If physics, mathematics or computing have allowed us to unravel riddles like black holes, why not use them against cancer, that true universe of diseases whose nebulas we still cannot understand? That is the objective of Raúl Rabadán (Madrid, 1974), a theoretical training physicist who began his career at the European Laboratory of Particle Physics (CERN) and today directs an ambitious cancer research project at Columbia University (USA). ). Through an interdisciplinary approach, this Madrid tries to solve biomedical problems through the study of big data. "The answer to our questions is in the biological data. We just have to learn to interpret them," he recently said on the day 'New horizons of cancer research: from the laboratory to the patient', organized in Madrid by the CNIO with the support of the "la Caixa".

How did the jump from theoretical physics to cancer research? Like all the important things in life, it was something that happened by chance. It was 2003, I was at the Institute of Advanced Studies in Princeton, where I arrived from CERN to continue researching string theory. But I went to a seminar taught by Arnold Levine, the discoverer of the most mutated gene in cancer, p53, on problems in biology for physicists. And I like it a lot. In physics I was investigating very interesting problems, but they were very theoretical, very academic, with which it takes many years to do experiments. And biology offered me the opposite: a lot of data and a theory to develop. I thought it was a good time to make important contributions in that field and I made the leap. He directs the Mathematical Genomics program at Columbia University, what is his laboratory trying to figure out? There are no test tubes in my laboratory. Only computers and blackboards. So what we do is ask ourselves things and try to answer them through biological data. One of the questions we ask ourselves is why immunotherapy only works in some cases. For example, we know that in glioblastoma, a very aggressive tumor, only 10% of patients respond to therapy. Why does this happen? Are these tumors different from others? Is it something in the patient's immune system that makes the difference? We are extracting genomic data from different classes and collecting many data associated with different patients to look for common patterns that lead us to those responses. Do you already have any idea where the shots are going? When we have a hypothesis, we collaborate with experimental groups that validate it or not. We know that the genetics of tumors that respond are different from those that do not respond to therapy. We have identified some associated mutations and we are trying to know what these mutations do, what their role is. Is it more difficult to understand the complexity of a system like cancer than a black hole? It is very different. The black hole can be understood theoretically and we can use mathematics to study it. In biology, however, there is not such a rich theory. We have a large amount of data, but the problem is that we still don't know what the structure of that data is. We have an instruction manual, but we don't know how to interpret it ... Each human genome is made up of 6,000 million bases. That is like having a thousand-volume encyclopedia with 1,000 pages each. There, hidden, are the instructions of each person. How does a cell know that it has to become part of an eye and not a nail? We know how to read some parts, for example those that carry information about proteins, but that is a small proportion. In general, there are many things we don't understand yet. We are trying to take that information, find statistical patterns and start drawing conclusions. How long will it take to interpret it? It depends on what we want to know. It advances quite quickly. Keep in mind that the first draft of the human genome was made in the early 2000s, not even 20 years ago, and we already understand many things. There will be problems that will resist, sure, but many advances will be made. Does winning cancer happen, yes or yes, by the study of big data? One of the things that is clear, not only in cancer, but in all biology and In the global society, we have more and more data and that these data are not random, but have a structure. We have to find ways to find that structure. I don't know if the final victory against cancer will come from there or not, but the truth is that there is a lot of information that is hidden there and that can make the way easier. In the coming years there will be more development of methods, hypotheses and ideas ... because many times the data gives us things that we did not expect and that I think is the most interesting part of what we are doing. He has also worked with viruses ... Yes, the first problem I was working on is trying to understand how viruses that are in other species can jump to humans. For example, with the flu. Every 30 years or so there is a pandemic from a virus that is in another species and jumps to humans. The question we ask is very simple: What makes a virus that is in another species jump to humans? Now what we have is 100,000 genomes of viruses that have been collected in all parts of the world, at different times, in different species. And you are trying to predict which of them are capable of jumping. In his team there are mathematicians, physicists, engineers ... Do you need an interdisciplinary point of view to address medical problems? I like to hire people who come from other fields, with different ideas than I have, with different challenges, because that allows to contribute different angles to the investigation. When I was in the Institute of Advanced Studies, I was very lucky because I had absolute academic freedom. This institution favors that people who have an idea can develop it without having to be aware of publishing or financing. There are very few places where that can be done, but it is essential to be able to look at things a little more in the long term.

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