TRARESA: a tissue microarray-based hospital system for biomarker validation and discovery

Formalin fixed and paraffin embedded tissue (FFPE) collections in pathology departments are the largest resource for retrospective biomedical research studies. Based on the literature analysis of FFPE related research, as well as our own technical validation, we present the Translational Research Arrays (TRARESA), a tissue microarray centred, hospital based, translational research conceptual framework for both validation and/or discovery of novel biomarkers. TRARESA incorporates the analysis of protein, DNA and RNA in the same samples, correlating with clinical and pathological parameters from each case, and allowing (a) the confirmation of new biomarkers, disease hypotheses and drug targets, and (b) the postulation of novel hypotheses on disease mechanisms and drug targets based on known biomarkers. While presenting TRARESA, we illustrate the use of such a comprehensive approach. The conceptualisation of the role of FFPE-based studies in translational research allows the utilisation of this commodity, and adds to the hypothesis-generating armamentarium of existing high-throughput technologies.     

Implementations of translational medicine

New developments in science are rapidly influencing and shaping basic and clinical research and medicine. This has led to the emergence of multiple opportunities and challenges on many levels in the bio-medical and other associated fields. To face these opportunities and challenges, new concepts and strategies are needed. These can be provided by translational research/medicine as an integrative concept based on a multidirectional understanding of research and medicine embedded in a socio-economical environment. Although the implementation of translational research/medicine faces many obstacles, some of its goals have already been part of new programs in local institutions and in medical or scientific societies. These implementations are important in creating a unified national and international system of translational research/medicine.     

Toward a genome-wide systems biology analysis of host-pathogen interactions in group A Streptococcus

Genome-wide analysis of microbial pathogens and molecular pathogenesis processes has become an area of considerable activity in the last 5 years. These studies have been made possible by several advances, including completion of the human genome sequence, publication of genome sequences for many human pathogens, development of microarray technology and high-throughput proteomics, and maturation of bioinformatics. Despite these advances, relatively little effort has been expended in the bacterial pathogenesis arena to develop and use integrated research platforms in a systems biology approach to enhance our understanding of disease processes. This review discusses progress made in exploiting an integrated genome-wide research platform to gain new knowledge about how the human bacterial pathogen group A Streptococcus causes disease. Results of these studies have provided many new avenues for basic pathogenesis research and translational research focused on development of an efficacious human vaccine and novel therapeutics. One goal in summarizing this line of study is to bring exciting new findings to the attention of the investigative pathology community. In addition, we hope the review will stimulate investigators to consider using analogous approaches for analysis of the molecular pathogenesis of other microbes.     

The place of implementation science in the translational medicine continuum

There is a growing consensus that the transfer of knowledge from biomedical discoveries into patient and public benefit should be accelerated. At the same time there is a persistent lack of conceptual clarity about the precise nature of the phases of the translational continuum necessary to implement this. In this paper, we: (i) propose an integrated schema to understand the five sequential phases that link basic biomedical research with clinical science and implementation; (ii) discuss the nature of three blocks along this translational pathway; (iii) outline key issues that need to be addressed in removing such barriers. The five research phases described are: (0) basic science discovery; (1) early human studies; (2) early clinical trials; (3) late clinical trials; (4) implementation (which includes adoption in principle, early implementation and persistence of implementation). This schema also sets out three points at which communication blocks can occur. The application of 'implementation science' is in its early stages within mental health and psychiatric research. This paper therefore aims to develop a consistent terminology to understand the discovery, development, dissemination and implementation of new interventions. By better understanding the factors that promote or delay knowledge to flow across these blocks, we can accelerate progression along translational medicine pathways and so realize earlier patient benefit.     

