Oscar Rueda – email@example.com
I am interested in the development of statistical models for the analysis of large genomic and transcriptomic datasets. Specifically, my goal is to integrate large breast cancer datasets in order to identify biomarkers that can be used to stratify patients and to identify potential drug candidates for specific subtypes. The combination of clinical samples and preclinical models can accelerate the clinical implementation of new drugs, however there are multiple statistical challenges that need to be solved in order to bridge the gap between what we observe in vitro, in animal models and in the patient.
There are several PhD students in the group, some of them co-supervised with other group leaders. You can expect a good mix of backgrounds and expertise within the group.
Identification of biomarkers for response to checkpoint inhibitors in breast cancer using transfer learning
Co-supervisors: Oscar Rueda and Sach Mukherjee
Immunotherapy, in the form of checkpoint inhibitors, has proved to be a very successful avenue for treatment in certain cancer types, such as melanoma or lung cancer. In the case of breast cancer, a lot of effort has been put in the development and testing of these drugs in the last few years, however we haven’t been able to identify accurately the subset of tumours that might benefit from these drugs. Apart from the tumour mutational burden or PD-L1 expression levels, there are no robust biomarkers of prediction to checkpoint inhibitors in breast cancer. This project will attempt to find common patterns between the subset of cancer types that respond to checkpoint inhibitors and a subset of breast tumours using an integrated analysis of different data modalities (DNA and RNA sequencing analysis, TME image analysis, etc) deriving techniques from transfer analysis. The ultimate goal of this study will be to derive and evaluate a prediction model for response to immunotherapy.
Modelling of genomic datasets with functional data analysis
Functional data analysis allows to represent spatial/temporal dependence using smooth functions. This project will attempt derive representations of different types of genomic data using these methods that can be used in different predictive models to answer important questions in breast cancer
How to apply
For details of the MRC BSU application process please see How to apply
To be considered for funding applications need to be submitted to the University of Cambridge application system by 23:59 (GMT) on January 5th 2023