Deep learning algorithm on H&E whole slide images to characterize TP53 alterations frequency and spatial distribution in breast cancer.

The tumor suppressor TP53 is frequently mutated in hormone receptor-negative, HER2-positive breast cancer (BC), contributing to tumor aggressiveness. Traditional ancillary methods like immunohistochemistry (IHC) to assess TP53 functionality face pre- and post-analytical challenges. This proof-of-concept study employed a deep learning (DL) algorithm to predict TP53 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 TP53 mutations, in BC. DL-based histopathological analysis represents a valuable tool for improving patient management and tailoring treatment approaches based on molecular biomarker status.
Competing Interests: A. M. has received support from Menarini Group and served on the Speakers' Bureau for Roche and AstraZeneca. C. C. has participated in advisory or consultancy roles and speakers' bureau engagements for Eli Lilly, Pfizer, Novartis, Roche, AstraZeneca, MSD, Daiichi Sankyo, Gilead, and Seagen. G. Curi. has received honoraria for speaker engagements from Roche, Seattle Genetics, Novartis, Lilly, Pfizer, Foundation Medicine, NanoString, Samsung, Celltrion, BMS, and MSD; honoraria for consultancy from Roche, Seattle Genetics, and NanoString; honoraria for participation in advisory boards from Roche, Lilly, Pfizer, Foundation Medicine, Samsung, Celltrion, and Mylan; honoraria for writing engagements from Novartis and BMS; and honoraria for participation in the Ellipsis Scientific Affairs Group. He has also received institutional research funding for conducting phase I and II clinical trials from Pfizer, Roche, Novartis, Sanofi, Celgene, Servier, Orion, AstraZeneca, Seattle Genetics, AbbVie, Tesaro, BMS, Merck Serono, Merck Sharp & Dohme, Janssen-Cilag, Philogen, Bayer, Medivation, and Medimmune. E. G-R. has received advisory fees, honoraria, travel accommodations/expenses, grants, and/or non-financial support from AstraZeneca, Exact Sciences, GSK, Illumina, MSD, Novartis, Roche, and Thermo Fisher Scientific. N.F. has received honoraria for consulting, advisory roles, speakers' bureau participation, travel, and/or research grants from Merck Sharp & Dohme (MSD), Novartis, AstraZeneca, Roche, Menarini, Daiichi Sankyo, GlaxoSmithKline (GSK), Gilead, Diaceutics, Adicet Bio, Sermonix, Reply, and Leica Biosystems. The remaining authors declare no conflicts of interest.
(© 2024 The Authors.)