Deep learning algorithm on H&E whole slide images to characterize <i>TP53</i> alterations frequency and spatial distribution in breast cancer
Chiara Frascarelli, Konstantinos Venetis, Antonio Marra, Eltjona Mane, Mariia Ivanova, Giulia Cursano, Francesca Porta, Alberto Concardi, Arnaud Céol, Annarosa Farina, Carmen Criscitiello, Giuseppe Curigliano, Elena Guerini-Rocco, Nicola Fusco
Abstract
<h2>Abstract</h2> The tumor suppressor <i>TP53</i> is frequently mutated in hormone receptor-negative, HER2-positive breast cancer (BC), contributing to tumor aggressiveness. Traditional ancillary methods like immunohistochemistry (IHC) to assess <i>TP53</i> functionality face pre- and post-analytical challenges. This proof-of-concept study employed a deep learning (DL) algorithm to predict <i>TP53</i> mutational status from H&E-stained whole slide images (WSIs) of BC tissue. Using a pre-trained convolutional neural network, the model identified tumor areas and predicted TP53 mutations with a Dice coefficient score of 0.82. Predictions were validated through IHC and next-generation sequencing (NGS), confirming TP53 aberrant expression in 92 % of the tumor area, closely matching IHC findings (90 %). The DL model exhibited high accuracy in tissue quantification and TP53 status prediction, outperforming traditional methods in terms of precision and efficiency. DL-based approaches offer significant promise for enhancing biomarker testing and precision oncology by reducing intra- and inter-observer variability, but further validation is required to optimize their integration into real-world clinical workflows. This study underscores the potential of DL algorithms to predict key genetic alterations, such as <i>TP53</i> mutations, in BC. DL-based histopathological analysis represents a valuable tool for improving patient management and tailoring treatment approaches based on molecular biomarker status.