University of Groningen - University Medical Center Groningen
Using machine learning to unravel the 'cancer-immune setpoint' in rare cancers (SETPOINT)
During cancer development, tumor cells undergo molecular ‘rewiring’ to escape the immune system, often by activating mechanisms that suppress an anti-cancer immune response. Immunotherapy can circumvent some of these mechanisms and thus trigger an anti-cancer response, however, little is known about the efficacy of immunotherapy in patients with rare cancers. Most research is being done in a limited set of common cancer types and little is known about the factors that influence the anti-cancer immune setpoint in rare cancers. To improve our understanding of the mechanisms that determine the triggering of an anti-cancer immune response in rare cancers, we need to increase information by pooling big data from common cancers while utilizing small data from rare cancers. We propose to build a big-data warehouse containing molecular features and immunological parameters from patients with rare and common cancer and by applying machine learning algorithms to this large-scale warehouse, we can reliably identify molecular features associated with immunological parameters potentially relevant to the cancer-immune setpoint. These molecular features may contain targetable components that could ultimately lead to enhanced anti-cancer immune responses in patients with rare or common cancers. This step will also enable us to select molecular features with the highest likelihood of contributing to accurate predictive models for response on immunotherapy.
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