ATAXIC: An Algorithm to Quantify Transcriptomic Perturbation Heterogeneity in Single Cancer Cells.

Area of Science:

• Genomics and Bioinformatics
• Cancer Research
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables detailed analysis of intratumor heterogeneity.
• Understanding transcriptomic profiles in individual cancer cells is crucial for deciphering tumor complexity.
• Existing methods may not fully capture the nuances of transcriptomic alterations at the single-cell level.

Purpose of the Study:

• To introduce ATAXIC, a novel algorithm for quantifying transcriptomic perturbation (TP) heterogeneity in single cancer cells.
• To assess the correlation between ATAXIC scores and key cancer phenotypes, including proliferation, oncogenic signatures, DNA damage repair, and treatment resistance.
• To explore the relationship between TP heterogeneity and clinical outcomes across various cancer types.

Main Methods:

• Developed the ATAXIC algorithm to calculate TP heterogeneity based on the standard deviation of absolute z-scored gene expression values.
• Applied ATAXIC to scRNA-seq datasets from eight distinct cancer types.
• Correlated ATAXIC scores with established biological signatures and clinical outcome data.

Main Results:

• ATAXIC scores positively correlated with proliferation, oncogenic signatures, DNA damage repair, and treatment resistance.
• Significant variation in ATAXIC scores was observed across cancer types, with lung cancer and melanoma showing lower heterogeneity than clear cell renal cell carcinoma.
• Lower TP heterogeneity in lung cancer and melanoma may be linked to higher response rates to immune checkpoint inhibitors.

Conclusions:

• ATAXIC provides a robust method for quantifying TP heterogeneity in single cancer cells.
• TP heterogeneity is associated with tumor aggressiveness and clinical outcomes.
• ATAXIC offers valuable insights into tumor biology and potential therapeutic strategies, particularly for immune checkpoint inhibitor response.