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

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

• Review • Previous Articles Next Articles

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 Online:2024-08-19 Published:2024-08-19
Contact: Chunyan Li E-mail:zhangziyi@buaa.edu.cn;lichunyan@buaa.edu.cn
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)

Abstract

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

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.

Figures/Tables 6

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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

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

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