Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours

High-grade serous ovarian carcinoma (HGSOC) is genetically unstable and characterised by the presence of subclones with distinct genotypes. Intratumoural heterogeneity is linked to recurrence, chemotherapy resistance, and poor prognosis. Here, we use spatial transcriptomics to identify HGSOC subclones and study their association with infiltrating cell populations. Visium spatial transcriptomics reveals multiple tumour subclones with different copy number alterations present within individual tumour sections. These subclones differentially express various ligands and receptors and are predicted to differentially associate with different stromal and immune cell populations. In one sample, CosMx single molecule imaging reveals subclones differentially associating with immune cell populations, fibroblasts, and endothelial cells. Cell-to-cell communication analysis identifies subclone-specific signalling to stromal and immune cells and multiple subclone-specific autocrine loops. Our study highlights the high degree of subclonal heterogeneity in HGSOC and suggests that subclone-specific ligand and receptor expression patterns likely modulate how HGSOC cells interact with their local microenvironment. 

Spatial transcriptomics reveals discrete tumour microenvironments and autocrine loops within ovarian cancer subclones

The copy number and mutational landscape of recurrent ovarian high-grade serous carcinoma

Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches. 

Spatial oncology: Translating contextual biology to the clinic

Ovarian cancer is resistant to immunotherapy, and this is influenced by the immunosuppressed tumor microenvironment (TME) dominated by macrophages. Resistance is also affected by intratumoral heterogeneity, whose development is poorly understood. To identify regulators of ovarian cancer immunity, we employed a spatial functional genomics screen (Perturb-map), focused on receptor/ligands hypothesized to be involved in tumor-macrophage communication. Perturb-map recapitulated tumor heterogeneity and revealed that interleukin-4 (IL-4) promotes resistance to anti-PD-1. We find ovarian cancer cells are the key source of IL-4, which directs the formation of an immunosuppressive TME via macrophage control. IL-4 loss was not compensated by nearby IL-4-expressing clones, revealing short-range regulation of TME composition dictating tumor evolution. Our studies show heterogeneous TMEs can emerge from localized altered expression of cancer-derived cytokines/chemokines that establish immune-rich and immune-excluded neighborhoods, which drive clone selection and immunotherapy resistance. They also demonstrate the potential of targeting IL-4 signaling to enhance ovarian cancer response to immunotherapy.

Ovarian cancer-derived IL-4 promotes immunotherapy resistance

High-grade serous ovarian carcinoma (HGSOC) is characterised by recurrence, chemotherapy resistance and overall poor prognosis. Genetic heterogeneity of tumour cells and the microenvironment of the tumour have been hypothesised as key determinants of treatment resistance and relapse. Here, using a combination of spatial and single cell transcriptomics (10x Visium and Chromium platforms), we examine tumour genetic heterogeneity and infiltrating populations of HGSOC samples from eight patients with variable response to neoadjuvant chemotherapy. By inferring gross copy number alterations (CNAs), we identified distinct tumour subclones co-existing within individual tumour sections. These tumour subclones have unique CNA profiles and spatial locations within each tumour section, which were further validated by ultra-low-pass whole genome sequencing. Differential expression analysis between subclones within the same section identified both tumour cell intrinsic expression differences and markers indicative of different infiltrating cell populations. The gene sets differentially expressed between subclones were significantly enriched for genes encoding plasma membrane and secreted proteins, indicative of subclone-specific microenvironments. Furthermore, we identified tumour derived ligands with variable expression levels between subclones that correlated or anticorrelated with various non-malignant cell infiltration patterns. We highlight several of these that are potentially direct tumour-stroma/immune cell relationships as the non-malignant cell type expresses a cognate receptor for the tumour derived ligand. These include predictions of CXCL10-CXCR3 mediated recruitment of T and B cells to associate with the subclones of one patient and CD47-SIRPA mediated exclusion of macrophages from association with subclones of another. Finally, we show that published HGSOC molecular subtype signatures associated with prognosis are heterogeneously expressed across tumour sections and that areas containing different tumour subclones with different infiltration patterns can match different subtypes. Our study highlights the high degree of intratumoural subclonal and infiltrative heterogeneity in HGSOC which will be critical to better understand resistance and relapse.

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Spatial transcriptomics reveals ovarian cancer subclones with distinct tumour microenvironments

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Spatial transcriptomics reveals discrete tumour microenvironments and autocrine loops within ovarian cancer subclones

Spatial transcriptomics reveals ovarian cancer subclones with distinct tumour microenvironments