A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective

Pancreatic neuroendocrine tumors (PanNETs) are a rare tumor entity with largely unpredictable progression and increasing incidence in developed countries. Molecular pathways involved in PanNETs development are still not elucidated, and specific biomarkers are missing. Moreover, the heterogeneity of PanNETs makes their treatment challenging and most approved targeted therapeutic options for PanNETs lack objective responses. Here, we applied a systems biology approach integrating dynamic modeling strategies, foreign classifier tailored approaches, and patient expression profiles to predict PanNETs progression as well as resistance mechanisms to clinically approved treatments such as the mammalian target of rapamycin complex 1 (mTORC1) inhibitors. We set up a model able to represent frequently reported PanNETs drivers in patient cohorts, such as Menin-1 (MEN1), Death domain associated protein (DAXX), Tuberous Sclerosis (TSC), as well as wild-type tumors. Model-based simulations suggested drivers of cancer progression as both first and second hits after MEN1 loss. In addition, we could predict the benefit of mTORC1 inhibitors on differentially mutated cohorts and hypothesize resistance mechanisms. Our approach sheds light on a more personalized prediction and treatment of PanNET mutant phenotypes.

Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images

In recent years, several deep learning approaches have been successfully applied in the field of medical image analysis. More specifically, different deep neural network architectures have been proposed and assessed for the detection of various pathologies based on chest X-ray images. While the performed assessments have shown very promising results, most of them consist in training and evaluating the performance of the proposed approaches on a single data set. However, the generalization of such models is quite limited in a cross-domain setting, since a significant performance degradation can be observed when these models are evaluated on data sets stemming from different medical centers or recorded under different protocols. The performance degradation is mostly caused by the domain shift between the training set and the evaluation set. To alleviate this problem, different unsupervised domain adaptation approaches are proposed and evaluated in the current work, for the detection of cardiomegaly based on chest X-ray images, in a cross-domain setting. The proposed approaches generate domain invariant feature representations by adapting the parameters of a model optimized on a large set of labeled samples, to a set of unlabeled images stemming from a different data set. The performed evaluation points to the effectiveness of the proposed approaches, since the adapted models outperform optimized models which are directly applied to the evaluation sets without any form of domain adaptation.

Efficient cross-validation traversals in feature subset selection

Sparse and robust classification models have the potential for revealing common predictive patterns that not only allow for categorizing objects into classes but also for generating mechanistic hypotheses. Identifying a small and informative subset of features is their main ingredient. However, the exponential search space of feature subsets and the heuristic nature of selection algorithms limit the coverage of these analyses, even for low-dimensional datasets. We present methods for reducing the computational complexity of feature selection criteria allowing for higher efficiency and coverage of screenings. We achieve this by reducing the preparation costs of high-dimensional subsets [Formula: see text] to those of one-dimensional ones [Formula: see text]. Our methods are based on a tight interaction between a parallelizable cross-validation traversal strategy and distance-based classification algorithms and can be used with any product distance or kernel. We evaluate the traversal strategy exemplarily in exhaustive feature subset selection experiments (perfect coverage). Its runtime, fitness landscape, and predictive performance are analyzed on publicly available datasets. Even in low-dimensional settings, we achieve approximately a 15-fold increase in exhaustively generating distance matrices for feature combinations bringing a new level of evaluations into reach.

Predicting disease progression in behavioral variant frontotemporal dementia

INTRODUCTION

The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline.

METHODS

One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups ("fast progressors" vs. "slow progressors"), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models.

RESULTS

We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models.

DISCUSSION

Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model.

NADH Fluorescence Lifetime Imaging Microscopy Reveals Selective Mitochondrial Dysfunction in Neurons Overexpressing Alzheimer's Disease-Related Proteins

