£6,000 FindAPhD Scholarship | APPLICATIONS CLOSING SOON! £6,000 FindAPhD Scholarship | APPLICATIONS CLOSING SOON!

SFI Centre for Research Training in Genomic Data Science: Multi-Omic prediction of treatment induced vulnerabilities to develop more effective treatments for Colorectal Cancer


   School of Medicine, Dentistry & Biomedical Sciences

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Dr S McDade, Prof D Longley, Dr Colm J. Ryan  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

About the Project

Start date: 1 September 2022

Large scale functional genomics screens using genome-wide CRISPR screens have enabled the identification of cancer-specific vulnerabilities or “synthetic lethalities” (SLs) at an unprecedented scale. These SLs are the starting points for potentially highly effective novel treatment strategies. To date, the majority of these analyses have focussed on identifying SLs linked to genomic events (primarily mutations); however, recent data from our groups at QUB and UCD indicate that gene and/or protein expression analyses provide an alternative, improved route to the identification of context-specific vulnerabilities (Lord, Quinn & Ryan 2020, Wappett et al. 2021). 

Despite significant progress in our understanding of genetic/molecular subgroups of Colorectal cancer (CRC), 5-Fluororuacil (5FU) based-chemotherapeutic regimens remain Standard-of-Care (SoC) for the vast majority of CRC patients. In order to leverage functional genomics approaches to improve treatment of colorectal cancer, our group is performing unique CRISPR-SoC Drug combination screens in human CRC cell lines, with matched gene-expression, epigenetic and proteomic profiling, which will enable genome-wide identification of SoC treatment-induced vulnerabilities.

Building upon our novel analysis methodologies, our expertise in CRC and these unique datasets, this project aims to:

1) Use existing software tools to identify basal and SoC-induced vulnerabilities in molecularly defined CRC sub-groups.

2) Develop novel methodologies and data analysis pipelines for identification of drug-induced effects on gene expression and identification of vulnerabilities this imposes on CRC cells

3) Identify clinically-tractable patient population and relevant models for pre-clinical validation of predictions.

4) Develop a Public “Shiny” interface for analysis and sharing of this data with the scientific/clinical community.

Embedding of the student within the colorectal cancer research cluster in QUB, through which the student will have access to unique expertise and extensive highly curated/annotated clinical datasets with Multi-Omic data (e.g. Allen Et al. 2018) as well as other pre-clinical genomics datasets in human and mouse models of CRC to achieve the project goals. The student will benefit from the supervisory team’s expertise in genomics and methods for analysing CRISPR screen data and their strong international collaborative networks; as well as complementary expertise in the analysis of transcriptional and epigenomic data (McDade QUB), machine, deep-learning (O’Broin NUIG) synthetic lethality and analysis of proteomic data (Ryan UCD), which will include a visits and/or periods of work at both the O’Broin and Ryan labs. 

Queen’s University Belfast is a partner of the SFI CRT in Genomics Data Science, which was formed in 2019. Over seven years of its operation, the Centre will train 100 PhD students in data analytical and computational skills that will help power the implementation of genomics on the island of Ireland. The Centre is built on a national cohort-based model of advanced training. At the outset of their training programme, each student cohort undertakes an intensive, semester-long series of training courses. Participation in the SFI CRT in Genomics Data Science includes a mandatory 3-month residential training programme for the students at NUI Galway, held in Semester 1 (start of September to mid-December) of Year 1 of the PhD programme. Through their experience of working and living together on this phase of the training programme, student cohorts form tight knit groups, enabling them to work collectively on the data science challenges they will encounter during their research. For more information on the SFI CRT in Genomics Data Science and the residential training programme see: https://genomicsdatascience.ie/

Students would benefit from having some experience with bioinformatics / computational biology tools and analyses, although training in these areas will be provided in the residential training programme at the start of the project.

Further funding eligibility: ROI (and EU applicants with pre-settled/settled) status may be eligible for funding if 3 year UK residency met.

ENTRY REQUIREMENTS

You must hold or expect to get an upper second class honours degree from a university in the UK or Ireland, or qualifications and experience considered by the University as equivalent to that standard. Candidates who already hold a doctoral degree, or who have registered on a PhD for one year (or part-time equivalent) or not eligible.

English Language

Candidates applying from countries where the first language is not English should produce evidence of their competence through a qualification such as IELTS or TOEFL score.

The minimum recommended score for the School of Medicine, Dentistry and Biomedical Science is:

·        IELTS score of 6.0 with not less than 5.5 in each of the four component elements of listening, reading, speaking and writing taken within the last 2 years;

·        TOEFL score of 80+ (internet basted test), taken within the last 2 years, with minimum component scores of; Listening 17, Reading 18, Speaking 20, Writing 17);

·        A valid Certificate of Proficiency in English grade A or B;

·        A valid Certificate of Advanced English grade A; or

·        A first or upper second class honours degree from a university based in the UK, Republic of Ireland or other suitably quality assured location in a country deemed by the UK Border Agency to be majority English speaking.

For a list of English Language qualifications also accepted by the School and University please see the following link:

http://www.qub.ac.uk/International/International-students/Applying/English-language-requirements/#English

INTO Queen’s English Language Courses offers both pre-sessional and in-sessional courses in English for academic purposes and study skills. Courses vary in length and full information can be obtained at: https://www.qub.ac.uk/International/International-students/Applying/University-Preparation-Courses/INTOEnglishlanguagecoursesatQueens/


Funding Notes

Funded by the Department for the Economy (DfE). For UK domiciled students the value of an award includes the cost of approved tuition fees and maintenance support (Fees £4,596, Stipend £16,062) for 2022/23. To be considered eligible you must have been ordinarily resident in the UK for the full 3 year period prior to the start of the studentship and you must be ordinarily resident in Northern Ireland on the first day of the start of the studentship. For further information about eligibility criteria please refer to the DfE Postgraduate Studentship Terms and Conditions 2021-22 at https://go.qub.ac.uk/dfeterms

References

Allen WL, Dunne PD, McDade S et al.…..Longley DB. Transcriptional subtyping and CD8 immunohistochemistry identifies poor prognosis stage II/III colorectal cancer patients who benefit from adjuvant chemotherapy. JCO Precis Oncol. 2018 Jun 13. PMID: 30088816
Lord CJ, Quinn N, Ryan CJ. Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions.
Elife. 2020 May 28. PMID: 32463358
Wappett M, Harris A, Lubbock ALR, Lobb I, McDade S*, Overton IM*. SynLeGG:analysis and visualization of multiomics data for discovery of cancer 'Achilles Heels' and gene function relationships. Nucleic Acids Res. 2021 May 17: PMID: 33997893. * Co-Corresponding authors
Please click on the 'Apply Now' button to apply.
When applying, please choose 'MEDICINE' as your subject area/School
Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

PhD saved successfully
View saved PhDs