UCL Cancer Institute


PhD Studentship - Multi-modal clinical testing of prostate cancer patient plasma

Based within the the Treatment Resistance Team led by Prof Gerhardt Attard. The lab focuses on studying treatment resistance in advanced urological cancers.

Primary supervisor: Prof Gert Attard

Closing date: Applications are now closed (2 Dec 2019)

Funding: Candidates will need to qualify as UK/EU fee payers.


The Treatment Resistance Team led by Professor Gerhardt Attard focuses on studying treatment resistance in advanced urological cancers. The main focus of the team has been integrating genomics, transcriptomics and epigenomics of liquid biopsies with functional studies to track tumour clone dynamics in patients progressing through multiple lines of treatment. Some of the group’s recent work used plasma DNA to track androgen receptor (AR) gene aberrations in advanced prostate cancer patients and identified a strong association between plasma AR aberrations and resistance to second-line hormonal treatment. These data have led to the prospective evaluation of plasma DNA in clinical trials designed to improve the management of advanced prostate cancer patients. 

The group is now expanding this strategy for the discovery of novel drivers of resistance with the recently awarded CRUK £5 million Accelerator award using the PRIME (PRostate Cancer plasma integrative multi-modal) test. The successful candidate will join the Treatment Resistance Team at UCL Cancer Institute and will work together with collaborators at the Centre for Integrative Biology (CIBIO, University of Trento, Italy) developing new tools aimed to improve the analysis of Next Generation Sequencing (NGS) data from patient derived material and to identify tumour characteristics that allow the monitor of response to treatment in real-time.

Project description

We are seeking a highly motivated and talented applicant with a background in computational sciences, e.g. bioinformatics, biomathematics, biostatistics, computer sciences with biosciences interests, or similar. The project will focus on integrating genomic, epigenomic and transcriptomic data from plasma samples from clinically-relevant prostate cancer cohorts with carefully annotated clinical follow-up data collected in prospective randomized clinical trials. We aim to accelerate the implementation of discoveries into clinical practice in order to improve the outcomes of advanced cancer patients.

The successful candidate will work in a multidisciplinary team and will be involved in the analysis and interpretation of next generation sequencing data, both to identify genomic aberrations (e.g. single nucleotide variants, indels, copy numbers, etc) as well as transcriptomic and methylation changes.  Specifically the computational PhD student will be responsible for developing dedicated pipelines to evaluate aspects of methylation related to genomic and transcriptomic features from prostate cancer patients’ plasma samples. The applicant should have demonstrable experience in programming and scripting skills (e.g. R, Python, Perl, etc). Experience of working in a Linux/UNIX environment and scripting is desirable. Excellent organisational and communication skills are essential. Understanding of cancer biology would be advantageous. Experience with next generation sequencing data tools (e.g. samtools, picard, etc) is desirable but not essential. 

Further information about the research project is available on request from Dr Paolo Cremaschi p.cremaschi@ucl.ac.uk

Duties and responsibilities
  • Contribute in all phases of the project lifecycle, from study design to publication.
  • Work with the computational team perform the analysis and interpretation of next generation sequencing data generated in-house and/or publicly available.
  • Work with the computational team to identify and apply the most suitable computational methods to be used in the research project. 
  • Evaluate available software and develop new bioinformatics tools if required.
  • Keep abreast of relevant literature and methodology developments relevant to the field.
  • Communicate with the Group Leader and other members of the computational team on a regular  basis  to  discuss  projects updates.
  • Attend appropriate scientific seminars, meetings, conferences and relevant training opportunities.
  • Participate in the activities of the group, contributing scientific ideas and innovative solutions.
Person specification


  • Degree in computational science (or another related or quantitative discipline)
  • Excellent programming and scripting skills (e.g. R, Python, Perl, etc)
  • Excellent communication skills, both written and oral, and the ability to interact and communicate with colleagues with a wide variety of backgrounds 
  • Excellent organisational skills, attention to detail and ability to troubleshoot problem
  • Excellent data visualisation skills
  • Ability to work independently and as part of a team
  • Independent scientific thinking and eagerness to learn new technologies and science relevant to the project
  • Highly committed and dedicated to the research topic with a strong interest in actively developing and shaping an innovative project


  • Experience of working in a Linux/UNIX environment and scripting
  • Experience in the analysis of next-generation sequencing data 
  • Experience in cancer genetics and biology
  • Experience with commonly used public genomics datasets (eg COSMIC, TGCA, 1000 Genomes, ENCODE)
  • Demonstrable mathematical and biostatistical skills
Research environment

The UCL Cancer Institute is a state-of-the-art institute to consolidate cancer research at UCL and promote links with our partner teaching hospitals, in order to support excellence in basic and translational studies. The Institute draws together talented scientists who are working together to translate research discoveries into developing kinder, more effective therapies for cancer patients. It is a Cancer Research UK and Experimental Cancer Medicine Centre, and contains approximately 580 staff, including 80 PhD and MD (Res) students and 30-40 MSc students. Core facilities within the Institute include: Genomics Facility (gene expression microarrays); Proteomics Facility; Imaging and Cell Sorting (confocal, time-lapsed microscopy, MoFlo FACS); Pathology Suite (laser capture microdissection, tissue arrays); Experimental Imaging (with UCL Institute of Child Health); and Transgenesis.


Students will need to qualify as UK/EU fee payers and meet UCL general admissions criteria

Application Procedure

Applications are now closed (2 Dec 2019)

Queries about the application procedure or recruitment process should be directed to: ci.pgeducation@ucl.ac.uk