Transcriptome biomarkers and mechanisms of chemoresistance


   Institute of Dentistry

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Project Summary

This project aims to identify biomarkers linked to the most potent multimodal anticancer therapy with the least toxicity to counteract hemoresistance in head and neck squamous cell carcinoma (HNSCC). Multidrug resistance renders chemotherapeutic treatment failure in large proportion of HNSCC patients requiring multimodal therapy involving chemotherapy in conjunction with surgery and/or radiotherapy. Molecular events conferring chemoresistance remain complex. This project investigates transcriptome vulnerabilities and mechanisms of hemoresistance (1) using 3D tumour-spheroid culture model system in combination with RNAseq transcriptomics (2), high-throughput gene-expression (3) and drug-library screens to identify novel drug-gene interactions and repurpose existing drugs to enable tailored therapy based on individual tumour molecular profile to achieve the best clinical outcome for HNSCC patients. Findings from this project could potentially be transferable to other cancer types. The specific project will be tailored in line with the candidate's experience and interests.

Training Facilities & Environment

The project will involve interdisciplinary approaches involving cell and molecular techniques for mechanistic studies, gene expression analysis, drug-library screening for drug discovery, bioinformatics meta-analysis of transcriptome data, exploration of using mathematical model and artificial intelligence (AI) for pattern recognition and biomarker & drug discovery. This project will benefit from the stateof-the-art facilities of the Blizard Institute which support cutting-edge multidisciplinary research (https://www.qmul.ac.uk/blizard/). In addition, resources and help with developing AI models could be obtained from QMUL’s newly created Digital Environment Research Institute (DERI) (https://www.qmul.ac.uk/deri/) for digital data science and AI research with links to the Alan Turing Institute, the University Enterprise Zone and East London’s Tech City, a vibrant cluster of hightech companies.

Admission Requirements  

A graduate with at least an equivalent of an upper second-class BSc degree or a merit/distinction in an experimental life science MSc degree is required for this PhD project. The candidate should have strong interests and preferably with some experience in cellular and molecular biology, have some basic concept or background in coding or AI machine learning, pharmacology and bioinformatics. The candidate should have excellent manual dexterity for careful liquid handling and meticulous in experimental details and data recording. 

If English is not your first language, the standard requirement for English is an IELTS score of 6.5 overall for non-clinical projects and 7 overall for clinical projects (or equivalent). More details about language requirements can be found here

For more information on the project, please contact Dr Muy Tek Teh ()

For information on the application process, please contact  


Biological Sciences (4) Computer Science (8) Medicine (26)

Funding Notes

We will consider applications from prospective students with a source of funding to cover tuition fees and bench fees for three years full-time or 6 years part-time. Both self-funded and sponsored students will be considered.
UK nationals, Irish citizens and those with settled status under the EU Settlement Scheme or indefinite leave to remain in the UK might be eligible for a doctoral loan for both the cost of tuition fees and a yearly stipend over the course of the PhD programme from Student Finance England: View Website

References

1. Usman, S. et al., Major Molecular Signaling Pathways in Oral Cancer Associated With Therapeutic Resistance. Frontiers in Oral Health 1, (2021).
2. Qadir, F. et al., Transcriptome reprogramming by cancer exosomes: identification of novel molecular targets in matrix and immune modulation. Mol Cancer 17, 97 (2018).
3. Teh, M.T. et al., Molecular Signatures of Tumour and Its Microenvironment for Precise Quantitative Diagnosis of Oral Squamous Cell Carcinoma: An International Multi-Cohort Diagnostic Validation Study. Cancers (Basel) 14, (2022).

Register your interest for this project


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