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Combining CRISPR and RNAseq to dissect molecular circuits in a hypoxic tumour microenvironment

Project Description

The role of the microenvironment in driving tumour progression is increasingly recognized. Hypoxia is one of the key microenvironmental changes between tumour and normal tissue, and adaptation of cancer cells to this hostile environment contributes to their malignancy and aggressive phenotype. Such adaptation is governed by many factors, including metabolic reprogramming of tumour cells (1).

We have developed integrated approaches to identify transcriptional programs activated in response to hypoxia (2-4). We will use next generation sequencing (NGS) and CRISPR high-throughput methodologies to produce a comprehensive map of such response. We will interrogate not only protein-coding transcripts and micro-RNAs, which we have previously characterized (3-4), but also uncharacterized non-coding regions of the human genome (see e.g. 5).

This will elucidate for the first time the genome-wide networks underlying transcriptional response to hypoxia in different cancer types. Gene regulatory networks (GRNs) are complex and still uncharacterized sets of regulators and interactions that govern cellular processes. We have used co-expression networks to derive a hypoxia signature (3, 4), which is now being translated to the clinic as biomarker (5). Merging powerful computational techniques and GRNs, we have recently developed a computational framework to predict single- and multi-cellular behaviour in a heterogeneous microenvironment (6). Here we will combine this methodology with RNAseq and CRISPR technology to develop a comprehensive GRN model of HIF1 signaling. This will enable the discovery of new HIF1 targets and generation of testable biological hypotheses on their biology, which we will validate in the lab. It will also highlight candidate therapeutic targets, and inform the design of future biomarker studies.

Importantly, we expect the results produced in this project will have broader methodological impact on the development of integrative genomic approaches.

All the techniques mentioned are established in the Buffa, D’Angiolella and Harris labs. There is space for focusing this project more on the wet-lab or computational aspects, depending on the candidate background and preferences. In either cases, the candidate will benefit from a highly multidisciplinary environment. They will work closely with members of Prof Buffa’s CRUK Functional Genomics and ERC Tumour Microenvironment Modelling labs. They will benefit from supervision from Prof Buffa, Dr D’Angiolella and Prof Harris, on computational biology, bioinformatics, RNAseq, CRISPR and tumour biology; and collaboration with Prof Saez-Rodriguez on computational methods to reconstruct gene networks. Their work will be supervised daily by experienced laboratory and computational scientists.

Hayder et al (2016) Genome Biology 17:140

Masiero et al (2013) Cancer Cell, 24:229-41

Buffa et al (2011) Cancer Research, 71:5635-45

Funding Notes

All complete applications received by 12 noon (UK time) on Friday 11 January 2020 will automatically be considered for all relevant competitive University and funding opportunities, including the Clarendon Fund, Medical Research Council funding, and various College funds. Please refer to the Funding and Costs webpage (View Website) for this course for further details relating to funded scholarships and divisional funding opportunities.

Funded studentships are highly competitive and are awarded to the highest ranked applicant(s) based on the advertised entry requirements for each programme of study.


Whilst you must register three referees, the department may start the assessment of your application if two of the three references are submitted by the course deadline and your application is otherwise complete. Please note that you may still be required to ensure your third referee supplies a reference for consideration.

Academic references are strongly encouraged, though you may use up to one professional reference provided that it is relevant to the course.

How good is research at University of Oxford in Clinical Medicine?

FTE Category A staff submitted: 238.51

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