by: Toma Legrand, Track « Chemoinformatics for Organic Chemistry », Lisbon-Strasbourg, 2025
Cancer’s complexity and adaptability make it one of the most challenging diseases to treat. Conventional therapies like radiation and chemotherapy often fail to distinguish between cancerous and healthy cells, resulting in many unwanted side effects.
Several gene therapy approaches have been approved recently as cures for cancers. Behind the scenes, a number of computational approaches, involving chemoinformatics and bioinformatics, have made these successes possible. More accurate, personalized, and successful gene therapies are likely to revolutionize oncology.
A spectacular step toward personalized medicine for cancer treatment is monitoring gene expression in tumors relative to healthy tissues. In my opinion, one such game-changing computational approach is mapping out gene networks (Figure 1). Indeed, atypical gene expression processes are frequently associated with cancers. Gene networks rationalize the interactions between genes as observed in cells. Mapping such networks is a powerful way to identify potential weak gene therapy targets as they appear as anomalies in cancer cells compared to healthy ones.
Identification of a gene can lead to the identification of relevant protein targets—such as those resulting from the transcription of the identified gene. It is then possible to design small molecules targeting these proteins. Their 3D structures are invaluable for this task when known experimentally. If not, they can be deduced from homology modeling or artificial intelligence models, using AlphaFold [2], for instance.
Analogous to gene interactions, protein-protein interaction networks have become an extremely valuable strategy. This is illustrated, for instance, by the MaSIF software [3]: it builds a network connecting two proteins if their shape allows for molecular recognition. This complements the potential to develop personalized anti-cancer drugs disrupting presumably pathogenic protein-protein recognition or protein sequences suitable for gene therapy.
Developing a one-size-fits-all treatment is extremely difficult because tumors within the same person might differ greatly from one another. Computational techniques, on the other hand, are becoming increasingly efficient and enable the examination of large quantities of genetic data from various malignancies, facilitating personalized gene therapy. However, there exists a huge gap between numerical models of a tumor and reality: how can we ensure gene treatments are effectively delivered to the appropriate cells? Are these treatments safe? What are the ethical pitfalls of such therapies?
References:
[1] Rosenthal, S.B., Wright, S.N., Liu, S. et al. “Mapping the common gene networks that underlie related diseases.” Nat Protoc 18, 1745–1759 (2023). https://doi.org/10.1038/s41596-022-00797-1
[2] Jumper, J., Evans, R., Pritzel, A. et al. “Highly accurate protein structure prediction with AlphaFold.” Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2
[3] Gainza, P., Sverrisson, F., Monti, F. et al. “Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.” Nat Methods 17, 184–192 (2020). https://doi.org/10.1038/s41592-019-0666-6