Radiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity?

Abstract

Adverse reactions in normal tissue after radiotherapy (RT) limit the dose that can be given to tumour cells. Since 80% of individual variation in clinical response is estimated to be caused by patient-related factors, identifying these factors might allow prediction of patients with increased risk of developing severe reactions. While inactivation of cell renewal is considered a major cause of toxicity in early-reacting normal tissues, complex interactions involving multiple cell types, cytokines, and hypoxia seem important for late reactions. Here, we review 'omics' approaches such as screening of genetic polymorphisms or gene expression analysis, and assess the potential of epigenetic factors, posttranslational modification, signal transduction, and metabolism. Furthermore, functional assays have suggested possible associations with clinical risk of adverse reaction. Pathway analysis incorporating different 'omics' approaches may be more efficient in identifying critical pathways than pathway analysis based on single 'omics' data sets. Integrating these pathways with functional assays may be powerful in identifying multiple subgroups of RT patients characterised by different mechanisms. Thus 'omics' and functional approaches may synergise if they are integrated into radiogenomics 'systems biology' to facilitate the goal of individualised radiotherapy.

Keywords: Gene expression microarrays; Genome-wide association studies; Normal-tissue reaction; Predictive tests; Radiotherapy; Single-nucleotide polymorphisms.

Figure 1

Schematic induction of normal-tissue reaction after radiotherapy (RT). Different cell types (stroma, vascular, parenchymal, and immune cells) in the irradiated tissue interact with each other and with the immune system via cytokines to produce inflammatory and pro-fibrotic reactions. Cell depletion, inflammation, repopulation and remodelling are reminiscent of the wound healing process and lead to deterministic effects. Radiation-induced mutations and genomic instability lead to stochastic effects.

Figure 2

Schematic relationship of ‘omics’ and functional endpoints. Epigenetic factors influence gene expression while protein levels and function are influenced by posttranslational modification. Abbreviations: SNPs (single nucleotide polymorphisms); CNV (copy number variations); INDELs (insertions and deletions); mtDNA (mitochondrial DNA); miRNA (microRNA).

Figure 3

Integration of ‘omics’ pathway analysis, functional assays, and experimental studies into a Systems Biology approach.