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

Targeted Cancer Therapies

Targeted Cancer Therapies

The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...

You might also read

Related Articles

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

Sort by

Same author

A multi-scale cognitive interaction model of instrument operations at the Linac Coherent Light Source.

The Review of scientific instruments·2025

Same author

Virtual Trials: Causally-validated treatment effects efficiently learned from an observational cancer registry.

Artificial intelligence in medicine·2023

Same author

Is Cancer Solvable? Towards Efficient and Ethical Biomedical Science.

The Journal of law, medicine & ethics : a journal of the American Society of Law, Medicine & Ethics·2019

Same author

Prototyping a precision oncology 3.0 rapid learning platform.

BMC bioinformatics·2018

Same author

Precision medicine: Fantasy meets reality.

Science (New York, N.Y.)·2016

Same author

UNDERSTANDING NITROGEN LIMITATION IN AUREOCOCCUS ANOPHAGEFFERENS (PELAGOPHYCEAE) THROUGH cDNA AND qRT-PCR ANALYSIS(1).

Journal of phycology·2016

Same journal

Improving neoadjuvant and perioperative therapy in non-small-cell lung cancer.

Nature reviews. Clinical oncology·2026

Same journal

Patient value over patent value: the mandate for open-source oncology.

Nature reviews. Clinical oncology·2026

Same journal

Cancer burden and control in China: landscape, trends and challenges.

Nature reviews. Clinical oncology·2026

Same journal

Antibody-drug conjugates in gynaecological cancers: opportunities and challenges.

Nature reviews. Clinical oncology·2026

Same journal

New standards and new constraints redefine treatment sequencing in platinum-resistant ovarian cancer.

Nature reviews. Clinical oncology·2026

Same journal

Teclistamab monotherapy improves survival in RRMM.

Nature reviews. Clinical oncology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 3, 2026

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

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

Rapid learning for precision oncology.

Jeff Shrager¹, Jay M Tenenbaum²

¹CommerceNet, 955-A Alma Street, Palo Alto, CA 94301, USA.

Nature Reviews. Clinical Oncology

|January 22, 2014

Summary

This summary is machine-generated.

Precision Oncology 3.0 advances cancer care by analyzing tumor networks to guide targeted therapies. Rapid Learning Precision Oncology refines these models with each treatment, improving patient outcomes.

More Related Videos

Automating Tumor Implantation in Zebrafish Larvae for Cancer Research and Medicine

Automating Tumor Implantation in Zebrafish Larvae for Cancer Research and Medicine

Published on: September 19, 2025

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Published on: September 8, 2023

Related Experiment Videos

Last Updated: May 3, 2026

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

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

Automating Tumor Implantation in Zebrafish Larvae for Cancer Research and Medicine

Automating Tumor Implantation in Zebrafish Larvae for Cancer Research and Medicine

Published on: September 19, 2025

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

Published on: September 8, 2023

Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Precision Oncology 3.0 utilizes panomics and statistical reverse engineering to identify tumor-driving networks.
Targeted therapies are combined to attack these identified drivers in cancer patients.

Purpose of the Study:

To review the paradigm of Rapid Learning Precision Oncology (RLPO).
To highlight RLPO's approach of using each treatment as a data-generating probe.
To discuss the challenges in implementing RLPO.

Main Methods:

Review of the RLPO paradigm.
Discussion of integrating patient treatment data for model refinement.
Analysis of challenges in data capture, extrapolation, and coordination.

Main Results:

RLPO treats patients while simultaneously validating and refining predictive models.
Every treatment event serves as a probe for model improvement.
Successful implementation requires advanced analytical tools and efficient data extrapolation.

Conclusions:

RLPO offers a dynamic approach to personalize cancer treatment.
Overcoming implementation challenges is crucial for advancing precision oncology.
Addressing economic, social, and structural barriers is essential for widespread adoption.