Alzheimer's disease (AD), the most prevalent form of dementia, affects globally more than 30 million people suffering from cognitive deficits and neuropsychiatric symptoms. Substantial evidence for the involvement of mitochondrial dysfunction in the development and/or progression of AD has been shown in addition to the pathological hallmarks amyloid beta (Aβ) and tau. Still, the selective vulnerability and associated selective mitochondrial dysfunction cannot even be resolved to date. We aimed at optically quantifying mitochondrial function on a single-cell level in primary hippocampal neuron models of AD, unraveling differential involvement of cell and mitochondrial populations in amyloid precursor protein (APP)-associated mitochondrial dysfunction. NADH lifetime imaging is a highly sensitive marker-free method with high spatial resolution. However, deciphering cellular bioenergetics of complex cells like primary neurons has still not succeeded yet. To achieve this, we combined highly sensitive NADH lifetime imaging with respiratory inhibitor treatment, allowing characterization of mitochondrial function down to even the subcellular level in primary neurons. Measuring NADH lifetime of the same neuron before and after respiratory treatment reveals the metabolic delta, which can be taken as a surrogate for cellular redox capacity. Correlating NADH lifetime delta with overexpression strength of Aβ-related proteins on the single-cell level, we could verify the important role of intracellular Aβ-mediated mitochondrial toxicity. Subcellularly, we could demonstrate a higher respiration in neuronal somata in general than dendrites, but a similar impairment of somatic and dendritic mitochondria in our AD models. This illustrates the power of NADH lifetime imaging in revealing mitochondrial function on a single and even subcellular level and its potential to shed light into bioenergetic alterations in neuropsychiatric diseases and beyond.

Capturing dynamic relevance in Boolean networks using graph theoretical measures

Motivation

Interaction graphs are able to describe regulatory dependencies between compounds without capturing dynamics. In contrast, mathematical models that are based on interaction graphs allow to investigate the dynamics of biological systems. However, since dynamic complexity of these models grows exponentially with their size, exhaustive analyses of the dynamics and consequently screening all possible interventions eventually becomes infeasible. Thus, we designed an approach to identify dynamically relevant compounds based on the static network topology.

Results

Here, we present a method only based on static properties to identify dynamically influencing nodes. Coupling vertex betweenness and determinative power, we could capture relevant nodes for changing dynamics with an accuracy of 75% in a set of 35 published logical models. Further analyses of the selected compounds’ connectivity unravelled a new class of not highly connected nodes with high impact on the networks’ dynamics, which we call gatekeepers. We validated our method’s working concept on logical models, which can be readily scaled up to complex interaction networks, where dynamic analyses are not even feasible.

Availability and implementation

Code is freely available at https://github.com/sysbio-bioinf/BNStatic.

Supplementary information

Supplementary data are available at Bioinformatics online.

Predicting resistance to first-line FOLFOX plus bevacizumab in metastatic colorectal cancer: Final results of the multicenter, international PERMAD trial

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Background: Antiangiogenic agents, in particular monoclonal antibodies (mAbs) against VEGF, a major driver of tumor angiogenesis, are widely used in cancer therapy including metastatic colorectal cancer (mCRC). However, some patients do not profit from antiangiogenic treatments (AT), other patients benefit initially, but subsequently develop resistance not only to chemotherapy but also to AT. So far, no biomarkers are available to predict resistance to AT. Having an accurate assessment of imminent resistance to an AT may e.g. enable to respond by treating the patient with a more broadly acting antiangiogenic agent and thereby further delay resistance to the treatment and at the same time avoid employing a not anymore efficacious treatment. We hypothesized that repeated analysis of multiple cytokines related to angiogenesis together with machine learning approaches may enable an accurate prediction of anti-VEGF resistance during first-line treatment of mCRC patients with FOLFOX plus bevacizumab. The PERMAD trial aimed at establishing a CAF marker combination that enables the prediction of treatment resistance of patients with mCRC receiving Bevacizumab plus mFOLFOX6 in a palliative first-line setting about three months prior to radiological progress using an omics approach and bioinformatics. Methods: A phase I/II biomarker trial was conducted, including 15 centers in Germany and Austria. All mCRC patients included were treatment naïve and received FOLFOX plus Bevacizumab treatment. 102 different, preselected CAFs were prospectively collected and centrally analyzed in plasma samples (n = 647) obtained prior to treatment and biweekly until radiological progress determined by CT scan every 2 months. The values of CAFs affected in a similar fashion by both chemotherapy and disease progress were excluded. Using the remaining CAFs we employed a random forest predictor to define a combination of 5 CAF (CAF marker combination) whose change in values/pattern correlated with subsequent progress 3 months prior to radiological progress according to RECIST 1.1. Results: Using the samples described above and a random forest predictor we established a CAF marker combination comprising 5 CAF whose specific change in value/pattern over time indicated treatment resistance 3 months prior to radiological progress. The model allowed to differentiate timepoints without progress from timepoints predicting progress 100 days before radiological progress with an accuracy of 83%, a sensitivity of 76% and specificity of 88%. Conclusions: Using advanced bioinformatics, we identified a CAF marker combination that points out treatment resistance to FOLFOX plus Bevacizumab in patients with mCRC 3 months prior to radiological progress. Clinical trial information: NCT02331927.

