More information: https://goo.gl/uMnFAH
Cancer is not a single disease but a collection of diseases in different tissues. Even within a single cancer type such as breast cancer, dividing up the disease into subtypes is important to help predict survival, inform treatment options, and suggest new drug treatment strategies to improve the lives of those affected by cancer. Due to technological advances we now have measurements on the amounts of all genes expressed in cells of a large number of cancer samples. Unfortunately, determining subtypes has been largely unsuccessful using traditional techniques, with the notable exception of breast cancer
One approach that is better suited to this structure is the Bayesian clustering method latent process decomposition (LPD). We have successfully applied LPD to prostate cancer and have found that different cancer subtypes can be identified in a single tumour sample, including a subtype called DESNT associated with poor prognosis. We plan to apply LPD to transcriptome data from The Cancer Genome Atlas, a large US project that has generated comprehensive, multi-dimensional maps of the key ‘omic changes in a large number of cancer types (https://cancergenome.nih.gov). Analysis pipelines will be set up to automatically analyse large amounts of data, before reviewing and performing further analysis on interesting findings. These studies are expected to detect and characterise novel stratifications for many cancer types.
This is a bioinformatics/data analysis based PhD. During the PhD you will gain knowledge on how to deal with big data, high performance computing, developing pipelines and statistical analyses. You will be part of the Cancer Genetics team at the Norwich Medical School, which is an interdisciplinary team comprising a mixture of bioinformaticians and lab-based scientists. We have a broad interest in translational cancer based molecular studies. Research includes urine based biomarker, whole genome sequencing, subtype detection and bacteria in cancer studies.
For more information on the supervisor for this project, please go here: https://www.uea.ac.uk/medicine/people/profile/d-brewer
Type of programme: PhD
Start date of project: October 2018
Mode of study: Full time
Acceptable first degree: Computer Science, Physics, Mathematics, Engineering, Biological Sciences, Biochemistry, Biomedical Science
2:1 or relevant Masters.
Cancer is not a single disease but a collection of diseases in different tissues. Even within a single cancer type such as breast cancer, dividing up the disease into subtypes is important to help predict survival, inform treatment options, and suggest new drug treatment strategies to improve the lives of those affected by cancer. Due to technological advances we now have measurements on the amounts of all genes expressed in cells of a large number of cancer samples. Unfortunately, determining subtypes has been largely unsuccessful using traditional techniques, with the notable exception of breast cancer
One approach that is better suited to this structure is the Bayesian clustering method latent process decomposition (LPD). We have successfully applied LPD to prostate cancer and have found that different cancer subtypes can be identified in a single tumour sample, including a subtype called DESNT associated with poor prognosis. We plan to apply LPD to transcriptome data from The Cancer Genome Atlas, a large US project that has generated comprehensive, multi-dimensional maps of the key ‘omic changes in a large number of cancer types (https://cancergenome.nih.gov). Analysis pipelines will be set up to automatically analyse large amounts of data, before reviewing and performing further analysis on interesting findings. These studies are expected to detect and characterise novel stratifications for many cancer types.
This is a bioinformatics/data analysis based PhD. During the PhD you will gain knowledge on how to deal with big data, high performance computing, developing pipelines and statistical analyses. You will be part of the Cancer Genetics team at the Norwich Medical School, which is an interdisciplinary team comprising a mixture of bioinformaticians and lab-based scientists. We have a broad interest in translational cancer based molecular studies. Research includes urine based biomarker, whole genome sequencing, subtype detection and bacteria in cancer studies.
For more information on the supervisor for this project, please go here: https://www.uea.ac.uk/medicine/people/profile/d-brewer
Type of programme: PhD
Start date of project: October 2018
Mode of study: Full time
Acceptable first degree: Computer Science, Physics, Mathematics, Engineering, Biological Sciences, Biochemistry, Biomedical Science
2:1 or relevant Masters.