Current methodologies for translational bioinformatics

Current methodologies for translational bioinformatics

Journal of Biomedical Informatics 43 (2010) 355–357 Contents lists available at ScienceDirect Journal of Biomedical Informatics journal homepage: ww...

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Journal of Biomedical Informatics 43 (2010) 355–357

Contents lists available at ScienceDirect

Journal of Biomedical Informatics journal homepage: www.elsevier.com/locate/yjbin

Guest Editorial

Current methodologies for translational bioinformatics Since the completion of human and model organism genomes, integration of computational methods in biomedical research is accelerating and becomes an inexorable facet of the 21th century biomedical research. Translational bioinformatics, which aims at developing storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomolecular data into proactive, predictive, preventive, and participatory health [1], has rapidly evolved from an emerging field into a complex multidisciplinary science intersecting with nearly all areas of biological, biomedical, and clinical research. Indeed, molecular bioinformatics and clinical informatics plus statistical genetics and genomic medicine – the core areas of translational bioinformatics – jointly are enticed to play an ever-increasing role in accelerating the translation of knowledge discovery from genome-scale studies to hypothesis-driven biological modeling or effective treatment as well as tailored disease management or prevention. In the era of molecular medicine, determinant factors that are pivotal to the realization of personalized medicine have congregated momentous opportunities for translational bioinformatics [2]: (i) the steadily improving availability and cost reduction of molecular measurements, (ii) public accessibility to molecular measurements in healthy and disease states (e.g., Gene Expression Omnibus), (iii) the evolving need to share molecular data and tools, (iv) the increasing expectation that clinicians should synthesize and interpret new discoveries in molecular medicine for their patients’ benefit, and (v) substantial increases in research funding for translational bioinformatics (e.g., NIH Roadmaps, Clinical and Translational Science Awards). The time for genomic medicine has arrived and recent developments promulgate an extraordinary opportunity for translational biomedical informatics research and development to play a key role, since novel discoveries in this field will lay the foundations of genomic medicine for decades to come. Undeniably, translational bioinformatics may lead to a pivotal moment in history when the fundamental discovery of the molecular underpinnings of diseases will be timely and efficiently translated into enabling technologies that bring the goal of personalized medicine within our grasp. The past success of clinical informatics in transforming medical research and patient care is merely a glimpse into the remarkable promises that this energizing field holds in an era of accelerated advancements in molecular medicine. This special issue highlights major computational advances in the field: (i) finding molecular mechanisms of therapies and for the underlying disease, (ii) bridging gaps between genetics discoveries and clinical practice, and (iii) building infrastructures enabling translational bioinformatics research. Novel modalities for molecular measurements, including those for epigenetics, interactions, and proteins, will continue to emerge 1532-0464/$ - see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.jbi.2010.05.002

each year. In this special issue, Chen et al. present customary molecular clinical measurements, such as creatinine and alkaline phosphatase, as new predictive biomarkers for aging [3]. These regression models, conducted for chronological ages of adolescents in the 2001–2002 National Health and Nutrition Examination Survey (NHANES) datasets [4] and validated using the 2003–2004 data, satisfactorily match the results seen in the aging process using cohorts collected from a hospital-based electronic health record system, and holds the potential to identify adolescent development-related disorders. This approach is likely scalable to model and identify other clinically relevant molecular differences between males and females as well as underlying pathophysiological processes. It exemplifies the ‘‘reverse translational bioinformatics” approach in which clinical data are used to gain insight into basic biological phenomena. In contrast, the second article illustrates the ‘‘forward translational bioinformatics” approach where primarily biomolecular computations provide mechanistic insight into the pathophysiology of diseases [5]. Liu and Zhao improve on modeling the underpinnings of ‘‘conformational diseases”, such as sickle cell anemia and transmissible spongiform encephalopathy caused by misfolding proteins or prions. Their algorithm can identify misfolding sites with a 93% accuracy, by comparing secondary structures of homologous segments and calculating the probabilities of a local protein segment shifting its native secondary structure to a pathogenic misfolded secondary structure. The gold standard consists of the 22 known proteins responsible for conformational diseases for which usable structural information is available among 31 such disease proteins. Of noteworthy importance, among their validated predictions, the authors identify conformation changes of transcriptional factor p53. This algorithm, or ones that are similar, may bear significant importance for predicting the impact of an individual-specific mutation on the structural changes in disease genes when personal genome sequencing becomes affordable. In other words, such algorithms may allow for disease predictions made based on the genomic information of a particular individual (n = 1). This approach will likely lead to the identification of additional conformational disease-associated genes. The next paper employs an ‘‘integrative translational bioinformatics” approach to analyze jointly clinical and molecular datasets [6]. Specifically, Yang et al. present a novel in silico method to predict mechanisms of toxicities caused by the off-targets inhibition of kinases. Kinase inhibitors are an increasingly popular class of medication for pharmacotherapy of cancers. Although initially developed as specific inhibitors, subsequent studies show that they typically inhibit multiple kinases with varying potency. Preclinical animal testing fails to provide accurate predictions of the numerous rate-limiting toxicities encountered in clinical trials. Indeed,

