Background: Invasive lobular carcinoma (ILC) of the breast is the second most common histologic subtype of breast cancer (BC), following invasive ductal carcinoma of no special type (IDC-NST). The hallmark histologic feature of ILC is cellular discohesiveness, the result of bi-allelic inactivation of CDH1, and represents an important genotypic-phenotypic correlation in BC. Although most ILCs harbor CDH1 loss-of function mutations associated to loss-of-heterozygosity (LOH) of the wild type-allele, a subset of ILCs lack these alterations despite displaying a typical lobular phenotype. Here, we sought to identify alternative molecular mechanisms converging on CDH1 inactivation by employing an integrative artificial intelligence (AI) and genomics approach. Materials and Methods: A genomics-driven AI-based algorithm using hematoxylin and eosin (H&E) whole slide images (WSIs) as input, previously developed to detect bi-allelic CDH1 mutations (inactivating mutation associated to LOH) in BC was employed. WSIs of 1,057 BCs including ILCs (n=187) and non-lobular BCs (n=870) previously subjected to FDA-cleared tumor/normal targeted sequencing were subjected to analysis with the AI-based algorithm. Cases predicted to harbor CDH1 bi-allelic mutations by the AI-model but lacking CDH1 bi-allelic mutations by targeted sequencing were assessed through targeted sequencing data re-analysis, CDH1 gene promoter methylation evaluation and/or whole genome sequencing analysis. Results: AI-based analysis WSIs corresponding to 1,057 BCs resulted in the identification of 34 cases found to lack CDH1 bi-allelic mutations by targeted sequencing but predicted to harbor these genetic alterations by the AI-based model. CDH1 gene promoter methylation assessment revealed CDH1 promoter methylation in 18 cases. Targeted sequencing data reanalysis revealed other genetic mechanisms of CDH1 inactivation including CDH1 homozygous deletions (n=3), intragenic deletion with LOH (n=1), and likely pathogenic non-coding CDH1 alterations associated with LOH (n=2). WGS analysis of an ILC revealed a novel deleterious CDH1 fusion stemming from translocation t(13;16), resulting in loss of the 5’UTR, transcription start site and exons 1 and 2 of CDH1, associated with complete loss of E-cadherin protein expression. Taken together, we identified alternative/novel mechanisms of bi-allelic CDH1inactivation in 74% (25/34) cases analyzed. Conclusions: By applying an AI-based algorithm trained to detect a genetic alteration (i.e., CDH1 bi-allelic mutations), we were able to identify alternative epigenetic and genetic molecular mechanisms of CDH1 inactivation in ILCs, including novel non-coding CDH1 genetic alterations and a new inactivating CDH1 fusion gene. These findings indicate that molecular mechanisms affecting a single gene or process converging on the same phenotype can be unveiled by the integration of AI and genomics, highlighting the robustness of this approach for the discovery of novel biology.

Citation Format: Fresia Pareja, Higinio Dopeso, Yikan Wang, Andrea Gazzo, David Brown, Pier Selenica, Jan Bernhard, Fatemeh Derakhshan, Edaise M. da Silva, Lorraine Colon-Cartagena, Thais Basili, Antonio Marra, Jillian Sue, Qiqi Ye, Arnaud Da Cruz Paula, Selma Yeni, Xin Pei, Hunter Green, Kaitlyn Gill, Yingjie Zhu, Matthew Lee, Ran Godrich, Adam Casson, Britta Weigelt, Nadeem Riaz, Hanna Y Wen, Edi Brogi, Matthew Hanna, Diana Mandelker, Jeremy Kunz, Brandon Rothrock, Sarat Chandarlapaty, Christopher Kanan, Gerard Oakley III, David Klimstra, Thomas Fuchs, Jorge Reis-Filho. Novel Mechanisms of CDH1 Inactivation in Breast Invasive Lobular Carcinoma Unveiled by the Integration of Artificial Intelligence and Genomics [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr GS03-04.