Perspective: Transforming science into medicine: how clinician-scientists can build bridges across research's "valley of death"

Significant increases in National Institutes of Health (NIH) spending on medical research have not produced corresponding increases in new treatments and cures. Instead, laboratory discoveries remain in what has been termed the "valley of death," the gap between bench research and clinical application. Recently, there has been considerable discussion in the literature and scientific community about the causes of this phenomenon and how to bridge the abyss. In this article, the authors examine one possible explanation: Clinician-scientists' declining role in the medical research enterprise has had a dilatory effect on the successful translation of laboratory breakthroughs into new clinical applications. In recent decades, the percentage of MDs receiving NIH funding has drastically decreased compared with PhDs. The growing gap between the research and clinical enterprises has resulted in fewer scientists with a true understanding of clinical problems as well as scientists who are unable to or uninterested in gleaning new basic research hypotheses from failed clinical trials. The NIH and many U.S. medical schools have recognized the decline of the clinician-scientist as a major problem and adopted innovative programs to reverse the trend. However, more radical action may be required, including major changes to the NIH peer-review process, greater funding for translational research, and significantly more resources for the training, debt relief, and early career support of potential clinician-scientists. Such improvements are required for clinician-scientists to conduct translational research that bridges the valley of death and transforms biomedical research discoveries into tangible clinical treatments and technologies.     

Materializing research promises: opportunities,priorities and conflicts in translational medicine

There is considerable evidence that the translation rate of major basic science promises to clinical applications has been inefficient and disappointing. The deficiencies of translational science have often been proposed as an explanation for this failure. An alternative explanation is that until recently basic science advances have made oversimplified assumptions that have not matched the true etiological complexity of most common diseases; while clinical science has suffered from poor research practices, overt biases and conflicts of interest. The advent of molecular medicine and the recasting of clinical science along the principles of evidence-based medicine provide a better environment where translational research may now materialize its goals. At the same time, priority issues need to be addressed in order to exploit the new opportunities. Translational research should focus on diseases with global impact, if true progress is to be made against human suffering. The health outcomes of interest for translational efforts need to be carefully defined and a balance must be struck between the subjective needs of healthcare consumers and objective health outcomes. Development of more simple, practical and safer interventions may be as important a target for translational research as the development of cures for diseases where no effective interventions are available at all. Moreover, while the role of the industry is catalytic in translating research advances to licensed interventions, academic independence needs to be sustained and strengthened at a global level. Conflicts of interest may stifle translational research efforts internationally. The profit motive is unlikely to be sufficient alone to advance biomedical research towards genuine progress.     

Text mining for traditional Chinese medical knowledge discovery: A survey

Extracting meaningful information and knowledge from free text is the subject of considerable research interest in the machine learning and data mining fields. Text data mining (or text mining) has become one of the most active research sub-fields in data mining. Significant developments in the area of biomedical text mining during the past years have demonstrated its great promise for supporting scientists in developing novel hypotheses and new knowledge from the biomedical literature. Traditional Chinese medicine (TCM) provides a distinct methodology with which to view human life. It is one of the most complete and distinguished traditional medicines with a history of several thousand years of studying and practicing the diagnosis and treatment of human disease. It has been shown that the TCM knowledge obtained from clinical practice has become a significant complementary source of information for modern biomedical sciences. TCM literature obtained from the historical period and from modern clinical studies has recently been transformed into digital data in the form of relational databases or text documents, which provide an effective platform for information sharing and retrieval. This motivates and facilitates research and development into knowledge discovery approaches and to modernize TCM. In order to contribute to this still growing field, this paper presents (1) a comparative introduction to TCM and modern biomedicine, (2) a survey of the related information sources of TCM, (3) a review and discussion of the state of the art and the development of text mining techniques with applications to TCM, (4) a discussion of the research issues around TCM text mining and its future directions.     

