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

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...
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

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...
Mismatch Repair01:20

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
Mismatch Repair01:36

Mismatch Repair

Overview
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

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,...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

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,...

You might also read

Related Articles

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

Sort by
Same author

Evidence for G6PD variant classification from multiplexed functional assays.

Genome biology·2026
Same author

Image-based, pooled phenotyping reveals multidimensional, disease-specific variant effects.

Cell·2026
Same author

CountESS: a flexible, graphical pipeline tool for deep mutational scanning analysis.

bioRxiv : the preprint server for biology·2026
Same author

ClinGen API platform for classification of human genetic variants.

Cell genomics·2026
Same author

VEFill: accurate and generalizable deep mutational scanning score imputation across protein domains.

Molecular systems biology·2026
Same author

A scalable approach to resolving variants of uncertain significance.

bioRxiv : the preprint server for biology·2026
Same journal

Tau protein as a regulator of mitochondrial function and dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A scalable, dividing cell model for the robust propagation and quantification of human sporadic Creutzfeldt-Jakob disease prions.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Epigenetic regulation of mesenchymal BMP signaling directs postnatal organ innervation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Single-shot wide-field biochemical imaging at 1 kHz frame rate.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Morphogenesis and topological evolution of a frustrated nematic liquid crystal under confinement.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

B cell-intrinsic CXCR3 drives efficient generation of ectopic pulmonary germinal center responses to influenza A virus infection.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

Related Experiment Video

Updated: Jun 17, 2026

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
11:15

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

Mutation patterns in cancer genomes.

Alan F Rubin1, Phil Green

  • 1Department of Genome Sciences, University of Washington, Foege Building S-250, Box 355065, 1705 NE Pacific Street, Seattle, WA 98195-5065, USA. afrubin@u.washington.edu

Proceedings of the National Academy of Sciences of the United States of America
|December 10, 2009
PubMed
Summary
This summary is machine-generated.

Cancer sequencing data reveals mutation patterns, not just genes. Analysis shows mutation trends are driven by underlying processes, not selection, offering insights into cancer development.

More Related Videos

Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Related Experiment Videos

Last Updated: Jun 17, 2026

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
11:15

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors

Published on: September 20, 2016

Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Area of Science:

  • Genomics
  • Cancer Biology
  • Molecular Evolution

Background:

  • Large-scale cancer sequencing studies generate vast amounts of data, including mutations not directly linked to cancer progression (passenger mutations).
  • Passenger mutations can provide valuable insights into the mutational processes active within cancer cells.
  • Understanding these processes is crucial for deciphering cancer development and identifying potential therapeutic targets.

Purpose of the Study:

  • To analyze patterns of nucleotide substitution in various cancer types using existing sequencing data.
  • To differentiate between mutational processes and selection pressures in shaping observed mutation frequencies.
  • To investigate the origins of increased CpG mutation frequency and explore potential influences of gene expression on mutation patterns.

Main Methods:

  • Analysis of published large-scale cancer sequencing datasets.
  • Examination of nucleotide substitution patterns across different cancer types.
  • Statistical assessment of mutation frequencies and their distribution relative to genomic features like CpG islands.
  • Comparison of observed mutation asymmetries with known patterns in germline mutations.

Main Results:

  • Selection pressures were found to affect only a small fraction of mutations, indicating that observed trends are primarily due to mutational processes.
  • Increased CpG mutation frequency in some cancers occurs mainly outside CpG islands and shores, refuting the methylation-deamination hypothesis.
  • A directional mutational bias (A-->G vs. T-->C) was observed in some cancers, potentially linked to gene expression levels.
  • Mutation frequencies at specific dinucleotide hotspots can serve as a reliable indicator for detecting technical artifacts in sequencing studies.

Conclusions:

  • Passenger mutation data from cancer sequencing is a rich resource for understanding mutational mechanisms.
  • The study refutes the hypothesis that increased CpG mutations are solely a byproduct of methylation in CpG islands/shores.
  • Gene expression may influence cancer-specific mutational processes, suggesting a link between transcription and mutation.
  • Analyzing mutation hotspots offers a method to improve the quality control of large-scale cancer genomic studies.