Title: Validation of multiparametric models and Circulating and imaging biomarkers to improve Lung cancer EARLY detection.
Giulia VERONESI (Italy) IRCCS, Istituto Clinico Humanitas, Milan
Fabrizio BIANCHI (Italy) IRCCS Fondazione Casa Sollievo della Sofferenza (ISBReMIT), San Giovanni Rotondo (FG)
Thomas DANDEKAR (Germany) University of Würzburg, Würzburg
Witold K. RZYMAN (Poland) Medical University of Gdansk, Gdansk
Patrizia PATERLINI-BRECHOT (France) University Paris Descartes, Paris
Philippe LAMBIN (The Netherlands) Maastricht University, Maastricht
Harry DE KONING (The Netherlands) Erasmus MC, Rotterdam
Lung cancer is an aggressive disease with a high fatality rate; early diagnosis through screening is therefore paramount. Current strategies relying on Low-Dose Computed Tomography (LDCT), still show certain false positives rates, thus effective and validated methods to assist LDCT for early detection and to increase treatment efficacy are strongly needed to reduce mortality. Recently, non-invasive approaches based on biomarkers were proposed as promising for early detection and for informing on acquired cancer mutations involved in resistance to chemotherapy. However, translation into preventive and clinical care is yet unfulfilled due to insufficient validation. CLEARLY will focus on validation of a multifactorial "bio-radiomic" protocol for early diagnosis of lung cancer that combines circulating biomarkers and radiomic analysis. We will (a) assess the role of molecular and cellular biomarkers (exosomes, protein signatures, CTCs, microRNA) and radiomic signature, as complementary to assist early detection of lung cancer by LDCT, using bioinformatics techniques; (b) assess the prognostic role of CTCs including the role of cells epithelial mesenchymal transition (EMT) and (c) standardize a method for genomic analysis of CTCs for early detection of treatment resistance. In a 2 years prospective study we will enrol, 150 lung tumor patients from Italy and Poland, 120 matched high-risk controls and additional 30 stage IV patients, collect biological samples and LDCTs, and evaluate biomarkers in blood and radiomic features in LDCTs. Radiomic signatures will be further validated in an independent screening dataset from NELSON study. Statistical methods will validate the combined signatures. The available sample size is calculated to allow a statistical power sufficient to identify predictive biomarker signatures with a p-value < 0.05.
Clinical endpoints are: to increase accuracy of lung cancer screening to facilitate large-scale implementation and improve target treatments of patients with advanced lung cancer.
Translational endpoints are: to validate effective biomarkers and radiomics signature for lung cancer early detection, to refine liquid biopsy using CTC for advanced cancer. We expect that the study will produce and validate a bio-radiomic protocol for lung cancer screening, will characterize the genetic features of CTCs and exosomal protein antigens and explore their theranostic potential. These results, achieved by a transnational team of internationally recognized high-profile scientists, will accelerate the undertaking of lung cancer screening programs in Europe. The proposed biomarkers and methods could increase the ratio of screening positive cancer diagnosis, facilitate selection of screening candidates and help development of molecularly targeted drugs to stop lung cancer progression.
(Project funded under JTC 2016)
This is the project page of the German collaboration partner
Remis 3R - Alternative methods for the reduction of animal experiments by a combined 3D tissue in vitro/in silico lung tumor model in oncological research and development
Funding BMBF (FKZ: 031L0129B)
Funding Period: 4/2017 - 3/2020
3D Tissue Models for Studying Microbial Infections by Human Pathogens / Project 2 (Chlamydia)
Funding: GSLS / University of Würzburg
Funding Period: 5/2016 - 4/2019
EFRE project on bee observation: „Bienen-Komplex“
Funding: EU / EFRE
Funding Period: 1/2016 - 12/2019
- Modeling of the interaction between host and pathogenic fungi combining metabolic modelling with evolutionary game theory / Project B01
- Interaction networks of signalling molecules and pathways of the pathogenic fungi A. fumigatus and C. albicans and their human host / Project B02
Funding: DFG (FKZ: TR124/2, B01; TR124/2, B02)
Funding period 1-2: 7/2013 - 6/2021
- A systems biology perspective of regulatory and metabolic adaption of S. aureus / Project A08
- New Integration of bioinformatics tools in OMICs databanks in a Staphylococcus aureus WIKI-environment/ Project INF
Funding: DFG (FKZ: TR34/3, A08 & TR34/3 INF)
Funding period 1-3: 7/2010 - 6/2018