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Guest Editorial / Journal of Biomedical Informatics 43 (2010) 355–357

exotic species of animal models are required for specific toxicities. For example, rodents are highly resistant to, and thus not optimal for, modeling nausea and vomiting. Here, the authors reviewed extensively the adverse events reported with clinical trials of kinase inhibitors. They propose a novel quantitative computational method to predict associations between kinase targets from a physical kinome map and kinase inhibitor toxicities derived from clinical trials. The kinome map consists of published binding affinity between kinase inhibitors (compounds and medications) and kinases. Forty-one prioritized associations between 7 adverse events and 19 kinase targets were identified and 8 associations were confirmed by the literature. This in silico pharmaco-toxicity clinical trial is potentially useful in uncovering the toxicities of hidden off-targets and provides clues for improving the drug efficacy. Further, this approach can identify rate-limiting toxicities that will be missed in pre-clinical animal model testing so that the anticipated toxicities will be considered during the decision making process of initiating clinical testing of a new class of agent or a new combination rather than startlingly observed at a later clinical trial stage. There are two studies describing observations derived from meta-analyses of multiple expression array datasets in cancers. Chen et al. unveil a molecular network of breast cancer connecting ten gene expression signatures that share concordant prognostics and paradoxically showed limited gene overlap [7]. This is achieved through protein interaction modeling. Their study predicts seven well established mechanisms of breast cancer oncogenesis and progression to relate eight of the ten expression signatures. They further confirm the inherent ability of this 54gene ‘‘network-signature” to identify ‘‘poor outcome” robustly in estrogen-receptor positive breast cancer using a Kaplan–Meier analysis. This approach demonstrates the feasibility of developing a mechanism-anchored road map to understand, utilize and interpret cancer gene expression signatures in clinical settings or for biomarker discovery. In another study, Ancona and colleagues identify 53 overlapping pathways among distinct datasets of patients affected by colorectal cancer [8]. These predictions stem from independent analyses of four distinct gene expression data sets comparing two well-established gene set analyses and enrichment methods. Meticulous literature review of common pathways across the datasets (e.g. cell cycle) demonstrates the significance of the uncovered pathways in colorectal cancer. In both studies, Chen et al. as well as Ancona and colleagues point out that, while analyses carried over gene expression datasets such as profiling, enrichment or signatures have surprisingly little gene overlaps, their approaches uncover biologically and clinically-relevant mechanistic concordance among gene expression datasets. Over many decades, thousands of single gene inheritances have been reported in human diseases through laborious conventional genetics science. In the last three years, over 250 novel gene polymorphisms have been discovered in high-throughput genomewide association studies (GWAS), of which the majority are associated with diseases of complex inheritance (complex diseases) such as diabetes mellitus or essential hypertension. The next set of papers illustrates that molecular bioinformatics methods are increasingly required for the identification of additional disease gene candidates over the plethora of new high-throughput genetic platforms. Shen and colleagues describe the design, implementation, and evaluation of SNPit, a knowledge-based system that incorporates probabilistic and logical reasoning about the clinical consequences of the specific physical location of a single nucleotide polymorphism (SNP) by integrating point and regional alterations of SNP annotations (e.g. protein structures, gene function, pathway function) [9]. SNPit is evaluated by comparing its predictions over 250 established disease SNPs with 250 randomly selected ones.

