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Research

​The ATAGC histology regression equations and Reference Standard classification for kidney transplant biopsies​​​

The Alberta Transplant Applied Genomics Centre (ATAGC) addresses unmet needs in medicine in terms of understanding disease states, including diagnosis, activity, stage, prognosis, and guidance for therapy.  Currently, microscope-based diagnostic labels are imperfect measures for assessing disease. Existing systems are arbitrary, subjective, and lack quantification. Consequently, there are inaccuracies and delays in treatment, and impediments to development of new drugs.

The current “gold standard” to biopsy based diagnosis of the true organ disease process has up to 50% error rates:

 

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Our approach:
We focus on developing molecular definitions of the disease states, starting with organ transplants but aiming to apply the lessons to primary organ diseases.  The strategy is to develop databases and data locks with our matrix strategy, and let the data from patients guide us to new systems.  Beyond providing insights into disease mechanisms, this approach offers an opportunity to assess and improve conventional diagnostic platforms (microscopes), and ultimately to introduce new platforms that offer insights  which the microscope cannot provide.  The goal is an integrated assessment of the disease state.
                                                                                                        
Our integrated approach provides greater accuracy to the diagnosis of the true organ disease process:

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There are four central elements to our research approach:

  1.  Applying the Organ Disease Model
    Organ transplant phenotypes reflect three main biological elements which we explore: pre-existing limitations on somatic tissues (senescence limits), the mechanisms of organ injury, and the organ's response to injury.
  2. A New Mechanism-Based Approach to Data Analysis
    While analyzing microarray data, our interdisciplinary team employs a method heavily influenced by pathology and mechanism-based expertise. We believe pathology, pathogenesis, and a priori knowledge are the solution to establishing a relationship between standard clinical phenotypes and data derived from new technologies.
  3. The Creation of a Comprehensive Database of Disease Phenotypes
    This provides a permanent record to reference new observations and assign probabilities of diagnoses and outcomes. This record will serve as a resource of data for future studies and assessing new biopsies.
  4. Applying our discoveries to patients
    The value of knowledge acquired through new technologies is measured by its clinical applications. By identify the molecular features of diseases, we can provide a new understanding of the behavior of organ transplants and primary organ diseases at the level of the individual patient – truly personalized medicine. Our project is thus embedded in the clinic.  This link between patients and research processes allows us to see population trends and outcomes, and provides the motivation that propels our research.