Objective
Currently cervical histology is the gold standard procedure in the field. However, there is a need for alternative approaches and specific biomarkers to aid objective CIN lesion grading and to identify true high grade cervical disease, especially in the advent of primary HPV screening and widespread HPV vaccination.
Panel of mRNA markers has been developed using systems biology and datamining tools and has demonstrated high specificity [93%] and sensitivity [88%] for detecting CIN 2-3 lesions. Clinical data supports its application as a cervical cancer grading biomarker as well. Our goal is to ascertain the utility of this novel panel and extend it using systems biology approaches with experimentally and computationally derived proteomic biomarkers in cervical pre cancer and cancer disease, for more accurate diagnosis of CIN disease. Toward that goal we establish a general framework for validation of proteomic biomarkers. This will be achieved by combining advanced technologies in a robust and time-effective way, including DNA array to protein array copying, Human Combinatorial Antibody Library of phage display and iRIfS, imaging Reflectometric Interference Spectroscopy technology. Systems biology approached would focus on both the existing and new markers and will use this framework rapid validation. Systems biology exploration of some specific pathways is justified by our previous data, that the CIN1/CIN2+ discrimination could be related to some given pathways.
The proposed approach potentially reduce costs of cervical clinical studies providing a reliable, quantitative, multi-content diagnostic approaches and results in reducing the number of colposcopic events. Cytology specimens will be of greater clinical utility and will result in earlier detection of disease. Molecular characterization of CIN lesions provides a basis for re-definition of histological categories.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesbiological sciencesmicrobiologyvirology
- medical and health sciencesbasic medicinephysiologycytology
- medical and health sciencesclinical medicineoncologycervical cancer
- medical and health scienceshealth sciencesinfectious diseasesDNA viruses
- natural sciencesbiological scienceshistology
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Call for proposal
FP7-HEALTH-2012-INNOVATION-1
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Funding Scheme
CP-FP - Small or medium-scale focused research projectCoordinator
D02 CX56 Dublin
Ireland