Gnocis: An integrated system for interactive and reproducible analysis and modelling of cis-regulatory elements in Python 3

Gene expression is regulated through cis-regulatory elements (CREs), among which are promoters, enhancers, Polycomb/Trithorax Response Elements (PREs), silencers and insulators. Computational prediction of CREs can be achieved using a variety of statistical and machine learning methods combined with different feature space formulations. Although Python packages for DNA sequence feature sets and for machine learning are available, no existing package facilitates the combination of DNA sequence feature sets with machine learning methods for the genome-wide prediction of candidate CREs. We here present Gnocis, a Python package that streamlines the analysis and the modelling of CRE sequences by providing extensible APIs and implementing the glue required for combining feature sets and models for genome-wide prediction. Gnocis implements a variety of base feature sets, including motif pair occurrence frequencies and the k-spectrum mismatch kernel. It integrates with Scikit-learn and TensorFlow for state-of-the-art machine learning. Gnocis additionally implements a broad suite of tools for the handling and preparation of sequence, region and curve data, which can be useful for general DNA bioinformatics in Python. We also present Deep-MOCCA, a neural network architecture inspired by SVM-MOCCA that achieves moderate to high generalization without prior motif knowledge. To demonstrate the use of Gnocis, we applied multiple machine learning methods to the modelling of D. melanogaster PREs, including a Convolutional Neural Network (CNN), making this the first study to model PREs with CNNs. The models are readily adapted to new CRE modelling problems and to other organisms. In order to produce a high-performance, compiled package for Python 3, we implemented Gnocis in Cython. Gnocis can be installed using the PyPI package manager by running ‘pip install gnocis’.publishedVersio

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images

Mathematical modeling approaches for the diagnosis and treatment of reentrant atrial tachyarrhythmias

