Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Adaptive Mechanisms in Cancer Cells

Adaptive Mechanisms in Cancer Cells

Adaptive Mechanisms in Cancer Cells

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,...

Mouse Models of Cancer Study

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...

Mouse Models of Cancer Study

Mouse Models of Cancer Study

Cancer Therapies

Cancer Therapies

Cancer therapies are various modes of treatment, such as surgery, radiation therapy, and chemotherapy that are administered to cancer patients.
However, cancer treatments can pose several challenges, as therapies used to kill cancer cells are generally also toxic to normal cells. Moreover, cancer cells mutate rapidly and can develop resistance to chemical agents or radiation therapy. Besides, all types of cancer cells may not respond to the same therapy. Some cancer cells respond to one...

Cancer Therapies

Cancer Therapies

You might also read

Related Articles

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

Sort by

Same author

Updated ENIGMA recommendations for reporting germline variants in cancer susceptibility genes and their translation into twenty languages.

Journal of medical genetics·2026

Same author

A stress-induced PI3P complex controls autophagy in erythroid precursors.

iScience·2026

Same author

Mutagenesis of the PALB2 WD40 domain identifies variants defective in interaction with BRCA2 and DNA repair.

The Journal of biological chemistry·2025

Same author

Real-world effects of using gnomAD 4.1.0 and All of Us population reference data sets on reporting of variants of uncertain significance.

Genetics in medicine : official journal of the American College of Medical Genetics·2025

Same author

A Phosphoinositide Interacting Protein Coordinates Stress Precursor Activities.

bioRxiv : the preprint server for biology·2025

Same author

Heterozygous CELF4 variants in the N-term region crucial for the RNA-binding activity lead to neurodevelopmental disorder and obesity.

European journal of human genetics : EJHG·2025

Search research articles

Related Experiment Video

Updated: Apr 22, 2026

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

Incorporating computational resources in a cancer research program.

Nicholas T Woods¹, Ankita Jhuraney, Alvaro N A Monteiro

¹Cancer Epidemiology Program, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive, Tampa, FL, 33612, USA.

|October 18, 2014

Summary

This summary is machine-generated.

Cancer genetics research is rapidly advancing with new technologies and datasets. This guide helps molecular biology labs integrate diverse data for effective analysis and visualization, offering practical recommendations for a successful transition.

More Related Videos

Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

Related Experiment Videos

Last Updated: Apr 22, 2026

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Author Spotlight: Integrating Computational and Experimental Approaches in Precision Oncology

Published on: December 1, 2023

Reusable Single Cell for Iterative Epigenomic Analyses

Reusable Single Cell for Iterative Epigenomic Analyses

Published on: February 11, 2022

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

Area of Science:

Cancer Genetics
Computational Biology
Molecular Biology Research

Background:

Technological advancements have generated extensive, annotated cancer genetics datasets.
These resources are increasingly accessible to smaller laboratories for specific research questions.
Integrating diverse data sources is a critical challenge for experimental cancer biology programs.

Purpose of the Study:

To discuss the challenges and lessons learned in adapting a molecular biology lab to utilize new cancer genetics datasets.
To provide recommendations for effectively incorporating multi-source data for annotation, visualization, and analysis.
To highlight selected computational resources beneficial for cancer genetics research programs.

Main Methods:

Review of laboratory experiences in adapting to new data integration techniques.
Selection and incorporation of specific computational resources for data analysis.
Development of recommendations based on practical implementation and lessons learned.

Main Results:

Successful integration of diverse datasets requires significant adaptation of traditional molecular biology workflows.
Careful selection of computational tools is crucial for efficient data annotation, visualization, and analysis.
Practical recommendations are provided to facilitate this transition in experimental research settings.

Conclusions:

Transitioning to data-intensive cancer genetics research necessitates strategic planning and resource adaptation.
The discussed approach and selected tools can enhance the analytical capabilities of molecular biology laboratories.
Choosing appropriate computational resources is key to leveraging new datasets for impactful cancer research.