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An automated reasoning framework for translational research.

ABSTRACT: In this paper we propose a novel approach to the design and implementation of knowledge-based decision support systems for translational research, specifically tailored to the analysis and interpretation of data from high-throughput experiments. Our approach is based on a general epistemological model of the scientific discovery process that provides a well-founded framework for integrating experimental data with preexisting knowledge and with automated inference tools. In order to demonstrate the usefulness and power of the proposed framework, we present its application to Genome-Wide Association Studies, and we use it to reproduce a portion of the initial analysis performed on the well-known WTCCC dataset. Finally, we describe a computational system we are developing, aimed at assisting translational research. The system, based on the proposed model, will be able to automatically plan and perform knowledge discovery steps, to keep track of the inferences performed, and to explain the obtained results.

SUBMITTER: Riva A

PROVIDER: S-EPMC2878845 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

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An automated reasoning framework for translational research.

Riva Alberto A Nuzzo Angelo A Stefanelli Mario M Bellazzi Riccardo R

Journal of biomedical informatics 20091118 3

In this paper we propose a novel approach to the design and implementation of knowledge-based decision support systems for translational research, specifically tailored to the analysis and interpretation of data from high-throughput experiments. Our approach is based on a general epistemological model of the scientific discovery process that provides a well-founded framework for integrating experimental data with preexisting knowledge and with automated inference tools. In order to demonstrate t ...[more]

PMID: 19931420

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Project description:BackgroundWith the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process.MethodologyIn this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications.Conclusion/significanceTo evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.

Project description:Glioblastoma (GBM) is the most aggressive primary brain tumor in adults. Designing effective individualized therapies for GBM requires quality fresh tissue specimens, and a comprehensive molecular profile of this highly heterogenous neoplasm. Novel neuro-surgical approaches, such as the automated resection NICO Myriad™ system, are increasingly used by neurosurgeons to better reach the invasive front of tumors. However, no information exists on how harvesting GBM tissue using this approach may impact the translational research value of the sample. Here, we set out to characterize matched specimens from 15 patients, where one tissue sample was obtained using traditional tumor de-bulking (herein referred to as "en bloc" sample), and the other sample was obtained using the MyriadTM System (herein referred to as "Myriad" sample). We investigated the fidelity of patient derived xenografts (PDXs) for each sample type to the corresponding human tissues and evaluated the added value of sequencing both samples for each patient. Matched en bloc and Myriad samples processed in parallel, were subjected to the following assays: cell viability, self-renewal, in vivo tumorigenicity using an orthotopic model of glioma, genomic sequencing, and pharmacological testing using PI3K-MTOR pathway inhibitors. Our results demonstrate that primary GBM cultures derived from matched specimens grew at similar rates (correlation coefficient R = 0.72), generated equivalent number of neurospheres, and had equivalent tumorigenic potential in vivo (mouse survival correlation coefficient R = 0.93). DNA Sequencing using the Illumina tumor panel amplicons revealed over 70% concordance in non-synonymous mutations between matched human GBM specimens. PDX genomic profiles were also highly concordant with the corresponding patient tissues (>70%). RNA sequencing of paired GBM samples revealed unique genomic variants and differential gene expression between the en bloc and Myriad specimens, with the former molecularly resembling the "tumor core" and the latter resembling the "invasive tumor front" signature. Functionally, we show that primary-derived GBM cells-obtained after fresh specimen's dissociation-are more effectively growth-inhibited by co-targeting non-overlapping mutations enriched in each sample type, suggesting that profiling both specimens more adequately capture the molecular heterogeneity of GBM and may enhance the design accuracy and efficacy of individualized therapies.

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An automated reasoning framework for translational research.

Publications

An automated reasoning framework for translational research.

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