Last week The New York Academy of Sciences hosted the symposium “Quantitative Approaches in Immuno-Oncology”. Dr Carlos Torroja from Centro National de Investigationes Cardiovasculares Carlos III (CNIC) thereby presented first APERIM results in a much-noticed poster.
The symposium aimed to explore the promising field of immunotherapy in cancer treatment, covering the breadth of approaches needed to quantify interactions between tumors and the immune system. Quantitative Immuno-Oncology—sitting at the interface between immuno-oncology and quantitative approaches from mathematics, physics, and computer science—has emerged as a field that can significantly advance the ability to interpret existing large datasets, and perform predictive analyses.
In his poster Carlos Torroja presented first results of the APERIM project. The reserachers around Fátima Sánchez-Cabo (CNIC) and Zlatko Trajanoski from the Medical University of Innsbruck applied deep learning on a set of markers selected as very predictive of the amount of lymphocytes and tested it on the 1207 breast cancer samples from TCGA. The results agreed relatively well with the annotated amount of lymphocytes from TCGA and furthermore also the predicted survival time of the groups.
Prof Dr Fátima Sánchez Cabo
Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid 28029, Spain
Fátima Sánchez Cabo: firstname.lastname@example.org