This is a Technicom podcast.

Algorithms and looking for effective treatment for children with cancer.

At first, this seems like an odd pairing, but by the end of today's podcast, you might think a little differently.

I'm Peter Balland from Technicon, and today we look again at the

IPCH-2020 European project. In this project, medical doctors, researchers, and clinicians

come together with computer scientists and experts in artificial intelligence to arrive at an

answer to one simple question. How can we assign treatment plans to children with cancer without causing harm or major side effects?

And this is where algorithms and artificial intelligence come in.

Today from the IPC project, we speak with Peter Mestach, principal investigator at the Cancer Research

Institute, Gemp.

He is a project partner in IPC, and he shares his team's methods with us for a better

understanding of their motives. Let's have a listen.

Welcome to the podcast, Peter. Thanks for coming on.

You're welcome.

You are using an efficient method called computational deconvolution to understand the different

cell types in a tumor sample in order to predict response to immunotherapy.

This could quickly become a complex conversation, but I'd like you to tell us what this means in simple terms.

Sure. So I think the first thing you need to realize is that a tumor tissue or any tissue for that matter.

So a tumor tissue does not only consist of tumor cells. So these tissues are very heterogeneous. They're a

heterogeneous mixture of various different cell types, including tumor cells, but also, for

instance, immune cells. And on top of that, even a tumor cell within a tumor tissue is not

identical or per se identical to other tumor cells in that

...