Discovery Network on ‘Children and sarcomas’

Inspire2Live is preparing a Discovery Network on ‘Children and Sarcoma’. There is little attention for this difficult to treat cancer. Only few children are diagnosed with a sarcoma and their survival is poor. Together with the best scientists and clinicians around the word and of course with the Princess Maxima Center for Pediatric Oncology we want to set up a Discovery Network and work towards solutions. The exact date and location is not yet known but it will be an international setting in the first quarter of 2020.

Discovery Network on ‘HIV/AIDS and cancer’

Inspire2Live works on a Discovery Network ‘Cancer, viruses and vaccines’. Now that the HIV virus sometimes is used to treat cancer patients, Inspire2Live took the initiative together with scientists from the IrsiCaixa – Institut de Recerca de la Sida, to work out a Discovery Network on this theme. How does it work when viruses attack cancer cells, or vaccines? What is the impact of inflammation in this process? And the influence of the metabolism? And of course: how can we set up new treatments for patients? In a Discovery Network setting we will find this out. The exact date is not yet known but it will be in the first quarter of 2020. The location is ‘La Pedrera’ in Barcelona.


The added value and even necessity of Artificial Intelligence (AI) becomes clearer every year. AI simulates what people already do well (like chess, driverless cars, recognizing patterns et cetera). In our initiative, The Discovery Network on AI and Cancer, we want it to contribute to health, biology and cancer in particular. That is a lot more complicated.

In healthcare AI is already pretty good in radiology. AI is exceptional in pattern recognition, therefore it enters the world of radiology. But the biology of patients is different and difficult. What can it contribute to this field of expertise? When we are looking for added value in this area we need to penetrate and understand the biology of patients. It is not sufficient to replace this black box by another one, namely the workings of the learned algorithms. Trey outlines this clearly in the 2018 article on Using Deep Learning to model the hierarchical structure and function of a cell.

We are looking for the integration of different datasets. Datasets with information on all the levels from molecular processes in the cell, about tumor cells, tissue, RNA, organoids, drug response and valuable other clinical data to population statistics and lifestyle data. We probably will also need information of citizens that do not have the disease (yet) and have similar lifestyles. Information about resilience. Analysis of these data requires very subtle and sparse patterns to be recognized, and to be biologically or medically relevant these discovered patterns need to be more or less “proved true or extremely likely” rather than just deemed 95% probable.

In this Discovery Network we aim to bring together biologists, mathematicians, experienced machine learning experts, epidemiologists, and perhaps others?

Expected date: Q3
Meeting place: to be decided.