Back to search results

PhD Studentship: Analysis of multi-omics data to rank anti-cancer drugs based on their predicted efficacy in individual patients

Queen Mary University of London

Qualification Type: PhD
Location: London
Funding for: UK Students
Funding amount: £21,000 stipend, home fees and a bench fee
Hours: Full Time
Placed On: 18th February 2019
Closes: 18th May 2019

We are now accepting applications to a KCH Charity funded studentship to start in September 2019.

Project Title: Analysis of multi-omics data to rank anti-cancer drugs based on their predicted efficacy in individual patients

A graduate with an interest in computational biology, with or is expecting at least an upper second class honours degree in biochemistry, genetics or related subject with a bioinformatics component, is required for this project involving analysis of multiomic data. The project will commence in October 2019 and has funding for 3 years. The student will be based primarily at the Barts Cancer Institute, Barts and the London School of Medicine and Dentistry (SMD), Charterhouse Square in the City of London.

Project Outline:

Intense research in cancer biology has resulted in the development of a large array of new targeted drugs to treat different forms of cancer. A problem for their use in the clinic is that most treatments based on these drugs are only effective in small patient subpopulations. To address this problem, genomic determinants of drug sensitivity have been investigated and some of them are routinely evaluated to select patients for treatment, but genomic alterations are not always accurate in predicting responses, suggesting that other molecular markers are required for accurate rationalisation of drug responses and for patient stratification.

This project will investigate machine learning algorithms that use big biochemical data to predict the efficacy of anti-cancer drugs in model systems. These computational tools have the potential to be an important step for the development of companion diagnostic tests based on artificial intelligence. Ultimately, algorithms that use an optimised combination of genomic and proteomic markers are expected to allow the personalization of treatments with greater accuracy than currently possible. Our previous work on this field used leukaemia and other cancer models to map kinase networks, identify markers of drug sensitivity and to develop predictive algorithms of drug response.

The student will receive training in cancer biology and biochemistry, state-of-the-art mass spectrometry for proteomics and phosphoproteomics and in computational biology methods for the analysis of multi-layered data. 


  1. Casado, P. et al. Leukemia 32, 1818-1822 (2018).
  2. Wilkes, E.H., et al. Molecular & cellular proteomics: MCP 16, 1694-1704 (2017).
  3. Wilkes, E.H., et al. PNAS 112, 7719-7724 (2015).
  4. Terfve, C.D., et al. Nat Commun 6, 8033 (2015).
  5. Casado, P. et al.. Science signaling 6, rs6 (2013).
  6. Alcolea, M.P., et al. Molecular & cellular proteomics: MCP 11, 453-466 (2012).

For an informal discussion, please contact the lead project supervisor: Pedro R. Cutillas; Email: ; telephone:0207 882 8266

How to apply:

Please make an online application for this project at

Candidate requirements:  An upper second-class honours degree (or international equivalent) in in biochemistry, genetics or related subject with a bioinformatics component.

Funding: Studentship covers a stipend at £21,000, home fees and a bench fee.

We value your feedback on the quality of our adverts. If you have a comment to make about the overall quality of this advert, or its categorisation then please send us your feedback
Advert information

Type / Role:

Subject Area(s):


PhD tools
More PhDs like this
Join in and follow us

Browser Upgrade Recommended has been optimised for the latest browsers.

For the best user experience, we recommend viewing on one of the following:

Google Chrome Firefox Microsoft Edge