PMLCP : Precision Medicine approach for effective treatment of Liver Cancer Patients
Professor Adil Mardinoglu
Professor of Systems Biology
Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London
Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer and affects more than half a million people worldwide. It is the third leading cause of cancer death, and the global burden of HCC continues to increase. The frequent causes of HCC are alcoholic liver disease (ALD), chronic hepatitis B (CHB), chronic hepatitis C (CHC), and nonalcoholic steatohepatitis (NASH). Multiple etiologic factors are also implicated in the development of HCC and these factors have a direct impact on patient characteristics as well as the tumor progression. System level treatments and cures for HCC and other cancers is the ultimate goal of the medical community for many years, but they have proven elusive. The Hallmarks of Cancer (HoC) provides an overview of an approach that could be utilized for systematic treatment. A Systems-engineering based Treatment Methodology (STM) that seeks to capture all important cancer processes and encompasses a broad array of disease and treatment data has been identified using the HoC. A key aspect of this method is a disease model based on biological functions comprising all component hallmarks of the HoC. However, it is clear that uses of single biological networks alone are not sufficient to define the progression of such complex disease. A more comprehensive representation of the operation of the cellular and whole-body system is required to truly define how HCC will progress.
Here we propose a study that consider why traditional strategies have not provided a treatment strategy for HCC, and develop an integrated, detailed, systemic representation of HCC using the HoC as an overall guide. The overall objective of this proposal is to reveal, mathematize, parameterize, and integrate the molecular mechanisms of HCC progression with different causes (ALD, CHB, CHC and NASH) into a Disease Progression Model.
The proposed study will be the first example of such an integration of regulatory, signalling, protein-protein interaction and metabolic networks for cancer. The derivative Disease Progression Model will be used, for example, by medical and pharmaceutical researchers to provide systematic, targeted, effective and efficient treatments for HCC and improve health care. The Systems Biology based approach used in this study will allow for identification of key genes/metabolites for a systematic analysis to obtain increased understanding of the underlying molecular mechanisms associated with HCC and other cancers.
We will integrate genomics, transcriptomics, proteomics, metabolomics and metagenomics data for HCC patients, generate whole body model accounting the interactions between the host and microbiome, analyse the relationship between omics data and tumor development, identify the mechanisms of action of the existing cancer drugs, identify the right drugs for the right patient group and finally provide information for development of novel treatment strategies. The results will be used in identification of novel drug targets for the effective treatment of HCC patients and identify biomarkers for the early detection of HCC. Moreover, a new methodology that will assist clinicians in selecting and prescribing the existing cancer drugs based on the characteristics of the HCC patients will be developed.
Liver Cancer, Systems Biology, Molecular Networks, Biomarker, Multiomics