Birkbeck, University of London Featured PhD Programmes
Imperial College London Featured PhD Programmes
University of Kent Featured PhD Programmes
Engineering and Physical Sciences Research Council Featured PhD Programmes
The Hong Kong Polytechnic University Featured PhD Programmes

Precision Medicine: Using machine learning and classification methods to identify immune evasion signatures in high-risk malignancies

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

Click here to search for PhD studentship opportunities
  • Full or part time
    Dr C Proby
    Prof R Petty
  • Application Deadline
    Applications accepted all year round

Project Description

Squamous cell carcinomas across multiple sites (lung, head and neck, skin, oesophagus) represent the most frequent human malignancies and are a major cause of cancer mortality with limited therapeutic options for advanced disease. Immunotherapies such as immune checkpoint inhibitors (ICi) have changed the therapeutic landscape for many cancers, but the strong association between immunosuppression/immune evasion and poor SCC outcomes implies that early use of such agents will be essential necessitating robust identification of ‘high-risk’ SCC at diagnosis for stratification to receive adjuvant strategies. New technologies such as NanoString and Ion Torrent allow immune-based transcriptomes to be produced rapidly and reliably from formalin-fixed, paraffin-embedded (FFPE) pathology blocks. Pilot data from this group has shown great success in successfully stratifying cancer transcriptomes from cutaneous SCC using machine learning algorithms (decision tree building and linear discriminant analysis). We propose to develop, optimize and validate this approach in SCC from multiple anatomical sites using machine learning and classification to unravel ‘omics’ data and create precision medicine applicable to NHS delivery.

To apply please send a cover letter, curriculum vitae and two references to: [Email Address Removed]

Funding Notes

Please note this is a self-funded PhD project

Related Subjects

FindAPhD. Copyright 2005-2019
All rights reserved.