Measured accuracies vary from 65% to 85% according to different algorithms relying on combinations of decision trees with a priori and a posteriori probabilities, resulting in a sensitivity of 85%. In another study, Riva et al. propose the Select and Test Model (ST-Model), a scalable epistemological framework designed for high-throughput translational bioinformatics experiments [10]. The ST-Model incorporates automated reasoning principles originating from the fields of cognitive science and artificial intelligence to formalize and operationalize five logical operations of discovery science: (i) abstraction, (ii) abduction and ranking, (iii) deduction, (iv) induction, and (v) hypothesis-space maintenance. In order to demonstrate the ST-Model’s usefulness with a case study, the authors identified the Wnt pathway from single nucleotide polymorphisms prioritized in a GWAS study of adult-onset diabetes mellitus and validated the finding in a second distinct study of juvenile-onset diabetes mellitus (p < 1%; Wilcoxon test). While the predicted pathway is well-characterized in diabetes, this study demonstrates the applicability of the ST-Model to recapitulate the result with an original analysis of GWAS studies, which have not otherwise been considered useful for pathway prioritization. With the rapidly plunging costs of whole genome sequencing, the opportunities for translational bioinformatics discoveries to affect the interpretation of personal genome maps are escalating. Kriseman et al. developed BING, an integrated pipeline of methods for automatically deriving nucleotide base-calls from raw signals produced by next generation sequencing (NGS) [11]. BING’s innovations are evident in (i) image alignment, (ii) signal correlation, compensation and separation, as well as in pixel-based cluster registration, (iii) signal measurement and base calling, and finally in (iv) quality control and accuracy measures. A superior accuracy and time efficiency is demonstrated when it is benchmarked against the commercially-available Illumina Genome Analysis Pipeline. The following two manuscripts propose methods that enable integrative translational bioinformatics research. Song et al. pioneer the data attributes of TMA-OM and TMA-TAB, the first comprehensive format for exchanging tissue microarray (TMA) data [12]. Tissue microarrays are particularly well adapted for cancer research, as they consist of paraffin blocks in which up to 1000 separate miniature tissue cores are assembled in array fashion to allow multiplex histological or immunohistochemistry analyses. TMA-TAB is a foundational data representation formalism that is expected to facilitate the creation of a broadly accepted standard and corresponds for TMAs to the MIAME format of gene expression arrays. To accelerate clinically relevant genomic hypotheses, Chen and Sarkar present a method to link based-pair sequences of GenBank with relevant clinical trials [13]. MeSH terms mutually occurring in GenBank, PubMed, and Clinicaltrials.gov are automatically discovered and annotated. Thirty percent of GenBank sequences are thus linked with 90% of clinical trials in ClinicalTrials.gov. Meaningful relationships are identified in a cursory evaluation. This system may allow high-throughput generation of relevant hypotheses for clinician trialists. Finally, Weng and colleagues close this special issue with a review of literature on formal representation eligibility criteria of patients for clinical trials [14]. These formalisms are required for accelerating clinical trials, for automated patient screening, and for computer-interpretable clinical guidelines. With the accelerated discovery of biomarkers and gene expression signatures, the automations are of prime importance for quality control as the complexity of clinical trials is increasing with greater patient stratification, increasingly complex ‘‘conditional” treatment plans, and the added challenges of uniformity of protocol-based patient management across a larger number of clinical research organizations. The authors review 27 models using their categorization of data, expressiveness, and representation of patient data and of medical concepts, use cases, and domains.

