a Global Agenda for Oncology Research

Abstract

The main topics of interest on oncology research stem from animal models, hypoxia, angiogenesis, metastasis, cellular signaling, cancer stem cells, DNA damage and repair, cell cycle and apoptosis. Biomarker studies of diagnostic and prognostic markers which may ultimately be applicable in a clinical setting are required. These should report on biomarkers that have a clear biologic relevance to a particular tumor type and be validated in at least one independent validation cohort and with biological validation in vitro or in vivo. Cancer epidemiology and related cancer research including risk factors for disease initiation and development, social determinants, environmental, behavioural, and occupational correlate, should also be advocated. Experimental therapeutics and drug development including aspects of preclinical investigations on therapeutic agents should also be welcomed submissions concerning anti-cancer drug discovery & development, and targeted therapies. Hence, we should also welcome studies on genetics, genomics and epigenetics including aspects of genome-scale analysis, functional genomics, genetic association studies, pharmacogenomics and epigenetics in relation to cancer biology, diagnosis and therapy. Tumor immunology including on aspects of infectious agents associated with cancer and tumor immunology, tumor immunity, immunotherapy, cancer vaccines, viral carcinogenesis and virus-host interactions. Therapy studies focusing on on clinical research that impacts on the treatment of cancer using systemic chemotherapy, immunotherapy, targeted therapy, and radiation, as well as research looking at therapy resistance mechanisms. Also studies on surgical oncology with a focus on clinical research that impacts on the diagnosis and treatment of cancer using surgery, diagnostic imaging, interventional therapeutics, and surgical pathology. Systems biology, post-genomic analysis and emerging technologies including aspects of the function of biological systems at the molecular and cellular level, in particular those addressing network modeling, quantitative analyses and the integration of different levels of information, deep sequencing-based technologies, computational biology and machine learning in relation to cancer biology.