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.
Abstract:
Oncogene amplification on extrachromosomal DNA (ecDNA) is a common driver of tumor progression and is associated with acquired drug resistance and poor patient survival. While whole genome sequencing (WGS) studies have revealed the landscape of genes amplified on ecDNA in tumors, it remains challenging to study the subclonal heterogeneity and functional (e.g., transcriptomic) consequences of ecDNA on tumors. To address this, we introduce scAmp: a probabilistic algorithm for detecting and analyzing ecDNA from single-cell datasets. We demonstrate scAmp's improved accuracy over WGS approaches on well-characterized cell-lines and its applicability to clinical histopathology. We further showcase scAmp by analyzing 73 patient tumors profiled with single-cell ATAC-seq, where we analyze the subclonal evolution of ecDNA+ subclones and identify the effect of ecDNA amplifications on the chromatin accessibility landscape of cancer cells. Together, we anticipate that scAmp will broadly enable further studies - both retrospective and prospective - that dissect critical questions of how ecDNA affect cancer cells and the tumors in which they reside.
Insights
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.
