Pre-clinical exploration of cancer neoepitope immunotherapy

  • Jappe, Emma Christine (PhD Student)
  • Kringelum, Jens Vindahl (Supervisor)
  • Olsen, Lars Rønn (Main Supervisor)
  • Purcell, Anthony Wayne (Supervisor)
  • Marcatili, Paolo (Examiner)
  • Holberg Blicher, Thomas (Examiner)
  • Ternette, Nicola M. N. (Examiner)

Project Details

Description

The complex nature of cancer has complicated the development of an effective treatment immensely. This has resulted in an increased interest for immuno-oncology treatment strategies that focus on the deliberate use of the patient’s immune system to fight the cancer. Particularly interesting are personalised
vaccines based on tumour-specific neoepitopes, which are created by the unique set of mutations characteristic of the individual tumour. However, pre-clinical approaches require time-consuming screening steps to identify neoepitopes and current in silico (computational) tools for efficient identification of
neoepitopes are scarce and of limited accuracy. The overall objective of the project is to develop an advanced, pre-clinically validated in silico pipeline for
the identification of murine cancer neoepitopes. In parallel, the development of a human pipeline will enable the translation of pre-clinical findings to clinically relevant information for the construction of personalised human cancer vaccines. A range of features, which are hypothesised to be linked to neoepitope immunogenicity, are determined through the development of various computational tools using data mining and advanced machine learning methods, and subsequently incorporated into a complete neoepitope identification pipeline. The aim of this is to improve the predictive performance of
the pipeline enabling a link between mouse and human models to be established, which will ultimately allow for translation of project findings into human cancer vaccines.
StatusFinished
Effective start/end date01/01/201717/11/2020

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.