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Original Articles
- Breast cancer
- Machine Learning–Based Prognostic Gene Signature for Early Triple-Negative Breast Cancer
- Ju Won Kim, Jonghyun Lee, Sung Hak Lee, Sangjeong Ahn, Kyong Hwa Park
- Cancer Res Treat. 2025;57(3):731-740. Published online November 19, 2024
- DOI: https://doi.org/10.4143/crt.2024.937
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Abstract
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Supplementary Material
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ePub - Purpose
This study aimed to develop a machine learning–based approach to identify prognostic gene signatures for early-stage triple-negative breast cancer (TNBC) using next-generation sequencing data from Asian populations.
Materials and Methods
We utilized next-generation sequencing data to analyze gene expression profiles and identify potential biomarkers. Our methodology involved integrating various machine learning techniques, including feature selection and model optimization. We employed logistic regression, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) curves to validate the identified gene signatures.
Results
We identified a gene signature significantly associated with relapse in TNBC patients. The predictive model demonstrated robustness and accuracy, with an area under the ROC curve of 0.9087, sensitivity of 0.8750, and specificity of 0.9231. The Kaplan-Meier survival analysis revealed a strong association between the gene signature and patient relapse, further validated by logistic regression analysis.
Conclusion
This study presents a novel machine learning-based prognostic tool for TNBC, offering significant implications for early detection and personalized treatment. The identified gene signature provides a promising approach for improving the management of TNBC, contributing to the advancement of precision oncology. -
Citations
Citations to this article as recorded by
- Machine Learning in Clinical Decision Making: Applications, Data Limitations and Multidisciplinary Perspectives
Augusta Raţiu, Emilia-Loredana Pop
Applied Sciences.2026; 16(2): 785. CrossRef
- Machine Learning in Clinical Decision Making: Applications, Data Limitations and Multidisciplinary Perspectives
- 3,847 View
- 147 Download
- 2 Web of Science
- 1 Crossref
- Molecular Classification of Breast Cancer Using Weakly Supervised Learning
- Wooyoung Jang, Jonghyun Lee, Kyong Hwa Park, Aeree Kim, Sung Hak Lee, Sangjeong Ahn
- Cancer Res Treat. 2025;57(1):116-125. Published online June 25, 2024
- DOI: https://doi.org/10.4143/crt.2024.113
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Abstract
PDFSupplementary MaterialPubReaderePub
- Purpose
The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer.
Materials and Methods
Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches.
Results
The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers.
Conclusion
The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice. -
Citations
Citations to this article as recorded by- Computational Methods for Breast Cancer Molecular Profiling using Routine Histopathology: A Review
Suchithra Kunhoth, Somaya Al-maadeed, Younes Akbari, Rafif Mahmood Al Saady
Archives of Computational Methods in Engineering.2025;[Epub] CrossRef - Development and validation of an artificial intelligence system for triple-negative breast cancer identification and prognosis prediction: a multicentre retrospective study
Xiu-Ming Zhang, Hua-Jun Zhou, Qing Chen, Xi Wang, Yu-Juan Fu, Cheng Jin, Feng-Tao Zhou, Jing-Ping Wang, Qiu-Yu Cai, Ji-Li Wang, Bo Luo, Mao-Tong Hu, Cai-Yun Yao, Xia Yang, Ya-Li Xu, Jing Zhang, Hao Chen
eClinicalMedicine.2025; 89: 103557. CrossRef - An interpretable hybrid deep learning framework for gastric cancer diagnosis using histopathological imaging
Tengfei Ren, Vijay Govindarajan, Sami Bourouis, Xiangkun Wang, Shanbao Ke
Scientific Reports.2025;[Epub] CrossRef - Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models
Chiara Frascarelli, Konstantinos Venetis, Antonio Marra, Alberto Concardi, Marianna D’Ercole, Elisa Mangione, Mariachiara Negrelli, Francesca Maria Porta, Sakshi Keswani, Giuseppe Curigliano, Elena Guerini-Rocco, Nicola Fusco
npj Breast Cancer.2025;[Epub] CrossRef
- Computational Methods for Breast Cancer Molecular Profiling using Routine Histopathology: A Review
- 5,289 View
- 264 Download
- 4 Web of Science
- 4 Crossref
- Gastrointestinal cancer
- Molecular and Immune Profiling of Syngeneic Mouse Models Predict Response to Immune Checkpoint Inhibitors in Gastric Cancer
- Dagyeong Lee, Junyong Choi, Hye Jeong Oh, In-Hye Ham, Sung Hak Lee, Sachiyo Nomura, Sang-Uk Han, Hoon Hur
- Cancer Res Treat. 2023;55(1):167-178. Published online May 20, 2022
- DOI: https://doi.org/10.4143/crt.2022.094
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Abstract
PDFSupplementary MaterialPubReaderePub
- Purpose
Appropriate preclinical mouse models are needed to evaluate the response to immunotherapeutic agents. Immunocompetent mouse models have rarely been reported for gastric cancer. Thus, we investigated immunophenotypes and responses to immune checkpoint inhibitor (ICI) in immunocompetent mouse models using various murine gastric cancer cell lines.
