Personalising Cancer Treatment With Data
PanDrugs is the first drug prescription tool that takes into account pathway context, collective gene impact, and information from functional experiments.
The complexity of cancers makes the identification and prioritisation of treatment options based on genetic profiles a tough challenge. The use of data-driven, computational, approaches are offering a much needed solution. That being said, there are few solutions that give clinicians answers, rather than more information to help them make a decision.
Researchers led by Fátima Al-Shahrour, head of the Bioinformatics Unit at the CNIO (Spanish National Cancer Research Centre), have addressed this through a new tool called PanDrugs. “The main novelty introduced in this methodology compared with current tools is the broadening of the search space to provide therapeutic options”, explained Al-Shahrour.
In other words, PanDrugs suggests treatments for direct targets and biomarkers as well as integrating a systems biology knowledge-based layer that automatically inspects biological circuits expanding cancer candidate therapies from beyond limited cancer-related gene lists to the whole druggable pathway.
“This novel strategy (called ‘pathway member’) extends the treatment opportunities of cancer patients by enriching the therapeutic arsenal against tumours and opens new avenues for personalized medicine”, states Al-Shahrour.
Simply as a database, PanDrugs represents an important contribution. It is the largest public repository of drug-target associations available from well-known targeted therapies to preclinical drugs. At present, the database integrates data from 24 primary sources supporting over 56,000 drug-target associations.
And for the best news of all, it’s all entirely open-source.