Periodic Reporting for period 1 - IPMT (Integrated Precision Medicine Technologies Research Centre of Excellence)
Reporting period: 2017-09-01 to 2018-08-31
The proposed Centre aims to establish a complete ecosystem which will nurture interdisciplinary convergence and integration and provide enabling infrastructure, leading to the identification of common targets and a focused effort to create the technologies (hardware and algorithms) necessary to overcome some of the limitations plaguing precision medicine (see Fig. 1).
Precision medicine requires a multipronged approach requiring technological support in a combination of areas for it to succeed: (1) data management and information technologies, (2) computational and analytics tools and systems and (3) sensing technologies and (4) biological validation systems. The Integrated Precision Medicine Technologies Centre of Excellence (IPMT CoE) will integrate the components key to the implementation of complex precision medicine tasks including: (i) Bioinformatics & Systems Medicine, (ii) In silico Modelling, (iii) Sensing, Electronics and Embedded Systems, (iv) Medical Imaging & Biosignal Analysis, (v) eHealth Systems, and (vi) Micro- and Nano-Biotechnologies, with the support of (vii) Molecular/Cellular Biosciences (Translational –omics) and (viii) Clinical Development & Validation. This will achieve the level of focus and integration that does not currently exist.
Further, the Centre aims to investigate the application of the developed methodologies in key precision medicine applications such as (i) Primary and metastatic brain malignancies, (ii) Neurodegenerative disorders, and (iii) Brain injury and response in critical care.
(i) Bioinformatics & Systems Medicine: The development of network-based multi-source integration of omics data and the computational construction of a comprehensive molecular profile for each patient or disease/disease stage is proposed.
(ii) In silico Modelling: A novel data-driven in-silico modelling framework for disease monitoring and biomarker identification will be developed, integrated with medical imaging algorithms and machine learning.
(iii) Sensing, Electronics and Embedded Systems: Biosensors using combinations of carbon nanomaterials, CMOS nucleic acid detectors, and miniaturized optical sensor systems in combination with luminescent probes will be developed, linked with custom hardware specifically for machine learning algorithms.
(iv) Medical Imaging & Biosignal Analysis: Novel, explainable AI solutions that lie in the intersection of shallow and deep machine learning and imaging methods will be investigated linked to computer assisted diagnosis (CAD) systems.
(v) eHealth Systems: Data mining workflow designs exploiting ontology-based semantic interconnection of heterogeneous data leveraging customized big data and deep learning frameworks resulting in clinically relevant metadata generation that underpin visualization will be implemented based on FHIR and mHealth services.
(vi) Micro- and Nano-Biotechnologies: Novel enabling technologies for point-of-care testing in precision medicine will be developed. Moreover, systems that can overcome the blood-brain barrier (BBB) will be investigated, as well as humanized models that could predict medicinal effectiveness could be feasible using organ-on-a-chip systems for preclinical studies.
At the two ends of the research spectrum lie the disciplines that will provide:
(i) Molecular/Cellular Biosciences (Translational –omics): It is proposed to create a comprehensive cohort of well annotated and systematically followed up pool of Cypriot patients and healthy individuals, in order to establish for the first time the much needed Cypriot genome, reference data resource.
(ii) Clinical Development and Validation: For validating the tools and methods developed.
Dissemination activities were carried out as follows:
Journal and Conference Papers: 17
Special Sessions Organised at International Conferences: 3
Book Chapters: 1
Refereed Abstracts Published: 10
Invited Presentations in International Fora: 9
Presentations to Secondary Education Schools: 5
Media and Other Presentations: 15.
(i) Primary and metastatic brain malignancies: (a) Integration of rationally engineered cells secreting extracellular vesicles carrying proteins or nucleic acids, serving as disease biomarkers in a self-controlled system. (b) Predictive models of spatiotemporal biodistribution and response to targeted therapies. (c) Cell-based and optoelectronic-based interfaces, translated into completely implantable or wearable devices, for molecular communications taking Brain Machine Interfaces to a drastically new level.
In addition, regarding cancer metastases to the brain the following technologies will be investigated: (a) Identifying ‘biomarker (s)’ of metastatic potential through retrospective review of genetic, proteomic, imaging, biochemical, environmental, lifestyle patient data. (b) Creating prediction models of the metastatic potential to the brain of different subgroups (prospective). (c) Utilizing imaging and liquid biopsy techniques to help localize brain lesions early, even at microscopic size. (d) further exploration of the concepts of the blood brain barrier (BBB) and resistance (primary or acquired) to systemic drugs whilst promising targeted systemic therapies are tested in this field.
(ii) Neurodegenerative disorders: Precision medicine technologies will be developed focusing on: (a) electronics, sensors and wearable devices for precise remote monitoring; (b) advanced image analytics and radiogenomics for more accurate diagnosis and monitoring of progression; (c) novel point of care technologies for liquid biopsy analysis for detection of disease markers; (d) development of blood brain barrier models for understanding disease pathogenesis; (e) development of new multimodal imaging contrast agents (e.g. MRI-Optical); and (f) development of an integrated Decision Support System (DSS) as well as patient oriented apps.
(iii) Brain injury and response in critical care: (a) To use interdisciplinary approaches in working out barriers for data integration/curation currently preventing us to combine diverse databases from patients with TBI. (b) To work with our existing small cohort of TBI patients with full scale data in combination with datasets from large European cohorts of TBI patients to model this multi-factorial, complex disease and identify specific bio-patterns/bio-markers for 2 distinct pathophysiological mechanisms (brain global edema and brain contusions), amenable to different monitoring modalities/medical-surgical interventions.