Detecting Ordinal Subcascades

Ordinal classifier cascades are constrained by a hypothesised order of the semantic class labels of a dataset. This order determines the overall structure of the decision regions in feature space. Assuming the correct order on these class labels will allow a high generalisation performance, while an incorrect one will lead to diminished results. In this way ordinal classifier systems can facilitate explorative data analysis allowing to screen for potential candidate orders of the class labels. Previously, we have shown that screening is possible for total orders of all class labels. However, as datasets might comprise samples of ordinal as well as non-ordinal classes, the assumption of a total ordering might be not appropriate. An analysis of subsets of classes is required to detect such hidden ordinal substructures. In this work, we devise a novel screening procedure for exhaustive evaluations of all order permutations of all subsets of classes by bounding the number of enumerations we have to examine. Experiments with multi-class data from diverse applications revealed ordinal substructures that generate new and support known relations.

Chained correlations for feature selection

Data-driven algorithms stand and fall with the availability and quality of existing data sources. Both can be limited in high-dimensional settings ($$n \gg m$$ n ≫ m ). For example, supervised learning algorithms designed for molecular pheno- or genotyping are restricted to samples of the corresponding diagnostic classes. Samples of other related entities, such as arise in differential diagnosis, are usually not utilized in this learning scheme. Nevertheless, they might provide domain knowledge on the background or context of the original diagnostic task. In this work, we discuss the possibility of incorporating samples of foreign classes in the training of diagnostic classification models that can be related to the task of differential diagnosis. Especially in heterogeneous data collections comprising multiple diagnostic categories, the foreign ones can change the magnitude of available samples. More precisely, we utilize this information for the internal feature selection process of diagnostic models. We propose the use of chained correlations of original and foreign diagnostic classes. This method allows the detection of intermediate foreign classes by evaluating the correlation between class labels and features for each pair of original and foreign categories. Interestingly, this criterion does not require direct comparisons of the initial diagnostic groups and therefore, might be suitable for settings with restricted data access.

Constraining classifiers in molecular analysis: invariance and robustness

Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of these data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by ad hoc simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik-Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that a priori chosen invariant models can replace ad hoc robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.

3D Network exploration and visualisation for lifespan data

BACKGROUND

The Ageing Factor Database AgeFactDB contains a large number of lifespan observations for ageing-related factors like genes, chemical compounds, and other factors such as dietary restriction in different organisms. These data provide quantitative information on the effect of ageing factors from genetic interventions or manipulations of lifespan. Analysis strategies beyond common static database queries are highly desirable for the inspection of complex relationships between AgeFactDB data sets. 3D visualisation can be extremely valuable for advanced data exploration.

RESULTS

Different types of networks and visualisation strategies are proposed, ranging from basic networks of individual ageing factors for a single species to complex multi-species networks. The augmentation of lifespan observation networks by annotation nodes, like gene ontology terms, is shown to facilitate and speed up data analysis. We developed a new Javascript 3D network viewer JANet that provides the proposed visualisation strategies and has a customised interface for AgeFactDB data. It enables the analysis of gene lists in combination with AgeFactDB data and the interactive visualisation of the results.

CONCLUSION

Interactive 3D network visualisation allows to supplement complex database queries by a visually guided exploration process. The JANet interface allows gaining deeper insights into lifespan data patterns not accessible by common database queries alone. These concepts can be utilised in many other research fields.

Loss of the novel Vcp (valosin containing protein) interactor Washc4 interferes with autophagy-mediated proteostasis in striated muscle and leads to myopathy in vivo

