Making sense of the cancer genome: computing strategies to support personalised cancer care in a clinical setting
It is now becoming possible to examine the genomic data contained within a tumour sample. A number of projects are now collecting this data from large patient groups, including the 100,000 genome project that is helping to develop genomic medicine services for the NHS. This data has the potential to be transformational in cancer care through improved diagnosis, prognosis and the design of "personalised" drug treatment that properly matches the molecular basis of a cancer to an individual's therapeutic requirements.
However this is an enormous task. A cancer is an ecology of cells, representing many different mutated versions of the patient's own genome. As the patient's immune system and the cancer drugs attacks a cancer, the cancer cells themeselves can evolve to respond to this threat.
Cancer genomic data is large, complex and noisy, making it difficult to analyse effectively. Some progress is being made to make medical sense of such data, but this is a complex task. A number of analytical methodologies have been developed in the research setting to work with this data - however much less use is being made of this data to support the treatment of individual patients.
The Manchester Centre for Genomic Medicine (MCGM; based at St Mary's hospital) is has a strong background in translational cancer research and including:
- discovery of the genetic basis of rare cancers
- development of companion genomic testing that can target cancer chemotherapy
- development of screening algorithms to understand familial cancer risk
The MCGM is working as part of the 100,000 genomes project to understand the genomic basic of a wide range of cancer subtypes. Bringing the benefits of genomic science to cancer care will need computer scientists, medical researchers and clinicians to work collaboratively.
This project will therefore be focused very specifically in exploring the ways in which modern computational, bioinformatics and machine learning strategies can be used to analyse this data in ways that directly benefit patients by supporting the development of a "personalised" cancer strategy based on genomic data. To support this work, the student will work of a collaborative team that spans from computer science, through medicine and genomics to the patient.
The successful candidate should have an excellent first degree in Computer Science or related discipline (e.g. mathematics, physics), with interests in bioinformatics. Excellent communication skills are essential. Additional experience in genetics and genomics or other areas of biology are desirable, but are not mandatory.