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GENEPI-LOWRT Résumé de rapport

Project ID: 36452
Financé au titre de: FP6-EURATOM-RADPROT
Pays: Belgium

Final Report Summary - GENEPI-LOWRT (Genetic Pathways for the Prediction of the Effects of Ionising Radiation: Low Dose Radiosensitivity and Risk to Normal Tissue after Radiotherapy)

Radiotherapy (RT) is one of the most effective cancer treatments and is used alone or in combination with surgery and chemotherapy. However, a fraction of patients react badly to radiotherapy. Adverse reactions to radiotherapy are seen in approximately 5 % of patients but clinical observations indicate these reactions vary widely between individuals. This variation arises from the intrinsic sensitivity of normal tissue. These adverse reactions are commonly classified as acute (occurring during or within a few weeks of treatment) or late (occurring six months to many years later). As late effects can be permanent, they provide the basis for dose constraints to radiation toxicity. If sensitive and specific predictive test or biomarkers could identify which patients are more sensitive to radiotherapy, the treatment could then be tailored to deliver doses of ionising radiation at levels more appropriate to their genetic make-up. The problem is that little is known about the biological factors underlying such normal tissue complications and attempts to link normal tissue responses in patients and various phenotypical cell and molecular responses to high doses in vitro have not generally been very successful. It had been shown that different genes are expressed after low relative to high dose radiation. Therefore the project had two broad aims: firstly, to explore links between the development of severe, normal tissue toxicity following radiotherapy, with transcriptional changes and modulation of gene expression induced at low doses; and secondly, to identify links between individual radiosensitivity and genetic factors.

Analysis of cellular responses at low dose was linked with the GENEPI bio-bank, which provides a valuable resource on normal and adverse tissue responses in a large population of radiotherapy treated breast cancer patients who have been followed up over several years. This gave an ideal opportunity to address whether there is any predictive power from differential genome-wide expression responses induced at low doses and whether inter-individual genetic differences are associated with the development of severe, normal tissue effects. The rationale was that responses to low doses may be hidden underneath any high dose responses as 'high' doses are used in radiotherapy.

The team of leading European clinical and basic scientists concentrated on non-irradiated skin biopsies or T-cells from breast cancer patients in the GENEPI databank by:
i) identifying two groups of patients (non- and over responders) who were statistically different in their normal tissue response to RT;
ii) taking into account national regulations, ethical guidelines and patient consent, non-irradiated skin biopsies and blood were collected and fibroblast and T-cells established and used to;
iii) undertake genotyping and functional analysis after low dose; and
iv) retrospective analyse for correlations between the genetic and functional findings to the severity of normal tissue responses.

Significant differences in gene expression were seen at high and low radiation doses with different gene sets being differentially expressed from the 108 clinical samples tested. From bioinformatic analysis of the gene profiles from lymphocytes and skin fibroblasts, several candidate biomarkers were identified. It was apparent though that a robust classifier(s) for radiosensitivity could not be established to identify those patients who showed adverse effects from the radiation (late effects). Functional analysis which focussed on the ability of the cells to repair DNA damage was also unable to distinguish between individual radiosensitivity. However, significant differences in gene expression were seen at high and low radiation doses with different gene sets being differentially expressed from the 108 clinical samples tested. This data has provided important information of relevance to environmental, diagnostic and occupational exposures.

In summary, a robust classifier for radiosensitivity to late effects of radiation could not be established due in part to unidentified confounding factors which may contribute to the radiosensitivity, in addition to any genetic contributions.

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