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脑神经前体细胞是乳腺癌激素治疗的新预测性生物标志物。

Brain Neural Progenitors are New Predictive Biomarkers for Breast Cancer Hormonotherapy.

机构信息

Omics Data Science Unit, Institut de Cancérologie de l'Ouest (ICO), Angers-Nantes, France.

SIRIC ILIAD, Angers-Nantes, France.

出版信息

Cancer Res Commun. 2022 Aug 24;2(8):857-869. doi: 10.1158/2767-9764.CRC-21-0090. eCollection 2022 Aug.

Abstract

UNLABELLED

Heterogeneity of the tumor microenvironment (TME) is one of the major causes of treatment resistance in breast cancer. Among TME components, nervous system role in clinical outcome has been underestimated. Identifying neuronal signatures associated with treatment response will help to characterize neuronal influence on tumor progression and identify new treatment targets. The search for hormonotherapy-predictive biomarkers was implemented by supervised machine learning (ML) analysis on merged transcriptomics datasets from public databases. ML-derived genes were investigated by pathway enrichment analysis, and potential gene signatures were curated by removing the variables that were not strictly nervous system specific. The predictive and prognostic abilities of the generated signatures were examined by Cox models, in the initial cohort and seven external cohorts. Generated signature performances were compared with 14 other published signatures, in both the initial and external cohorts. Underlying biological mechanisms were explored using deconvolution tools (CIBERSORTx and xCell). Our pipeline generated two nervous system-related signatures of 24 genes and 97 genes (NervSign24 and NervSign97). These signatures were prognostic and hormonotherapy-predictive, but not chemotherapy-predictive. When comparing their predictive performance with 14 published risk signatures in six hormonotherapy-treated cohorts, NervSign97 and NervSign24 were the two best performers. Pathway enrichment score and deconvolution analysis identified brain neural progenitor presence and perineural invasion as nervous system-related mechanisms positively associated with NervSign97 and poor clinical prognosis in hormonotherapy-treated patients. Transcriptomic profiling has identified two nervous system-related signatures that were validated in clinical samples as hormonotherapy-predictive signatures, meriting further exploration of neuronal component involvement in tumor progression.

SIGNIFICANCE

The development of personalized and precision medicine is the future of cancer therapy. With only two gene expression signatures approved by FDA for breast cancer, we are in need of new ones that can reliably stratify patients for optimal treatment. This study provides two hormonotherapy-predictive and prognostic signatures that are related to nervous system in TME. It highlights tumor neuronal components as potential new targets for breast cancer therapy.

摘要

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肿瘤微环境(TME)的异质性是乳腺癌治疗耐药的主要原因之一。在 TME 成分中,神经系统在临床结果中的作用被低估了。确定与治疗反应相关的神经元特征将有助于描述神经元对肿瘤进展的影响,并确定新的治疗靶点。通过对来自公共数据库的合并转录组数据集进行有监督的机器学习(ML)分析,寻找激素治疗预测性生物标志物。通过途径富集分析研究 ML 衍生基因,并通过去除不是严格神经系统特异性的变量来整理潜在的基因特征。通过 Cox 模型在初始队列和七个外部队列中检查生成特征的预测和预后能力。在初始和外部队列中,将生成的特征性能与其他 14 个已发表的特征进行比较。使用去卷积工具(CIBERSORTx 和 xCell)探索潜在的生物学机制。我们的流水线生成了两个与神经系统相关的 24 个基因和 97 个基因的特征(NervSign24 和 NervSign97)。这些特征具有预后和激素治疗预测性,但不具有化疗预测性。当将其与 6 个接受激素治疗的队列中的 14 个已发表风险特征进行比较时,NervSign97 和 NervSign24 是表现最好的两个。途径富集评分和去卷积分析确定了脑神经祖细胞的存在和周围神经浸润,作为与 NervSign97 相关的神经系统相关机制,并与接受激素治疗的患者的不良临床预后相关。转录组谱分析确定了两个与神经系统相关的特征,这些特征在临床样本中得到验证,作为激素治疗预测特征,值得进一步探索肿瘤进展中神经元成分的参与。

意义

个性化和精准医学的发展是癌症治疗的未来。由于只有两个基因表达特征被 FDA 批准用于乳腺癌,我们需要新的特征来可靠地对患者进行分层,以获得最佳治疗效果。本研究提供了两个与 TME 中的神经系统相关的激素治疗预测和预后特征,强调了肿瘤神经元成分作为乳腺癌治疗的潜在新靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/45c5/10010318/45011710a3a1/crc-21-0090_fig1.jpg

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