Abstract
Cancer is a complex disease that causes the alterations in the levels of gene, RNA, protein and metabolite. With the development of genomics, transcriptomics, proteomics and metabolomic techniques, the characterisation of key mutations and molecular pathways responsible for tumour progression has led to the identification of a large number of potential targets. The increasing understanding of molecular carcinogenesis has begun to change paradigms in oncology from traditional single-factor strategy to multi-parameter systematic strategy. The therapeutic model of cancer has changed from adopting the general radiotherapy and chemotherapy to personalised strategy. The development of predictive, preventive and personalised medicine (PPPM) will allow prediction of response with substantially increased accuracy, stratification of particular patient groups and eventual personalisation of medicine. The PPPM will change the approach to tumour diseases from a systematic and comprehensive point of view in the future. Patients will be treated according to the specific molecular profiles that are found in the individual tumour tissue and preferentially with targeted substances, if available.
Highlights
Cancer is a complex disease that is caused by the interplay of multiple internal factors and extrinsic factors [1,2]
We propose the use of the multi-parameter systematic strategy to predict, prevent and personalise the treatment of a cancer
The multiparameter systematic strategy for predictive, preventive and personalised medicine (PPPM) in cancer was initially conceived by the Zhan and Desiderio [10]; this concept was addressed by XZ as a keynote speaker and panellist at the first EPMA-World Congress 2011 and was collected into the post-meeting report of the first EPMA-World Congress 2011 [11]
Summary
Cancer is a complex disease that is caused by the interplay of multiple internal factors and extrinsic factors [1,2]. Many technical challenges remain for systems biology, including (a) data quality and standardisation of ‘omics’-based large-scale data, (b) the immaturity of network biology, (c) the requirement of high-sensitivity tools for detection and quantification of the concentrations, fluxes and interactions of various types of molecules at a given space and time, (d) the necessity of miniaturised and automated microfluidics/nanotechnology platforms that are capable of multi-parameter analyses of cell sorting and single cell gene and protein profiling, (e) the need of imaging technologies that enable the dynamic, spatial and multi-parameter measurements within single cells, and (f ) even challenges regarding fair credit and data ownership [95]. Systems biology can systematically integrate those ‘omics’ data to form a panel change of genes, proteins, metabolites and clinical features to predict, prevent and personalise the treatment of cancer (Figure 2). From the angle of systems biology, four significant signalling pathway network variations that were
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