Guest Editorial / Journal of Biomedical Informatics 43 (2010) 355–357

It is interesting to reflect back on how much has already changed in biomedicine since we issued the Call for Papers for this special issue. Already, whole human genome sequencing is becoming routine, as entire families are sequenced and the causes of rare Mendelian disorders are identified with handfuls of individuals, while platforms for genotyping have again doubled in capacity. Hundreds of specific cancers are now being sequenced for mutations, and already the first individual sequences have been released from the 1000 Human Genomes project. Despite these impressive gains, the storage, analysis, and interpretation of molecular measurements and entire genomes is far from trivial or inexpensive, while their clinical interpretations remain as extremely challenging and vexing problems. We hope that this special issue will stimulate and catalyze translational bioinformatics research and development to embrace the era of personal and genomic medicine. Acknowledgments We thank Dr. Haiquan Li for his critical review of the editorial. This work was supported in part by the NIH/NCRR Clinical & Translational Science Awards (1U54 RR023560-01A1), the NIH/NLM/NCI National Center for Multiscale Analyses of Genomic and Cellular Networks (MAGNET, 1U54CA121852), the Cancer Research Foundation, The University of Chicago Cancer Research Center, and The Ludwig Center for Metastasis Research. References [1] American Medical Informatics Association, AMIA strategic plan, ; 2006. [accessed 05.05.10]. [2] Atul J Butte. Translational bioinformatics: coming of age. J Am Med Inform Assoc 2008;15(6):709–14. [3] David P Chen, Alexander A Morgan, Atul J Butte. Validating pathophysiological models of aging using clinical electronic medical records. J Biomed Informatics 2010;43(3):358–64. [4] . [5] Xin Liu, Ya-Pu Zhao. Simulated pathogenic conformational switch regions matched well with the biochemical findings. J Biomed Informatics 2010;43(3):365–75. [6] Xinan Yang, Yong Huang, Matthew Crowson, Jianrong Li, Michael L Maitland, Yves A Lussier. Kinase inhibition causing adverse event predicted from clinical trial data and in vitro kinome. J Biomed Informatics 2010;43(3):376–84.

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[7] James Chen, Lee Sam, Yong Huang, Younghee Lee, Jianrong Li, Yang Liu, et al. Protein interaction network underpins concordant prognosis among heterogeneous breast cancer signatures. J Biomed Informatics 2010;43(3):385–96. [8] Rosalia Maglietta, Angela Distaso, Ada Piepoli, Orazio Palumbo, Massimo Carella, Annarita D’Addabbo, et al. On the reproducibility of results of pathway analysis in genome-wide expression studis of colorectal cancers. J Biomed Informatics 2010;43(3):397–406. [9] Terry H Shen, Peter Tarczy-Hornoch, Landon T Detwiler, Eithon Cadag, Christopher S Carlson. Evaluation of probabilistic and logical inference for a SNP annotation system. J Biomed Informatics 2010;43(3):407–18. [10] Alberto Riva, Angelo Nuzzo, Mario Stefanelli, Riccardo Bellazzi. An automated reasoning framework for translational research. J Biomed Informatics 2010;43(3):419–27. [11] Jeffrey Kriseman, Christopher Busick, Szabolcs Szelinger, Valentin Dinu. Bing: biomedical informatics pipeline for next generation sequencing. J Biomed Informatics 2010;43(3):428–34. [12] Young Soo Song, Hye Won Lee, Yu Rang Park, Do Kyoon Kim, Jaehyun Sim, Hyunseok Peter Kang. TMA-TAB: a spreadsheet-based document for exchange of tissue microarray data based on the Tissue MicroArray-Object Model. J Biomed Informatics 2010;43(3):435–41. [13] Elizabeth S Chen, Indra Neil Sarkar. Meshing molecular sequences and clinic trials: a feasibility study. J Biomed Informatics 2010;43(3):442–50. [14] Chunchua Weng, Samson W Tu, Ida Sim, Rachel Richesson. Formal representations of eligibility criteria: a literature review journal of biomedical informatics. J Biomed Informatics 2010;43(3):451–67.

Guest Editors Yves A. Lussier Center for Biomedical Informatics, UC Institute for Translational Medicine, Institute for Genomics and Systems Biology, The University of Chicago, Chicago, IL 60634, USA E-mail address: [email protected] Atul J. Butte Departments of Pediatrics and Medicine, Stanford University, Stanford, CA 94305, USA Lawrence Hunter Center for Computational Pharmacology, Computational Bioscience Program, Department of Pharmacology, University of Colorado Health Sciences Center, Denver, CO 80262, USA