Computational Precision Medicine

• explain the concepts of applied precision medicine in the clinic.
• explain the concepts of treatment stratification of cancer patients and describe strengths and weaknesses of using bulk gene expression profiling.
• explain the concepts of cancer immunotherapy and discuss the challenges of target selection.
• apply basic methods for unsupervised clustering based on gene expression.
• apply basic methods for sample classification based on gene expression.
• apply differential gene expression analysis to find potential tumor associated antigens.
• analyze isoform switches to identify potential cell membrane antigens.
• create bioinformatics workflows for diagnostics, prognostics, and antigen identification.

In this course, the students will be introduced to bioinformatics methods in precision medicine. The students will learn how transcriptomics data from microarrays or RNA sequencing can be used for diagnostics and patient stratification for selecting optimal treatments. The students will also learn the principles of precision and personalized therapeutics by analyzing RNA and DNA sequencing data.

Each lecture consists of an introduction to a clinical problem by a domain expert from translational research and/or a clinician, followed by an introduction to how to solve the problem bioinformatically, followed by hands on exercises. The latter two will be based on the programming language R.

The evaluation is based on hands on group projects, in which a precision medicine problem is solved. The students can either choose a predefined project or come up with their own.

02402/22110/22100/22126/27002/27008, The course assumes that the student has basic experience in R programming, as all exercises and group assignments will be based on this language. A basic understanding of next generation sequencing and a good understanding of statistics are highly recommended.

Lectures, computer exercises, and group work

Minimum: 10.

Please be aware that this course will only be held if the required minimum number of participants is met. You will be informed 8 days before the start of the course, whether the course will be held.