 The emergence of drug resistance in oncology is the biggest, arguably, the biggest problem we have in the field right now. We don't know in the large majority of cases what are the mechanisms that drive resistance. And so understanding what the mechanisms are means that we are going to develop an entirely new generation of cancer treatments as well as optimize the use of existing treatments in the future thanks to our understanding of these molecular mechanisms and dynamics. So in the ERC funded dark matter project we are interested in understanding why and how patients who respond very well to treatment at some point stop responding. And this is due to the big problem we have in oncology today which is called the emergence of drug resistance. So with the ERC Consolidator Award we will hire two new lab members that will be fully dedicated to this project. We will use these model systems we derive from patients in clinical trials and standard of care called organoids. So they are cells that are grown in the lab that emulate what happens in the patient. So we simulate treatment in these model systems, use different drugs to understand how these cells in a dish evolve over time to become resistant to treatment. And we will then look at their molecular profiles to understand what are the mechanism of this process and try to solve it with the idea of developing new sequences of drugs or identify new targets with a specific focus on molecular mechanisms related to what's called cancer epigenetics. So cancer epigenetics is the field that studies the changes that happen in cancer cells that are not strictly related to DNA alterations but are rather molecules and markers pieces of information that are wrapped on top of the DNA and regulate the expression of the genes so how the cell uses these genes. And we realize that it's not just DNA mutations that change in cancer but also these additional molecular markers that we want to study in this grant. Cancer drug resistance as I said it's a huge problem and behind this phenomenon there is an enormous complexity. So cells adapt to treatment in many different ways at the same time in the same patient. So the challenge here is the convoluting like disassembling this enormous complexity with the model systems that we derive from the patient. This is a very big challenge but thanks to new generation of technologies that are available at Human Technopole such as single cell sequencing and profiling we are quite confident that we'll be able to overcome this complexity and understand the real root of the problem. The thing that excites me the most is the use of mathematical modeling as well as machine learning and artificial intelligence methods to understand the complicated data that we generate in the lab. So this from the lab we do complex experiments, the data are complicated to understand but through the use of mathematics and computation particularly artificial intelligence we can simplify this data and extract the fundamental rules that drive these biological processes and use these rules to then advance further cancer research and treatment.