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Original Article | Open Access

A machine learning model to predict efficacy of neoadjuvant therapy in breast cancer based on dynamic changes in systemic immunity

Yusong Wang1,*, Mozhi Wang1,*, Keda Yu2, Shouping Xu3, Pengfei Qiu4, Zhidong Lyu5, Mingke Cui6, Qiang Zhang6 ( ), Yingying Xu1( )
Department of Breast Surgery, The First Hospital of China Medical University, Shenyang 110001, China
Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, China
Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan 250117, China
Breast Center, The Affiliated Hospital of Qingdao University, Qingdao 266003, China
Department of Breast Surgery, Liaoning Cancer Hospital and Institute, Shenyang 110801, China

*These authors contributed equally to this work.

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Abstract

Objective

Neoadjuvant therapy (NAT) has been widely implemented as an essential treatment to improve therapeutic efficacy in patients with locally-advanced cancer to reduce tumor burden and prolong survival, particularly for human epidermal growth receptor 2-positive and triple-negative breast cancer. The role of peripheral immune components in predicting therapeutic responses has received limited attention. Herein we determined the relationship between dynamic changes in peripheral immune indices and therapeutic responses during NAT administration.

Methods

Peripheral immune index data were collected from 134 patients before and after NAT. Logistic regression and machine learning algorithms were applied to the feature selection and model construction processes, respectively.

Results

Peripheral immune status with a greater number of CD3+ T cells before and after NAT, and a greater number of CD8+ T cells, fewer CD4+ T cells, and fewer NK cells after NAT was significantly related to a pathological complete response (P < 0.05). The post-NAT NK cell-to-pre-NAT NK cell ratio was negatively correlated with the response to NAT (HR = 0.13, P = 0.008). Based on the results of logistic regression, 14 reliable features (P < 0.05) were selected to construct the machine learning model. The random forest model exhibited the best power to predict efficacy of NAT among 10 machine learning model approaches (AUC = 0.733).

Conclusions

Statistically significant relationships between several specific immune indices and the efficacy of NAT were revealed. A random forest model based on dynamic changes in peripheral immune indices showed robust performance in predicting NAT efficacy.

Keywords

machine learningBreast cancerprediction modelneoadjuvant therapyperipheral blood lymphocytes

Electronic Supplementary Material

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Cancer Biology & Medicine
Volume 20 Issue 3,
March 2023
Pages 218-228
DOI: 10.20892/j.issn.2095-3941.2022.0513
Cite this article:
Wang Y, Wang M, Yu K, et al. A machine learning model to predict efficacy of neoadjuvant therapy in breast cancer based on dynamic changes in systemic immunity. Cancer Biology & Medicine, 2023, 20(3): 218-228. https://doi.org/10.20892/j.issn.2095-3941.2022.0513

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Received: 22 August 2022
Accepted: 16 January 2023
Published: 24 March 2023
©2023 Cancer Biology & Medicine.

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