VCP/p97 (valosin containing protein) is a key regulator of cellular proteostasis. It orchestrates protein turnover and quality control in vivo, processes fundamental for proper cell function. In humans, mutations in VCP lead to severe myo- and neuro-degenerative disorders such as inclusion body myopathy with Paget disease of the bone and frontotemporal dementia (IBMPFD), amyotrophic lateral sclerosis (ALS) or and hereditary spastic paraplegia (HSP). We analyzed here the in vivo role of Vcp and its novel interactor Washc4/Swip (WASH complex subunit 4) in the vertebrate model zebrafish (Danio rerio). We found that targeted inactivation of either Vcp or Washc4, led to progressive impairment of cardiac and skeletal muscle function, structure and cytoarchitecture without interfering with the differentiation of both organ systems. Notably, loss of Vcp resulted in compromised protein degradation via the proteasome and the macroautophagy/autophagy machinery, whereas Washc4 deficiency did not affect the function of the ubiquitin-proteasome system (UPS) but caused ER stress and interfered with autophagy function in vivo. In summary, our findings provide novel insights into the in vivo functions of Vcp and its novel interactor Washc4 and their particular and distinct roles during proteostasis in striated muscle cells.

sAPPβ and sAPPα increase structural complexity and E/I input ratio in primary hippocampal neurons and alter Ca2+ homeostasis and CREB1-signaling

One major pathophysiological hallmark of Alzheimer's disease (AD) is senile plaques composed of amyloid β (Aβ). In the amyloidogenic pathway, cleavage of the amyloid precursor protein (APP) is shifted towards Aβ production and soluble APPβ (sAPPβ) levels. Aβ is known to impair synaptic function; however, much less is known about the physiological functions of sAPPβ. The neurotrophic properties of sAPPα, derived from the non-amyloidogenic pathway of APP cleavage, are well-established, whereas only a few, conflicting studies on sAPPβ exist. The intracellular pathways of sAPPβ are largely unknown. Since sAPPβ is generated alongside Aβ by β-secretase (BACE1) cleavage, we tested the hypothesis that sAPPβ effects differ from sAPPα effects as a neurotrophic factor. We therefore performed a head-to-head comparison of both mammalian recombinant peptides in developing primary hippocampal neurons (PHN). We found that sAPPα significantly increases axon length (p = 0.0002) and that both sAPPα and sAPPβ increase neurite number (p < 0.0001) of PHN at 7 days in culture (DIV7) but not at DIV4. Moreover, both sAPPα- and sAPPβ-treated neurons showed a higher neuritic complexity in Sholl analysis. The number of glutamatergic synapses (p < 0.0001), as well as layer thickness of postsynaptic densities (PSDs), were significantly increased, and GABAergic synapses decreased upon sAPP overexpression in PHN. Furthermore, we showed that sAPPα enhances ERK and CREB1 phosphorylation upon glutamate stimulation at DIV7, but not DIV4 or DIV14. These neurotrophic effects are further associated with increased glutamate sensitivity and CREB1-signaling. Finally, we found that sAPPα levels are significantly reduced in brain homogenates of AD patients compared to control subjects. Taken together, our data indicate critical stage-dependent roles of sAPPs in the developing glutamatergic system in vitro, which might help to understand deleterious consequences of altered APP shedding in AD patients, beyond Aβ pathophysiology.

Combined microRNA and mRNA microfluidic TaqMan array cards for the diagnosis of malignancy of multiple types of pancreatico-biliary tumors in fine-needle aspiration material

Pancreatic ductal adenocarcinoma (PDAC) continues to carry the lowest survival rates among all solid tumors. A marked resistance against available therapies, late clinical presentation and insufficient means for early diagnosis contribute to the dismal prognosis. Novel biomarkers are thus required to aid treatment decisions and improve patient outcomes.

We describe here a multi-omics molecular platform that allows for the first time to simultaneously analyze miRNA and mRNA expression patterns from minimal amounts of biopsy material on a single microfluidic TaqMan Array card. Expression profiles were generated from 113 prospectively collected fine needle aspiration biopsies (FNAB) from patients undergoing surgery for suspect masses in the pancreas. Molecular classifiers were constructed using support vector machines, and rigorously evaluated for diagnostic performance using 10×10fold cross validation. The final combined miRNA/mRNA classifier demonstrated a sensitivity of 91.7%, a specificity of 94.5%, and an overall diagnostic accuracy of 93.0% for the differentiation between PDAC and benign pancreatic masses, clearly outperfoming miRNA-only classifiers. The classification algorithm also performed very well in the diagnosis of other types of solid tumors (acinar cell carcinomas, ampullary cancer and distal bile duct carcinomas), but was less suited for the diagnostic analysis of cystic lesions.

We thus demonstrate that simultaneous analysis of miRNA and mRNA biomarkers from FNAB samples using multi-omics TaqMan Array cards is suitable to differentiate suspect solid pancreatic masses with high precision.

