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

Combination Therapies and Personalized Medicine

Combination Therapies and Personalized Medicine

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...

You might also read

Related Articles

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

Sort by

Same author

Editorial: New advances in embryo development and embryo-endometrial interface.

Frontiers in endocrinology·2026

Same author

ExposoGraph: An Interactive Platform for Carcinogen Bioactivation and Detoxification Pathway Visualization.

Medical oncology (Northwood, London, England)·2026

Same author

Tryptophan metabolism in embryo implantation and decidualization.

Tissue & cell·2026

Same author

ExposoGraph: An Interactive Platform for Carcinogen Bioactivation and Detoxification Pathway Visualization.

bioRxiv : the preprint server for biology·2026

Same author

Innate-like T Cell Biology in the Tumor Microenvironment Implications for Cancer Immunotherapy.

Cells·2026

Same author

Proteomics discovery of MTDH and SND1 interaction vulnerabilities in ovarian cancer.

Scientific reports·2025

Search research articles

Related Experiment Video

Updated: Aug 11, 2025

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

Published on: February 6, 2015

A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer.

Ahmad Nasimian^1,2, Mehreen Ahmed^1,2, Ingrid Hedenfalk³

¹Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden.

Computational and Structural Biotechnology Journal

|February 3, 2023

Summary

This study developed a machine learning model to predict cisplatin chemotherapy resistance using gene expression data. BCL2L1 was identified as a key gene, and its inhibition enhanced cisplatin efficacy.

Keywords:

BCL-XL Elastic net Ovarian cancer Random Forest WNT/β-catenin XGBoost

More Related Videos

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Author Spotlight: Unveiling the Role of TMOD3 in Platinum Resistance and Immune Infiltration in Ovarian Cancer

Author Spotlight: Unveiling the Role of TMOD3 in Platinum Resistance and Immune Infiltration in Ovarian Cancer

Published on: August 2, 2024

Related Experiment Videos

Last Updated: Aug 11, 2025

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

Published on: February 6, 2015

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Author Spotlight: Unveiling the Role of TMOD3 in Platinum Resistance and Immune Infiltration in Ovarian Cancer

Author Spotlight: Unveiling the Role of TMOD3 in Platinum Resistance and Immune Infiltration in Ovarian Cancer

Published on: August 2, 2024

Area of Science:

Oncology
Bioinformatics
Genomics

Background:

Cisplatin is a cornerstone chemotherapy for many cancers, but variable patient responses due to resistance limit its effectiveness.
Understanding the molecular mechanisms of cisplatin resistance is crucial for improving treatment outcomes.

Purpose of the Study:

To develop a predictive model for cisplatin sensitivity using gene expression data.
To identify key genes associated with cisplatin resistance.
To explore therapeutic strategies targeting identified resistance mechanisms.

Main Methods:

Analysis of cisplatin-perturbed gene expression and pathway enrichment.
Development of a cisplatin sensitivity prediction model using the TabNet machine learning algorithm.
Feature importance analysis to identify key genes and validation through pharmacological inhibition.

Main Results:

The TabNet model achieved over 80% accuracy in predicting cisplatin sensitivity, outperforming other machine learning algorithms.
BCL2L1 was identified as a significant gene contributing to cisplatin resistance, particularly in ovarian cancer.
Pharmacological inhibition of BCL2L1 demonstrated synergistic effects, enhancing cisplatin efficacy.

Conclusions:

A novel tool for predicting cisplatin sensitivity based on gene expression signatures has been developed.
BCL2L1 is a critical determinant of cisplatin resistance and a potential therapeutic target.
Targeting BCL2L1 offers a promising strategy to overcome cisplatin resistance and improve cancer treatment.