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

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

5.1K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
5.1K
Cancer Survival Analysis01:21

Cancer Survival Analysis

456
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...
456
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

7.8K
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...
7.8K
Treatment Resistant Cancers02:56

Treatment Resistant Cancers

3.4K
Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
3.4K
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

5.9K
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,...
5.9K

You might also read

Related Articles

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

Sort by
Same author

Reoperation rates following breast-conserving surgery in a contemporary cohort.

BMC surgery·2026
Same author

Immune profiling in residual disease: refining risk stratification after neoadjuvant therapy in HER2-positive breast cancer.

Translational cancer research·2026
Same author

Clinical characteristics and prognostic impact of HER2 low expression in breast cancer subtypes from a Brazilian real-world cohort.

Scientific reports·2025
Same author

Is axillary surgery still justified in DCIS diagnosed via vacuum-assisted biopsy?

World journal of surgical oncology·2025
Same author

Gastrin-releasing peptide receptor: a promising new biomarker to identify cervical precursor lesions and cancer.

Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia·2025
Same author

Oncological outcomes of breast-conserving surgery versus mastectomy following neoadjuvant chemotherapy in a contemporary multicenter cohort.

Scientific reports·2025

Related Experiment Video

Updated: Sep 15, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Optimizing breast cancer therapy: chemoressitance and machine learning for precision prediction.

Martina Lichtenfels1, Matheus G S Dalmolin2, Julia Caroline Marcolin1

  • 1Translational Research, Ziel Biosciences, Porto Alegre, Brazil.

Personalized Medicine
|July 16, 2025
PubMed
Summary

A novel platform accurately predicts breast cancer (BC) chemoresistance. Machine learning models identified key biomarkers to predict treatment response, paving the way for personalized BC medicine.

Keywords:
Breast neoplasmsdrug resistancedrug therapyneoadjuvant chemotherapyresidual neoplasms

More Related Videos

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
07:45

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer

Published on: May 18, 2020

6.3K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

200

Related Experiment Videos

Last Updated: Sep 15, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
07:45

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer

Published on: May 18, 2020

6.3K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

200

Area of Science:

  • Oncology
  • Biotechnology
  • Artificial Intelligence in Medicine

Background:

  • Breast cancer (BC) poses a significant challenge, with treatment response varying widely among patients.
  • Neoadjuvant chemotherapy (NACT) is a common treatment, but predicting response remains difficult.
  • Understanding tumor chemoresistance is crucial for developing effective, personalized treatment strategies.

Purpose of the Study:

  • To validate a novel in vitro platform for assessing breast cancer chemoresistance.
  • To evaluate resistance profiles of treatment-naïve and residual tumors post-NACT.
  • To develop a machine learning model predicting NACT response using clinical biomarkers.

Main Methods:

  • Breast cancer cells from primary and residual tumors were cultured on a chemoresistance platform.
  • Drug resistance was quantified based on cell viability after 72-hour chemotherapy exposure.
  • XGBoost algorithm and SHAP interpretation analyzed clinicopathological data to predict NACT response.

Main Results:

  • Residual disease tumors showed higher drug resistance and poorer prognosis than upfront surgery cases.
  • AI analysis of 1,012 patients achieved 82% accuracy in predicting pathological response and residual disease.
  • Key predictors for NACT response included age, ER status, tumor grade/size, axillary status, and HER2 status.

Conclusions:

  • The chemoresistance platform demonstrated utility in identifying resistance patterns for precision medicine.
  • The XGBoost algorithm accurately predicted NACT response, supporting AI integration in personalized BC treatment.
  • Combining functional precision medicine with AI offers a promising approach for tailoring breast cancer therapies.