Kidney cancer cells are hijacking a normal oxygen-sensing system to fuel their own growth, and this project will map exactly how that happens in hundreds of patient tumours. The problem is that kidney cancer is notoriously unpredictable. Doctors cannot reliably tell which patients will have aggressive disease, and existing drugs work only for some. The underlying biology is complex: a single genetic switch—the loss of the VHL tumour suppressor—activates a protein called HIF, which in turn triggers thousands of molecular changes that can either promote or suppress cancer. Current prognostic tools ignore this complexity. The researcher will combine genome-wide mapping of HIF binding sites with patient DNA and tumour samples to identify which components of the HIF response are truly driving cancer progression versus restraining it. This will produce new algorithms that integrate both pro- and anti-tumour signals to stratify patients more accurately. If successful, this could give clinicians a practical test to predict which kidney cancer patients need aggressive treatment and which can be spared it. It may also reveal new drug targets within the HIF pathway and help optimise the use of existing therapies for patients most likely to benefit.
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Background: Renal cell carcinoma (RCC) is the seventh commonest cancer in Europe and its incidence is rising. Clinical prognosis is notoriously difficult to predict and although new medical treatments (rapamycin analogues and multi-kinase inhibitors) show some efficacy, in the absence of surgical excision the prognosis of most patients remains very poor. There is an urgent need for better stratification and new treatment strategies. Most RCCs (both sporadic and familial) manifest biallelic inactivation of the von Hippel Lindau tumour suppressor (pVHL), a ubiquitin E3 ligase that normally (in oxygenated cells) targets the transcription factor hypoxia-inducible factor (HIF) for degradation. Thus, in VHL-defective RCC, HIF is constitutively activated. New opportunities to study the HIF pathway at the pan-genomic level have revealed the enormous complexity of primary and secondary transcription cascades, and multiple interactions with non-coding RNA networks and protein translational controls that result in thousands of changes in the molecular profile of the cell. Although activation of HIF (particularly the HIF2 iso-form) promotes cancer progression, it has become clear that HIF pathway activation results in multiple pro- and anti-tumourigenic effects and that it is the balance of these activities that is important. In RCC, evolution to a more oncogenic HIF2-driven profile is observed, but even within HIF1 and HIF2 target repertoires there are multiple pro- and anti-tumour activities. It is argued that the 'co-selection' penalty of activating anti-tumourigenic components of large pathways imposes a general restraint on cancer evolution, the importance of which has been under-estimated in approaches to clinical stratification and therapy. Aims and experimental strategy: The aims are (i) to define the pro- and anti-tumourigenic components of HIF activation in RCC (ii) to define the mechanisms that modulate this balance during RCC progression (iii) to use this data to define new prognostic algorithms which integrate this data for patient stratification (iv) to define new treatment targets and optimise the use of existing treatments, based on this framework. The pan-genomic architecture of HIF binding and associated chromatin conformation will be defined in RCC cells and patient tumour samples, and interfaced with large-scale cancer genomics databases and parallel sequencing programmes to define germ-line and somatic signatures of selection and epigenetic mechanisms that affect key driver or restrictor components of the HIF pathway. This data will be used to derive and test new prognostic algorithms that focus on functionally important components of the HIF pathway, incorporate complex bi-directional changes in gene expression, and integrate expression and DNA sequence data. RCC-relevant cell based assays (including the use of patient material and genetic engineering of markers at key HIF target loci) will be derived. These cells and other assays (e.g. for HIF1/2 alpha expression ratios) will be used in focussed screens to identify targets and lead molecules that modify the HIF response to generate a less oncogenic profile, and to determine whether, in selected patients, existing agents have the potential to do this. Context: The NIHR chair would enable DRM to capitalise on, and link several major strengths in Oxford, including the University's recently commissioned Target Discovery Institute, Big Data Centre (due for completion Q4, 2016), the new £109M NHS Cancer Hospital and the regional referral centre for kidney cancer. It would also build on local strengths in hypoxia biology, including those of the applicant. Patient benefit: The work should be of direct benefit to patients with RCC where there is an urgent need to define new methods for prognostic stratification, develop new treatments and optimise existing treatments. It leverages enormous accruing cancer (RCC) genetic databases to develop a framework in which this type of information can be interpreted in terms relevant to the patient.
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