Instituto Gulbenkian de Ciência
The immuno-oncology approach leverages on the unique capability of the immune system to recognize and kill tumour cells. This action is hampered by escape mechanisms put in place by tumour cells like, for instance, the engagement immune checkpoints, i.e. inhibitory molecules that modulate the amplitude and duration of immune responses. Immunotherapies that block checkpoint molecules are amongst the most promising approaches in immuno-oncology for the enhancement of antitumour immunity. Thanks to high-throughput technologies, such as next-generation sequencing (NGS) and proteomics, we have now access to large-scale tumour data that can be used to investigate the interplay between tumour and immune cells and the role of the immune system in tumour progression and response to therapy. In this course, you will learn to use bioinformatics tools and mathematical modelling techniques operating on high-throughput tumour data, in order to extract features that can be used to characterise this complex tumour-immune cell interface, such as:
- tumour antigens recognized by T cells
- tumour-infiltrating immune cells
- deregulated signalling pathways in cancer and immune cells
A fully practical, hands-on approach will ensure that the newly acquire skills can be used with a great deal of autonomy.
This is one of our "Foundations" type courses, providing a systematic and detailed review of fundamental concepts and techniques in Biostatistics. Participants can expect to go through a set of exercises that are based on biomedical problems. These exercises are preceded by short lectures that are simple to follow. The lectures are designed to provide the conceptual framework that is needed to release the training power of the exercises, not to flood the participants with formality, which will be kept to a minimum. We will make use of a highly interactive methodology, taking advantage of our well equipped Bioinformatics training room. With this approach, we expect to bring the participants to a high degree of usage independence in using the methods that we cover.
Main topics: Descriptive Statistics; Probability; Statistical Inference; Design of Experiments.