Algorithmically defined therapeutic targets: integrating single-cell transfer learning frameworks with small molecule...

Area of Science:

• Computational Biology
• Pharmacology
• Genomics

Background:

• Disease microenvironment heterogeneity drives therapeutic failure and drug resistance.
• Traditional pharmacogenomics struggles with single-cell resolution, obscuring rare resistant cell populations.
• Predicting drug responses precisely at the single-cell level remains a significant challenge.

Purpose of the Study:

• To present a closed-loop strategy integrating computational pharmacology and cell biology.
• To enable virtual prediction of cellular drug sensitivity using computational frameworks.
• To elucidate mechanisms of drug resistance and demonstrate therapeutic reprogramming strategies.

Main Methods:

• Utilized Deep Transfer Learning and Domain Adaptation algorithms (e.g., scDEAL, SCAD) for knowledge transfer from cell lines to clinical single-cell data.
• Investigated chemotherapy-induced Transcriptional Stress States and their interactions with stromal cells.
• Demonstrated reprogramming of pathological states using small-molecule drugs.

Main Results:

• Successfully predicted cellular drug sensitivity virtually, bypassing the need for experimental labels.
• Elucidated co-evolutionary mechanisms between resistant cells and inflammatory stromal cells, forming an immunosuppressive barrier.
• Showcased drug-induced reprogramming of specific pathological states, including macrophage polarization and osteogenic differentiation.

Conclusions:

• The integrated "algorithm prediction-mechanism elucidation-drug intervention" strategy offers a novel paradigm for precision therapy.
• This approach can reverse disease-associated cell fates by targeting resistant subpopulations.
• Enables more accurate and personalized treatment strategies for complex diseases.