Tissue-, sex-, and age-specific DNA methylation of rat glucocorticoid receptor gene promoter and insulin-like growth factor 2 imprinting control region

Tissue-, sex-, and age-specific epigenetic modifications such as DNA methylation are largely unknown. Changes in DNA methylation of the glucocorticoid receptor gene (NR3C1) and imprinting control region (ICR) of IGF2 and H19 genes during the lifespan are particularly interesting since these genes are susceptible to epigenetic modifications by prenatal stress or malnutrition. They are important regulators of development and aging. Methylation changes of NR3C1 affect glucocorticoid receptor expression, which is associated with stress sensitivity and stress-related diseases predominantly occurring during aging. Methylation changes of IGF2/H19 affect growth trajectory and nutrient use with risk of metabolic syndrome. Using a locus-specific approach, we characterized DNA methylation patterns of different Nr3c1 promoters and Igf2/H19 ICR in seven tissues of rats at 3, 9, and 24 mo of age. We found a complex pattern of locus-, tissue-, sex-, and age-specific DNA methylation. Tissue-specific methylation was most prominent at the shores of the Nr3c1 CpG island (CGI). Sex-specific differences in methylation peaked at 9 mo. During aging, Nr3c1 predominantly displayed hypomethylation mainly in females and at shores, whereas hypermethylation occurred within the CGI. Igf2/H19 ICR exhibited age-related hypomethylation occurring mainly in males. Methylation patterns of Nr3c1 in the skin correlated with those in the cortex, hippocampus, and hypothalamus. Skin may serve as proxy for methylation changes in central parts of the hypothalamic-pituitary-adrenal axis and hence for vulnerability to stress- and age-associated diseases. Thus, we provide in-depth insight into the complex DNA methylation changes of rat Nr3c1 and Igf2/H19 during aging that are tissue and sex specific.

Reduced cGMP levels in CSF of AD patients correlate with severity of dementia and current depression

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disorder, primarily affecting memory. That disorder is thought to be a consequence of neuronal network disturbances and synapse loss. Decline in cognitive function is associated with a high burden of neuropsychiatric symptoms (NPSs) such as depression. The cyclic nucleotides cyclic adenosine-3',5'-monophosphate (cAMP) and cyclic guanosine-3',5'-monophosphate (cGMP) are essential second messengers that play a crucial role in memory processing as well as synaptic plasticity and are potential therapeutic targets. Biomarkers that are able to monitor potential treatment effects and that reflect the underlying pathology are of crucial interest.

METHODS

In this study, we measured cGMP and cAMP in cerebrospinal fluid (CSF) in a cohort of 133 subjects including 68 AD patients and 65 control subjects. To address the association with disease progression we correlated cognitive status with cyclic nucleotide levels. Because a high burden of NPSs is associated with decrease in cognitive function, we performed an exhaustive evaluation of AD-relevant marker combinations in a depressive subgroup.

RESULTS

We show that cGMP, but not cAMP, levels in the CSF of AD patients are significantly reduced compared with the control group. Reduced cGMP levels in AD patients correlate with memory impairment based on Mini-Mental State Examination score (r = 0.17, p = 0.048) and tau as a marker of neurodegeneration (r = -0.28, p = 0.001). Moreover, we were able to show that AD patients suffering from current depression show reduced cGMP levels (p = 0.07) and exhibit a higher degree of cognitive impairment than non-depressed AD patients.

CONCLUSION

These results provide further evidence for an involvement of cGMP in AD pathogenesis and accompanying co-morbidities, and may contribute to elucidating synaptic plasticity alterations during disease progression.

Genetic Factors of the Disease Course After Sepsis: Rare Deleterious Variants Are Predictive

Sepsis is a life-threatening organ dysfunction caused by dysregulated host response to infection. For its clinical course, host genetic factors are important and rare genomic variants are suspected to contribute. We sequenced the exomes of 59 Greek and 15 German patients with bacterial sepsis divided into two groups with extremely different disease courses. Variant analysis was focusing on rare deleterious single nucleotide variants (SNVs).

We identified significant differences in the number of rare deleterious SNVs per patient between the ethnic groups. Classification experiments based on the data of the Greek patients allowed discrimination between the disease courses with estimated sensitivity and specificity > 75%. By application of the trained model to the German patients we observed comparable discriminatory properties despite lower population-specific rare SNV load. Furthermore, rare SNVs in genes of cell signaling and innate immunity related pathways were identified as classifiers discriminating between the sepsis courses.

