The overarching aim of our research programme is to understand the correlation between transcriptional regulation of nuclear encoded mitochondrial genes, cancer cell bioenergetics and genome scale metabolic networks and tumour biology, progression, architecture and chemosensitivity. We use (i) unbiased approaches, including bioinformatic analysis of genome sequencing, modifications and gene expression data as well as (ii) high content functional imaging of metabolic function on cellular and patient derived organotypic cultures and xenograft mouse models, (iii) quantify metabolic fluxes using isotope labelled metabolites and mass spectrometry. Overall, we aim (i) to establish gene expression based biomarkers for metabolic stratification of tumours to predict tumour progression and ultimately chemosensitivity, and (ii) to identify therapeutic targets in metabolic pathways to develop novel treatment strategies.
The PhD studentship will address the following project:
Identification and functional characterisation of metabolic subtypes of breast cancer.
Here we will validate a novel, predictive biomarker tool based on mitochondrial gene expression (mGEP), which reveals fundamental biology of tumours and thus can more effectively predict pathology and clinical outcome. The algorithm effectively predicts the metabolic phenotype of breast tumours, which in turn informs on their chemosensitivity. The identified clusters of metabolic pathways are characteristic of specific tumour types, which can lead to the identification of novel metabolic pharmacological targets, occurring in specific subgroups of tumours. We have tested and functionally verified the prediction tool in cellular models. Accordingly, the principal objective of the project is to perform validation on human tumours. In addition to the aim to stratify breast cancers using mGEPs as biomarkers, we also plan to identify molecular mechanisms underlying the relationship between mitochondrial activity and nutrient requirements of tumours and develop novel mitochondria/metabolism targeting therapeutic protocols for the treatment of these tumours.
a. Classify human breast cancer samples according to their mitochondrial biogenesis patterns driven by specific transcription factors/nuclear receptors and co-regulators.
b. Verify the functional mitochondrial and metabolic phenotype in in vivo human tumour xenograft models and ex vivo organotypic cultures using the biochemical and functional imaging platform (UCL) and metabolomics (Crick) facilities to identify possible metabolic targets.
c. Identify the functional relationship between mitochondrial and metabolic phenotypes using cellular and in vivo models.
d. Identify the correlation between mGEP patterns and chemosensitivity using cellular and in vivo models.
The partner institution for this project is UCL.
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