Background Medulloblastoma (MB) is a malignant tumour of the cerebellum which can be classified into four major subgroups based on gene expression and genomic features. Single-cell transcriptome studies have defined the cellular states underlying each MB subgroup; however, the spatial organisation of these diverse cell states and how this impacts response to therapy remains to be determined. Methods Here, we used spatially resolved transcriptomics to define the cellular diversity within a sonic hedgehog (SHH) patient-derived model of MB and show that cells specific to a transcriptional state or spatial location are pivotal for CDK4/6 inhibitor, Palbociclib, treatment response. We integrated spatial gene expression with histological annotation and single-cell gene expression data from MB, developing an analysis strategy to spatially map cell type responses within the hybrid system of human and mouse cells and their interface within an intact brain tumour section. Results We distinguish neoplastic and non-neoplastic cells within tumours and from the surrounding cerebellar tissue, further refining pathological annotation. We identify a regional response to Palbociclib, with reduced proliferation and induced neuronal differentiation in both treated tumours. Additionally, we resolve at a cellular resolution a distinct tumour interface where the tumour contacts neighbouring mouse brain tissue consisting of abundant astrocytes and microglia and continues to proliferate despite Palbociclib treatment. Conclusions Our data highlight the power of using spatial transcriptomics to characterise the response of a tumour to a targeted therapy and provide further insights into the molecular and cellular basis underlying the response and resistance to CDK4/6 inhibitors in SHH MB.

Background: Medulloblastoma (MB) is a malignant tumour of the cerebellum which can be classified into four major subgroups based on gene expression and genomic features. Single cell transcriptome studies have defined the cellular states underlying each MB subgroup, however the spatial organisation of these diverse cell states and how this impacts response to therapy remains to be determined. Methods: Here, we used spatially resolved transcriptomics to define the cellular diversity within a sonic hedgehog (SHH) patient-derived model of MB and identify how cells specific to a transcriptional state or spatial location are pivotal in responses to treatment with the CDK4/6 inhibitor, Palbociclib. We integrated spatial gene expression with histological annotation and single cell gene expression data from MB, developing a analysis strategy to spatially map cell type responses within the hybrid system of human and mouse cells and their interface within an intact brain tumour section. Results: We distinguish neoplastic and non-neoplastic cells within tumours and from the surrounding cerebellar tissue, further refining pathological annotation. We identify a regional response to Palbociclib, with reduced proliferation and induced neuronal differentiation in both treated tumours. Additionally, we resolve at a cellular resolution a distinct tumour interface where the tumour contacts neighbouring mouse brain tissue consisting of abundant astrocytes and microglia and continues to proliferate despite Palbociclib treatment. Conclusions: Our data highlight the power of using spatial transcriptomics to characterise the response of a tumour to a targeted therapy and provide further insights into the molecular and cellular basis underlying the response and resistance to CDK4/6 inhibitors in SHH MB.

Spatial transcriptomic (ST) data enables us to link tissue morphological features with thousands of unseen gene expression values, opening a horizon for breakthroughs in digital pathology. Models to predict the presence/absence, high/low, or continuous expression of a gene using images as the only input have a huge potential clinical applications, but such models require improvements in accuracy, interpretability, and robustness. We developed STimage models to estimate parameters of gene expression as distributions rather than fixed data points, thereby allowing for the essential quantification of uncertainty in the predicted results. We assessed aleatoric and epistemic uncertainty of the models across a diverse range of test cases and proposed an ensemble approach to improve the model performance and trust. STimage can train prediction models for one gene marker or a panel of markers and provides important interpretability analyses at a single- cell level, and in the histopathological annotation context. Through a comprehensive benchmarking with existing models, we found that STimage is more robust to technical variation in platforms, data types, and sample types. Using images from the cancer genome atlas, we showed that STimage can be applied to non-spatial omics data. STimage also performs better than other models when only a small training dataset is available. Overall, STimage contributes an important methodological advance needed for the potential application of spatial technology in cancer digital pathology.

Skin cancer is by far the most common cancer, encompassing squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and melanoma. The diversity of cell types and tissue organisation in skin cancer remains poorly understood yet is required to improve diagnosis and treatment. In this work, we integrated six imaging and sequencing technologies to build the first spatial single cell reference for all three major skin cancer types and create a comprehensive skin cancer interactome. Using single-cell RNA-Seq (RNA) of >100,000 cells from 11 paired patient biopsies, we identified 28 SCC cell types, including 10 immune cell types, and found core suites of 39 cancer genes and 222 healthy genes shared across ≥80% patient samples. Using independent Nanostring Digital Spatial Profiling (RNA, protein), we validated most immune cell types and gene markers at protein and RNA levels. The enrichment of an immune signalling signature in SCC was further revealed by spatial Nanostring Single Molecular Imaging - SMI (RNA). Strikingly, we found the high consistency in mapping cell types in scRNAseq data and the independent SMI data, for example, the distribution of the three keratinocyte layers (basal, cycling and differentiated). This observation suggested the power of combining scRNAseq data with spatial SMI data. Furthermore, we implemented three approaches to validate the spatial distribution and cell type co-localisation by both Visium Spatial Transcriptomics (RNA), SMI (RNA) and Opal Multiplex Polaris (protein). Finally, cell-cell interactions were inferred at the global level using scRNAseq data (no spatial information) and Visium data (with spatial dimension), which were then validated at high throughput (517 ligands/receptors) and single-cell resolution using SMI. These in situ interaction maps were built across all three cancer types to create a comprehensive spatial interaction atlas of skin cancer. We also used targeted approaches with Polaris (protein) and RNAScope (RNA) to confirm and visualise clinically-important ligand-receptor pairs, including checkpoint inhibitor drug targets PD-1 and PD-L1. By integrating six distinct yet complementary spatial and single cell technologies, this study highlights the power of a spatial multi-omics approach for understanding cell types and their activities in cancer tissues. Citation Format: Laura Grice, Guiyan Ni, Xinnan Jin, Minh Tran, Emily Killingbeck, Mark Gregory, Onkar Mulay, Siok-Min Teoh, Arutha Kulasinghe, Michael Leon, Sarah Murphy, Sarah Warren, Youngmi Kim, Quan Nguyen. A single-cell, spatial multiomics atlas and cellular interactome of all major skin cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3817.

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