Going MAD: development of a "matrix academic division" to facilitate translating research to personalized medicine

Personalized medicine integrates an individual's genetic and other information for the prevention or treatment of complex disorders, and translational research seeks to identify those data most important to disease processes based on observations at the bench and the bedside. To understand complex disorders such as chronic pancreatitis, inflammatory bowel disease, liver cirrhosis, and other idiopathic chronic inflammatory diseases, physician-scientists must systematically collect data on relevant risks, clinical status, biomarkers, and outcomes. The author describes a "matrix academic division" (MAD), a highly effective academic program created at the University of Pittsburgh School of Medicine and the University of Pittsburgh Medical Center using translational research to rapidly develop personalized medicine for digestive diseases. MAD is designed to capture patient-specific data and biologic samples for analysis of steps in a complex process (reverse engineering), reconstructing the system conceptually and mathematically (disease modeling), and deciphering disease mechanism in individual patients to predict the effects of interventions (personalized medicine). MAD draws on the expertise of the medical school's and medical center's physician-scientists to translate essential disease information between the bed and the bench and to communicate with researchers from multiple domains, including epidemiology, genetics, cell biology, immunology, regenerative medicine, neuroscience, and oncology. The author illustrates this approach by describing its successful application to the reverse engineering of chronic pancreatitis.     

Induced pluripotent stem cells as a next-generation biomedical interface

Recent advances in DNA sequencing technologies and subsequent progress in genome-wide association study (GWAS) are rapidly changing the landscape of human diseases. Our knowledge on disease-gene linkage has been exponentially growing, and soon we will obtain complete maps of SNPs and mutations linked to nearly all major disease conditions. These studies will undoubtedly lead us to a more comprehensive understanding of how multiple genetic modifications link to human pathobiology. But what comes next after we discover these genetic linkages? To truly understand the mechanisms of how polygenic modifications identified through GWAS lead to disease conditions, we need an experimental interface to study their pathobiological effects. In this study, induced pluripotent stem cells (iPSCs), retaining all the genetic information from patients, will likely serve as a powerful resource. Indeed, pioneering studies have demonstrated that disease-specific iPSCs are useful for understanding disease mechanisms. Moreover, iPSC-derived cells, when recapitulating some disease phenotypes in vitro, can be a fast track screening tool for drug discovery. Further, with GWAS information, iPSCs will become a valuable tool to predict drug efficacy and toxicity for individuals, thus promoting personalized medicine. In this review, we will discuss how patient-specific iPSCs will become a powerful biomedical interface in clinical translational research.     

Stacked ensemble combined with fuzzy matching for biomedical named entity recognition of diseases

Biomedical Named Entity Recognition (Bio-NER) is the crucial initial step in the information extraction process and a majorly focused research area in biomedical text mining. In the past years, several models and methodologies have been proposed for the recognition of semantic types related to gene, protein, chemical, drug and other biological relevant named entities. In this paper, we implemented a stacked ensemble approach combined with fuzzy matching for biomedical named entity recognition of disease names. The underlying concept of stacked generalization is to combine the outputs of base-level classifiers using a second-level meta-classifier in an ensemble. We used Conditional Random Field (CRF) as the underlying classification method that makes use of a diverse set of features, mostly based on domain specific, and are orthographic and morphologically relevant. In addition, we used fuzzy string matching to tag rare disease names from our in-house disease dictionary. For fuzzy matching, we incorporated two best fuzzy search algorithms Rabin Karp and Tuned Boyer Moore. Our proposed approach shows promised result of 94.66%, 89.12%, 84.10%, and 76.71% of F-measure while on evaluating training and testing set of both NCBI disease and BioCreative V CDR Corpora.     

World hypotheses and the evolution of integrative medicine: combining categorical diagnoses and cause-effect interventions with whole systems research and nonvisualizable (seemingly "impossible") healing

