Digital Twins to find biomarkers for early diagnosis of cancer
Approved Research ID: 102162
Approval date: April 26th 2023
Pancreatic cancer is one of the deadliest among major cancers and is projected to be the third major cancer in the EU by 2030. In most cases, insufficient detection methods at early stages lead to poor prognosis and eventual death of the patients. The diversity of cell types in the tumor microenvironment is one of the major hindrances to finding early detection biomarkers. We propose to create a 3D multicellular model (3D-MCM) of pancreatic cancer to find combinations of biomarkers for the prediction of this cancer at its early stages.
Our 3D MCM approach would provide an integrated perspective of discovery across multiple levels of cellular phenomena by combining genetic diversity, metabolites, protein, etc, and other modalities of medical data such as age, sex, medical history, habit as well as other risk factors. Rich genotypic and phenotypic data in UK Biobank's would open the windows of opportunity to examine and expose pancreatic cancer risk that has not been investigated previously.
We next aim to expand the same methodology to other common cancers, namely lung, breast, colon, prostate, melanoma, and bladder cancers. Once we have the 3D MCM model we will further process to create digital twins to find the optimal drug for individuals. We anticipate that the whole analysis will be completed within 3 years with the involvement of several researchers.
Our analysis will enable the identification of biomarkers based on an approach for comprehensive understanding and prioritizations of molecular changes associated with cancers and thus allow doctors to intervene in cancer at its early stages.