Dimitrios Anastasiou & Saeed Shoaie


Combined computational and mouse models to identify novel therapeutic targets for liver cancer

Joint Crick/King's College London position

Hepatocellular carcinoma (HCC) is one of the deadliest human cancers with no available cure. HCC is strongly linked to liver disease, the incidence of which has been increasing exponentially [1], so the need for novel HCC therapies is urgent. We have recently found that liver tumours interact metabolically with the surrounding tissue in a previously unappreciated bidirectional manner that fuels biosynthetic processes predicted to be important for tumour growth. This project aims to computationally identify and experimentally validate novel therapeutic targets that mediate the metabolic communication of HCC with its environment by combining high-throughput measurements in mouse cancer models acquired in the Anastasiou lab with computational methods and in-silico models developed in the Shoaie lab.

A major interest in the Anastasiou lab is to understand the metabolic requirements for HCC development. Using stable isotope tracers and metabolic flux analysis in mouse models of HCC, the lab has shown that the host liver undergoes a metabolic reprogramming in order to provide nutrients specifically required for tumour growth. These findings raise the possibility that interfering with this metabolic communication could inhibit tumour development. A key approach of this project is the use of genome-scale models (GEMs) reconstruction [2] to understand the mechanisms underlying the metabolic co-dependence between tumour and host tissue, identify the proteins that mediate these mechanisms and validate their disruption as a therapeutic approach for HCC.

An important feature of GEMs is that they incorporate information at the gene, protein and metabolite level, allowing an efficient exploration of cellular metabolism in a holistic manner. GEMs can therefore integrate with various other biological networks and are a powerful tool to combine and model diverse high-throughput experimental datasets. The Shoaie lab has extensive expertise in the reconstruction of GEMs at all scales (from cells to entire organisms), which they use, along with multi-level optimisation algorithms they develop in-house, to elucidate metabolic interactions between cells, tissues and microbes. They have successfully applied these approaches to understand the genotype-phenotype relationship in metabolic disorders, reveal underlying molecular mechanisms, and identify novel therapeutic targets [3-5].

In this joint project, we will use new proteomics and transcriptomics data combined with existing in vivo metabolic data from the mouse HCC models to construct and validate GEMs for tumours and the surrounding tissue and, reciprocally, we will use these refined GEMs to uncover novel ways to disrupt HCC development. Although there is ample evidence for the importance of cell autonomous roles of metabolism in cancer, very little is known about tumour-host metabolic interactions; so in addition to their potential clinical impact, the outcomes of this work will also provide fundamental insights into how tumours interact metabolically with their surroundings. Importantly, at the end of this project the student will have a solid training in both GEMs reconstruction, an emerging cutting-edge computational approach, and their experimental application to understand how metabolism supports tumorigenesis, which is an area of great therapeutic interest and research activity.

1. The Lancet Commission. (2014). "UK liver disease crisis." from http://www.thelancet.com/pb/assets/raw/Lancet/stories/commissions/lancet-liver-disease-infographic.pdf.

2. Anastasiou, D. (2017)
Tumour microenvironment factors shaping the cancer metabolism landscape.
British Journal of Cancer 116: 277-286. PubMed abstract

3. Nilsson, A. and Nielsen, J. (2016)
Genome scale metabolic modeling of cancer.
Metabolic Engineering: Epub ahead of print. PubMed abstract

4. Shoaie, S., Ghaffari, P., Kovatcheva-Datchary, P., Mardinoglu, A., Sen, P., Pujos-Guillot, E., de Wouters, T., Juste, C., Rizkalla, S., Chilloux, J., Hoyles, L., Nicholson, J. K., MICRO-Obes Consortium, Dore, J., Dumas, M. E., Clement, K., Bäckhed, F. and Nielsen, J. (2015)
Quantifying diet-induced metabolic changes of the human gut microbiome.
Cell Metabolism 22: 320-331. PubMed abstract

5. Ghaffari, P., Mardinoglu, A., Asplund, A., Shoaie, S., Kampf, C., Uhlen, M. and Nielsen, J. (2015)
Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling.
Scientific Reports 5: 8183. PubMed abstract