Leveraging Single-Cell RNA Sequencing Experiments to Model Intratumor Heterogeneity

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Targeted cancer therapies are crucial but often face tumor resistance.
• Intratumor heterogeneity, driven by cellular subclones, fuels this resistance.
• Single-cell methods are essential for revealing gene expression heterogeneity.

Purpose of the Study:

• To introduce a General Diversity Index (GDI) for quantifying cellular heterogeneity.
• To relate GDI to cancer evolution and resistance mechanisms.
• To demonstrate the utility of GDI using single-cell RNA sequencing data.

Main Methods:

• Analysis of single-cell RNA sequencing data from diverse patient samples.
• Development of a computational pipeline for sample normalization and clustering.
• Application of GDI for mathematical interpretation of cellular diversity.

Main Results:

• GDI effectively quantified heterogeneity in various cancer and healthy samples.
• GDI revealed differences in clonal richness and potential key drivers.
• GDI identified heterogeneity changes where standard measures like Shannon index failed.

Conclusions:

• GDI is a robust tool for quantifying cellular heterogeneity across diverse conditions.
• The GDI approach offers a broader spectrum for assessing heterogeneity compared to traditional indices.
• This method can be widely applied to understand disease evolution and treatment resistance.