Biomarker discovery for detection of cancer
We are looking for students that want to participate in analysis of large-scale omics data to find and validate cancer biomarkers.
The aim is to discover and/or validate new cancer biomarkers for early detection of cancer, prognosis determination, and selection of treatment. The long-term goal is to develop biomarker-based tests for use in health care. With our research, we also want to contribute to improved methods and study designs for biomarker discovery to allow more and better biomarkers to reach clinical use.
Biomarkers hold huge potential for improved health care. They can e.g. be used in diagnostic tests or support selection of the best treatment option. However, although there are thousands of scientific publications on new potential biomarkers, very few biomarkers obtain regulatory approval and end up as clinical tests. Some computations in the discovery phase have previously been difficult to perform due to lack of efficient computational methods. Therefore, we have developed new computational and statistical tools that allow us to efficiently select for strong composite biomarkers already in the discovery screening phase. These novel tools, along with the current state-of-art methods, will here be used for analysis of genomics, proteomics and metabolomics data from thousands of cancer patients and healthy individuals.
We are searching for tumor-type specific biomarkers for several solid tumor types, and you may be involved in work on one or several of the diagnoses. Different types of omics data collected from cancer patient blood or tissue samples, or retrieved from databases, will be analyzed through computational approaches, targeting clinical needs where access to regulatory approved biomarkers can improve the prognosis or the clinical handling of a patient. Testing and improvement of algorithms for the computations may be part of the project.
Basic programming skills and statistics knowledge are required. It is also good if you have knowledge of biochemistry and cell biology, and can understand scientific literature on e.g. proteomics and biomarker research.
Professor Tobias Sjöblom
Read more about our research here.