Single cell and spatial technologies that profile gene expression across a whole tissue are revolutionizing the resolution of molecular states in clinical tissue samples. Commercially available methods that characterize either single cell or spatial gene expression are currently limited by low sample throughput and/or gene plexy, lack of on-instrument analysis, and the destruction of histological features and epitopes during the workflow. Here, we analyzed large, serial formalin-fixed, paraffin-embedded (FFPE) human breast cancer sections using a novel FFPE-compatible single cell gene expression workflow (Chromium Fixed RNA Profiling; scFFPE-seq), spatial transcriptomics (Visium CytAssist), and automated microscopy-based in situ technology using a 313-plex gene panel (Xenium In Situ). Whole transcriptome profiling of the FFPE tissue using scFFPE-seq and Visium facilitated the identification of 17 different cell types. Xenium allowed us to spatially resolve these cell types and their gene expression profiles with single cell resolution. Due to the non-destructive nature of the Xenium workflow, we were able to perform H&E staining and immunofluorescence on the same section post-processing which allowed us to spatially register protein, histological, and RNA data together into a single image. Integration of data from Chromium scFFPE-seq, Visium, and Xenium across serial sections allowed us to do extensive benchmarking of sensitivity and specificity between the technologies. Furthermore, data integration inspired the interrogation of three molecularly distinct tumor subtypes (low-grade and high-grade ductal carcinoma in situ (DCIS), and invasive carcinoma). We used Xenium to characterize the cellular composition and differentially expressed genes within these subtypes. This analysis allowed us to draw biological insights about DCIS progression to infiltrating carcinoma, as the myoepithelial layer degrades and tumor cells invade the surrounding stroma. Xenium also allowed us to further predict the hormone receptor status of tumor subtypes, including a small 0.1 mm2 DCIS region that was triple positive for ESR1 (estrogen receptor), PGR (progesterone receptor) and ERBB2 (human epidermal growth factor receptor 2, a.k.a. HER2) RNA. In order to derive whole transcriptome information about these cells, we used Xenium data to interpolate the cell composition of Visium spots, and leveraged Visium whole transcriptome information to discover new biomarkers of breast tumor subtypes. We demonstrate that scFFPE-seq, Visium, and Xenium independently provide information about molecular signatures relevant to understanding cancer heterogeneity. However, it is the integration of these technologies that leads to even deeper insights, ushering in discoveries that will progress oncology research and the development of diagnostics and therapeutics.

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The tumor microenvironment is composed of highly heterogeneous structures and cell types that dynamically influence and communicate with each other. Although examination of singular biospecimens is sufficient for diagnostic purposes, it is inadequate and cost prohibitive when scaling for complex and overarching studies. Thus, high density multi-tumor tissue microarrays (TMAs) have been a practical and effective solution for high-throughput molecular analysis of tissues. Introduced more than a decade ago, TMAs have been instrumental in the recent study of tumor biology, the development of diagnostic tests, the establishment of quality control, and the investigation and identification of oncological biomarkers. Here, we demonstrate the pairing of the 10x Genomics Visium Cytassist Spatial Gene Expression Solution and Xenium In-Situ Platform on multi-tumor TMAs to screen for common biomarkers among a cohort of samples. Spatial transcriptomics technology has proven valuable in mapping the whole transcriptome with spatial context (Visium), whereas In Situ (Xenium) enables high-throughput cellular characterization at single-cell resolution. With the addition of our CytAssist platform, we expand on the pre-existing standard Visium solution by facilitating the retrieval of RNA transcriptomic information from tissues placed on standard or archival slides. On the other hand, the novel Xenium platform compliments whole transcriptome Visium data by unlocking the potential to assign transcripts to a particular cell with spatial context and subcellular resolution. The combination of spatial transcriptomics and targeted in situ data with FFPE TMAs promotes a high-throughput method to accelerate the uncovering of molecular signatures suitable to understanding the tumor microenvironment. We showcase the ability to spatially and comprehensively resolve individual oncogene and tumor suppressor genes associated with multiple tumors from a cohort of cancer patients and from multiple different tumor samples. In addition, these markers are mapped back to distinct morphological features within each tissue core, and use differential gene expression data to identify distinct cell types throughout the different patient tissues. By combining the throughput of TMA samples and depth of the Visium and Xenium platforms, the strategy enables greater insights into cell-type specifics while also expanding the spectrum of biospecimen types that can be analyzed. Citation Format: Syrus Mohabbat, Hardeep Singh, Stephen R. Williams, Lauren M M. Gutgesell, David J. Sukovich, Govinda M. Kamath, Hanyoup Kim, Amanda Janesick, Robert Shelansky, Ghezal Beliakoff, Augusto M. Tentori, Albert Kim, Cedric R. Uytingco, Sarah Taylor. Application of spatially resolved transcriptomics to screen multiple tumor biospecimens using tissue microarrays. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4708.