[EN] Atrial tachyarrhythmias present a high prevalence in the developed world, and several studies predict that in the coming decades it will be increased. Micro or macro-reentrant mechanisms of the electrical wavefronts that govern the mechanical behavior of the heart are one of the main responsibles for the maintenance of these arrhythmias. Atrial flutter is maintained by a macro-reentry around an anatomical or functional obstacle located in the atria. In the case of atrial fibrillation, the hypothesis which describes high frequency rotors as dominant sources of the fibrillation and responsible for the maintenance of the arrhythmia, has been gaining relevance in the last years. However, the therapies that target high frequency sources have a limited efficacy with current techniques. Radiofrequency ablation allows the destruction of parts of the cardiac tissue resulting in the interruption of the reentrant circuit in case of macro-reentries or the isolation of micro-reentrant circuits. The non-invasive location of reentrant circuits would increment the efficacy of these therapies and would shorten surgery interventions. In parallel, pharmacological therapies modify ionic expressions associated to the excitability and electrical refractoriness of the cardiac tissue with the objective of hindering the maintenance of reentrant behaviors. These therapies require a deep knowledge of the ionic mechanisms underlying the reentrant behavior and its properties in order to be effective. The research in these mechanisms allows the evaluation of new targets for the treatment and thus may improve the efficacy in atrial fibrillation termination. In this thesis, mathematical modeling is used to go forward in the minimization of the limitations associated to these treatments. Body surface potential mapping has been evaluated, both clinically and by means of mathematical simulations for the diagnosis and location of macro-reentrant circuits. The analysis of phase maps obtained from multiple lead electrocardiographic recordings distributed in the whole torso allowed the discrimination between different reentrant circuits. It is the reason why this technique is presented as a tool for the non-invasive location of macro and micro-reentrant circuits. A population of mathematical models designed in this thesis based on the action potentials recordings of atrial cardiomyocites from 149 patients, allowed the evaluation of the ionic mechanisms defining the properties of reentrant behaviors. This study has allowed us defining the blockade of ICaL as a target for the pharmacological treatment. The blockade of this current is associated with the increase of the movement in the core of the rotor which easies the collision of the rotor with other wavefronts or anatomical obstacles promoting the extinction of the reentry. The variability observed between patients modeled in our population has allowed showing and explaining the mechanisms promoting divergent results of a single treatment. This is why the introduction of populations of models will allow the prevention of side effects associated to inter-subject variability and to go forward in the development of individualized therapies. These works are built through a simulation platform of cardiac electrophysiology based in Graphic Processing Units (GPUs) and developed in this thesis. The platform allows the simulation of cellular models, tissues and organs with a realistic geometry and shows features comparable to that of the platforms used by the most relevant electrophysiology research groups at the moment.[ES] Las taquiarritmias auriculares tienen una alta prevalencia en el mundo desarrollado, además diversos estudios poblacionales indican que en las próximas décadas ésta se verá incrementada. Los mecanismos de micro o macro-reentrada de los frentes de onda eléctricos que rigen el comportamiento mecánico del corazón, se presentan como una de las principales causas del mantenimiento de estas arritmias. El flutter auricular es mantenido por un macro-reentrada alrededor de un obstáculo anatómico o funcional en las aurículas, mientras que en el caso de la fibrilación auricular la hipótesis que define a los rotores de alta frecuencia como elementos dominantes y responsables del mantenimiento de la arritmia se ha ido imponiendo al resto en los últimos años. Sin embargo, las terapias que tienen como objetivo finalizar o aislar estas reentradas tienen todavía una eficacia limitada. La ablación por radiofrecuencia permite eliminar zonas del tejido cardiaco resultando en la interrupción del circuito de reentrada en el caso de macro-reentradas o el aislamiento de comportamientos micro-reentrantes. La localización no invasiva de los circuitos reentrantes incrementaría la eficacia de estas terapias y reduciría la duración de las intervenciones quirúrgicas. Por otro lado, las terapias farmacológicas alteran las expresiones iónicas asociadas a la excitabilidad y la refractoriedad del tejido con el fin de dificultar el mantenimiento de comportamientos reentrantes. Este tipo de terapias exigen incrementar el conocimiento de los mecanismos subyacentes que explican el proceso de reentrada y sus propiedades, la investigación de estos mecanismos permite definir las dianas terapéuticas que mejoran la eficacia en la extinción de estos comportamientos. En esta tesis el modelado matemático se utiliza para dar un paso importante en la minimización de las limitaciones asociadas a estos tratamientos. La cartografía eléctrica de superficie ha sido testada, clínicamente y con simulaciones matemática,s como técnica de diagnóstico y localización de circuitos macro-reentrantes. El análisis de mapas de fase obtenidos a partir de los registros multicanal de derivaciones electrocardiográficas distribuidas en la superficie del torso permite diferenciar distintos circuitos de reentrada. Es por ello que esta técnica de registro y análisis se presenta como una herramienta para la localización no invasiva de circuitos macro y micro-reentrantes. Una población de modelos matemáticos, diseñada en esta tesis a partir de los registros de los potenciales de acción de 149 pacientes, ha permitido evaluar los mecanismos iónicos que definen las propiedades asociadas a los procesos de reentrada. Esto ha permitido apuntar al bloqueo de la corriente ICaL como diana terapéutica. Ésta se asocia al incremento del movimiento del núcleo que facilita el impacto del rotor con otros frentes de onda u obstáculos extinguiéndose así el comportamiento reentrante. La variabilidad entre pacientes reflejada en la población de modelos ha permitido además mostrar los mecanismos por los cuales un mismo tratamiento puede mostrar efectos divergentes, así el uso de poblaciones de modelos matemáticos permitirá prevenir efectos secundarios asociados a la variabilidad entre pacientes y profundizar en el desarrollo de terapias individualizadas. Estos trabajos se cimientan sobre una plataforma de simulación de electrofisiología cardiaca de basado en Unidades de Procesado Gráfico (GPUs) y desarrollada en esta tesis. La plataforma permite la simulación de modelos celulares cardiacos así como de tejidos u órganos con geometría realista, mostrando unas prestaciones comparables con las de las utilizadas por los grupos de investigación más potentes en el campo de la electrofisiología.[CA] Les taquiarítmies auriculars tenen una alta prevalença en el món desenvolupat, a més diversos estudis poblacionals indiquen que en les pròximes dècades aquesta es veurà incrementada. Els mecanismes de micro o macro-reentrada dels fronts d'ona elèctrics que regeixen el comportament mecànic del cor, es presenten com una de les principals causes del manteniment d'aquestes arítmies. El flutter auricular és mantingut per una macro-reentrada al voltant d'un obstacle anatòmic o funcional en les aurícules, mentre que en el cas de la fibril·lació auricular la hipòtesi que defineix als rotors d'alta freqüència com a elements dominants i responsables del manteniment de l'arítmia s'ha anat imposant a la resta en els últims anys. No obstant això, les teràpies que tenen com a objectiu finalitzar o aïllar aquestes reentrades tenen encara una eficàcia limitada. L'ablació per radiofreqüència permet eliminar zones del teixit cardíac resultant en la interrupció del circuit de reentrada en el cas de macro-reentrades o l'aïllament de comportaments micro-reentrants. La localització no invasiva dels circuits reentrants incrementaria l'eficàcia d'aquestes teràpies i reduiria la durada de les intervencions quirúrgiques. D'altra banda, les teràpies farmacològiques alteren les expressions iòniques associades a la excitabilitat i la refractaritat del teixit amb la finalitat de dificultar el manteniment de comportaments reentrants. Aquest tipus de teràpies exigeixen incrementar el coneixement dels mecanismes subjacents que expliquen el procés de reentrada i les seues propietats, la recerca d'aquests mecanismes permet definir les dianes terapèutiques que milloren l'eficàcia en l'extinció d'aquests comportaments. En aquesta tesi el modelatge matemàtic s'utilitza per a fer un pas important en la minimització de les limitacions associades a aquests tractaments. La cartografia elèctrica de superfície ha sigut testada, clínicament i amb simulacions matemàtiques com a tècnica de diagnòstic i localització de circuits macro-reentrants. L'anàlisi de mapes de fase obtinguts a partir dels registres multicanal de derivacions electrocardiogràfiques distribuïdes en la superfície del tors permet diferenciar diferents circuits de reentrada. És per açò que aquesta tècnica de registre i anàlisi es presenta com una eina per a la localització no invasiva de circuits macro i micro-reentrants. Una població de models matemàtics, dissenyada en aquesta tesi a partir dels registres dels potencials d'acció de 149 pacients, ha permès avaluar els mecanismes iònics que defineixen les propietats associades als processos de reentrada. Açò ha permès apuntar al bloqueig del corrent ICaL com a diana terapèutica. Aquesta s'associa a l'increment del moviment del nucli que facilita l'impacte del rotor amb altres fronts d'ona o obstacles extingint-se així el comportament reentrant. La variabilitat entre pacients reflectida en la població de models ha permès a més mostrar els mecanismes pels quals un mateix tractament pot mostrar efectes divergents, així l'ús de poblacions de models matemàtics permetrà prevenir efectes secundaris associats a la variabilitat entre pacients i aprofundir en el desenvolupament de teràpies individualitzades. Aquests treballs es fonamenten sobre una plataforma de simulació de electrofisiologia cardíaca basat en Unitats de Processament Gràfic (GPUs) i desenvolupada en aquesta tesi. La plataforma permet la simulació de models cel·lulars cardíacs així com de teixits o òrgans amb geometria realista, mostrant unes prestacions comparables amb les de les utilitzades per els grups de recerca més importants en aquesta área.Liberos Mascarell, A. (2016). Mathematical modeling approaches for the diagnosis and treatment of reentrant atrial tachyarrhythmias [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/62166TESI

