An Immune Model to Predict Prognosis of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy Based on Support Vector Machine

Wang, Mozhi; Pang, Zhigang; Wang, Yusong; Cui, Mingke; Yao, Litong; Li, Shuang; Wang, Mengshen; Zheng, Yanfu; Sun, Xiangyu; Hui, Dong; Qiang, Zhang; Xu, Yingying

doi:10.3389/fonc.2021.651809

“…However, tissue biopsy during NAC is challenging, and circulating immune cells have been extensively evaluated during NAC. Previous studies have indicated that these circulating cells may also be informative and capable of serving as promising biomarkers to predict treatment response ( 59 – 61 ). A study quantifying myeloid-derived suppressor cells (MDSCs) and Tregs by flow cytometry in blood samples from 34 pre-NAC TNBC patients has revealed a negative correlation between early MDSC (eMDSC) levels and NAC response ( 61 ).…”

Section: Cytometrymentioning

confidence: 99%

Multiomics technologies for comprehensive tumor microenvironment analysis in triple-negative breast cancer under neoadjuvant chemotherapy

Wang

¹

,

Yao

²

,

Huang

³

et al. 2023

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Triple-negative breast cancer (TNBC) is one of the most aggressive breast cancer subtypes and is characterized by abundant infiltrating immune cells within the microenvironment. As standard care, chemotherapy remains the fundamental neoadjuvant treatment in TNBC, and there is increasing evidence that supplementation with immune checkpoint inhibitors may potentiate the therapeutic efficiency of neoadjuvant chemotherapy (NAC). However, 20-60% of TNBC patients still have residual tumor burden after NAC and require additional chemotherapy; therefore, it is critical to understand the dynamic change in the tumor microenvironment (TME) during treatment to help improve the rate of complete pathological response and long-term prognosis. Traditional methods, including immunohistochemistry, bulk tumor sequencing, and flow cytometry, have been applied to elucidate the TME of breast cancer, but the low resolution and throughput may overlook key information. With the development of diverse high-throughput technologies, recent reports have provided new insights into TME alterations during NAC in four fields, including tissue imaging, cytometry, next-generation sequencing, and spatial omics. In this review, we discuss the traditional methods and the latest advances in high-throughput techniques to decipher the TME of TNBC and the prospect of translating these techniques to clinical practice.

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“…The following post-NAT/pre-NAT immune indicator values were related to the long-term prognosis of patients: CD4 + :CD8 + T cell ratio; CD3 + CD8 + T cell percentage; CD16 + CD56 + natural killer (NK) cell absolute value (Abs); CD3 + CD4 + helper T cell percentage; and summation of T, B, and NK cell percentages [lymphosum (T+B+NK)]. We then used the support vector machine (SVM) to train the prediction model 9 . In this study we focused on the efficacy of NAT and optimized the prediction model using machine learning (ML).…”

Section: Introductionmentioning

confidence: 99%

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

Wang

¹

,

Wang

²

,

Yu

³

et al. 2023

Cancer Biol Med

2

0

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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.

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“…However, traditional biostatistical methods (e.g., logistic regression or linear regression) could not provide higher precision in health-data prediction, especially in real-world research. Support vector machine (SVM) is widely used as a type of supervised learning algorithm which analyzes data and recognizes patterns, mainly used for binary classification and regression by linear or nonlinear decision boundary [21]. It aims to divide samples into worthy bifurcations that enable the prediction of labels from one or more feature vectors.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors

Yang

¹

,

Li

²

,

Lin

³

et al. 2022

Journal of Healthcare Engineering

0

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Objective. Immune checkpoint inhibitors, such as programmed death-1/ligand-1 (PD-1/L1), exhibited autoimmune-like disorders, and hyperglycemia was on the top of grade 3 or higher immune-related adverse events. Machine learning is a model from past data for future data prediction. From post-marketing monitoring, we aimed to construct a machine learning algorithm to efficiently and rapidly predict hyperglycemic adverse reaction in patients using PD-1/L1 inhibitors. Methods. In original data downloaded from Food and Drug Administration Adverse Event Reporting System (US FAERS), a multivariate pattern classification of support vector machine (SVM) was used to construct a classifier to separate adverse hyperglycemic reaction patients. With correct core SVM function, a 10-fold 3-time cross validation optimized parameter value composition in model setup with R language software. Results. The SVM prediction model was set up from the number type/number optimization method, as well as the kernel and type of “rbf” and “nu-regression” composition. Two key values (nu and gamma) and case number displayed high adjusted r2 in curve regressions ( n u = 0.5649 × e − case / 6984 , gamma = 9.005 × 10 − 4 × case − 4.877 × 10 − 8 × case 2 ). This SVM model with computable parameters greatly improved the assessing indexes (accuracy, F1 score, and kappa) as well as coequal sensitivity and the area under the curve (AUC). Conclusion. We constructed an effective machine learning model based on compositions of exact kernels and computable parameters; the SVM prediction model can noninvasively and precisely predict hyperglycemic adverse drug reaction (ADR) in patients treated with PD-1/L1 inhibitors, which could greatly help clinical practitioners to identify high-risk patients and perform preventive measurements in time. Besides, this model setup process provided an analytic conception for promotion to other ADR prediction, such ADR information is vital for outcome improvement by identifying high-risk patients, and this machine learning algorithm can eventually add value to clinical decision making.

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An Immune Model to Predict Prognosis of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy Based on Support Vector Machine

Cited by 3 publications

References 23 publications

Multiomics technologies for comprehensive tumor microenvironment analysis in triple-negative breast cancer under neoadjuvant chemotherapy

Multiomics technologies for comprehensive tumor microenvironment analysis in triple-negative breast cancer under neoadjuvant chemotherapy

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

Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors

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