Principles and methods of integrative genomic analyses in cancer

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.

Integrative methods for analyzing big data in precision medicine

“…However, a majority of recent methods use integrative approaches to combine multiple types of molecular data, such as DNA copy number alteration, DNA methylation, mRNA and protein expression, and molecular interaction data, accounting for different levels of variations among affected individuals and thereby providing more accurate sub-typing [122,123]. For example, Shen et al [124] developed iCluster, an unsupervised learning framework that can simultaneously perform clustering, data integration, feature selection and dimension reduction of multiple data types.…”

Transforming the Future of Treatment for Ovarian Cancer

“…Over the past 2 decades following completion of the human genome project and advancing technologies in transcriptome analysis, cancer patient datasets, with and without treatment, have been generated, such as in the The Cancer Genome Atlas (TCGA, http:// cancergenome.nih.gov/) and Oncomine (https://www.oncomine.org) [88]. For example, transcriptome analysis has revealed chemoresponse associated genes in ovarian cancer patient tissue samples, such as for platinum and taxane drugs [89,90].…”

Biomarker discovery, high performance and cloud computing: A comprehensive review

“…For example, a protein, tissue, group of cells or fluids found in a human biopsy sample may be predictive or prognostic of a particular disease. A list of differentially expressed gene when clustered could stratify patients by prognosis or response to treatment [3]. Analysis from clinical data can be used to determine gene signatures with appropriate thresholds that can be used in the development of clinical tests, used for example, in treatment guidance.…”