Personalized anticancer therapy selection using molecular landscape topology and thermodynamics

Area of Science:

• Bioinformatics
• Computational Biology
• Oncology

Background:

• Personalized anticancer therapy necessitates integrating complex bioinformatics data.
• Novel mathematical and physical approaches can enhance therapeutic target selection.

Purpose of the Study:

• To develop and apply a method integrating chemical thermodynamics and topology with RNA-seq data for low-grade glioma (LGG) target identification.
• To evaluate the potential for individualized therapies in distinct LGG subtypes.

Main Methods:

• Utilized chemical thermodynamics and two topology measures to link patient RNA-seq data with protein-protein interaction networks.
• Computed Gibbs free energy of protein-protein interaction networks and applied topological filtration (persistent homology).
• Determined optimal therapeutic targets using the Betti number, a topological network measure.

Main Results:

• Demonstrated that while some shared targets exist across astrocytoma, oligoastrocytoma, and oligodendroglioma, most patients require individualized therapies.
• Successfully applied the algorithm to identify potential therapeutic targets in patient data.

Conclusions:

• The developed method effectively integrates diverse data types for accurate therapeutic target selection in LGG.
• Highlights the importance of personalized therapeutic strategies over generalized approaches for LGG treatment.