Pan-cancer classifications of tumor histological images using deep learning

Histopathological images are essential for the diagnosis of cancer type and selection of optimal treatment. However, the current clinical process of manual inspection of images is time consuming and prone to intra- and inter-observer variability. Here we show that key aspects of cancer image analysis can be performed by deep convolutional neural networks (CNNs) across a wide spectrum of cancer types. In particular, we implement CNN architectures based on Google Inception v3 transfer learning to analyze 27815 H&E slides from 23 cohorts in The Cancer Genome Atlas in studies of tumor/normal status, cancer subtype, and mutation status. For 19 solid cancer types we are able to classify tumor/normal status of whole slide images with extremely high AUCs (0.995±0.008). We are also able to classify cancer subtypes within 10 tissue types with AUC values well above random expectations (micro-average 0.87±0.1). We then perform a cross-classification analysis of tumor/normal status across tumor types. We find that classifiers trained on one type are often effective in distinguishing tumor from normal in other cancer types, with the relationships among classifiers matching known cancer tissue relationships. For the more challenging problem of mutational status, we are able to classify TP53 mutations in three cancer types with AUCs from 0.65-0.80 using a fully-trained CNN, and with similar cross-classification accuracy across tissues. These studies demonstrate the power of CNNs for not only classifying histopathological images in diverse cancer types, but also for revealing shared biology between tumors. We have made software available at: https://github.com/javadnoorb/HistCNNFirst author draf

Microscopy with ultraviolet surface excitation for rapid slide-free histology.

