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Related Concept Videos

Mouse Models of Cancer Study02:43

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.
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Related Experiment Video

Updated: May 24, 2026

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

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Published on: October 10, 2018

Develop a Deep-Learning Model to Predict Cancer Immunotherapy Response Using In-Born Genomes.

Kai Yan, Zhiheng Zhou, Sihao Liu

    IEEE Journal of Biomedical and Health Informatics
    |March 28, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Predicting cancer patient response to immune checkpoint inhibitors (ICIs) is crucial. A novel deep learning model accurately identifies patients likely to benefit from ICIs based on germline whole-genome sequencing data.

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    Published on: April 30, 2021

    Area of Science:

    • Oncology
    • Genomics
    • Bioinformatics

    Background:

    • Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy, but response rates remain low (15-30%).
    • Predicting patient response to ICIs is essential due to high costs and potential adverse effects.
    • Accurate prediction methods are needed to optimize ICI treatment selection.

    Purpose of the Study:

    • To develop and validate a novel computational model for predicting cancer patient response to ICIs.
    • To identify germline genomic variants associated with ICI treatment efficacy and patient survival.
    • To enhance personalized cancer treatment strategies through predictive biomarkers.

    Main Methods:

    • Compiled germline whole-genome sequencing (WES) data from 37 melanoma patients and 700 publicly available ICI-treated cancer patients.
    • Developed a novel double-channel attention neural network (DANN) model for predicting ICI response.
    • Performed enrichment analysis on DANN-identified genes and correlated genomic variants with patient survival.

    Main Results:

    • The DANN model achieved high prediction accuracy (mean 0.95) and AUC (0.98), outperforming traditional machine learning methods.
    • Enrichment analysis suggested germline variants impacting the host immune system broadly influence ICI response.
    • Identified a set of 12 genes with genomic variants significantly associated with patient survival post-ICI treatment.

    Conclusions:

    • The DANN model offers a promising tool for accurately predicting ICI response in cancer patients.
    • Germline genomic variations play a significant role in modulating host immunity and subsequent response to ICIs.
    • The identified 12-gene set may serve as potential biomarkers for predicting survival outcomes in ICI-treated cancer patients.