Optimizing the ER Cutoff in HER2-Positive Breast Cancer: ER ≥ 50% Predicts Low pCR Rates and Resistance to Antibody-Drug Conjugates in the Neoadjuvant Setting

IF 3.5 2区 医学 Q2 ONCOLOGY
Cancer Medicine Pub Date : 2026-09-29 DOI:10.1002/cam4.72331
Tian Du, Min Lin, Gehao Liang, Yan Wang, Hao Wu, Zixuan Zhao, Luhao Sun, Jun Tang
{"title":"Optimizing the ER Cutoff in HER2-Positive Breast Cancer: ER ≥ 50% Predicts Low pCR Rates and Resistance to Antibody-Drug Conjugates in the Neoadjuvant Setting","authors":"Tian Du,&nbsp;Min Lin,&nbsp;Gehao Liang,&nbsp;Yan Wang,&nbsp;Hao Wu,&nbsp;Zixuan Zhao,&nbsp;Luhao Sun,&nbsp;Jun Tang","doi":"10.1002/cam4.72331","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Estrogen receptor (ER)-positive/HER2-positive breast cancer demonstrates significantly lower rates of pathological complete response (pCR) to neoadjuvant HER2-targeted therapies compared to ER-negative/HER2-positive disease. However, the optimal ER positivity cutoff for clinically meaningful patient stratification remains undefined.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We analyzed a retrospective cohort of 741 HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus dual HER2 blockade (trastuzumab and pertuzumab) at Sun Yat-sen University Cancer Center. The optimal ER cutoff for predicting pCR was determined by ROC analysis with Youden index and validated by bootstrap resampling (1000 iterations). Transcriptomic data from TCGA and SCAN-B cohorts were analyzed to characterize biological differences between ER subgroups. Drug response was predicted using the oncoPredict algorithm, and cancer dependency was assessed using DepMap CRISPR screening data.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>ER ≥ 50% positivity was identified as the optimal predictive cutoff (bootstrap 95% CI: 25.0%–77.5%) and was an independent predictor of significantly lower pCR rates in multivariate analysis (OR = 0.27; 95% CI: 0.19–0.40; <i>p</i> &lt; 0.001). Transcriptomic analysis revealed that ER ≥ 50% tumors are characterized by activated estrogen response signaling, downregulated cell cycle and immune pathways. Predicted resistance to trastuzumab, T-DM1, and T-DXd was consistently enriched in this subgroup, consistent with lower HER2 and CD16A expression observed in ER ≥ 50% tumors. CCND1, a canonical transcriptional target of ESR1, was significantly upregulated in ER ≥ 50% tumors across both cohorts, and ER + /HER2 + cell lines exhibited significantly higher CCND1 and CDK4 dependency scores in DepMap CRISPR screening (<i>p</i> &lt; 0.05), supporting activation of the ESR1-CCND1-CDK4/6 axis in this subgroup.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>ER ≥ 50% positivity defines a clinically and biologically distinct HER2-positive subgroup with poor response to standard neoadjuvant therapy and predicted resistance to ADCs, consistent with lower HER2 and CD16A expression in this subgroup. The convergent transcriptomic and functional evidence for ESR1-CCND1 axis activation provides mechanistic support for combining CDK4/6 inhibitors with endocrine and anti-HER2 therapies to improve outcomes in this resistant subgroup.</p>\n </section>\n </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"15 10","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624533/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Medicine","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cam4.72331","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Background

Estrogen receptor (ER)-positive/HER2-positive breast cancer demonstrates significantly lower rates of pathological complete response (pCR) to neoadjuvant HER2-targeted therapies compared to ER-negative/HER2-positive disease. However, the optimal ER positivity cutoff for clinically meaningful patient stratification remains undefined.

Methods

We analyzed a retrospective cohort of 741 HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus dual HER2 blockade (trastuzumab and pertuzumab) at Sun Yat-sen University Cancer Center. The optimal ER cutoff for predicting pCR was determined by ROC analysis with Youden index and validated by bootstrap resampling (1000 iterations). Transcriptomic data from TCGA and SCAN-B cohorts were analyzed to characterize biological differences between ER subgroups. Drug response was predicted using the oncoPredict algorithm, and cancer dependency was assessed using DepMap CRISPR screening data.

Results

ER ≥ 50% positivity was identified as the optimal predictive cutoff (bootstrap 95% CI: 25.0%–77.5%) and was an independent predictor of significantly lower pCR rates in multivariate analysis (OR = 0.27; 95% CI: 0.19–0.40; p < 0.001). Transcriptomic analysis revealed that ER ≥ 50% tumors are characterized by activated estrogen response signaling, downregulated cell cycle and immune pathways. Predicted resistance to trastuzumab, T-DM1, and T-DXd was consistently enriched in this subgroup, consistent with lower HER2 and CD16A expression observed in ER ≥ 50% tumors. CCND1, a canonical transcriptional target of ESR1, was significantly upregulated in ER ≥ 50% tumors across both cohorts, and ER + /HER2 + cell lines exhibited significantly higher CCND1 and CDK4 dependency scores in DepMap CRISPR screening (p < 0.05), supporting activation of the ESR1-CCND1-CDK4/6 axis in this subgroup.

Conclusions

ER ≥ 50% positivity defines a clinically and biologically distinct HER2-positive subgroup with poor response to standard neoadjuvant therapy and predicted resistance to ADCs, consistent with lower HER2 and CD16A expression in this subgroup. The convergent transcriptomic and functional evidence for ESR1-CCND1 axis activation provides mechanistic support for combining CDK4/6 inhibitors with endocrine and anti-HER2 therapies to improve outcomes in this resistant subgroup.

优化her2阳性乳腺癌的ER截止:ER≥50%预测新辅助环境中低pCR率和对抗体-药物偶联物的耐药性
背景:与雌激素受体(ER)阴性/ her2阳性疾病相比,雌激素受体(ER)阳性/ her2阳性乳腺癌对新辅助her2靶向治疗的病理完全缓解(pCR)率显著降低。然而,临床上有意义的患者分层的最佳ER阳性临界值仍未确定。方法:我们对中山大学肿瘤中心接受新辅助化疗加双HER2阻断(曲妥珠单抗和帕妥珠单抗)治疗的741例HER2阳性乳腺癌患者进行回顾性队列分析。采用约登指数(Youden index)进行ROC分析,确定预测pCR的最佳ER截断值,并通过自举重采样(1000次迭代)进行验证。分析来自TCGA和SCAN-B队列的转录组学数据,以表征ER亚组之间的生物学差异。使用oncoppredict算法预测药物反应,使用DepMap CRISPR筛选数据评估癌症依赖性。结果:ER≥50%阳性被确定为最佳预测截止点(bootstrap 95% CI: 25.0%-77.5%),并且是多因素分析中显著降低pCR率的独立预测因子(OR = 0.27; 95% CI: 0.19-0.40; p)。结论:ER≥50%阳性定义了临床和生物学上不同的HER2阳性亚组,对标准新辅助治疗反应较差,预测对adc的耐药,与该亚组中HER2和CD16A表达较低一致。ESR1-CCND1轴激活的趋同转录组学和功能证据为CDK4/6抑制剂联合内分泌和抗her2治疗改善该耐药亚组的预后提供了机制支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Cancer Medicine
Cancer Medicine ONCOLOGY-
CiteScore
5.50
自引率
2.50%
发文量
907
审稿时长
19 weeks
期刊介绍: Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书