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

doi:10.16288/j.yczz.24-156

遗传 ›› 2024, Vol. 46 ›› Issue (10): 820-832.doi: 10.16288/j.yczz.24-156

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

章子怡¹^,²(), 王棨临¹^,², 张俊有¹^,², 段迎迎¹^,², 刘家欣¹^,², 刘赵硕¹^,², 李春燕¹^,²^,³^,⁴()

1.北京航空航天大学，医学科学与工程学院，北京 100191
2.北京航空航天大学，生物与医学工程学院，北京 100191
3.北京航空航天大学，工业和信息化部大数据精准医疗重点实验室，北京 100191
4.北京航空航天大学，大数据精准医疗高精尖创新中心，北京 100191

收稿日期:2024-05-31 修回日期:2024-08-18 出版日期:2024-08-19 发布日期:2024-08-19
通讯作者: 李春燕，博士，副教授，研究方向：功能基因组学。E-mail: lichunyan@buaa.edu.cn
作者简介:章子怡，硕士研究生，专业方向：生物医学工程。E-mail: zhangziyi@buaa.edu.cn
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

Ziyi Zhang¹^,²(), Qilin Wang¹^,², Junyou Zhang¹^,², Yingying Duan¹^,², Jiaxin Liu¹^,², Zhaoshuo Liu¹^,², Chunyan Li¹^,²^,³^,⁴()

1. School of Engineering Medicine, Beihang University, Beijing 100191, China
2. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
3. Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing 100191, China
4. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China

Received:2024-05-31 Revised:2024-08-18 Published:2024-08-19 Online: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)

摘要/Abstract

摘要：

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。

关键词: 乳腺癌, 机器学习, 多组学数据整合分析, 生存预测, 治疗响应

Abstract:

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.

Key words: breast cancer, machine learning, multi-omics data analysis, survival prediction, treatment response

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.

Ziyi Zhang, Qilin Wang, Junyou Zhang, Yingying Duan, Jiaxin Liu, Zhaoshuo Liu, Chunyan Li. Machine learning applications in breast cancer survival and therapeutic outcome prediction based on multi-omic analysis[J]. Hereditas(Beijing), 2024, 46(10): 820-832.

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分型	HR特征	ER/PR特征	HER2特征	Ki-67水平	预后情况
Luminal A型	HR+	ER+或PR+	HER2-	低	预后较好
Luminal B型	HR+	ER+或PR+	HER2-或HER2+	中等至高水平	生长速度较LuminalA型快，预后稍差
HER2过表达型	HR-	ER-且PR-	HER2+	中等至高水平	生长速度比LuminalB型快，预后更差
三阴性乳腺癌	HR-	ER-且PR-	HER2-	中等至高水平	具有侵袭性且预后较差

分型	研究对象
基因组学(genomics)	所有遗传物质的结构功能与遗传变异
转录组学(transcriptomics)	特定条件下DNA转录为RNA的基因表达调控
表观组学(epigenomics)	包含DNA甲基化、组蛋白修饰在内的基因表达调控的表观遗传机制
蛋白组学(proteomics)	所有蛋白质的组成、结构和功能
代谢组学(metabolomics)	代谢产物的组成与代谢通路的调控
免疫组学(immunomics)	应对疾病时免疫系统调节的机制
病理组学(pathomics)	疾病的形态学特征及组织和细胞的结构与功能

任务	代表方法	应用
回归	线性回归、LASSO回归、岭回归	挖掘乳腺癌的潜在预后标志物，预测无进展生存期和总生存期
分类	LR、SVM、RF、Adaboost、XGBoost、NB、CNN	预测乳腺癌患者对治疗的反应、复发情况及转移的风险
聚类	k-means、层次聚类	识别基因表达模式，诊断转移性癌病理亚型
降维	PCA、t-SNE、AE	数据处理和特征筛选，降低数据维度，突出重要特征

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

Machine learning applications in breast cancer survival and therapeutic outcome prediction based on multi-omic analysis

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