Breast cancer is caused by the uncontrolled proliferation and invasion of tumor cells in the breast and is the most common cancer affecting women. In recent years, improved understanding of tumor biology led to more effective therapies that target cancer vulnerabilities.
Immunotherapy aims to unleash the power of the immune system against the tumor and has become a powerful tool. The success rate of immunotherapies, however, remains low. Tumor cells have numerous strategies to evade the immune system and can even subdue it to promote tumor growth.
Identifying the mechanisms responsible for this phenomenon, as well as predicting which therapy will work better for each patient remains a big challenge. Tumor microenvironment is complex and consists of a mixture of tumor and healthy cells - including immune cells. How are the gene expression programs of those cells altered in comparison to the healthy state? Does the immune system favour or antagonize tumor growth in specific patients?
In this Q-team the students will use state-of-the-art tools to analyze genomic datasets and explore these questions. The main research question will be to identify how different cell types are altered between healthy and diseased tissue in breast tumors. This entails the study of breast tissue and of the immune cells present within the tumor. The students will leverage dataset integration tools and publicly available datasets from healthy and diseased patient samples, as well as disease models. In particular, the students will explore how immune cells alter their behaviour when they infiltrate the tumor microenvironment, assess if immune and tumor cells directly interact with each other and understand if this may favour or antagonize tumor growth. The Q-team will include students from both biological and computational backgrounds to work on these questions.