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Repurposing Existing Drugs as Cancer Therapies

  • Full or part time
  • Application Deadline
    Saturday, November 30, 2019
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

The extraordinary genomic complexity of copy-number aberration in high grade serous ovarian carcinoma (HGSOC) has prevented effective molecular stratification in the clinic. We have identified copy-number (CN) signatures that can detect specific patterns of DNA damage response (DDR) from clinical biopsies [1]. These signatures now offer new predictive tests for therapy with PARP inhibitors such as olaparib. PARP inhibitors are currently the only FDA-approved class of DDR inhibiting cancer therapies and are significantly improving survival for women with HGSOC. However, resistance to PARP inhibitor therapy is a significant and growing clinical problem underscoring the need to identify new PARP inhibitor compounds with alternative mechanisms of action. This project will use functional genomic approaches to characterize novel PARP inhibitors identified by Cycle Pharma and the University of Cambridge including using CN signatures to develop biomarkers and mechanisms of action for these medicines. The PhD project will be based in the CRUK Cambridge Institute in the Brenton laboratory and will use experimental approaches based on well characterized HGSOC models including cancer organoids, primary patient samples and unique HGSOC cell lines. The main techniques will include whole genome sequencing analysis, high throughput RNA profiling with L1000 for therapeutic phenotyping [2] and high throughput microscopy for DDR biomarkers. The project has direct translational relevance as identifying new PARP inhibitors has major impact for women with high grade serous ovarian cancer. In addition, the strategy of identifying new activities from existing medicines that have already completed safety testing and entered clinical practice, will ensure rapid transition into therapeutic studies in cancer patients.

Preferred skills/knowledge
The project will require strong computational abilities (or enthusiastic potential) and would be ideal for candidates who have completed both a biology undergraduate degree and a MSc in bioinformatics. Experience in some of the areas mentioned in the project description would be beneficial. The successful candidate must be strongly motivated to drive an independent research project, but also be a highly collaborative individual.

Please click on ’Visit Website’ for details on how to apply. You must submit an application on-line to be considered for this studentship.

Funding Notes

This project is co-funded by Cancer Research UK and Cycle Pharma. The studentship includes full funding for University and College fees and a stipend of £19,000 per annum.

No nationality restrictions apply to this studentship. Applications are invited from recent graduates or final year undergraduates who hold or expect to gain a first/upper second class degree (or equivalent) in a relevant subject from any recognised university worldwide.

The study start date will be October 2020 (Michaelmas Term 2020).


[1] Macintyre G, Goranova TE, De Silva D, Ennis D, Piskorz AM, Eldridge M, Sie D, Lewsley LA, Hanif A, Wilson C, Dowson S, Glasspool RM, Lockley M, Brockbank E, Montes A, Walther A, Sundar S, Edmondson R, Hall GD, Clamp A, Gourley C, Hall M, Fotopoulou C, Gabra H, Paul J, Supernat A, Millan D, Hoyle A, Bryson G, Nourse C, Mincarelli L, Sanchez LN, Ylstra B, Jimenez-Linan M, Moore L, Hofmann O, Markowetz F, McNeish IA, Brenton JD. Copy number signatures and mutational processes in ovarian carcinoma. Nat Genet. 2018;50(9):1262-70.

[2] Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE, Lu X, Gould J, Davis JF, Tubelli AA, Asiedu JK, Lahr DL, Hirschman JE, Liu Z, Donahue M, Julian B, Khan M, Wadden D, Smith IC, Lam D, Liberzon A, Toder C, Bagul M, Orzechowski M, Enache OM, Piccioni F, Johnson SA, Lyons NJ, Berger AH, Shamji AF, Brooks AN, Vrcic A, Flynn C, Rosains J, Takeda DY, Hu R, Davison D, Lamb J, Ardlie K, Hogstrom L, Greenside P, Gray NS, Clemons PA, Silver S, Wu X, Zhao WN, Read-Button W, Wu X, Haggarty SJ, Ronco LV, Boehm JS, Schreiber SL, Doench JG, Bittker JA, Root DE, Wong B, Golub TR. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell. 2017;171(6):1437-52 e17.

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

FTE Category A staff submitted: 192.05

Research output data provided by the Research Excellence Framework (REF)

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