm applies transition invariants and the novel concept of Manatee invariants. A knockout matrix visualizes the results. The tool enables the analysis of dependencies, for example, in signal flows from the receptor activation to the cell response at steady state. AVAILABILITY AND IMPLEMENTATION: isiKnock is an open-source tool, freely available at http://www.bioinformatik.uni-frankfurt.de/tools/isiKnock/. It requires at least Java 8 and runs under Microsoft Windows, Linux, and Mac OS.

Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features.

OBJECTIVES: To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). METHODS: Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. RESULTS: PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p < 0.001) while also identifying combinations of features and models which reduced overall accuracy. CONCLUSIONS: The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance. KEY POINTS: • Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. • Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. • Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone.

Common functional variants of the glutamatergic system in Autism spectrum disorder with high and low intellectual abilities.

The genetic architecture underlying Autism spectrum disorder (ASD) has been suggested to differ between individuals with lower (IQ ≤ 70; LIQ) and higher intellectual abilities (IQ > 70; HIQ). Among the identified pathomechanisms, the glutamatergic signalling pathway is of specific interest in ASD. We investigated 187 common functional variants of this neurotransmitter system for association with ASD and with symptom severity in two independent samples, a German (German-ALL: N = 583 families) and the Autism Genome Project cohort (AGP-ALL: N = 2001 families), split into HIQ, and LIQ subgroups. We did not identify any association withstanding correction for multiple testing. However, we report a replicated nominal significant under-transmission (OR < 0.79, p < 0.04) of the AKAP13 rs745191-T allele in both LIQ cohorts, but not in the much larger HIQ cohorts. At the phenotypic level, we nominally replicated associations of CAMK2A-rs2241694 with non-verbal communication in both combined LIQ and HIQ ASD cohorts. Variants PLD1-rs2124147 and ADCY1-rs2461127 were nominally associated with impaired non-verbal abilities and AKAP2-rs3739456 with repetitive behaviour in both LIQ cohorts. All four LIQ-associated genes are involved in G-protein coupled signal transduction, a downstream pathway of metabotropic glutamate receptor activation. We conclude that functional common variants of glutamatergic genes do not have a strong impact on ASD, but seem to moderately affect ASD risk and phenotypic expression. Since most of our nominally replicated hits were identified in the LIQ cohort, further investigation of the glutamatergic system in this subpopulation might be warranted.