The devastating effect that cancer has on the lives of many individuals and families remains a reality from which we still cannot escape. Nearer or further away, we all have loved ones who encounter this disease unexpectedly and fight, hand in hand with exceptional doctors and nurses, to regain their former life or get as close to it as possible. This is the case of my mother, exemplary and inspiring in her illness, whose suffering we live, share and accept as best we can, in our case with the hope that Someone from above is watching her and will not leave her.

In parallel to this tremendous reality, which we cannot ignore, the scientific community awaits expectantly and enthusiastically the results of a large number of projects, initiatives and clinical trials in the field of precision oncology, the current paradigm in the fight and research against cancer. In short, precision oncology seeks to emphasise the heterogeneity of tumours and the need to apply personalised and targeted therapies to each patient, using information from high-resolution molecular technologies from the fields of genomics, transcriptomics and metabolomics, among others. This concept is not new and, in fact, the use of "omics" data in cancer research has been the norm for two decades. What is new now? What makes us more optimistic?

The reasons for this optimism are varied. Firstly, massive DNA and RNA sequencing technologies have become substantially cheaper and are becoming commonplace in hospitals. For example, the acquired mutations (specific genomic alteration type) in the disease are already specified in the clinical report of many patients. On the other hand, large-scale gene editing initiatives are growing, mainly based on CRISPR-Cas9 technology, which allow us to identify possible vulnerabilities and genetic dependencies in tumours.

In addition, our increasing computing power, with ever cheaper and more powerful computers, is coupled with a growing number of professionals and researchers trained in data analysis and predictive modelling. These advances place us in a research environment with an unprecedented frequency and quantity of data that can now be analysed quickly and efficiently to find more effective therapies with fewer side effects.  

The fact that there are already successful cases of precision oncology in the clinic also increases our optimism. This is the case, for example, with the combined use of MEK and BRAF inhibitors to treat metastatic melanoma in BRAF-mutated patients, a treatment that has dramatically reversed the survival of these patients. In summary, we are approaching crucial years in cancer research in which we expect precision oncology to explode and generate a large number of new therapies and markers that will optimise clinical decision-making.      

In the Bioinformatics Group of Tecnun, School of Engineering of the University of Navarrawe have been working in the field of precision oncology for years. In collaboration with exceptional biomedical researchers, we are involved in different projects and collaborations with the clear objective of capitalising on all this data and generating new hypotheses, usually through algorithms and mathematical models. Coincidentally (or not), in collaboration with the CIMA of the University of Navarra and other European entities, this February we started a new European project at Tecnun focused on the disease my mother suffers from, which, naturally, I am approaching with tremendous enthusiasm.

Through these projects, one of our tasks at Tecnun, intrinsic to the University's mission, is to train young researchers and future PhDs in the field of data analysis and predictive modelling. We are proud that some of them already form part of leading research centres and pharmaceutical companies in the field of cancer. This fact encourages us to continue with this training and research work because the scientific community is very clear that the speed and success of precision oncology will depend, to a large extent, on having a large number of researchers capable of managing, analysing, integrating and capitalising on the huge amount of biological and clinical data being generated in the fight against cancer.