Histologic examination of tissues is central to the diagnosis and management of neoplasms and many other diseases, and is a foundational technique for preclinical and basic research. However, commonly used bright-field microscopy requires prior preparation of micrometre-thick tissue sections mounted on glass slides, a process that can require hours or days, that contributes to cost, and that delays access to critical information. Here, we introduce a simple, non-destructive slide-free technique that within minutes provides high-resolution diagnostic histological images resembling those obtained from conventional haematoxylin-and-eosin-histology. The approach, which we named microscopy with ultraviolet surface excitation (MUSE), can also generate shape and colour-contrast information. MUSE relies on ~280-nm ultraviolet light to restrict the excitation of conventional fluorescent stains to tissue surfaces, and it has no significant effects on downstream molecular assays (including fluorescence in situ hybridization and RNA-seq). MUSE promises to improve the speed and efficiency of patient care in both state-of-the-art and low-resource settings, and to provide opportunities for rapid histology in research

Equine penile squamous cell carcinoma: expression of biomarker proteins and EcPV2

Equine penile squamous cell carcinoma (EpSCC) is a relatively common cutaneous neoplasm with a poor prognosis. In this study, we aimed to determine the protein expression and colocalisation of FRA1, c-Myc, Cyclin D1, and MMP7 in normal (NT), tumour (T), hyperplastic epidermis and/or squamous papilloma (Hyp/Pap), poorly-differentiated (PDSCC), or well-differentiated (WDSCC) EpSCC using a tissue array approach. Further objectives were to correlate protein expression to (i) levels of inflammation, using a convolutional neural network (ii) equine papillomavirus 2 (EcPV2) infection, detected using PCR amplification. We found an increase in expression of FRA1 in EpSCC compared to NT samples. c-Myc expression was higher in Hyp/Pap and WDSCC but not PDSCC whereas MMP7 was reduced in WDSCC compared with NT. There was a significant increase in the global intersection coefficient (GIC) of FRA1 with MMP7, c-Myc, and Cyclin D1 in EpSCC. Conversely, GIC for MMP7 with c-Myc was reduced in EpSCC tissue. Inflammation was positively associated with EcPV2 infection in both NT and EpSCC but not Hyp/Pap. Changes in protein expression could be correlated with EcPV2 for Cyclin D1 and c-Myc. Our results evaluate novel biomarkers of EpSCC and a putative correlation between the expression of biomarkers, EcPV2 infection and inflammation

Applying Deep Learning To Identify Imaging Biomarkers To Predict Cardiac Outcomes In Cancer Patients

Cancer patients are a unique population with increased mortality from cardiovascular disease, however only half of high-risk patients are medically optimized. Physicians ascertain cardiovascular risk from several risk predictors using demographic information, family history, and imaging data. The Agatston score, a measure of total calcium burden in coronary arteries on CT scans, is the current best predictor for major adverse cardiac events (MACE). Yet, the score is limited as it does not provide information on atherosclerotic plaque characteristics or distribution. In this study, we use deep learning techniques to develop an imaging-based biomarker that can robustly predict MACE in lung cancer patients. We selected participants with screen-detected lung cancer from the National Lung Screening Trial (NLST) and used cardiovascular mortality as our primary outcome. We applied automated segmentation algorithms to low-dose chest CT scans from NLST participants to segment cardiac substructures. Following segmentation, we extracted radiomic features from selected cardiac structures. We then used this dataset to train a regression model to predict cardiovascular death. We used a pre-trained nnU-Net model to successfully segment large cardiac structures on CT scans. These automated large cardiac structures had features that were predictive of MACE. We then successfully extract radiomic features from our areas of interest and use this high-dimensional dataset to train a regression model to predict MACE. We demonstrated that automated segmentation algorithms can result in low-cost non-invasive predictive biomarkers for MACE. We were able to demonstrate that radiomic feature extraction from segmented substructures can be used to develop a high-dimensional biomarker. We hope that such a scoring system can help physicians adequately determine cardiovascular risk and intervene, resulting in better patient outcomes