Abstract P4-02-06: Clinical and biological predictors of lymph node involvement in patients with early breast cancer for adjuvant treatment personalization
Authors: Pivetta Tania , Pastò Brenno , Urbani Martina , Benozzi Elisabetta , De Pascalis Nicola , Perin Tiziana , Mileto Mario , Pasquotti Bruno , Piccoli Erica , Vinante Lorenzo , Bampo Chiara , Bolzonello Silvia , Garutti Mattia , Nicoloso Milena , Corsetti Serena , Scalone Simona , da Ros Lucia , di Nardo Paola , Lisanti Camilla , Spazzapan Simon , Belletti Barbara , Bartoletti Michele , Gerratana Lorenzo , Massarut Samuele , Puglisi Fabio 
Published: March 2023

Background: Over the last years, the management of patients with node positive early breast cancer has gone through important innovations. On the medical side, new targeted therapies such as olaparib and abemaciclib have been introduced, with promising results on the invasive disease-free survival. Moreover, sparing axillary lymph node dissection has proven to be noninferior in terms of overall survival. However, no tools are currently available to predict lymph node involvement before definitive surgical evaluation. The aim of the study was to analyze clinical and pathological characteristics of patients with node positive early breast cancer to explore potential risk profiles associated with a ≥3 nodal involvement. Methods: The study retrospectively analyzed 335 node-positive breast cancer patients treated at the Breast Unit of the CRO Aviano National Cancer Institute, between 2017 and 2021. Data regarding primary tumor biological features, lymph node involvement and surgical approach were collected. Associations between clinico-pathological characteristics and ≥3 lymph node involvement were tested through stepwise logistic regression and the gradient boosting machine learning algorithm (GBM). Results: Among the 335 analyzed patients, 87.0% had a primary tumor &lt; 5 cm, with a single positive lymph node in 73.3% of cases. Hormone receptors were mainly positive (respectively 93.5% and 83.4% for estrogen and progesterone receptors). Tumor grade was most frequently well differentiated (Grade 1 in 60.7%), with a Ki67 &lt; 20% (59.5%). After multivariable logistic regression, a tumor size ≥ 3 cm (OR 3.24, CI95% 1.47-7.17, p = 0.004), the presence of massive lymphovascular stromal invasion (OR 2.50, CI95% 1.02-6.14, p = 0.045) and 2 or more positive sentinel lymph nodes at surgical evaluation (OR 6.08, CI95% 3.34-11.05, p &lt; 0.001) were associated with a higher risk of identifying ≥ 3 positive lymph nodes after subsequent axillary dissection. Similar results were observed in the luminal-like cohort. A GBM machine learning model was then developed with a 0.77 Area Under the Curve. Features with the highest relative importance (RI) were single sentinel node involvement (RI 16.1873), followed by tumor size ≥ 3 cm (RI 10.2024), ≥2 positive sentinel lymph nodes (RI 8.5050) and lymphovascular stromal invasion (4.0217). Consistently, number of positive sentinel lymph nodes and tumor size were the predominant features in all top 20 GBM models. Conclusions: The present study explored the definition of risk profiles linked to 3 or more positive lymph nodes based on clinical and pathological features. It, moreover, tested the feasibility of developing machine learning classifiers to support future clinical decision-making. Due to the growing complexity of the adjuvant setting, finding a balance between minimally invasive surgical and staging approaches and risk definition for treatment personalization will become increasingly critical.

Citation Format: Tania Pivetta, Brenno Pastò, Martina Urbani, Elisabetta Benozzi, Nicola De Pascalis, Tiziana Perin, Mario Mileto, Bruno Pasquotti, Erica Piccoli, Lorenzo Vinante, Chiara Bampo, Silvia Bolzonello, Mattia Garutti, Milena Nicoloso, Serena Corsetti, Simona Scalone, Lucia da Ros, Paola di Nardo, Camilla Lisanti, Simon Spazzapan, Barbara Belletti, Michele Bartoletti, Lorenzo Gerratana, Samuele Massarut, Fabio Puglisi. Clinical and biological predictors of lymph node involvement in patients with early breast cancer for adjuvant treatment personalization [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-02-06.