scAmp analyzes focal gene amplifications at single-cell resolution
Matthew G Jones1,2,3,4, Natasha E Weiser1,2,5, King L Hung1,6
1Center for Personal Dynamic Regulomes, Stanford University, Stanford, CA, USA.
Biorxiv : the Preprint Server for Biology
|February 23, 2026
Summary
We developed scAmp, a new algorithm to detect extrachromosomal DNA (ecDNA) in single cells. This tool helps understand how ecDNA drives cancer progression and drug resistance.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- Extrachromosomal DNA (ecDNA) amplification drives tumor progression, drug resistance, and poor survival.
- Studying ecDNA heterogeneity and functional impact is challenging with current methods like whole genome sequencing (WGS).
Purpose of the Study:
- Introduce scAmp, a novel probabilistic algorithm for detecting and analyzing ecDNA from single-cell data.
- Enable detailed investigation of ecDNA's role in cancer at a single-cell level.
Main Methods:
- Developed scAmp, a probabilistic algorithm for ecDNA detection in single-cell datasets.
- Validated scAmp's accuracy against WGS using cell lines.
- Applied scAmp to analyze 73 patient tumors using single-cell ATAC-seq.
Main Results:
- scAmp demonstrates improved accuracy over WGS for ecDNA detection.
- The algorithm successfully analyzed subclonal evolution of ecDNA in patient tumors.
- Identified the impact of ecDNA amplification on chromatin accessibility.
Conclusions:
- scAmp is an effective tool for analyzing ecDNA from single-cell data.
- The algorithm facilitates research into ecDNA's effects on cancer cells and tumor development.
- scAmp is applicable to clinical histopathology and future retrospective/prospective studies.
