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Key Technologies

We will utilise advanced histology techniques in order to fast-track the development of protein diagnostics for clinical use. For this, we will employ antibody-based profiling of TMAs, as well as harnessing the power of digital pathology. The development of Tissue Micro Arrays (TMAs), wherein hundreds of clinical specimens can be arranged in an ordered fashion on a single tissue section, has allowed for candidate cancer biomarkers to be rapidly and uniformly assessed for their potential clinical utility. Techniques such as Immunohistochemistry (IHC), when applied to TMAs, can obtain a simultaneous view of protein expression across a wide cohort of patients with different clinical outcomes. This high-throughput technique allows for standardisation of the assay, by reducing the variables, and will be carried out by OncoMark, with input from Pathology Diagnostics

For quantitation, there are two methods that can be employed: (1) ‘gold-standard’ manual assessment, which is based on visual inspection and scoring by a trained pathologist; and (2) automatic analysis using computational approaches on digital slides. Manual assessment takes time, and there are also issues associated with inter- and intra-observer reproducibility of scoring due to the subjectivity of visual interpretation by the naked eye, and thus computerised image analysis has come to the fore. In this project, both manual and automated quantitation will be employed and results compared. University College Dublin and OncoMark have developed proprietary automatic quantitation methods that will be utilised.

To aid in the development of tools that allow clinicians to predict treatment outcome on a case-by-case basis, systems biology and systems medicine approaches will be undertaken, where quantitative measurements of multiple biological parameters are integrated with knowledge on pathway topologies and signalling networks. Integrating information on protein-protein interplay and pathway topology with protein expression levels should significantly increase the predictive (and/ or prognostic) capacity that can be achieved. To this end, our colleagues in Royal College of Surgeons in Ireland have developed a novel systems-level approach, using knowledge- and data-driven multivariate statistical modelling, in order to identify whether baseline protein expression patterns can predict susceptibility to apoptosis inducing drugs. The methodological framework for this systems modelling strategy will be further developed to include data from epigenomic analyses and to aid the associated definition of a prognostic tool.

Pathology Diagnostics Ltd (PDL) is GCLP accredited with full quality control at all stages of the tissue handling cycle including sample acquisition and throughput in the laboratory, QC of laboratory processes, equipment validation and monitoring and standardisation of all operating procedures. An integrated quality management system is in place to continually monitor improve standards. Clients can be assured that all results are fully validated and are accurate, consistent and reliable.
PDL has over 30 years combined experience from academia, NHS and industry.

Using whole slide scanning technology, PDL can convert your microscope slides to superb high-resolution digital images as a starting point for computerised image analysis, telepathology, quantitative biomarker and companion diagnostics evaluation. PDL's whole slide hosting service is of benefit to industry experts looking to centralise the dissemination and global review of microscope slides from clinical trials or other international studies; or to perform automated image analysis as first stage of automated analysis/TMA analysis. Clinicians may also benefit from this service to access slides for teaching, MDT review meetings and archiving. 

Computational modeling integrates heterogeneous experimental studies into a unified framework to build common hypotheses and complement experiments by making predictions that limit the breath of experiments performed, thus avoiding unnecessary experiment. It can be utilized to investigate hidden dynamics of signaling pathway and design principle of biological circuit underling diverse biological phenomena. Identifying key participants of biological phenomena, we can focus on the most important player in experimental design. Computational modeling also permit unique
in silico experiments that may not yet be physically possible, e.g., because of instrument resolution/dynamics range of specific and quantitative up/downregulation of single or combinations of biological species.