Abstract PS09-09: Multiomics profiling and molecular classification refine precision treatment strategies for HER2-positive breast cancer

Abstract Background: Anti-HER2 targeted therapy has achieved a series of breakthroughs. However, the current treatment strategy regarding HER2-positive breast cancer remains indiscriminate and lacks specificity, which limits the further improvement of overall treatment response and may lead to overtreatment and extra cost for some patients. Our study aims to reveal the molecular heterogeneity of HER2-positive breast cancer to guide a more precise treatment. Patients and methods: We selected HER2-positive breast cancer patients treated at Fudan University Shanghai Cancer Center between 2013 and 2014 and conducted genomic, transcriptomic, proteomic and metabolomic profiling. We then applied a non-negative matrix factorization algorithm on transcriptomic data to obtain an unsupervised classification. And we further studied the correlation between subtypes and corresponding treatment strategies in multiple cohorts of adjuvant and neoadjuvant therapy. For clinical accessibility, we developed convolutional neural network models through deep learning algorithm based on digital pathology to identify different subtypes. Additionally, we explored novel treatment strategies using the patient-derived organoids (PDOs) models. Results: We established a novel multiomics cohort of 180 HER2- breast cancer patients and classified them into four clinically significant molecular subtypes: (1) A classical HER2-enriched (HER2-CLA, N=51) subtype characterized by strong ERBB2 signaling and remarkable sensitivity to anti-HER2-targeted therapy (pathologic complete response with dual-targeted therapy: 93%). (2) an immunomodulatory (HER2-IM, N=36) subtype characterized by an immune-activated microenvironment and excellent prognosis with current treatment (no relapse in 97% of patients with a median follow-up of 86 months). Tumors of this subtype were therefore candidates for de-escalatory treatment. (3) A luminal-like (HER2-LUM, N=55) subtype distinguished by activated estrogen receptor signaling and (4) a basal/mesenchymal-like (HER2-BM, N=38) subtype enriched in activated receptor tyrosine kinase pathways. HER2-LUM and HER2- BM showed limited benefit from anti-HER2 therapy, and thus, add-on therapies might be needed. The overall area under the curve (AUC) of the convolutional neural network model based on digital pathology for identifying different subtypes is 0.77. In the exploration of novel treatment strategies, we found in the PDO model that the HER2-LUM subtype is more sensitive to a treatment regimen combining standard (chemotherapy and targeted therapy) with subsequent endocrine therapy and CDK4/6 inhibitors compared to other subtypes. Additionally, the HER2-BM subtype demonstrated greater sensitivity to treatment with a combination of EGFR inhibitors, PDGFR inhibitors or VEGFR inhibitors. Conclusion: We uncovered a high degree of molecular heterogeneity in HER2-positive breast cancer and illustrated its impact on treatment response. More precise treatment can be given according to the characteristics of different subtypes, which may achieve good efficacy and simultaneously reduce overtreatment and extra cost. The comprehensive profiling of HER2-positive breast cancers could also serve as an important resource for further exploration. Key Words: HER2-positive breast cancer cohort; molecular classification; targeted therapy; precision treatment; de-escalatory treatment. Citation Format: Yu-Wei Li, Ding Ma, Xiang-Rong Wu, Lei-Jie Dai, Shen Zhao, Yu-Zheng Xu, Xi Jin, Xiao Yi, Ying Wang, Cai-Jin Lin, Yi-Fan Zhou, Tong Fu, Wen-Tao Yang, Ming Li, Hong Lv, Yi-Zhou Jiang, Zhi-Ming Shao. Multiomics profiling and molecular classification refine precision treatment strategies for HER2-positive breast cancer abstract. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS09-09.