Harnessing the potential of zebrafish model system towards therapeutic programming of T lymphocytes in melanoma

Abstract

Malignant melanoma is one of the most aggressive types of malignancies in humans resulting in 50,000 deaths worldwide annually. Malignant melanoma is highly immunogenic and it is now well established that the host immune system can detect and kill melanoma. In this thesis, our work has focused on establishing zebrafish as a viable model system to study CD4+ T cell mediated immune responses against melanoma as well as an in-vivo model system compatible for testing multiple immunotherapy strategies. We have created BAC transgenic zebrafish with vibrantly labeled CD4+ cells allowing us to scrutinize the development and specialization of zebrafish CD4+ leukocytes in-vivo. We demonstrate the utility of this zebrafish resource for interrogating the complex behavior of immune cells at cellular resolution by the imaging of intimate contacts between CD4+ T cells and mononuclear phagocytes. We reveal that sub-specialization of CD4+ T cells in to TH1, TH2 and Treg cells is conserved in zebrafish and most importantly, as in mammals, we show that zebrafish CD4+ T cells will infiltrate melanoma tumours and obtain a phenotype consistent with a type 2 immune microenvironment. We have built a Tamoxifen inducible CreERT2 system, which can be used to specifically induce the expression of selected immune-regulatory molecules by the zebrafish melanocytes. We report that forced expression of IFNγ in the tumour microenvironment using this system has resulted in enhanced tumour regressing in our autochthonous zebrafish melanoma model. However, new tumour nodules were also observed to develop in the vicinity of regressing nodules which are unpigmented and potentially hypo-immunogenic. In parallel, we have observed that forced secretion of the ectopic domain of zebrafish PDL1 homologue suppresses tumour formation but again late escapee tumours were unpigmented. RNA sequencing of the resistant tumours (IFNγ or PDL1 expressing) could reveal unique but also shared signatures for progressing tumours, which can be used to predict immunotherapy responses in human patients.

Date of Award	1 Aug 2017
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Adam Hurlstone (Supervisor) & Werner Muller (Supervisor)