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Principles and methods of integrative genomic analyses in cancer
Vessela N Kristensen1, Ole Christian Lingjærde2, Hege G Russnes3
11] Department of Genetics, Institute for Cancer Research, Oslo University Hospital, The Norwegian Radium Hospital, Montebello, 0310 Oslo, Norway. [2] K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, Faculty of Medicine, University of Oslo, 0313 Oslo, Norway. [3] Department of Clinical Molecular Oncology, Division of Medicine, Akershus University Hospital, 1478 Ahus, Norway.
Abstract:
Combined analyses of molecular data, such as DNA copy-number alteration, mRNA and protein expression, point to biological functions and molecular pathways being deregulated in multiple cancers. Genomic, metabolomic and clinical data from various solid cancers and model systems are emerging and can be used to identify novel patient subgroups for tailored therapy and monitoring. The integrative genomics methodologies that are used to interpret these data require expertise in different disciplines, such as biology, medicine, mathematics, statistics and bioinformatics, and they can seem daunting. The objectives, methods and computational tools of integrative genomics that are available to date are reviewed here, as is their implementation in cancer research.
Insights
Integrative genomics analyzes molecular data to reveal deregulated pathways in cancer, aiding in identifying patient subgroups for targeted therapies. This review covers methods and tools for cancer research.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Molecular data analyses (DNA copy-number alteration, mRNA, protein expression) reveal deregulated biological functions and pathways in various cancers.
- Emerging genomic, metabolomic, and clinical data offer opportunities for identifying novel patient subgroups for tailored cancer therapy and monitoring.
Purpose of the Study:
- To review the objectives, methods, and computational tools of integrative genomics.
- To discuss the implementation of integrative genomics in cancer research.
Main Methods:
- Review of existing literature on integrative genomics methodologies.
- Discussion of computational tools and interdisciplinary expertise required for data interpretation.
Main Results:
- Integrative genomics enables the analysis of complex molecular, metabolomic, and clinical data.
- Identification of deregulated pathways and novel patient subgroups is facilitated by these analyses.
Conclusions:
- Integrative genomics provides a framework for understanding cancer biology and developing personalized treatment strategies.
- Despite its complexity, integrative genomics is crucial for advancing cancer research and clinical applications.
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