Optimizing the ER cutoff: Using ER≥50% to define a distinct HER2+ subgroup characterized by low pCR rates, ADC resistance, and CDK4 dependency.
e12646 Background: While estrogen receptor (ER)-positive/HER2-positive breast cancer generally demonstrates better long-term survival than ER-negative/HER2-positive disease, it shows significantly lower rates of pathological complete response (pCR) to standard neoadjuvant HER2-targeted therapies. Currently, the optimal ER positivity cutoff for stratifying patients remains undefined. Methods: We analyzed a retrospective cohort of 741 HER2-positive patients treated with neoadjuvant chemotherapy plus dual HER2 blockade (trastuzumab and pertuzumab). Receiver operating characteristic (ROC) analysis was used to define the optimal prognostic ER cutoff. Integrated multi-omics analysis (TCGA, SCAN-B) and CRISPR dependency screening (DepMap) were utilized to elucidate biological mechanisms and identify therapeutic vulnerabilities. Results: ROC analysis identified 50% ER positivity as the optimal threshold for stratifying response. In multivariate analysis, ER≥50% subgroup was characterized by activated estrogen signaling, downregulated cell cycle pathways, and predicted resistance to taxanes and antibody-drug conjugates (e.g., T-DXd). Conversely, CRISPR screening prioritized CDK4 as a top essential survival dependency specifically in ER-positive/HER2-positive models, suggesting functional addiction to the Cyclin D1-CDK4 axis. Conclusions: An ER cutoff of 50% optimally defines a distinct, chemo-resistant luminal subgroup within HER2-positive breast cancer. These findings support a precision medicine strategy integrating CDK4/6 inhibitors with endocrine and anti-HER2 therapies for patients with ER≥50% tumors.
Authors
- Tian Du (ORCID: https://orcid.org/0000-0003-0841-6484)
- Luhao Sun (ORCID: https://orcid.org/0009-0005-2937-4019)
- Zixuan Zhao (ORCID: https://orcid.org/0000-0001-7460-0074)
- Gehao Liang
- H Wu (ORCID: https://orcid.org/0000-0003-1628-0580)
- Ning Li
- Jun Tang
- Yan Wang
- Min Lin
Institutions
- Sun Yat-sen University (CN)
- Sun Yat-sen University Cancer Center (CN)
Publication Details
- Journal
- Journal of Clinical Oncology
- Published
- 2026-05-28
- DOI
- https://doi.org/10.1200/jco.2026.44.16_suppl.e12646
- Primary Topic
- Advanced Breast Cancer Therapies
- Type
- article
- Field-Weighted Citation Impact
- 0.00