Obesity is a well-known modifiable risk factor for breast cancer and is considered a poor prognostic factor in pre- and post-menopausal women. While the systemic effects of obesity have been extensively studied, less is known about the mechanisms underlying obesity-associated cancer risk and the local consequences of obesity. Thus, obesity-induced inflammation has become the focus of research interest. Biologically, the development of cancer involves a complex interaction with numerous components. As the tumor immune microenvironment changes due to obesity-triggered inflammation, an increase in infiltration occurs for proinflammatory cytokines and adipokines, as well as adipocytes, immune cells, and tumor cells in the expanded adipose tissue. Complicated cellular-molecular crosstalk networks change critical pathways, mediate metabolic and immune function reprogramming, and have a significant role in tumor metastasis, proliferation, resistance, angiogenesis, and tumorigenesis. This review summarizes recent research findings on how inflammatory mediators in the in situ tumor microenvironment regulate the occurrence and development of breast cancer in the context of obesity. We analyzed the heterogeneity and potential mechanisms of the breast cancer immune microenvironment from the perspective of inflammation to provide a reference for the clinical transformation of precision targeted cancer therapy.
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Open Access
Rapid Communication
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Open Access
Review
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Open Access
Original Article
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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.
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.
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).
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.
