scAmp analyzes focal gene amplifications at single-cell resolution

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 <i>scAmp</i>: a probabilistic algorithm for detecting and analyzing ecDNA from single-cell datasets. We demonstrate <i>scAmp's</i> improved accuracy over WGS approaches on well-characterized cell-lines and its applicability to clinical histopathology. We further showcase <i>scAmp</i> 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 <i>scAmp</i> 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.

Authors

Institutions

Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-02-15
DOI
https://doi.org/10.64898/2026.02.14.705928
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

scAmp analyzes focal gene amplifications at single-cell resolution

Kevin Yu, Aditi Gnanasekar, Ellis J. Curtis, B. Howitt et al.
bioRxiv (Cold Spring Harbor Laboratory)
Single-cell and spatial transcriptomics
article

scAmp analyzes focal gene amplifications at single-cell resolution

Kevin Yu, Aditi Gnanasekar, Ellis J. Curtis, B. Howitt, John C. Rose, Katerina Kraft, King L. Hung, Jens Luebeck, Paul S. Mischel, Valeh Valiollah Pour Amiri, Vineet Bafna, Sangya Agarwal, Howard Y Chang, Matthew G Jones, Leena Satpathy
article en

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 <i>scAmp</i>: a probabilistic algorithm for detecting and analyzing ecDNA from single-cell datasets. We demonstrate <i>scAmp's</i> improved accuracy over WGS approaches on well-characterized cell-lines and its applicability to clinical histopathology. We further showcase <i>scAmp</i> 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 <i>scAmp</i> 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.

bioRxiv (Cold Spring Harbor Laboratory)
Scripps Research Institute (US), Scripps Health (US), Amgen (United States) (US), Yale University (US), University of California San Diego (US), Massachusetts Institute of Technology (US), Stanford University (US)
Good health and well-being, No poverty
Openalex Percentile: Top 55%
Single-cell and spatial transcriptomics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.