Cancer Research and Treatment

Original Articles

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; Received September 26, 2024 Accepted November 18, 2024 Published online November 19, 2024; DOI: https://doi.org/10.4143/crt.2024.937 [Epub ahead of print]

Abstract PDF Supplementary Material: 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.

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Breast cancer

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

Abstract PDF Supplementary Material PubReader ePub: 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.

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