Located in the Gasthuisberg campus in the picturesque city of Leuven, the Computational Oncology team at KU Leuven (Department of Oncology) is at the interface of academic research and clinical practise. We are strongly interested in the use of machine intelligence to realise personalised health care. As such, our dedicated team of resarchers focuses on spatial and non-spatial omics, data integration and computer vision to power the next generation of biomarker detection strategies.
Renal cell carcinoma (RCC) ranks in the top ten most frequent cancers and is responsible for approximately 134,000 deaths per year worldwide. Clear-cell RCC (ccRCC) accounts for 70 to 80% of cases and is characterized by genomic instability and dysregulation of the normal cell to cell communication in a spatially aware manner. Although ccRCC are known as immunogenic tumors, patient response has remained highly variable and no reliable molecular markers for patient treatment selection have entered the clinic thus far. Proposed biomarkers, such as tumor mutational burden, have consistently failed to predict ICB response.
To overcome this issue, several tumour classification methods have been proposed for renal cell carcinoma. Typically, these transcriptomics-driven approaches identify 4 to 6 distinct subtypes. However, these also fail to reliably predict therapy response. In this PhD position, you will build a novel classification system, using public and in-house datasets to improve upon existing methods. Key is a better precision and recall in identifying patients sensitive to therapy. To this end, you will work with different omics data types at single-cell and bulk resolution, while also extrapolating your findings in a spatial context. Subtasks in this project involve omics integration (information transfer), bulk sample deconvolution, summarization of functional modules and creation of classification models. All analyses will be rigorously benchmarked in comparison to existing patient subtyping strategies and reviewed by subject matter experts in a multidisciplinary manner.
Your mission
In this position, you will:
Expected results
We are looking for highly motivated researchers who hold a Master of Science degree in either Bioinformatics, Biology, Biochemistry, Biomedical engineering or Medicine who either already have a strong affinity for machine learning or are motivated to pick it up. We welcome applications from individuals of any nationality and background, provided they meet the following requirements:
We offer:
For more information please contact Prof. Stefan Naulaerts (stefan.naulaerts@kuleuven.be) or Prof. Benoit Beuselinck (benoit.beuselinck@uzleuven.be).
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KU Leuven is an autonomous university. It was founded in 1425. It was born of and has grown within the Catholic tradition.
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