Abstract PD14-02: Unravelling spatial tumor organization and heterogeneity in lobular breast cancer using spatial transcriptomics
Authors: Collet Laetitia , Serra Matteo , Rediti Mattia , Lifrange Frédéric , Venet David , Wang XiaoXiao , Vincent Delphine , Rouas Ghizlane , Fimereli Danai , Gacquer David , Garcia Andrea Joaquin , Veys Isabelle , Craciun Ligia , Larsimont Denis , Vikkula Miikka , Duhoux François , Rothé Françoise , Sotiriou Christos 
Published: March 2022

Introduction: Invasive lobular breast cancer (ILC) is the most frequent special type of breast cancer. Recent studies showed the impact of intratumor heterogeneity (ITH) on patient outcome. Here, we aimed to better characterize ITH in ILC using spatial transcriptomics (ST) together with high-resolution morphological annotation. Methods: Spatial transcriptomics (Visium 10x Genomics) was performed on frozen tumor samples from 15 primary estrogen receptor positive, HER2-negative ILCs with 4 patients developing disease relapse. For each sample, we annotated hematoxylin/eosin sections (QuPath software) in two steps: manual annotation of specific histomorphological structures (e.g. normal breast ducts, fat tissue, vessels) and machine learning annotation of stroma and tumor cells at the single cell level. Proportion of each specific tissue type per sample was defined by pixel percentage computed within each ST spot. For each sample, the proportion of a given tissue type was defined as the proportion of pixels annotated with this specific type averaged across all spots within the tissue section. Spatially variable genes were used as input for non-negative matrix factorization and the reduced expression matrix was clustered with the Louvain algorithm implemented in Seurat. A cluster of spots was defined as tumoral if the average proportion of pixels annotated as tumor across its constitutive spots was higher than the average proportion of tumor pixels across all spots from the tissue section. Cluster characterization was performed using gene set enrichment analysis based on hallmarks (Molecular Signature Database) with a false discovery rate &lt; 0.05. Spatial heterogeneity and tumor organization within each sample were assessed according to the number and type of cluster interactions, homo-contacts being defined as interactions between spots belonging to the same cluster and hetero-contacts between spots from two different clusters. Comparisons between groups were assessed using Wilcoxon test. Results: A total of 45 tumor clusters were identified among the 15 patients, with a range of 2-4 tumor clusters per sample highlighting intra-tumor heterogeneity. Cluster characterization revealed the presence of clusters enriched in different biological pathways within a given sample. These analyses also showed inter-patient heterogeneity as witnessed by distinct tumor clusters only present in a subset of tissue samples. In particular, 66% (N=10/15) of the tissue samples harbored clusters enriched in oxidative phosphorylation, 53% (N=8/15) in epithelial-mesenchymal transition, 33% (N=5/15) in interferon gamma response, 27% (N=4/15) in TNF-alpha via NFkB, 27% (N=4/15) in androgen response and 13% (N=2/15) in protein secretion hallmarks. All samples shared at least one tumor cluster enriched in estrogen early and late response.Although the number of tumor clusters was similar between samples from patients with or without relapse (median of 3, range 2-4), the presence of clusters enriched in protein secretion was associated with disease recurrence (p = 0.02). Of note, higher number of hetero-contacts mirroring tumor disorganization was associated with poor outcome although not significant (p= 0.08). Conclusions: Here, we showed for the first time a substantial inter- and intra-tumor heterogeneity of ILC at an unprecedented level, characterized by the presence of specific tumor clusters and distinct tumor organization patterns associated with outcome. We also. uncovered potentially targetable clusters missed by bulk analysis, offering novel perspectives for optimized ILC care. Further validation is warranted.

Citation Format: Laetitia Collet, Matteo Serra, Mattia Rediti, Frédéric Lifrange, David Venet, XiaoXiao Wang, Delphine Vincent, Ghizlane Rouas, Danai Fimereli, David Gacquer, Andrea Joaquin Garcia, Isabelle Veys, Ligia Craciun, Denis Larsimont, Miikka Vikkula, François Duhoux, Françoise Rothé, Christos Sotiriou. Unravelling spatial tumor organization and heterogeneity in lobular breast cancer using spatial transcriptomics [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD14-02.