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

Tumor Progression02:07

Tumor Progression

7.1K
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
7.1K
Cancer Survival Analysis01:21

Cancer Survival Analysis

600
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
600

You might also read

Related Articles

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

Sort by
Same author

Association of Genetic Liability to Psychiatric Disorders with Peripheral Metabolic Dysregulation.

medRxiv : the preprint server for health sciences·2026
Same author

Population-scale Y chromosome assemblies reveal recurrent remodeling within constrained architectures.

bioRxiv : the preprint server for biology·2026
Same author

Disinfection of hospital sink drains enriches pseudomonadota and efflux pump-mediated antibiotic resistance in reestablished biofilms.

Nature communications·2026
Same author

Transcriptomic and phenotypic convergence of neurodevelopmental disorder risk genes in vitro and in vivo.

Nature neuroscience·2026
Same author

Epigenetic characterization of pseudogenes across human tissues.

Genome research·2026
Same author

Metabolic characterization of tumor-immune interactions by multiplexed immunofluorescence reveals spatial mechanisms of immunotherapy response in non-small cell lung carcinoma (NSCLC).

Nature communications·2026
Same journal

Large-scale discovery and annotation of substructure patterns in mass spectrometry profiles.

Nature communications·2026
Same journal

Salmonella SopB suppresses post-transcriptionally regulated cytokine release to reduce early tissue inflammation and delay disease progression.

Nature communications·2026
Same journal

A human-specific microRNA controls the timing of excitatory synaptogenesis.

Nature communications·2026
Same journal

An HMA-like integrated domain in the wheat tandem kinase WTK4 recognises an RNase-like pathogen effector.

Nature communications·2026
Same journal

Learning regularities in noise engages both neural predictive activity and representational changes.

Nature communications·2026
Same journal

The H3K4 methyltransferase KMT2D is an essential cofactor for GATA1 at erythroid gene enhancers.

Nature communications·2026
See all related articles

Related Experiment Video

Updated: Dec 29, 2025

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

10.5K

Estimating growth patterns and driver effects in tumor evolution from individual samples.

Leonidas Salichos1,2, William Meyerson1,2, Jonathan Warrell1,2

  • 1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA.

Nature Communications
|February 7, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to identify cancer drivers using variant-allele frequencies (VAFs) from a single tumor sample. This approach overcomes limitations of recurrence-based methods and aids in personalized cancer driver diagnosis.

More Related Videos

Longitudinal Intravital Imaging of Brain Tumor Cell Behavior in Response to an Invasive Surgical Biopsy
09:17

Longitudinal Intravital Imaging of Brain Tumor Cell Behavior in Response to an Invasive Surgical Biopsy

Published on: May 3, 2019

7.7K
Generation of Microtumors Using 3D Human Biogel Culture System and Patient-derived Glioblastoma Cells for Kinomic Profiling and Drug Response Testing
09:24

Generation of Microtumors Using 3D Human Biogel Culture System and Patient-derived Glioblastoma Cells for Kinomic Profiling and Drug Response Testing

Published on: June 9, 2016

9.4K

Related Experiment Videos

Last Updated: Dec 29, 2025

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds
13:34

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

10.5K
Longitudinal Intravital Imaging of Brain Tumor Cell Behavior in Response to an Invasive Surgical Biopsy
09:17

Longitudinal Intravital Imaging of Brain Tumor Cell Behavior in Response to an Invasive Surgical Biopsy

Published on: May 3, 2019

7.7K
Generation of Microtumors Using 3D Human Biogel Culture System and Patient-derived Glioblastoma Cells for Kinomic Profiling and Drug Response Testing
09:24

Generation of Microtumors Using 3D Human Biogel Culture System and Patient-derived Glioblastoma Cells for Kinomic Profiling and Drug Response Testing

Published on: June 9, 2016

9.4K

Area of Science:

  • Genomics
  • Cancer Biology
  • Computational Biology

Background:

  • Tumors accumulate numerous mutations, with recurrence-based methods identifying cancer drivers.
  • Traditional methods require large patient cohorts and struggle with low-recurrence mutations.
  • Ultra-deep sequencing enables precise variant-allele frequency (VAF) measurement, revealing tumor evolutionary paths.

Purpose of the Study:

  • To develop a method for identifying cancer drivers and quantifying tumor growth using only the VAF spectrum from an individual sample.
  • To overcome the limitations of cohort-based driver identification methods.
  • To enable personalized driver diagnosis from single-sample sequencing data.

Main Methods:

  • Developed a novel computational method analyzing the VAF spectrum of mutations within a single tumor.
  • Quantified driver impact by measuring hitchhiking mutations preceding a driver.
  • Validated the method using simulation models and 993 tumors from the PCAWG Consortium.

Main Results:

  • The method successfully identifies cancer drivers and quantifies tumor growth based on VAF spectrum perturbations.
  • Validation against existing datasets confirmed the method's accuracy.
  • Applied to an ultra-deep sequenced acute myeloid leukemia (AML) tumor, identifying known and novel driver candidates.

Conclusions:

  • The developed framework enables personalized cancer driver identification using single-sample sequencing data.
  • This approach offers a powerful tool for understanding individual tumor evolution.
  • Presents new opportunities for precision oncology and targeted therapies.