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Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
Published on: July 25, 2020
Objective assessment of cancer genes for drug discovery
Mishal N Patel1, Mark D Halling-Brown, Joseph E Tym
1Cancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London, UK.
Abstract:
Selecting the best targets is a key challenge for drug discovery, and achieving this effectively, efficiently and systematically is particularly important for prioritizing candidates from the sizeable lists of potential therapeutic targets that are now emerging from large-scale multi-omics initiatives, such as those in oncology. Here, we describe an objective, systematic, multifaceted computational assessment of biological and chemical space that can be applied to any human gene set to prioritize targets for therapeutic exploration. We use this approach to evaluate an exemplar set of 479 cancer-associated genes, reveal the tension between biological relevance and chemical tractability, and describe major gaps in available knowledge that could be addressed to aid objective decision-making. We also propose drug repurposing opportunities and identify potentially druggable cancer-associated proteins that have been poorly explored with regard to the discovery of small-molecule modulators, despite their biological relevance.
Insights
This study presents a computational method to systematically prioritize therapeutic targets from large gene sets, aiding drug discovery. It identifies druggable cancer proteins and suggests drug repurposing opportunities.
Area of Science:
- Computational biology
- Drug discovery
- Genomics
Background:
- Selecting effective therapeutic targets is crucial for drug discovery, especially with large gene sets from multi-omics initiatives in oncology.
- Prioritizing these targets requires systematic, efficient, and objective computational approaches.
Approach:
- Developed a multifaceted computational assessment framework to evaluate biological and chemical space for target prioritization.
- Applied the approach to a set of 479 cancer-associated genes to demonstrate its utility.
Key Points:
- Revealed the inherent conflict between biological significance and chemical tractability in target selection.
- Highlighted significant knowledge gaps hindering objective decision-making in drug development.
- Identified potential drug repurposing opportunities for existing therapeutics.
Conclusions:
- The computational method provides an objective framework for prioritizing therapeutic targets.
- Identified poorly explored, yet druggable, cancer-associated proteins for small-molecule drug development.
- Aimed to facilitate more informed and efficient drug discovery and development processes.
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