It has been proposed that to understand (1) the evolution of science and medicine, and (2) the integration of conventional, complementary and alternative medicine, it is essential to consider at least eight universal implicit meta-cognitive hypotheses. It has been suggested that these implicit "world" hypotheses can be applied in every discipline of science. The present paper reviews the eight world hypotheses and proposes an additional hypothesis, termed the nonvisualizable or "Nth" world hypothesis (adopting the mathematical concept of "N"; eg, as in N dimensional space). Drawing on contemporary mathematics and quantum physics, we propose that certain theories and data-by their inherent nature-can not be visualized, and therefore may seem "unimaginable" and "impossible" (if not "unbelievable"), even though they are real. Certain seemingly anomalous observations in mind-body and energy medicine, including areas historically labeled as parapsychology or spiritual energy healing, often elicit strongly skeptical and dismissive reactions. We propose that these skeptical and dismissive reactions to purportedly impossible (yet logical) theories and seemingly unbelievable (yet replicable) data can be tempered when the Nth world hypothesis is understood and incorporated. Integrity in evidence-based science and medicine may require that scientists and nonscientists alike develop comfort and humility in accepting the human mind's restricted ability to envision and imagine certain nonvisualizable-yet fundamental and real-concepts and effects, as illustrated in contemporary physics and complementary and alternative medicine.     

Bridging the gap. The separate worlds of evidence-based medicine and patient-centered medicine

Modern medical care is influenced by two paradigms: 'evidence-based medicine' and 'patient-centered medicine'. In the last decade, both paradigms rapidly gained in popularity and are now both supposed to affect the process of clinical decision making during the daily practice of physicians. However, careful analysis shows that they focus on different aspects of medical care and have, in fact, little in common. Evidence-based medicine is a rather young concept that entered the scientific literature in the early 1990s. It has basically a positivistic, biomedical perspective. Its focus is on offering clinicians the best available evidence about the most adequate treatment for their patients, considering medicine merely as a cognitive-rational enterprise. In this approach the uniqueness of patients, their individual needs and preferences, and their emotional status are easily neglected as relevant factors in decision-making. Patient-centered medicine, although not a new phenomenon, has recently attracted renewed attention. It has basically a humanistic, biopsychosocial perspective, combining ethical values on 'the ideal physician', with psychotherapeutic theories on facilitating patients' disclosure of real worries, and negotiation theories on decision making. It puts a strong focus on patient participation in clinical decision making by taking into account the patients' perspective, and tuning medical care to the patients' needs and preferences. However, in this approach the ideological base is better developed than its evidence base. In modern medicine both paradigms are highly relevant, but yet seem to belong to different worlds. The challenge for the near future is to bring these separate worlds together. The aim of this paper is to give an impulse to this integration. Developments within both paradigms can benefit from interchanging ideas and principles from which eventually medical care will benefit. In this process a key role is foreseen for communication and communication research.     

"Creation of mouse models for human diseases"

To translate research findings into medicine and drug development, we have been performing in vivo research using primary human samples. Development of mouse models with reconstituted human immunity would be critical to overcome technical and ethical constraints associated with in vivo human research. To this end, we have created humanized mice by intravenously injecting purified human hematopoietic stem cells (HSCs) into immune-compromised NOD/SCID/IL2rgKO newborns. This xenogeneic transplantation system allows long-term engraftment and multi-lineage differentiation of human HSCs. The humanized mouse fully reconstituted with human myeloid and lymphoid subsets is expected to serve as an in vivo platform for the investigation of human immune function. In addition to understanding normal human hematopoiesis and immunity, the use of humanized mice is expected to allow investigators to translate their research findings into therapeutic and pharmaceutical development. As a possibility for such translation, we developed an in vivo model of human acute myeloid leukemia (AML), a disease in which the majority of patients succumb to relapse. Using the model, we examined the pathogenesis of AML and tried to clarify the role of AML stem cells in chemotherapy resistance and relapse. Humanized mouse model for both normal and diseased immuno-hematopoietic system is opening a new era in translational medicine.     

Knowledgebase as a tool for monitoring post-genomic medico-biological research

On-going molecular biological researches are characterized by exponential increase in the amount of experimental data which dictates the necessity to develop relevant information technologies. The knowledge-based approach appears to be a most promising and flexible tool for analytical processing biomedical information that allows new relationships between study objects to be established. In this article we analyze the work of researchers based at the Institute of Biomedical Chemistry with the materials of the PubMed biomedical library. A list of publication IDs for a half-year period has been compiled to elucidate individual profiles of research activities. Statistical analysis of medical subject headings (MeSH) reveals typical profiles of research of interest for separate divisions of the Institute. The proposed approach may be recommended as a means for improving the efficiency and coordination of biomedical research.     

