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Bioinformatics for Personalized and Precision Neoantigen Therapeutics in Cancer

  • Full or part time
    Prof T Hupp
  • Application Deadline
    Applications accepted all year round
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

Despite the success of immune checkpoint inhibitors in the clinic, most cancer patients do not respond, and even among responders therapeutic resistance is a problem. Among the various mechanisms of therapeutic resistance, immune-editing to impair the antigen presentation pathway is common. This is a general theme developing in cancer immunology, and The International Center For Cancer Vaccine Science (a joint entity between the University of Edinburgh and the University of Gdansk) is now recruiting computational students working on different aspects of this problem.

Project: Comprehensive characterisation of antigen presentation in cancer.

Introduction. The classic concept of self and non-self antigen discrimination by the immune system is focused on the recognition of neoantigen peptides presented by MHC molecules. Yet, there are other modes of antigen presentation. MHC-class-I-like CD1 antigen-presentation molecules (CD1a, CD1b, CD1c, CD1d and CD1e) allow the immune system to recognize lipid-containing antigens. These CD1 molecules bind and present amphipathic lipid antigens for recognition by T-cell receptors. The MR1 protein binds to molecules derived from bacterial riboflavin biosynthesis. Yet, even with a seemingly unrelated function, the TCGA Pan-cancer studies indicate MR1 as amplified in ~8% of Cholangiocarcinomas, Breast Invasive Carcinomas and Liver cancers (cbioportal; [5]). Further, two preliminary studies have implicated the endogenous presentations of MR1 ligand in tumour cells.

Project outline. We are searching for a computational student to address questions in systems immunology and cancer. In Stage 1, the student will learn the computational analysis of MHC class I/classII antigen presentation, but also develop computational pipelines in lipidomics and metabolocs for the mass-spectrometry based study of CD1 and MR1 presented antigens. In Stage 2, the student will develop a database cataloguing the existing literature and datasets around lipid and metabolite antigen presentation. In stage 3, we will work with Dr. Irena Dapic and a team of biophysicists on new mass-spectrometry technologies and support applications towards the characterization of MHC Class I/II, CD1 and MR1 presented antigens.

Resources: Students will have an international supervisory team of professors and industry experts. This position offers the opportunity to be jointly supervised between Dr. Ted Hupp (University of Edinburgh), Dr. Robin Fahraeus (Universite Paris) and Dr. Javier Alfaro (University of Gdansk). Students will work closely and validate their results alongside collaborators at the three Universities, which are equipped with state of the art facilities in mass spectrometry, virology, protein biochemistry and vaccine technology. Students will also have the opportunity to develop skills in machine learning and high performance computing. The project has access to Cyfronet Prometheus (~55, 000 cores) and CI TASK Tryton (~38, 000 cores) clusters, which are consistently represented among the top 500 supercomputers in the world. As the work is international in nature, students will have the opportunity to travel between Edinburgh and Gdansk at various points during their PhD.

Please note, you are encouraged to contact Dr. Ted Hupp () and Dr. Javier Alfaro () before submitting an application.

Funding Notes

For this role, we seek a computer scientist willing to become fluent in the terminology of genetics, genomics, proteomics and immunology. Alternatively, we seek a highly motivated molecular biologist eager to pick up skills in data analysis, including statistics, programming and high performance computing.

Desired skills:
• Previous experience in Genomics, Transcriptomics or Proteomics
• Previous project in bioinformatics
• Experience in R
• Experience in a scripting language (Perl or Python)

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