Welcome to PREDICT Consortium

Personalised RNA Interference to Enhance the Delivery of Individualised Cytotoxic and Targeted therapeutics

PREDICT will define the next generation of predictive biomarkers through the integration of clinical trial design with functional cancer genomics to enable personalised medicine and the cost-effective delivery of beneficial cancer therapeutics.

  • seventh-framework-programme
  • ocelot
  • sanger
  • predict-consortium-dna-strand
  • marsden
  • predict-consortium-lab
  • horizon


Publications: 12

Conference talks given: 35

Data generation progress

Clinical samples collected (March 2014):

- A-PREDICT = 20 (expected by 06/2014 ~32)

- E-PREDICT = 29 (expected by 06/2014 = suspended to recruitment)

- NEO-RAD = 22 (expected by 06/2014 = 30)

RCC cell lines engineered: 18 (11 of established cell lines + 7 new from RCC-patient derived primary cell lines).

Latest News and Events

  • Improved gene editing efficiencies using AAV- donors in┬ácombination with nuclease based approaches. read more »
  • Our next GA meeting in Copenhagen will start at 9.30 am on 16th of April. To download the final age read more »
  • Charles Swanton gives the Goulstonian Prize Lecture at Royal College of Physicians on Renal Cancer E read more »

Software devlopment progress




predict-consortium-technician PREDICT is utilising whole genome RNA interference screens to identify which genes encoded by the cancer genome influence response to common cytotoxic and targeted therapeutics used in clinical practice.
predict-consortium-microscope PREDICT has initiated a series of single drug clinical trials in the pre-operative setting enabling tumour genomics analysis before and after treatment. The consortium is analysing clinical trial tumour tissue using whole genome sequencing and gene expression approaches in order to identify how tumour somatic mutations and gene expression changes evolve through therapy.
predict-consortium-technicians PREDICT is employing technological developments to develop new cancer models for in vivo and in vitro therapeutic assessments and utilising novel bioinformatics approaches to develop platforms for biomarker discovery.