Computer model targets more effective cancer treatments
A groundbreaking EU-funded project has created a new method for developing multidrug cancer treatments. Many of the cancers this method targets have proven very difficult to treat with conventional medicines. As a result, this innovation has the potential to save thousands of lives every year.
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Scientists have dedicated huge amounts of time and resources to better understanding the causes and mechanisms of cancer growth. This in turn has led to better treatments and better patient outcomes. The battle against cancer however is far from won.
Cancer begins when genes in a cell become abnormal, and the cell starts to grow and divide out of control. Researchers estimate that each cell contains an incredible 30 000 different genes.
These genes control cells by making proteins. When a gene mutates, or becomes abnormal, it creates an abnormal protein. This can cause cells to multiply uncontrollably and become cancerous.
A key focus in our research was on cancers that bear mutations in the RAS gene family, explains SAMNets project coordinator Boris Kholodenko, professor of systems biology at University College Dublin, Ireland. RAS genes regulate diverse cell behaviours.
This is important, because these mutations are key drivers for more than 30 % of all human cancers. These include some of the deadliest cancers, notably pancreatic, colorectal and melanoma.
At present, treatment options for cancers with RAS gene mutations are very limited. For pancreatic cancer, for example, chemotherapy still remains the only available option, even with recent advances in more targeted therapies such as immunotherapy.
The SAMNets project sought to address this unmet need. We wanted to bring new treatments into the field of mutant RAS-driven cancers, adds Kholodenko. To achieve this, Kholodenko and senior team member Oleksii Rukhlenko aimed to combine computational modelling with experimental lab work.
The project team began by developing a next-generation computer model. This was designed not only to integrate all known protein interactions, but also to take into consideration all known drug-protein interactions.
The objective of this was to develop a computational model, capable of predicting which combination of drugs would be most effective against any given RAS-driven cancer, details Kholodenko.
The point is that each drug on its own is not effective; it is the combination of the right drugs together that makes them effective. Drug combinations where two drugs affect the same primary target have not been studied before.
These computational predictions were next validated in experiments on cancer cell lines. This enabled the team to assess the accuracy of their computational modelling, and to evaluate the efficacy of this approach to diagnosing multidrug treatments for RAS-driven cancers.
Combined cancer therapies
Two main innovations came out of this project, notes Kholodenko. The first is a new type of computational modelling that we pioneered, which we call structure-based modelling. The second is a new principle of combining drugs, as a result of this structure-based modelling. Our findings have attracted the interest of a number of clinical investigators.
Kholodenko and his team are continuing their research, focusing at the moment on better understanding the mechanisms of resistance of cancer cells to targeted therapies. A patent application based on the project findings has been filed, and funding obtained from the National Institute of Health in the United States to continue this groundbreaking work. A clinical trial is also being prepared in the United States, based on our findings, adds Kholodenko.
Ultimately, the work pioneered through the SAMNets project could one day lead to potential new treatments of mutant RAS-driven cancers. This could save thousands of lives every year. The European Commission recently estimated that there will be 2.7 million new cases of cancer in the EU in 2020, with 1.3 million deaths. The fight goes on.