I was presenting in a session that was focussing on the role of metabolism in cancer, in haematological malignancies. We contributed to the session by telling how the immunoglobulin receptor which is expressed on the surface of all B-cell tumours controls metabolism in the malignant cells. So we basically started our story with using animal models where we inactive the B-cell receptor genetically and try to understand what happens to the tumours when you inhibit the receptor. This is relevant from a clinical point of view because there are drugs that are being actually currently used in the clinics that inhibit the B-cell receptor signalling pathway.

What we discovered through the animal models is that actually the immunoglobulin participates in a fundamental way in the control of tumour cell metabolism and when the cells lose the B-cell receptor, and this could be happening, for example, when you inhibit functionally the receptor to drugs, the cells learn how to adapt to this mechanism, to this treatment, and they use alternative ways to actually survive. So the message is that these drugs are effective but resistance can occur. This resistance is associated with changes in the metabolic profile of the tumour cells. Our animal models are helping us to understand how we can actually target these resistances and actually identify new ways through which we can inhibit the growth of the cells.

First we are actually identifying molecular targets, so genes and proteins that are actually responsible for the resistance to these drugs and then eventually develop drugs if they are not already available.

Is it possible that there are drugs out there already that can target these cells?

Yes. Actually what we are currently doing is a large scale screening on these resistant cells, so cells that are resistant to the current drugs. We are focussing particularly on FDA approved drugs so that if we find any drug that would hit these cells and kill them we could immediately start clinical trials without the need of all the development phase.

This model that started as a preclinical investigation has actually built up and has raised attention in a number of pathologies around us. We’re actually trying now to figure out which patients could resist the drugs before actually giving the drug to the patients. So I believe that these preclinical models are actually extremely useful to stratify better the patients that actually get ibrutinib or idelalisib.

So the move to personalised medicine will help?

Right. We need biomarkers to identify those and actually these preclinical models are helping us to identify those biomarkers in a way that we could actually personalise the treatment as much as we can.

Also will this need more computing power due to the amount of data?

Yes. It’s actually a combination of metabolomic data, transcriptomic data, genomic data, so it’s really omics coming into play. The difficulty is actually integrating all this information to try to find the right hits that we can actually exploit for overcoming these resistances.