Most newly transformed cancer cells are recognised and are eliminated by the immune surveillance. Drastic cancerous transformations grant immune-escape features and, the abilities to alter and hijack the immune system for cancer cell survival and growth. Here, we describe the multiparametric screening pipelines developed in our laboratory to identify cancer-driven biomarkers, starting in preclinical setting to train Machine Learning model that can predict the presence and the type of cancer. Our goal is to translate these workflows into the clinic.


Affiliations: Irradiation Immunity Interaction (I3) Laboratory, Division of Genome Sciences and Cancer, John Curtin School of Medical Research, Australian National University, Australia.