Periodic Reporting for period 1 - RADIOGENFF (RADIOGENOMICS: Finding Genetic Functional Variants Through Fine Mapping)
Reporting period: 2016-04-01 to 2018-03-31
In the European Union in 2003 approximately 17.8 million people have had a past diagnosis of cancer. Improvement in diagnosis and treatment leads to increased life-expectancy. Thus, the number of cancer survivors is expected to rise. As cancer increasingly becomes a chronic disease, patients’ quality-of-life (QoL) needs to be addressed. Half of all cancer patients (and ~60% of those treated with curative-intention) receive radiotherapy at some point. Radiotherapy schedules have been developed to maximize tumour-kill while minimizing surrounding normal tissue toxicity. However, approximately 5% of radiotherapy-treated patients suffer from severe long-term side-effects and yet more experience moderate toxicity, such as incontinence or chronic pain, which can have a marked effect on QoL. The identification of radio-sensitive patients would permit better-tailored radiation doses; thus, toxicity could be minimised in radiation-sensitive patients and, contrastingly, radiation-tolerant patients could be given increased tumour-doses, raising their probability of local recurrence-free survival.
The objectives of the proposed research, structured by working packages (WP), are:
WP1. For the Researcher to learn advanced genetic epidemiological and fine-mapping skills by participating in team-based analysis of large cohorts of genotyping data
WP2. Identify and validate additional novel genetic radiation toxicity genomic regions through analysis of specific radiotoxicity genetic variants
WP3. Apply high-level skills, gained in objective 1, to the fine scale mapping of radiation toxicity genomic regions with the aim of identifying the most likely causal variants for radiation toxicity
WP4. Carry out bioinformatic functional analysis of the top candidate variants to both narrow the candidate list further, and gain understanding of the molecular and genetic mechanisms that mediate the development of radiation-induced toxicity
The objectives of the proposed research, structured by working packages (WP), are:
WP1. For the Researcher to learn advanced genetic epidemiological and fine-mapping skills by participating in team-based analysis of large cohorts of genotyping data
WP2. Identify and validate additional novel genetic radiation toxicity genomic regions through analysis of specific radiotoxicity genetic variants
WP3. Apply high-level skills, gained in objective 1, to the fine scale mapping of radiation toxicity genomic regions with the aim of identifying the most likely causal variants for radiation toxicity
WP4. Carry out bioinformatic functional analysis of the top candidate variants to both narrow the candidate list further, and gain understanding of the molecular and genetic mechanisms that mediate the development of radiation-induced toxicity
As part of WP1, we have developed a fine scale mapping methodology that allows us to analyse subtype specific (Estrogen receptor positive and negative) breast cancer risk regions. This led to the identification of more than 360 independent breast cancer risk signals. We also determined whether the credible set of candidate causal variants at these signals significantly overlapped with (i) histone marks associated with active or repressed regulatory regions at breast cells; (ii) regions of the DNA that directly interact with the proteins responsible of regulating gene expression (transcription factors binding sites, TFBS); and (iii) variants associated with gene expression in breast tisues (eQTL), specifically relevant in breast tissue. Our results suggest significant overlap of credible causal variants with active gene regulatory elements and binding sites for certain TFs in a subtype specific manner in breast cancer. We presented our results at the European Society of Human Genetics (ESHG 2017) and the American Association of Human Genetics (ASHG 2017) conferences.
Combining genotyping data from different studies, we have identified three new regions associated with radiation-induced toxicity in prostate cancer patients (WP2). These results will be presented at the American Society for Radiation Oncology annual meeting (ASTRO 2018).
We used the methodology developed as part of WP1 and the data from WP2 to identify independent risk signals at the radiation-induced regions (WP3). Thus, four independent signals were identified, together with their set of credible candidate causal variants. As part of WP4, we systematically annotated and predicted the effect of these variants according to their genomic location (within coding or non-coding regions). The results from WP3 and WP4 will be presented at the annual Radiogenomics Consortium meeting (RGC 2018).
Three manuscripts with these results are under preparation.
Combining genotyping data from different studies, we have identified three new regions associated with radiation-induced toxicity in prostate cancer patients (WP2). These results will be presented at the American Society for Radiation Oncology annual meeting (ASTRO 2018).
We used the methodology developed as part of WP1 and the data from WP2 to identify independent risk signals at the radiation-induced regions (WP3). Thus, four independent signals were identified, together with their set of credible candidate causal variants. As part of WP4, we systematically annotated and predicted the effect of these variants according to their genomic location (within coding or non-coding regions). The results from WP3 and WP4 will be presented at the annual Radiogenomics Consortium meeting (RGC 2018).
Three manuscripts with these results are under preparation.
The methodology designed in WP1 and WP2 will be applied to the risk genomic regions identified as part of EU funded project REQUITE (EU FP7 Health-2013-601826) for cancers other than the prostate cohort (lung, breast). In addition, the variants identified through this project will be integrated into a prediction risk model for prostate cancer radiation induced toxicity and implemented in the clinical interventional trial design that will be generated as part of REQUITE.