预测乳腺癌新辅助化疗反应性的血浆代谢组学特征

Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer.

作者信息

Silva Alex Ap Rosini, Cardoso Marcella R, Oliveira Danilo Cardoso de, Godoy Pedro, Talarico Maria Cecília R, Gutiérrez Junier Marrero, Rodrigues Peres Raquel M, de Carvalho Lucas M, Miyaguti Natália Angelo da Silva, Sarian Luis O, Tata Alessandra, Derchain Sophie F M, Porcari Andreia M

机构信息

MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil.

Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, Faculty of Medical Sciences, University of Campinas (UNICAMP-Universidade Estadual de Campinas), Campinas 13083881, São Paulo, Brazil.

出版信息

Cancers (Basel). 2024 Jul 6;16(13):2473. doi: 10.3390/cancers16132473.

DOI:10.3390/cancers16132473

PMID:39001535

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11240312/

Abstract

BACKGROUND

Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data.

METHODS

Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC ( = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively.

RESULTS

The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance.

CONCLUSION

We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.

摘要

背景

新辅助化疗（NACT）已成为乳腺癌（BC）的一种治疗选择。然而，对NACT的反应仍然不可预测，且取决于癌症亚型。代谢组学是一种预测生物标志物和化疗反应的工具。我们使用血浆来验证NACT前BC患者的代谢组学改变，并将其与临床数据相关联。

方法

对75例BC患者NACT前的血浆进行液相色谱-质谱联用（LC-MS）分析。数据过滤后，分别用75%/25%的数据构建并验证了用于分类的支持向量机（SVM）模型。

结果

由19种已鉴定代谢物组成的模型对训练/验证集的NACT反应具有高效的预测能力，敏感性高（95.4%/93.3%）、特异性高（91.6%/100.0%）和准确性高（94.6%/94.7%）。在这两个数据集中，该模型正确分类了95%的耐药女性和94%的敏感女性。该模型鉴定出的大多数化合物为脂质和氨基酸，并揭示了与化疗耐药相关的代谢途径改变。