Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

11.4K
Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
11.4K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
5.7K
Synteny and Evolution02:31

Synteny and Evolution

3.2K
John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
Around 80 million years ago, the human and mice lineages diverged from the common ancestor. During the course of evolution, the ancestral...
3.2K
Next-generation Sequencing03:00

Next-generation Sequencing

86.8K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
86.8K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.0K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.0K
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

5.7K
Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
5.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Homologous recombination deficiency and hemizygosity drive resistance in breast cancer.

Nature·2026
Same author

Evolution of oncogene amplification across 86,000 cancer cell genomes.

bioRxiv : the preprint server for biology·2026
Same author

Determinants of sensitivity to HER2-targeted antibody drug conjugates in urothelial cancer.

Nature communications·2025
Same author

Enabling whole genome sequencing analysis from FFPE specimens in clinical oncology.

Nature communications·2025
Same author

Genomics Define Malignant Transformation in Myeloma Precursor Conditions.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2025
Same author

Tracking clonal evolution during treatment in ovarian cancer using cell-free DNA.

Nature·2025

Related Experiment Video

Updated: May 23, 2025

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

89

Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints.

Gryte Satas1,2, Matthew A Myers1,2, Andrew McPherson1,2

  • 1Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Biorxiv : the Preprint Server for Biology
|March 10, 2025
PubMed
Summary

Ultra-low-coverage single-cell whole-genome sequencing (scWGS) can distinguish active from historical cancer mutational processes. This method reveals dynamic mutational patterns linked to tumor evolution and therapeutic resistance.

More Related Videos

Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

11.6K
Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

19.4K

Related Experiment Videos

Last Updated: May 23, 2025

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

89
Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

11.6K
Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

19.4K

Area of Science:

  • Genomics
  • Cancer Biology
  • Evolutionary Biology

Background:

  • Cancer's natural history is shaped by ongoing mutagenesis, creating genetic diversity.
  • Distinguishing active from historical mutational processes is crucial for understanding tumor evolution, presentation, and therapeutic resistance.
  • Bulk sequencing typically captures only historical mutational signatures, limiting insights into dynamic processes.

Purpose of the Study:

  • To investigate if ultra-low-coverage single-cell whole-genome sequencing (scWGS) can differentiate between historical and active mutational processes in cancer.
  • To develop a method for robustly analyzing single nucleotide variants (SNVs) in sparse scWGS data.
  • To uncover temporal and spatial patterns of mutagenesis in various cancer types.

Main Methods:

  • Introduced ArtiCull, a novel method to identify and remove SNV artifacts in scWGS data by utilizing evolutionary constraints.
  • Applied ArtiCull to analyze scWGS data from pancreatic ductal adenocarcinoma (PDAC), triple-negative breast cancer (TNBC), and high-grade serous ovarian cancer (HGSOC).
  • Examined mutation patterns in therapy-treated and untreated cancer models to identify active mutational signatures.

Main Results:

  • Demonstrated that scWGS data, despite sparsity, contains valuable information on dynamic mutational processes.
  • Observed a temporal increase in mismatch repair deficiency (MMRd) in PDAC.
  • Identified therapy-induced mutagenesis and APOBEC3 inactivation in cisplatin-treated TNBC xenografts.
  • Revealed distinct APOBEC3 mutagenesis patterns and late tumor-wide activation in HGSOC.
  • Detected clone-specific increases in SBS17 activity associated with recurrence in HGSOC.

Conclusions:

  • Ultra-low-coverage scWGS is a powerful tool for studying active mutational processes in cancer.
  • This approach can provide insights into ongoing clonal evolution and mechanisms of therapeutic resistance.
  • Findings highlight the potential of scWGS for dissecting dynamic mutagenic landscapes in diverse cancers.