Objective Esophageal adenocarcinoma (EAC) is associated with a dismal prognosis. The identification of cancer biomarkers advances the possibility for early detection and better monitoring of tumor progression and/or response to therapy. The current study presents results of the development of a serum based four-protein (biglycan, myeloperoxidase, annexin-A6, and protein S100-A9) biomarker-panel for EAC. Design A vertically integrated proteomics-based biomarker discovery approach was used to identify candidate serum biomarkers for detection of EAC. Liquid chromatography-mass spectrometry (LC-MS/MS) analysis was performed on FFPE tissue samples that were collected from across the Barrett's esophagus (BE)-EAC disease spectrum. The MS-based spectral count data was used to guide the selection of candidate serum biomarkers. The serum ELISA data was validated in an independent cohort and used to develop a multi-parametric risk assessment model to predict the presence of disease. Results With a minimum threshold of 10 spectral counts, 351 proteins were identified as differentially abundant along the spectrum of BE, HGD and EAC (p < 0.05). Eleven proteins from this dataset were then tested using ELISAs in serum samples of which five proteins were significantly elevated in abundance in the EAC patients compared to normal controls, which mirrored trends across the disease spectrum present in the tissue data. Using serum data a Bayesian Rule Learning predictive model with four biomarkers was developed to accurately classify disease class; the cross-validation results for the merged dataset yielded accuracy of 87% and AUROC of 93 %. Conclusion Serum biomarkers hold significant promise for early non-invasive detection of EAC.

The comprehensibility of good predictive models learned from high-dimensional gene expression data is attractive because it can lead to biomarker discovery. Several good classifiers provide comparable predictive performance but differ in their abilities to summarize the observed data. We extend a Bayesian Rule Learning (BRL-GSS) algorithm, previously shown to be a significantly better predictor than other classical approaches in this domain. It searches a space of Bayesian networks using a decision tree representation of its parameters with global constraints, and infers a set of IF-THEN rules. The number of parameters and therefore the number of rules are combinatorial to the number of predictor variables in the model. We relax these global constraints to a more generalizable local structure (BRL-LSS). BRL-LSS entails more parsimonious set of rules because it does not have to generate all combinatorial rules. The search space of local structures is much richer than the space of global structures. We design the BRL-LSS with the same worst-case time-complexity as BRL-GSS while exploring a richer and more complex model space. We measure predictive performance using Area Under the ROC curve (AUC) and Accuracy. We measure model parsimony performance by noting the average number of rules and variables needed to describe the observed data. We evaluate the predictive and parsimony performance of BRL-GSS, BRL-LSS and the state-of-the-art C4.5 decision tree algorithm, across 10-fold cross-validation using ten microarray gene-expression diagnostic datasets. In these experiments, we observe that BRL-LSS is similar to BRL-GSS in terms of predictive performance, while generating a much more parsimonious set of rules to explain the same observed data. BRL-LSS also needs fewer variables than C4.5 to explain the data with similar predictive performance. We also conduct a feasibility study to demonstrate the general applicability of our BRL methods on the newer RNA sequencing gene-expression data.

BackgroundAdenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. Normally, histopathological analyses are used to distinguish between the two, where the tissue samples are collected based on small endoscopic samples or needle aspirations. However, the lack of cell architecture in these small tissue samples hampers the process of distinguishing between the two subtypes.Molecular profiling can also be used to discriminate between the two lung cancer subtypes, on condition that the biopsy is composed of at least 50 % of tumor cells. However, for some cases, the tissue composition of a biopsy might be a mix of tumor and tumor-adjacent histologically normal tissue (TAHN). When this happens, a new biopsy is required, with associated cost, risks and discomfort to the patient. To avoid this problem, we hypothesize that a computational method can distinguish between lung cancer subtypes given tumor and TAHN tissue.MethodsUsing publicly available datasets for gene expression and DNA methylation, we applied four classification tasks, depending on the possible combinations of tumor and TAHN tissue. First, we used a feature selector (ReliefF/Limma) to select relevant variables, which were then used to build a simple naïve Bayes classification model. Then, we evaluated the classification performance of our models by measuring the area under the receiver operating characteristic curve (AUC). Finally, we analyzed the relevance of the selected genes using hierarchical clustering and IPA® software for gene functional analysis.ResultsAll Bayesian models achieved high classification performance (AUC > 0.94), which were confirmed by hierarchical cluster analysis. From the genes selected, 25 (93 %) were found to be related to cancer (19 were associated with ADC or SCC), confirming the biological relevance of our method.ConclusionsThe results from this study confirm that computational methods using tumor and TAHN tissue can serve as a prognostic tool for lung cancer subtype classification. Our study complements results from other studies where TAHN tissue has been used as prognostic tool for prostate cancer. The clinical implications of this finding could greatly benefit lung cancer patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-016-2223-3) contains supplementary material, which is available to authorized users.

Data sharing is essential for reproducibility of epidemiological research, replication of findings, pooled analyses in consortia efforts and maximizing study value to address multiple research questions. However, barriers related to confidentiality, costs, and incentives often limit the extent and speed of data sharing. Epidemiological practices that follow FAIR principles can address these barriers by making data resources (F)indable with the necessary metadata, (A)ccessible to authorized users and (I)nteroperable with other data, to optimize the (R)e-use of resources with appropriate credit to its creators. We provide an overview of these principles and describe approaches for implementation in epidemiology. Increasing degrees of FAIRness can be achieved by moving data and code from on-site locations to the Cloud, using machine-readable and non-proprietary files, and developing open-source code. Adoption of these practices will improve daily work and collaborative analyses and facilitate compliance with data sharing policies from funders and scientific journals. Achieving a high degree of FAIRness will require funding, training, organizational support, recognition, and incentives for sharing research resources, both data and code. But these costs are outweighed by the benefits of making research more reproducible, impactful, and equitable by facilitating the re-use of precious research resources by the scientific community.

AIMTo develop a framework to incorporate background domain knowledge into classification rule learning for knowledge discovery in biomedicine.METHODSBayesian rule learning (BRL) is a rule-based classifier that uses a greedy best-first search over a space of Bayesian belief-networks (BN) to find the optimal BN to explain the input dataset, and then infers classification rules from this BN. BRL uses a Bayesian score to evaluate the quality of BNs. In this paper, we extended the Bayesian score to include informative structure priors, which encodes our prior domain knowledge about the dataset. We call this extension of BRL as BRLp. The structure prior has a λ hyperparameter that allows the user to tune the degree of incorporation of the prior knowledge in the model learning process. We studied the effect of λ on model learning using a simulated dataset and a real-world lung cancer prognostic biomarker dataset, by measuring the degree of incorporation of our specified prior knowledge. We also monitored its effect on the model predictive performance. Finally, we compared BRLp to other state-of-the-art classifiers commonly used in biomedicine.RESULTSWe evaluated the degree of incorporation of prior knowledge into BRLp, with simulated data by measuring the Graph Edit Distance between the true data-generating model and the model learned by BRLp. We specified the true model using informative structure priors. We observed that by increasing the value of λ we were able to increase the influence of the specified structure priors on model learning. A large value of λ of BRLp caused it to return the true model. This also led to a gain in predictive performance measured by area under the receiver operator characteristic curve (AUC). We then obtained a publicly available real-world lung cancer prognostic biomarker dataset and specified a known biomarker from literature [the epidermal growth factor receptor (EGFR) gene]. We again observed that larger values of λ led to an increased incorporation of EGFR into the final BRLp model. This relevant background knowledge also led to a gain in AUC.CONCLUSIONBRLp enables tunable structure priors to be incorporated during Bayesian classification rule learning that integrates data and knowledge as demonstrated using lung cancer biomarker data.