Developing a Bioinformatics Big Data approach to Identifying candidate drugs for triple negative breast cancer

Applications are invited for a DfE funded PhD studentship tenable in the Faculty of Life and Health Sciences at the Magee/C-TRIC Campus. Please note that a faculty reorganisation is underway at Ulster and these studentships will be based within the new structure in the Faculty of Life and Health Sciences.

Project Summary:

Developing a Bioinformatics Big Data approach to Identifying candidate drugs for triple negative breast cancer

Background to the project :

Connectivity mapping (Lamb et al 2006) is an advanced bioinformatics technique that establishes connections among different biological states via their gene expression profiles/signatures. An important application of connectivity mapping is the identification of small molecule compounds capable of inhibiting a disease state. Our research team has undertaken pioneering research in this area by developing a robust framework of connectivity mapping and releasing tested software for high throughput connectivity mapping tasks (Zhang & Gant 2008, 2009; McArt & Zhang2011; McArt et al 2013; Wen et al 2015, 2016). Connectivity mapping has been used to successfully identify medications with anti-cancer properties. Recently, our research team has used the connectivity map approach to predict and subsequently validate, in a mouse model, entinostat as a potential inhibitor of acute myeloid leukaemia (AML) (Ramsey et al 2013), and similarly with promising applications in other disease areas (Malcomson et al 2016, PNAS; Wen et al 2017).

This project aims to develop an integrated Bioinformatics and Big Data approach to the identification and subsequent validation of candidate drugs with potential anti-cancer properties. Triple negative breast cancer will be the primary disease to test out this novel approach. Once developed, the process can be similarly applied to other diseases.

Objectives of the research project:

The objectives of the research project are:
1. To use multi-omics data and clinical data available in the public data repository, TCGA, GEO, and ArrayExpress, to identify gene signatures that are characteristic of breast cancer disease state and also prognostic of breast cancer patient survival.
2. To process and analyse the drug-induced gene expression data to construct reference gene expression profiles for FDA-approved drugs and experimental compounds available through the LINCS database.
3. To establish the connections between the disease/prognostic gene signatures generated in Aim 1 and the reference drug profiles in Aim 2, to identify candidate compounds, particularly FDA approved drugs, which have potential desirable effects associated with specific gene signatures and therefore be able to inhibit the disease conditions and improve patient survival.
4. To validate the predicted effects of candidate drugs on model systems of the disease.

This is an interdisciplinary bioinformatics and BigData research project; it requires an individual with good computational skills and statistical knowledge. A good understanding of basic biological processes and some experience with basic biological lab experiments would be desirable, but training on these aspects will be provided during the course of PhD study.


Entrance Requirements:

Candidates should have ordinary UK residence to be eligible for both fees and maintenance. Non UK residents who hold ordinary EU residence may also apply but if successful will receive fees only. All applicants should hold a first or upper second class honours degree in computer science, bioinformatics, computational biology engineering, physical sciences, mathematics, biomedical sciences, medicine or a cognate area. Applications will be considered on a competitive basis with regard to the candidate’s qualifications, skills experience and interests. Successful candidates will enrol as of 1 January 2018, on a full-time programme of research studies leading to the award of the degree of Doctor of Philosophy.

The studentship will comprise fees together with an annual stipend of £14,553 and will be awarded for a period of up to three years subject to satisfactory progress.

If you wish to discuss your proposal or receive advice on this project please contact:-

Dr Shu-Dong Zhang, Northern Ireland Centre for Stratified Medicine, School of Biomedical Sciences, University of Ulster Email: [Email Address Removed]

Procedure

For more information on applying go to ulster.ac.uk/research
Apply online ulster.ac.uk/applyonline

The closing date for receipt of completed applications is 1st December 2017

Interviews will be held in Early Dec 2017