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MRC Precision Medicine DTP: Trascriptome-wide association analysis of survival in colorectal cancer patients

Project Description

Colorectal cancer (CRC) is the second most common cause of cancer death in UK with 15,900 CRC deaths in 2014. The risk of recurrence and death depends very much on the stage of disease at the time of diagnosis with 5-year survival rate >90% for stage I and <10% for stage IV. However, even individuals with the same surgical stage of disease may have substantial different prognosis and risk of recurrence. Hence, prediction at the individual patient level is very poor for survival outcome, likelihood of locoregional recurrence or metastatic spread, even within any given tumour stage. There is a pressing need for new approaches to guide clinical management and target treatment to those most likely to gain benefit. Precision medicine absolutely requires such predictive performance to be effective.

Recent analysis of all genes expressed (transcriptome) in tumours from 597 individuals with CRC using RNA sequencing (RNA-seq) approach has identified 593 genes associated with survival [3]. However, little is known about the association between the transcriptome of normal colonic tissue and cancer prognosis and survival. Published evidence does suggest that the transcriptome profile of normal tissue can contribute to prognosis prediction [4]. Recent progress in our understanding of gene expression and its relationship to genetics made it possible to predict and impute gene expression level into genome-wide data and perform transcriptome–wide association studies (TWAS) to measure the strength of association between gene expression and outcomes of interest.

Here we proposed to perform a TWAS of CRC survival in UK Biobank and Scottish Colorectal Cancer Study (SOCCS). The overarching aim is to generate data to develop and test new predictive algorithms for survival outcome, disease relapse patterns and potentially even with the ability to identify targets for individualised chemotherapy.

Aim 1: To develop genetic prediction models of transcriptome levels in normal colonic tissue. 406 normal colorectal mucosa samples with available expression (Illumina HT12, RNAseq) and whole-genome genotyping arrays have been collected previously. The student will be using this sample collection to build prediction model of whole-genome gene expression using range of machine learning technics and cross-validation to compare performance of predictions.

Aim 2: To investigate gene-expression-environment interactions that may influence transcriptome levels in normal colorectal mucosa tissue (n=406) with available genotype, expression and environmental data. RNA-seq and whole genome sequencing has been done on the tumours with matching normal mucosa samples (n=137). We will liaise expression in normal and tumour tissues and interrogate a molecular architecture of CRC in relation to the TWAS outputs.

Aim 3: To perform TWAS of all-cause and CRC-specific mortality of 6,000 incident CRC SOCCS cases and 3,306 incident and prevalent UK biobank CRC cases (taking into account the bias of underrepresentation of patients with aggressive and/or metastatic disease) with available genome-wide data. This analysis will include the following steps:
a.Imputation of transcriptome of normal colonic tissue, whole blood and other potentially relevant tissue (i.e. adipose tissue) using developed (aim 1) and publicly available prediction models.
b.TWAS of all-cause and CRC–specific mortality in SOCCS and UK biobank CRC cases adjusting for age, gender, stage of cancer and the tumour MSI status as well as other factors that can influence gene expression (i.e smoking, comorbidity or diet pattern).
c.Appraisal of the clinical usefulness of identified transcriptome profiles to discriminate high and low risk tumours within a given stage. We will focus on clinically important outcome measures - as isolated local recurrence, combined local and distant relapse, distant metastatic spread only.
d.Validation of predictive algorithms generated from the TWAS analysis in Cambridge CRC cohort (n=4830) as well as other external CRC cohorts within existing collaborations.
Aim 4 (optional): Functional validation of the identified loci. To explore the TWAS signal(s) and identified gene(s) in the available metastatic mouse models and potentially consider developing genetically modified mice for the identified genes in collaboration with Owen Sansom and Rene-Philip Jackstadt, Cancer Research UK Beatson Institute, Glasgow.

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location:

Please note, you must apply to one of the projects and you should contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.

For more information about Precision Medicine visit:

Funding Notes

Start: September 2019

Qualifications criteria: Applicants applying for a MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualifications, in an appropriate science/technology area.

Residence criteria: The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £14,777 (RCUK rate 2018/19) for UK and EU nationals that meet all required eligibility criteria.

Full eligibility details are available: View Website

Enquiries regarding programme:


1. Cancer Research UK, full URL of the page, Accessed September 2017
2. Burdett T (EBI), Hall PN (NHGRI), Hastings E (EBI), Hindorff LA (NHGRI), Junkins HA (NHGRI), Klemm AK (NHGRI), MacArthur J (EBI), Manolio TA (NHGRI), Morales J (EBI), Parkinson H (EBI) and Welter D (EBI).The NHGRI-EBI Catalog of published genome-wide association studies.
Available at: Accessed 12 September 2017, version v1.0.
3. Uhlen, M., C. Zhang, S. Lee, E. Sjostedt, L. Fagerberg, G. Bidkhori, R. Benfeitas, M. Arif, Z. Liu, F. Edfors, K. Sanli, K. von Feilitzen, P. Oksvold, E. Lundberg, S. Hober, P. Nilsson, J. Mattsson, J. M. Schwenk, H. Brunnstrom, B. Glimelius, T. Sjoblom, P. H. Edqvist, D. Djureinovic, P. Micke, C. Lindskog, A. Mardinoglu and F. Ponten (2017). "A pathology atlas of the human cancer transcriptome." Science 357(6352)
4. Huang, X., D. F. Stern and H. Zhao (2016). "Transcriptional Profiles from Paired Normal Samples Offer Complementary Information on Cancer Patient Survival - Evidence from TCGA Pan-Cancer Data." Sci Rep 6: 20567.

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