Abstract PS5-13: Holistic artificial intelligence-driven predictor in HER2-positive (HER2+) early breast cancer (BC) treated with neoadjuvant lapatinib and trastuzumab without chemotherapy: A correlative analysis from SOLTI-1114 PAMELA
Authors: Batistella Enzo , Paré Laia , Sahasrabudhe Mihir , Pascual Tomás , Vakalopoulou Maria , Villagrasa Patricia , Deutsch Eric , Chic Núria , Villacampa Guillermo , Nuciforo Paolo , Cortes Javier , Llombart-Cussac Antonio , Paragios Nikos , Prat Aleix 
Published: February 2021

Abstract
Background: Positive partial response was observed in patients with primary HER2+ early BC with dual HER2 blockade that were not treated with chemotherapy. In this context and beyond, the low (partial) response rate of chemotherapy-free treatment strategies creates the necessity for patient stratification prior to treatment selection. Here, we tackle the challenging task of evaluating the ability of clinical, gene expression and histopathology data to predict response following dual HER2 blockade without chemotherapy. Our aim is through artificial intelligence to automatically decipher the complementarity of clinical, genomic and histopathology data through an evidence-driven approach towards a low dimensional holistic signature that determines outcomes and could be subsequently used as a clinical biomarker for treatment patient inclusion. Methods: PAMELA (Lancet Oncology 2017) was a prospective study in HER2+ BC designed to evaluate the ability of the PAM50 HER2-enriched intrinsic subtype to predict pCR following 18-weeks of neoadjuvant lapatinib and trastuzumab (and hormonal therapy if hormone receptor-positive [HR+]). Clinical-pathological variables (15) were included such as tumor cellularity, tumor-infiltrating lymphocytes (TILs), the expression of BC-related genes/signatures (567) along with histopathological data from pre-treatment samples. Imaging information was obtained from H/E slides, through an unsupervised deep learning approach using an attention network. The semantic segmentation was used to derive at the patch level image and shape characteristics resulted on a pathomics-derived feature vector of (300) variables. An integrative approach that harnessed clinical, genomics and pathomics data into a unified prediction framework was used. Patients were divided into a training set 80% and a testing set 20% with proportions of pCR and non-pCR corresponding to the ones observed. A 100-fold Cross-validation (CV) was performed on the training. Linear and non-linear robust feature selection were used to recover a low dimensional holistic signature along with an ensemble learning approach to select the top 5 machine learning/artificial intelligence methods for prognosis. Results: From the high dimensional feature (882) space, a low dimensional holistic signature of 8 predictive variables was automatically retrieved through. The signature consisted of 4 genomics variables (expression levels of ERBB2, ESR1, Luminal A signature and Risk of Relapse score), 2 clinical-pathological variables (histologic grade and ER-status) and 2 imaging variables (mean Short Run Low Gray Level Emphasis of the gray level run length matrix and the mean absolute deviation). To ensure the robustness and generalizability of the results, we present results averaged over 100 splits into training and test. On all cases the same holistic signature was uses and the same prediction methods/principles. The proposed AI-driven prognosis mechanism reached 75% balanced accuracy, 69% precision, 65% sensitivity, 86% specificity and 0.84 AUC demonstrating the relevance of the approach. It was observed a successful classification of 86% for the non-pCR and 65% for the pCR cases. Ablation studies were performed to determine the relevance of the different categories of variables. Genomics variables were the most informative since their removal led to the highest decrease of the metrics (11% in average). Conclusion: The proposed method has great potentials for an effective and clinically meaningful implementation of pre-selecting patients that will not achieve a pCR after neoadjuvant dual HER2 blockade. Besides, the generality of the method used here makes it transposable to any type of cancer or therapy.
Citation Format: Enzo Batistella, Laia Paré, Mihir Sahasrabudhe, Tomás Pascual, Maria Vakalopoulou, Patricia Villagrasa, Eric Deutsch, Núria Chic, Guillermo Villacampa, Paolo Nuciforo, Javier Cortes, Antonio Llombart-Cussac, Nikos Paragios, Aleix Prat. Holistic artificial intelligence-driven predictor in HER2-positive (HER2+) early breast cancer (BC) treated with neoadjuvant lapatinib and trastuzumab without chemotherapy: A correlative analysis from SOLTI-1114 PAMELA [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS5-13.