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

doi:10.1016/j.canlet.2016.02.035

Cancer Letters

Volume 382, Issue 1, 1 November 2016, Pages 95-109

https://doi.org/10.1016/j.canlet.2016.02.035 Get rights and content

Highlights

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Different pathways are involved in normal-tissue reaction after radiotherapy.
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Combining different ‘omics’ techniques may identify critical pathways.
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Functional assays may identify patient subgroups with different mechanisms.
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A systems biology approach promises synergy from ‘omics’ and functional assays.

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.

Introduction

Radiation therapy (RT) is an important component of modern multimodality tumour therapy and is part of the treatment in approximately 60% of cancer patients treated with curative intent [1]. Although technological advances in the delivery of RT has reduced the volume of normal tissue receiving critical radiation doses, the tumour dose is still limited by adverse effects in adjacent late-reacting normal tissue. Previous studies on RT-induced telangiectasiaof the skin suggested that after considering the effects of absorbed dose and dose per fraction, up to 80% of the observed variation in risk was associated with individual patient-related factors [2], [3], [4]. The identification of patients' individual susceptibility for the development of adverse effects from RT is an important prerequisite for individualising tumour treatment. Thus the therapeutic window might be widened by increasing the dose to the tumour in patients with relatively radioresistant normal tissue. On the other hand, patients with high risk of developing severe normal-tissue reaction might be candidates for either altered radiotherapy regimens (alternative fractionation schemes, treatment planning, or modalities), changes to surgery (e.g. in breast cancer, mastectomy rather than wide local excision plus RT) or pharmacologic interventions to ameliorate symptoms. Therefore, several approaches to develop a predictive assay for normal-tissue toxicity have been pursued in the past two decades. Here we review the novel high-throughput ‘omics’ technologies and more classical functional assays and discuss how the two approaches may interact synergistically to facilitate the identification of subgroups with different risks of RT-induced toxicity.

Section snippets

Clinical end points, mechanisms and hypotheses

Before reviewing ‘omics’ approaches and functional assays for radiation-induced normal-tissue reaction, a brief overview of the clinical end points and possible mechanisms is given. This is intended to present the context and basic principles of early and late RT-toxicities based on selected representative examples. For a more detailed discussion of mechanistic aspects, the reader is encouraged to consult comprehensive reviews on the special topics.

In tumour therapy, the aim is to prevent

Candidate gene studies of single nucleotide polymorphisms (SNPs)

Earlier studies attempted correlations of genetic variants with the risk of developing normal-tissue reaction after RT using candidate gene SNPs. The majority of the candidate genes were selected on the basis of their relation with the DNA damage response (DDR), mostly related to DNA repair, or genes involved in inflammatory or pro-fibrotic processes. Various single-centre studies suggested correlations with specific polymorphisms in TGFB1, XRCC3, XRCC1, ATM, GSTP1, GSTA1, or the cumulated

Functional assays

A large number of studies have been performed with the purpose of establishing associations between normal-tissue reaction after RT and the functional response of individual patients' cells to irradiation. End points have been either clonogenic cell survival or processes considered to be related to cellular radiosensitivity, such as DSB repair, chromosome aberrations, or apoptosis.

Normal skin fibroblasts can be readily grown in vitro and are functional cells of connective tissue. Therefore,

Promise of systems biology for identification of pathways and subgroups

The ‘omics’ approaches used so far focus on identifying genetic factors and changes in the transcriptional activity of genes involved in the development of normal-tissue reaction after RT. However, cell and tissue function is also influenced by posttranslational modification, cell signalling networks, and the microenvironment. Thus cell/tissue function depends on the interaction of multiple biological components from the cell to the systemic level. A direct correlation between the level of mRNA

Conclusions

Candidate SNP studies and gene expression studies found some associations with patients' adverse reactions after radiotherapy, although few have been validated for risk prediction. Recent large GWAS and candidate SNP studies including 1000–3000 patients identified significant associations, but heterogeneity with respect to tissues and end points limits success. However, the notion of multiple low-effect genes showing additive effects assumes that the effect of each gene is independent. An

Conflict of interest

All authors declare no conflict of interest.

Acknowledgements

This work was supported by funding from the European Union Seventh Framework Programme for research, technological development and demonstration under grant agreement no 601826 (“REQUITE”). The funding source had no involvement in the writing of this review.

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Mini-reviewRadiogenomics: A systems biology approach to understanding genetic risk factors for radiotherapy toxicity?

Highlights

Abstract

Introduction

Section snippets

Clinical end points, mechanisms and hypotheses

Candidate gene studies of single nucleotide polymorphisms (SNPs)

Functional assays

Promise of systems biology for identification of pathways and subgroups

Conclusions

Conflict of interest

Acknowledgements

Int. J. Radiat. Oncol. Biol. Phys

Eur. J. Cancer

Int. J. Radiat. Oncol. Biol. Phys

Semin. Radiat. Oncol

Int. J. Radiat. Oncol. Biol. Phys

Cancer Lett

Cancer Lett

Cancer Lett

Cancer Lett

Arch. Gerontol. Geriatr

Radiother. Oncol

Exp. Cell Res

Radiother. Oncol

Exp. Gerontol

Eur. J. Cancer

Radiother. Oncol

Radiother. Oncol

Clin. Oncol

Radiother. Oncol

Int. J. Radiat. Oncol. Biol. Phys

Int. J. Radiat. Oncol. Biol. Phys

Biochim. Biophys. Acta

Radiother. Oncol

Radiother. Oncol

Radiother. Oncol

Int. J. Radiat. Oncol. Biol. Phys

Int. J. Radiat. Oncol. Biol. Phys

Int. J. Radiat. Oncol. Biol. Phys

Radiother. Oncol

Lancet Oncol

Radiother. Oncol

Radiother. Oncol

Int. J. Radiat. Oncol. Biol. Phys

Int. J. Radiat. Oncol. Biol. Phys

Int. J. Radiat. Oncol. Biol. Phys

Radiother. Oncol

Radiother. Oncol

Int. J. Radiat. Oncol. Biol. Phys

Int. J. Radiat. Oncol. Biol. Phys

Int. J. Radiat. Oncol. Biol. Phys

J. Urol

Radiother. Oncol

Radiother. Oncol

Radiother. Oncol

Radiother. Oncol

Radiother. Oncol

Radiother. Oncol

Cancer Lett

Global cancer statistics

CA Cancer J. Clin

Cell cycles in cell hierarchies

Int. J. Radiat. Biol. Relat. Stud. Phys. Chem. Med

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