Sepsis patients with favorable disease course after sepsis, even in the case of unfavorable preconditions, seem to be affected more often by rare deleterious SNVs in cell signaling and innate immunity related pathways, suggesting a protective role of impairments in these processes against a poor disease course.

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Rare SNV load is higher in the Greek vs. German population.

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Subsets of rare deleterious SNVs are predictive for the disease course after sepsis.

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Patients with favorable disease course seem to carry protective deleterious variants in sepsis related pathways.

Sepsis is a life-threatening disease caused by improper response to infection. Only little is known about the role of genetic factors. From > 4000 patients we selected the most extreme cases showing either a favorable or adverse disease course. We determined rare (< 1/200) protein-damaging genetic variants, as they may have a large effect. Using a computational model that includes knowledge on genes we can predict the disease course with > 75% accuracy. Surprisingly, favorable courses can be expected if defense mechanisms are damaged and prevented from overshooting. This underlines the relevance of rare variants for better understanding of sepsis and may offer new treatment options.

BiTrinA--multiscale binarization and trinarization with quality analysis

MOTIVATION

When processing gene expression profiles or other biological data, it is often required to assign measurements to distinct categories (e.g. 'high' and 'low' and possibly 'intermediate'). Subsequent analyses strongly depend on the results of this quantization. Poor quantization will have potentially misleading effects on further investigations. We propose the BiTrinA package that integrates different multiscale algorithms for binarization and for trinarization of one-dimensional data with methods for quality assessment and visualization of the results. By identifying measurements that show large variations over different time points or conditions, this quality assessment can determine candidates that are related to the specific experimental setting.

AVAILABILITY AND IMPLEMENTATION

BiTrinA is freely available on CRAN.

CONTACT

hans.kestler@leibniz-fli.de or hans.kestler@uni-ulm.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Sputnik: ad hoc distributed computation

MOTIVATION

In bioinformatic applications, computationally demanding algorithms are often parallelized to speed up computation. Nevertheless, setting up computational environments for distributed computation is often tedious. Aim of this project were the lightweight ad hoc set up and fault-tolerant computation requiring only a Java runtime, no administrator rights, while utilizing all CPU cores most effectively.

RESULTS

The Sputnik framework provides ad hoc distributed computation on the Java Virtual Machine which uses all supplied CPU cores fully. It provides a graphical user interface for deployment setup and a web user interface displaying the current status of current computation jobs. Neither a permanent setup nor administrator privileges are required. We demonstrate the utility of our approach on feature selection of microarray data.

AVAILABILITY AND IMPLEMENTATION

The Sputnik framework is available on Github http://github.com/sysbio-bioinf/sputnik under the Eclipse Public License.

CONTACT

hkestler@fli-leibniz.de or hans.kestler@uni-ulm.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Differentiation of multiple types of pancreatico-biliary tumors by molecular analysis of clinical specimens

Timely and accurate diagnosis of pancreatic ductal adenocarcinoma (PDAC) is critical in order to provide adequate treatment to patients. However, the clinical signs and symptoms of PDAC are shared by several types of malignant or benign tumors which may be difficult to differentiate from PDAC with conventional diagnostic procedures. Among others, these include ampullary cancers, solid pseudopapillary tumors, and adenocarcinomas of the distant bile duct, as well as inflammatory masses developing in chronic pancreatitis. Here, we report an approach to accurately differentiate between these different types of pancreatic masses based on molecular analysis of biopsy material. A total of 156 bulk tissue and fine needle aspiration biopsy samples were analyzed using a dedicated diagnostic cDNA array and a composite classification algorithm developed based on linear support vector machines. All five histological subtypes of pancreatic masses were clearly separable with 100% accuracy when using all 156 individual samples for classification. Generalized performance of the classification system was tested by 10 × 10-fold cross validation (100 test runs). Correct classification into the five diagnostic groups was demonstrated for 81.5% of 1,560 test set predictions. Performance increased to 85.3% accuracy when PDAC and distant bile duct carcinomas were combined in a single diagnostic class. Importantly, overall sensitivity of detection of malignant disease was 92.2%. The molecular diagnostic approach presented here is suitable to significantly aid in the differential diagnosis of undetermined pancreatic masses. To our knowledge, this is the first study reporting accurate differentiation between several types of pancreatico-biliary tumors in a single molecular analytical procedure.