Thrombosis in Cancer Patients
Machine learning based identification of immune-related biomarkers for thrombosis risk assessment in cancer patients
Thromboembolic complications are serious and life-threatening adverse events in cancer patients. Thus, in this project we aim to elucidate the mechanisms driving hypercoagulation in cancer patients and its interference with the immune system. To this end, we combine innovative research approaches in oncology, health-tech medicine, immunology and hemostaseology.
The consortium will study blood samples from healthy individuals and patients with different tumor entities to decipher possible mechanisms leading to hypercoagulation in cancer patients. Single cell sequencing and spectral flow cytometry will be used to predict the risk of developing thrombosis in cancer. Data analysis will be performed using machine learning methods and, to properly capture the complexity within the data, deep learning.