Breaking down the walls: thoughts on the scholarship of integration.

The scholarship of integration is concerned with making connections across scientific disciplines, placing the work of individual investigators and their specialty fields into a larger context, and educating nonspecialists. The authors focus their comments on the biomedical sciences, but observe that closer integration of the biomedical and behavioral sciences will be particularly crucial to advance understanding of the human brain. They observe that as biomedical sciences become more technologically sophisticated, progress is increasingly dependent on sciences such as physics, chemistry, engineering, and related fields. However, the scholarship of integration has been slower than other forms of scholarship to gain acceptance as an integral activity of the professoriate. The isolation of disciplines from one another, particularly at large universities, and the perception of interdisciplinary work as risky and professionally unrewarding are among the forces that may discourage integrative scholarship. In addition, a troubling disconnect exists between the scientific community and the larger public in the understanding of science. Leaders in academic medicine and science must develop strategies to move interdisciplinary work from the margins into the mainstream of academia. Solutions that have been proposed include creating new research entities and funding mechanisms dedicated to interdisciplinary work; reinvigorating the integrative role of the physician-scientist; and training specialists in translational research. The scientific community must also work to develop more effective means of communicating the importance of its work to the public.     

Detection and characterization of translational research in cancer and cardiovascular medicine

Background

Scientists and experts in science policy have become increasingly interested in strengthening translational research. Efforts to understand the nature of translational research and monitor policy interventions face an obstacle: how can translational research be defined in order to facilitate analysis of it? We describe methods of scientometric analysis that can do this.

Methods

We downloaded bibliographic and citation data from all articles published in 2009 in the 75 leading journals in cancer and in cardiovascular medicine (roughly 15,000 articles for each field). We calculated citation relationships between journals and between articles and we extracted the most prevalent natural language concepts.

Results

Network analysis and mapping revealed polarization between basic and clinical research, but with translational links between these poles. The structure of the translational research in cancer and cardiac medicine is, however, quite different. In the cancer literature the translational interface is composed of different techniques (e.g., gene expression analysis) that are used across the various subspecialties (e.g., specific tumor types) within cancer research and medicine. In the cardiac literature, the clinical problems are more disparate (i.e., from congenital anomalies to coronary artery disease); although no distinctive translational interface links these fields, translational research does occur in certain subdomains, especially in research on atherosclerosis and hypertension.

Conclusions

These techniques can be used to monitor the continuing evolution of translational research in medicine and the impact of interventions designed to enhance it.

     

Translation research: from accurate diagnosis to appropriate treatment

This review article focuses on the various aspects of translational research, where research on human subjects can ultimately enhance the diagnosis and treatment of future patients. While we will use specific examples relating to the asbestos related cancer mesothelioma, it should be stressed that the general approach outlined throughout this review is readily applicable to other diseases with an underlying molecular basis. Through the integration of molecular-based technologies, systematic tissue procurement and medical informatics, we now have the ability to identify clinically applicable "genotype"-"phenotype" associations across cohorts of patients that can rapidly be translated into useful diagnostic and treatment strategies. This review will touch on the various steps in the translational pipeline, and highlight some of the most essential elements as well as possible roadblocks that can impact success of the program. Critical issues with regard to Institutional Review Board (IRB) and Health Insurance Portability and Accountability Act (HIPAA) compliance, data standardization, sample procurement, quality control (QC), quality assurance (QA), data analysis, preclinical models and clinical trials are addressed. The various facets of the translational pipeline have been incorporated into a fully integrated computational system, appropriately named Dx2Tx. This system readily allows for the identification of new diagnostic tests, the discovery of biomarkers and drugable targets, and prediction of optimal treatments based upon the underlying molecular basis of the disease.