多组学数据驱动的机器学习模型在乳腺癌生存及治疗响应预测中的应用
收稿日期: 2024-05-31
修回日期: 2024-08-18
网络出版日期: 2024-08-19
基金资助
国家自然科学基金项目(32270610);国家自然科学基金项目(82072499);国家自然科学基金项目(31801094);北京航空航天大学青年科学家创新团队支持计划(YWF-21-BJ-J-T105)
Machine learning applications in breast cancer survival and therapeutic outcome prediction based on multi-omic analysis
Received date: 2024-05-31
Revised date: 2024-08-18
Online published: 2024-08-19
Supported by
National Natural Science Foundation of China(32270610);National Natural Science Foundation of China(82072499);National Natural Science Foundation of China(31801094);Fundamental Research Funds for the Central Universities(YWF-21-BJ-J-T105)
乳腺癌的高度异质性导致其治疗及预后评估较为复杂。治疗方案的选择受到肿瘤亚型、病变分级、基因型等多种因素的影响，因此需要制定个体化治疗策略。患者的预后效果因病情不同而产生显著差异。作为人工智能的一个重要分支，机器学习能高效处理海量数据，并实现决策过程的自动化。机器学习方法的引入将为乳腺癌治疗的选择和预后评估提供新的解决方案。在癌症治疗领域，传统方法预测生存与治疗效果往往依赖于单一或少量的生物标志物，难以全面捕捉复杂的生物学过程。机器学习通过分析患者的多组学数据以及它们在疾病发生发展过程中复杂的变化趋势，预测患者的生存和治疗响应效果，从而选择适合的治疗措施，实施早期干预，改善患者的治疗效果。本文首先介绍了常用的机器学习方法，在此基础上分别从评估生存情况和预测治疗效果这两方面展开，详细分析了机器学习在乳腺癌患者生存预测及预后领域中的应用，以期为乳腺癌患者提供精准医疗治疗策略，提高治疗效果和生存质量。
章子怡, 王棨临, 张俊有, 段迎迎, 刘家欣, 刘赵硕, 李春燕 . 多组学数据驱动的机器学习模型在乳腺癌生存及治疗响应预测中的应用[J]. 遗传, 2024 , 46(10) : 820 -832 . DOI: 10.16288/j.yczz.24-156
The high heterogeneity within and between breast cancer patients complicates treatment determination and prognosis assessment. Treatment decision-making is influenced by various factors, such as tumor subtype, histological grade, and genotype, necessitating personalized treatment strategies. Prognostic outcomes vary significantly depending on patient-specific conditions. As a critical branch of artificial intelligence, machine learning efficiently handles large datasets and automates decision-making processes. The introduction of machine learning offers new solutions for breast cancer treatment selection and prognosis assessment. In the field of cancer therapy, traditional methods for predicting treatment and survival outcomes often rely on single or few biomarkers, limiting their ability to capture the complexity of biological processes comprehensively. Machine learning analyzes patients’ multi-omic data and the intricate patterns of variations during cancer initiation and progression to predict patients’ survival and treatment outcomes. Consequently, it facilitates the selection of appropriate therapeutic interventions to implement early intervention and improve treatment efficacy for patients. Here, we first introduce common machine learning methods, and then elaborate on the application of machine learning in the field of survival prediction and prognosis from two aspects: evaluating survival and predicting treatment outcomes for breast cancer patients. The aim is to provide breast cancer patients with precise treatment strategies to improve therapeutic outcomes and quality of life.
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