Dr A McMahon, Mr T Hambrock, Dr N Thacker
No more applications being accepted
Competition Funded PhD Project (European/UK Students Only)
About the Project
The Gleason grading is currently the most important tissue marker to aid decisions on treatment, metastases assessment and prognosis. A definite need however exist for new biomarkers that can offer much improved metastatic risk stratification and prediction of therapy response. Prostate cancer is unique because of its heterogeneous lipid metabolism pathways, providing fuel for growth, metastasis and progression.
New promising mass spectrometric techniques have been developed that can provide insights into lipid metabolic changes and therefore be of potential great value in prostate cancer, by aiding biomarker discovery that aid our decisions on treatment, therapy response and prognosis.
In this project, we will aim to assess the value of two techniques: desorption electrospray ionisation (DESI) and rapid evaporation ionisation mass-spectrometry (REIMS) in prostate cancer. These techniques have been shown in a number of studies to be able to derive clinically meaningful lipid spectral maps that can provide valuable information on tumour biology and biomarker development. Doria(1) has shown the great potential of using DESI derived lipid signals to characterise ovarian cancer. Similarly Eberlin(2) used DESI to classify different brain tumours with high accuracy. REIMS has been shown to rapidly identify dissected tissues by determination of tissue structural lipid profiles in breast cancers (3).
Our project will first optimize and validate these techniques on different prostate cancer cell lines. Thereafter we will perform a state-of-the-art pathway to obtain tissue for human analysis: perform multimodality MRI, MR in-bore targeted biopsies of the tumour regions, freeze tissue samples directly prior to routine histological assessment followed by DESI/REIMS interrogation, all one the same biopsy specimen. We will focus on optimizing the workflow to make tissue interrogation, both for routine diagnostic histopathological analysis as well as additional spectroscopic analysis possible. This will allow direct comparison of histological identified tissue subtypes with associated metabolic spectral information.
Advanced machine learning tools developed in-house (http://tina-vision.net/) will be employed to derive signatures/biomarkers that can not only differentiate reliably between tumour and benign tissue, but also classify different tumour grades.
Entry Requirements
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.
Funding Notes
This project is to be funded under the MRC Doctoral Training Partnership. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the MRC DTP website www.manchester.ac.uk/mrcdtpstudentships
As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
References
1. Dória ML et al. Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging. Sci Rep.2016;6(December):1–11.
2. Eberlin L et. al. Classifying human brain tumors by lipid imaging with mass spectrometry. Cancer Res.2012;72(3):645–54.
3. St John ER et al. Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: Towards an intelligent knife for breast cancer surgery. Breast Cancer Res.2017;19(1):1–14.
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