IO17 - Large-scale bioinformatics for Immuno-Oncology
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.
Motivated researchers, clinicians, and students who want to gain an understanding on how bioinformatics tools and simple (logic-based) modelling approaches can be used to investigate the tumour-immune cell interface and its underlying signalling pathways from high-throughput data.
Programming/scripting skills are helpful, but not mandatory. An understanding of elementary operations with R will be required. Elementary command line instructions in UNIX will be used, so minimal familiarity with navigation in directory trees, copying files and folders, etc. will be needed.
- Francesca Finotello received her PhD in Bioengineering in 2014 from the Department of Information Engineering, University of Padova (Italy). Her PhD thesis, entitled "Computational methods for the analysis of gene expression from RNA sequencing data", was awarded with the "Marco Ramoni" doctoral research award by the Italian National Bioengineering Group. She has an extensive experience on computational methods for the analysis of different types of next-generation sequencing (NGS) data, including RNA-seq and 16S ribosomal RNA gene sequencing of the human microbiota. Currently, she is a postdoctoral researcher in the Division of Bioinformatics of Medical University of Innsbruck (Austria). She is interested in bioinformatics and computational biology for cancer immunology and precision medicine, with a particular focus on in silico prediction of tumor neoantigens and deconvolution of tumour-infiltrating immune cells from NGS data. She is principal investigator of the research project "QuanTIseq: dissecting the immune contexture of human cancers" funded by the Ã–sterreichischen Krebshilfe Tirol (Austria) and aimed at developing a computational tool for the quantification of immune cell fractions from RNA-seq data of cell mixtures.
Affiliation: Division of Bioinformatics, Medical University of Innsbruck, AT
- Federica Eduati received her PhD in Bioengineering in 2013 from the University of Padova, with a thesis (awarded the "Paolo Durst" best Italian PhD Thesis Award in Bioengineering) focusing on mechanistic modelling aspects of both large- and small-scale biological systems. In 2009 she participated to the DREAM4 "Predictive signaling network modeling" challenge classifying as best performing team. In 2011-2012 she was a visiting predoctoral fellow for 8 months in the Systems Biomedicine group at EBI. Since February 2013 she is a Postdoctoral EIPOD fellow - Marie Curie Fellow at EMBL (UK and Germany). Since May 2016 she is also a visiting scientist at JRC-COMBINE in RWTH Aachen (Germany). Currently, her main research interest is the investigation of why patients differentially respond to cancer therapy and how we can suggest personalized therapy. In particular, she is interested in approaching this problem by investigating signalling pathways, their deregulation in cancer and the specific effect of targeted therapy, using dynamic mathematical modelling approaches and machine learning techniques. She has also been working on the development of a microfluidics platform, which allows drug screening of live cells obtained from patient biopsies in a fast and cost-effective way. In 2013 she was also co-organizer of the NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge, where 213 registered participants from more than 30 countries had to predict cell line-specific cytotoxicity to chemical compounds based on genomic data and chemical attributes.
Affiliation: European Molecular Biology Laboratory (EMBL), Heidelberg, DE; JRC-COMBINE (RWTH Aachen), Aachen, DE
You can find here the detailed program.
Register using here until August the 30th
Contact: For any questions about this course, please contact Pedro Fernandes (e-mail address below)