While triple-negative breast cancer (TNBC) is known to be heterogeneous at the genomic and transcriptomic levels, spatial information on tumor organization and cell composition is still lacking. Here, we investigate TNBC tumor architecture including its microenvironment using spatial transcriptomics on a series of 92 patients. We perform an in-depth characterization of tumor and stroma organization and composition using an integrative approach combining histomorphological and spatial transcriptomics. Furthermore, a detailed molecular characterization of tertiary lymphoid structures leads to identify a gene signature strongly associated to disease outcome and response to immunotherapy in several tumor types beyond TNBC. A stepwise clustering analysis identifies nine TNBC spatial archetypes, further validated in external datasets. Several spatial archetypes are associated with disease outcome and characterized by potentially actionable features. In this work, we provide a comprehensive insight into the complexity of TNBC ecosystem with potential clinical relevance, opening avenues for treatment tailoring including immunotherapy.

Triple negative breast cancer (TNBC) is a highly heterogeneous disease with a poor prognosis and a paucity of therapeutic options. In recent years, immunotherapy has emerged as a new treatment option for patients with TNBC. However, this therapeutic evolution is paralleled by a growing need for biomarkers which allow for a better selection of patients who are most likely to benefit from this immune checkpoint inhibitor (ICI)-based regimen. These biomarkers will not only facilitate a better optimization of treatment strategies, but they will also avoid unnecessary side effects in non-responders, and limit the increasing financial toxicity linked to the use of these agents. Huge efforts have been deployed to identify predictive biomarkers for the ICI, but until now, the fruits of this labor remained largely unsatisfactory. Among clinically validated biomarkers, only programmed death-ligand 1 protein (PD-L1) expression has been prospectively assessed in TNBC trials. In addition to this, microsatellite instability and a high tumor mutational burden are approved as tumor agnostic biomarkers, but only a small percentage of TNBC fits this category. Furthermore, TNBC should no longer be approached as a single biological entity, but rather as a complex disease with different molecular, clinicopathological, and tumor microenvironment subgroups. This review provides an overview of the validated and evolving predictive biomarkers for a response to ICI in TNBC.

Immunotherapy has become the standard of care for various cancer types. The widespread use of immune checkpoints inhibitors confronts us with a whole range of novel immune-related adverse events. Skin toxicity is one of the most frequent adverse events. In this article, we report a case of anti-PD-1 induced late bullous pemphigoid (BP) with mucosal erosions and associated with a troublesome neurological disorder of undetermined origin in a patient with metastatic melanoma. Skin biopsy was essential to make the diagnosis and rapid initiation of systemic prednisolone played a role in favorable clinical outcome of BP. We will discuss the difficulty of early diagnosis of BP, its unusual association with neurological disorders, and the specific management of this particular dermatological entity.

Introduction Triple negative breast cancer (TNBC) is a heterogeneous disease. Spatial transcriptomics (ST) interrogates gene expression in spatially defined spots. Here, we assessed whether ST expression data could identify specific histomorphologic features from TNBC samples, the ultimate goal being to develop a tool allowing to automatically annotate ST slides from gene expression data. Methods ST expression profiles were obtained from 41 TNBC samples. T and B cell immune phenotyping was performed on a consecutive slide using double CD3/CD20 IHC. All samples were manually annotated in 11 histomorphological categories (see Table). ST spots with artifacts or less than 500 reads were removed from the analysis. Each spot was assigned to its most common annotation. A linear booster classifier was used to classify the spots into 11 histomorphological categories based on gene expression. Classification accuracy was summarized by area under the ROC curve (AUC), assessed using leave-a-patient-out (LPO) or in-patient 10-fold cross-validation (xVal). Results Most ST spots were annotated as invasive tumor and stroma with low TILs as well stroma with high TILs (see Table). Overall, the performance of the classifier was very high. However, xVal method obtained higher AUC than the LPO method, reflecting higher interpatient than intrapatient variations. Necrosis, in situ carcinoma and B cells were very variable between patients, but not intrapatient, as shown by low LPO and high xVal AUCs. Of note, misclassification of B cells could be due to poor distinction of B vs T cells on HE slides despite IHC support. Spots with multiple annotations were also more difficult to classify. Conclusion Here, we developed a high-performance classifier based on the biggest series of ST data allowing to identify several histomorphological features from TNBC tissues, representing a unique tool for further ST research. Citation Format: Xiaoxiao Wang, David Venet, Floriane Dupont, Ghizlane Rouas, Linnea Stenberg, Annelie Mollbrink, Denis Larsimont, Joakim Lundeberg, Françoise Rothé, Christos Sotiriou. Recapitulating spatial tumor morphology using automated classifier in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 182.

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 < 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.

While triple negative breast cancer (TNBC) is known to be heterogeneous at the genomic and transcriptomic levels, spatial information on tumor organization and cell composition is still lacking. Here, we investigated TNBC tumor architecture including its microenvironment using spatial transcriptomics on a series of 92 patients. We performed an in-depth characterization of tumor and stroma organization and composition using an integrative approach combining histomorphological and spatial transcriptomics. Furthermore, a detailed molecular characterization of tertiary lymphoid structures led to identify a gene signature strongly associated to disease outcome and response to immunotherapy in several tumor types beyond TNBC. A stepwise clustering analysis identified nine TNBC ecotypes, further validated in external datasets. Several ecotypes were associated with disease outcome and characterized by potentially actionable features. This is the largest TNBC series providing a comprehensive insight into the complexity of TNBC ecosystem with potential clinical relevance, opening avenues for treatment tailoring including immunotherapy.