Materials and Methods
We constructed subcutaneous syngeneic tumors with murine gastric cancer cell lines, YTN3 and YTN16, in C57BL/6J mice. Mice were intraperitoneally treated with IgG isotype control or an anti–programmed death-ligand 1 (PD-L1) neutralizing antibody. We used immunohistochemistry to evaluate the tumor-infiltrating immune cells of formalin-fixed paraffin-embedded mouse tumor tissues. We compared the protein and RNA expression between YTN3 and YTN16 cell lines using a mouse cytokine array and RNA sequencing.
Results
The mouse tumors revealed distinct histological and molecular characteristics. YTN16 cells showed upregulation of genes and proteins related to immunosuppression, such as Ccl2 (CCL2) and Csf1 (M-CSF). Macrophages and exhausted T cells were more enriched in YTN16 tumors than in YTN3 tumors. Several YTN3 tumors were completely regressed by the PD-L1 inhibitor, whereas YTN16 tumors were unaffected. Although treatment with a PD-L1 inhibitor increased infiltration of T cells in both the tumors, the proportion of exhausted immune cells did not decrease in the non-responder group.
Conclusion
We confirmed the histological and molecular features of cancer cells with various responses to ICI. Our models can be used in preclinical research on ICI resistance mechanisms to enhance clinical efficacy. -
Citations
Citations to this article as recorded by- The Therapeutic Potential of Targeting Tumor Microenvironment and Modulation of Immunotherapy in Gastrointestinal Cancer
Saeideh Khorshid Sokhangouy, Hamid Jamialahmadi, Mahdieh Sadat Mortazavi Sani, Ghazaleh Khalili-Tanha, Arian Karimi Rouzbahani, Golnaz Mahmoudvand, Zahra Goudarzi, Arshia Fakouri, Simin Farokhi, Seyed Mahdi Hassanian, Gordon A Ferns, Elham Nazari, Amir Av
Current Cancer Drug Targets.2025; 25(10): 1222. CrossRef - Tubulointerstitial nephritis antigen-like 1 from cancer-associated fibroblasts contribute to the progression of diffuse-type gastric cancers through the interaction with integrin β1
Dagyeong Lee, In-Hye Ham, Hye Jeong Oh, Dong Min Lee, Jung Hwan Yoon, Sang-Yong Son, Tae-Min Kim, Jae-Young Kim, Sang-Uk Han, Hoon Hur
Journal of Translational Medicine.2024;[Epub] CrossRef - Safety and Efficacy of Neoadjuvant Chemoimmunotherapy in Gastric Cancer Patients with a PD-L1 Positive Status: A Case Report
Alexandra V. Avgustinovich, Olga V. Bakina, Sergey G. Afanas’ev, Liudmila V. Spirina, Alexander M. Volkov
Current Issues in Molecular Biology.2023; 45(9): 7642. CrossRef - Targeting GAS6/AXL signaling improves the response to immunotherapy by restoring the anti-immunogenic tumor microenvironment in gastric cancer
Tae Hoon Kim, Dagyeong Lee, Hye Jeong Oh, In-Hye Ham, Dong Min Lee, Yulim Lee, Zhang Zhang, Ding Ke, Hoon Hur
Life Sciences.2023; 335: 122230. CrossRef
- The Therapeutic Potential of Targeting Tumor Microenvironment and Modulation of Immunotherapy in Gastrointestinal Cancer
- 12,238 View
- 493 Download
- 5 Web of Science
- 4 Crossref
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