Multi-panel Coding and Non-coding Transcriptional Responses as an Indicator of Individualised Responses to Radiation Effects in Radiation Therapy Patients
Multi-panel Coding and Non-coding Transcriptional Responses as an Indicator of Individualised Responses to Radiation Effects in Radiation Therapy Patients
Peripheral blood samples will be taken with informed consent from radiotherapy patients before and during treatment fractions for sarcoma, breast, lung, gut, genitourinary and head & neck tumours at The Royal Marsden. Candidate genes identified by PHE, Columbia and/or in the literature as being specific to radiation responses will be assessed, together with genes relevant to systemic inflammatory and immune responses, to identify transcriptional responses for a range of doses and exposures on an inter-individual basis. Data will be analysed using existing and new statistical tools focused on count data modelling. The intended outcome is identification of a radiation specific panel of genes to inform individual radiation responses and if the results are favourable, a large scale follow up to this project is expected.
Biomarkers of radiation exposure are recognised to form an important component of the 'toolkit' for initial triage assessment of potentially exposed individuals in a mass casualty radiation accident or incident. Furthermore, radiation is an important medical tool and biomarkers can contribute to longer term assessment of radiation effects and public health risks. The gene expression assay has been gaining popularity as a sensitive biological marker of radiation exposure with the potential to be used for truly individualised dosimetry. The possibility for this assay to be used for a large scale mass-casualty scenario has been proposed and tested in a recent inter-comparison exercise. However, a fuller understanding of genetic factors that contribute to individual radiation risks is needed to inform tailored screening approaches - i.e. to identify a truly radiation specific panel of genetics responses for use as a transcriptional biomarker.
At Columbia University, research has shown that transcriptional effects have the potential to be used as individualised predictors of radiosensitivity to early and late effects. At Public Health England (PHE), recently established molecular counting nCounter technology allows direct counting of nucleic acid molecules (DNA, mRNA, miRNA and lncRNA) without the need for enzymatic reaction or amplification steps hence reducing time for data collection. This new gene expression analysis technique has been assessed for radiation biodosimetry applications with promising results. Furthermore, gene expression has shown a high degree of promise as a marker for late effects of radiation, for instance normal tissue reactions following curative radiotherapy for breast cancer.
In the earlier pilot RTGene study the investigators found genes that are consistently down-regulated and up-regulated towards the end of the radiotherapy treatment. The next stage of this work (RTGene 2) will be to validate the nCounter data for a small number of new genes consistently found in the top 6 of differentially expressed. Importantly, in an attempt to identify genes which are promising biomarkers of susceptibility to radiation-induced toxicity, expression levels will need to be compared with normal tissue reaction to IR, and combined with longer term radiation toxicity data to identify genes with the most pronounced expression level fluctuations during and after radiotherapy.
Peripheral blood samples will be taken with informed consent from radiotherapy patients before and during treatment fractions for sarcoma, breast, lung, gut, genitourinary and head & neck tumours at The Royal Marsden. Candidate genes identified by PHE, Columbia and/or in the literature as being specific to radiation responses will be assessed, together with genes relevant to systemic inflammatory and immune responses, to identify transcriptional responses for a range of doses and exposures on an inter-individual basis. Data will be analysed using existing and new statistical tools focused on count data modelling. The intended outcome is identification of a radiation specific panel of genes to inform individual radiation responses and if the results are favourable, a large scale follow up to this project is expected.
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