PO.CL01.06 · 临床研究
与MUC4相关的基质-免疫空间转录组特征识别HER2阳性乳腺癌曲妥珠单抗耐药的潜在生物标志物
A stromal-immune spatial transcriptomic signature associated with MUC4 identifies potential biomarkers of trastuzumab resistance in HER2-positive breast cancer
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
基于曲妥珠单抗的疗法是HER2阳性乳腺癌的标准治疗方案。我们此前证明，黏蛋白4（MUC4）表达与曲妥珠单抗耐药相关，并且是不良临床反应的独立生物标志物。本研究的目的是完善MUC4的预后和预测价值。使用五例诊断时获取、分类为MUC4阳性/曲妥珠单抗无反应者或MUC4阴性/反应者的HER2阳性乳腺癌样本，采用10X Genomics的Visium平台分析转录本的空间表达。文库在NextSeq 550上测序，数据用Cell Ranger处理。为最大限度减少批次效应并优化信噪比，使用不同方法整合数据：互惠PCA、典型相关分析和Harmony。选择RPCA以实现最佳批次校正并保留生物学异质性。细胞注释包括三种方法：使用细胞特异性标志物的手动注释、使用来自另外五个空间转录组实验数据的自动预测，以及使用Unicell的半自动方法。当这些方法中至少两种在细胞注释上一致时，即对细胞进行注释。差异基因表达采用Seurat和limma进行，考虑供体和细胞类型组成。值得注意的是，下调的基因包括多个免疫球蛋白相关转录本，与先前报告的曲妥珠单抗反应特征（如HER2DX）一致。为减少基因列表可变性带来的假阳性，我们应用弹性网络惩罚逻辑回归，将最初267个MUC4相关基因的集合精炼为37个高置信度候选基因。对于每个基因，我们使用The Human Protein Atlas、Gene Cards、UniProt和PubMed等资源分析了结构特征、生物学功能、蛋白质-蛋白质相互作用及在癌症中的相关性。所分析的基因集在与细胞外基质重塑、基质活化、上皮-间充质转化、血管生成、细胞侵袭和迁移、免疫调节及代谢重编程相关的基因本体生物学过程类别中显示出强烈富集。因此，空间转录组学识别出37个在MUC4阳性乳腺癌中差异表达、与基质和免疫转录程序相关的基因，这些基因可能促进曲妥珠单抗耐药，并完善MUC4的预测和预后价值，从而改善对最可能从曲妥珠单抗治疗中获益的患者的识别。
查看英文原文 English abstract
Trastuzumab-based therapies are the standard of care treatment for HER2-positive breast cancer. We previously demonstrated that mucin 4 (MUC4) expression is associated with trastuzumab resistance and is an independent biomarker of poor clinical response. The aim of this work was to refine the prognostic and predictive value of MUC4.Five HER2-positive breast cancer samples obtained at diagnosis and classified as either MUC4-positive/non-responder or MUC4-negative/responder to trastuzumab were used to analyze spatial expression of transcripts using the Visium platform from 10X Genomics. Libraries were sequenced on a NextSeq 550 and data were processed with Cell Ranger. To minimize batch effects and to optimize noise-to-signal ratio, data were integrated using different approaches: reciprocal PCA, canonical correlation analysis and Harmony. RPCA was selected for optimal batch correction and preservation of biological heterogeneity.Cell annotation included three approaches: manual annotation using cell specific markers, automated prediction using data from other five spatial transcriptomic experiments and a semi-automated method using Unicell. Cells were annotated when at least two of these methods coincided in cell annotation. Differential gene expression was performed with Seurat and limma, accounting for donor and cell type composition. Notably, downregulated genes included multiple immunoglobulin-related transcripts consistent with previously reported trastuzumab response signatures such as HER2DX.To reduce false positives arising from gene list variability, we applied an elastic net penalized logistic regression which refined the initial set of 267 MUC4-associated genes to 37 high confidence candidates. For each gene, we analyzed structural features, biological function, protein-protein interactions and relevance in cancer using resources such as The Human Protein Atlas, Gene Cards, UniProt and PubMed. The analyzed gene set showed strong enrichment for Gene Ontology Biological Process categories related to extracellular matrix remodeling, stromal activation, epithelial-to-mesenchymal transition, angiogenesis, cell invasion and migration, immune modulation and metabolic reprogramming. Spatial transcriptomics therefore identified 37 differentially expressed genes in MUC4-positive breast cancer associated with stromal and immune transcriptional programs that may contribute to trastuzumab resistance and refine MUC4 predictive and prognostic value improving the identification of patients most likely to benefit from trastuzumab treatment.
利益披露 Disclosure
M. F. Mercogliano, None..
N. Schrode, None.