From trials to clinics: Real-world outcomes of neoadjuvant HER2 directed therapy in early stage breast cancer using AI-enhanced data pipelines.

Abstract

e12610 Background: Human Epidermal Growth Factor Receptor 2 (HER2)–positive breast cancers were historically associated with more aggressive disease before HER2-targeted therapies. Neoadjuvant combination regimens such as TCHP (docetaxel, carboplatin, trastuzumab, and pertuzumab) have demonstrated superior efficacy in clinical trials, achieving high pathological complete response (pCR) rates. However, translating these trial outcomes to real-world community settings remains challenging. Real-world evidence (RWE) outside of the controlled trial environment is limited, partly due to the labor-intensive nature of manually abstracting data from unstructured clinical notes. Scalable solutions, such as large language models (LLMs), offer a promising alternative by enabling more efficient data extraction. In this study, we utilized LLMs to extract and analyze clinical data, comparing outcomes in patients treated within the American Oncology Network (AON) to those reported in clinical trials. Methods: We conducted a retrospective study of patient records from AON. Patients with HER2-positive invasive ductal carcinoma (stages I–III) diagnosed between January 2018, and March 2024, who received neoadjuvant TCHP, were included. Clinical data were obtained from structured fields and manual chart abstraction and validated by oncology experts. Concurrently, we developed a locally hosted, quantized LLM pipeline to parse unstructured physician notes for key variables, including treatment timing, neoadjuvant intent, HER2-targeted therapy, and pCR status. Patients pCR rates were contrasted with published clinical trial results using chi-squared test. Results: A total of 335 eligible patients were identified from multiple community oncology clinics across 20 states. Stage I, II, and III disease accounted for 18%, 58%, and 24% of cases, respectively. The overall pCR rate was 52.63%, aligning with pCR rates reported in clinical trials such as KRISTINE, NEOSPHERE, PEONY, and TRYPHAENA. LLM-based abstraction achieved 96% accuracy for determining pCR and 87% for identifying neoadjuvant therapy details, reducing manual review time by over 98%. Conclusions: This retrospective analysis demonstrates that HER2-positive breast cancer patients who received neoadjuvant TCHP within AON achieved pCR rates comparable to those reported in clinical trials, confirming its efficacy in real-world populations. Additionally, the integration of LLMs significantly reduced labor and time enabling a more efficient and scalable approach to RWE studies. These findings underscore the transformative potential of AI in advancing cancer research. Comparison of pCR between AON and clinical trials. Trial/RWE Sample Size Pathological Complete Response P-value AON 115 52.6% * KRISTINE 222 55.7% 0.47 NeoSphere 107 45.8% 0.23 PEONY 218 39.5% 0.72 TRYPHENA 77 66.2% 0.08