Are you interested to work in the exciting field of genomics (big data) and eager to improve the prognosis of bladder cancer patients? Join our team!
Many diseases recur after recovery, e.g. recurrences in cancer and infections. Research into these recurrences is mainly focused on analyzing time-to-first recurrence using commonly applied survival models instead of analysis of the total recurrence burden. Thus, any subsequent recurrences that may occur after the first recurrence are ignored, and hence a substantial part of the clinical data is discarded. There are several statistical models available that enable modeling of the total recurrence burden, so-called recurrent time-to-event models.
However, these models are computationally demanding and therefore inefficient to apply to high-dimensional data (big data) analysis. This also holds for genome-wide association studies (GWAS), in which millions of DNA variants are analyzed simultaneously. Currently, several solutions for this problem are being developed. In this project, the PhD candidate will evaluate (and optionally: co-develop, extend and/or improve) these recurrent event methods for application in the context of GWAS.
Next, the PhD candidate will apply the recurrent event models to non-muscle invasive bladder cancer (NMIBC). NMIBC is an example of a disease that is characterized by a high risk of recurrences: >50% of the NMIBC patients experiences at least one recurrence within three years after diagnosis, and many patients experience multiple recurrences. This necessitates frequent follow-up visits and repeated surgery with adjuvant treatment, placing a heavy burden on patients' quality of life. The PhD candidate will perform a recurrent event GWAS using data from the Nijmegen Bladder Cancer Study and data of cohorts of international collaborators (these are all existing datasets) in order to identify novel genetic variants that are associated with the NMIBC recurrence rate.
Taken together, this project focuses on evaluation and application of state-of-the art methodology using a wealth of clinical and genetics data as obtained from bladder cancer patients. In addition, this project will allow you to contribute to the development of a personalized surveillance and treatment plan for NMIBC patients.Tasks and responsibilities
- Comparing and evaluating recurrent event methods for application in the context of GWAS
- Data cleaning and data management of big data, i.e. clinical data and genomics data of NMIBC patients.
- Execution of a large, international meta-analysis of GWAS for the total NMIBC recurrence burden.
- Working in a multidisciplinary team: work together with researchers from diverse professional backgrounds, e.g. in genetics, epidemiology, bioinformatics, biostatistics, pathobiology, but also with urologists and international collaborators from diverse backgrounds.
- Writing of scientific publications resulting in a PhD thesis.
- Presentation of results at internal and external meetings and national and international (in the future maybe digital?) conferences.
- Possibility to follow courses and training throughout the project to obtain or increase relevant knowledge and skills.
- Possibility to be involved in teaching (curriculum Biomedical Sciences and Medicine).