ArticleFrontiers in immunology2022
Machine learning models based on immunological genes to predict the response to neoadjuvant therapy in breast cancer patients.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- High Expression of Aurora Kinase A (AURKA) is Associated with Aggressive Tumor Biology and Therapeutic Response in ER+/HER2- Breast Cancer.Annals of surgical oncology · 2026Article
- Association of tumor Schwann cells with epithelial-mesenchymal transition and immune-suppressive microenvironment in triple-negative breast cancer.Breast cancer research and treatment · 2026Article
- A Neuron Projection Guidance Signature Associates with Immune-Cold Tumor Microenvironment and Poor Outcomes in Breast Cancer.Annals of surgical oncology · 2026Article
- Ki67 Gene Expression is Associated with Immune Cell Infiltration and Neoadjuvant Chemotherapy Response in ER+/HER2- Breast Cancer.Annals of surgical oncology · 2026Article
- Tumor-infiltrating lymphocytes display prognostic signatures associated with chemotherapy response in TNBC patients.iScience · 2026Article
- Expression of Bruton's Tyrosine Kinase Reflects Immune Cells Infiltration and Cell Proliferation in Breast Cancer.World journal of oncology · 2026Article
- Ensemble Machine Learning on Bulk RNA-Seq Identifies 17-Gene Signature Predicting Neoadjuvant Chemotherapy Response in Breast Cancer.Current issues in molecular biology · 2026Article
- Unpacking Genomic Biomarkers for Programmed Cell Death Receptor-1 Immunotherapy Success in Non-Small Cell Lung Cancer Using Deep Neural Networks: Quantitative Study.JMIR bioinformatics and biotechnology · 2026Article
- CA9+ cancer-associated fibroblasts cooperate with SPP1+ tumor-associated macrophages driving immune resistance in triple-negative breast cancer.Cellular and molecular life sciences : CMLS · 2026Article
- Identification of an Immunotherapy-Associated CD1cInternational journal of women's health · 2026Article
- Artificial neural network-based immune biomarker signature predicts pathological complete response to neoadjuvant chemotherapy in HER2-negative breast cancer.Frontiers in oncology · 2026Article
- The role of the intratumoral microbiota in breast cancer metastasis and immune regulation: mechanisms and therapeutic implications.Frontiers in immunology · 2026Review
- Stand-alone Transcriptional Immune Response Prediction in Primary Triple-Negative Breast Cancer.Cancer research communications · 2025Article
- DEPTH2 score was associated with cell proliferation and immune cell infiltrations but not with systemic treatment response in breast cancer.Scientific reports · 2025Article
- Computer-Aided Drug Design Across Breast Cancer Subtypes: Methods, Applications and Translational Outlook.International journal of molecular sciences · 2025Review
- Clinical significance of androgen receptor in unilateral invasive breast cancer in women.European journal of medical research · 2025Article
- Clinical prediction of pathological complete response in breast cancer: a machine learning study.BMC cancer · 2025Article
- MammOnc-DB, an integrative breast cancer data analysis platform for target discovery.NPJ breast cancer · 2025Article
- Comparing Random Survival Forests and Cox Regression for Nonresponders to Neoadjuvant Chemotherapy Among Patients With Breast Cancer: Multicenter Retrospective Cohort Study.Journal of medical Internet research · 2025Article
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Authors and funding
6 authors.
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Abstract
Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predict the responses to NAT. Although many models to predict the response to NAT based on gene expression determined by the microarray platform have been proposed, their applications in clinical practice are limited due to the data normalization methods during model building and the disadvantages of the microarray platform compared with the RNA-seq platform. In this study, we first reconfirmed the correlation between immune profiles and pCR in an RNA-seq dataset. Then, we employed multiple machine learning algorithms and a model stacking strategy to build an immunological gene based model (Ipredictor model) and an immunological gene and receptor status based model ICpredictor model) in the RNA-seq dataset. The areas under the receiver operator characteristic curves for the Ipredictor model and ICpredictor models were 0.745 and 0.769 in an independent external test set based on the RNA-seq platform, and were 0.716 and 0.752 in another independent external test set based on the microarray platform. Furthermore, we found that the predictive score of the Ipredictor model was correlated with immune microenvironment and genomic aberration markers. These results demonstrated that the models can accurately predict the response to NAT for BC patients and will contribute to individualized therapy.
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