Assessment of spatial transcriptomics for oncology discovery

Abstract

Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.

Keywords: biomarkers; cancer biology; cancer genomics; digital pathology; genomics; oncology; pancreatic cancer; spatial genomics; spatial transcriptomics; tumors.

Graphical abstract

Figure 1

Experimental summary and spatial validation framework (A) Summary of key questions addressed in this study, including cohorts, model systems, modalities, goals, and conclusions. (B) Schematic of our spatial validation framework using high-quality imaging and digital pathology classifications to provide orthogonal validation of spatial genomics data used for biomarker discovery and target identification. (C) Mean features identified per spot under tissue for each tissue type and protocol. Created with BioRender.com.

Figure 2

ST recovers known tissue architectures and cell types in frozen, normal, well-structured tissue (A–C) Gene expression signatures on a representative fresh-frozen rat colon section for colonocytes (A), myocytes (B), and neurons (C) as detected by ST. Sections imaged and processed with a coverslip. Scale bars represent average SCT-normalized expression levels of gene signatures (STAR Methods). (D) Pseudo-bulk gene expression comparison from ST data between fresh-frozen rat colon sections processed with a coverslip or without a coverslip (n = 2/condition). (E) Representative bright-field image of an H&E-stained frozen rat colon section used for ST. (F–H) De-novo-derived ST clusters and biomarkers for rat colon. (F) Uniform manifold approximation and projection (UMAP) embedding of spot expression profiles (dots) colored by de novo cluster. (G) Spatial visualization of tissue sections colored by cluster. (H) Corresponding cluster biomarkers (dot size: fraction of spots expressing each biomarker; dot color: mean expression level in expressing spots). (I and J) Tissue compartments as identified by digital pathology. (I) Textures as identified by digital pathology overlaid on the colon section. Colors represent distinct textures and are matched to pathology annotations (legend, right). (J) Comparison of transcriptional de novo cluster spot assignments and digital pathology textures. Clusters are numbered and depicted along the x axis. Digital pathology composition of each cluster is represented by the corresponding bar graph.

Figure 3

ST recovers known tumor heterogeneity in syngeneic mouse models (A–F) B16F10 (n = 7; see also Figure S2A) or MC38 (n = 6; see also Figure S2B) syngeneic tumor cohorts were profiled using ST. One representative section is visualized for each of the models; full cohort plots are available in Figure S2. (A and B) De-novo-derived clusters are visualized spatially for representative B16F10 (A) and MC38 (B) sections. (C and D) Digital pathology identification of tumor (blue for B16F10; red for MC38) and necrosis (yellow) compartments in representative tissue B16F10 (C) and MC38 (D) sections. (E and F) De-novo-derived cluster biomarkers for B16F10 (E) and MC38 (F) tumor cohorts (dot size: fraction of spots expressing each biomarker; dot color: mean expression level in expressing spots). (G and H) Pairwise correlation of canonical cell-type marker gene expression across all spots in B16F10 (G) and MC38 (H) tumor cohorts. (I and J) Gene expression of select canonical cell-type markers for B16F10 (I) and MC38 (J) tumors. Scale bars represent SCT-normalized gene expression levels (STAR Methods).

Figure 4

ST recovers tumor microenvironment depth correlates in syngeneic tumors (A and B) Gene expression profiles associated with the edge-to-center depth of B16F10 (A) and MC38 (B) tumors (x axis: linear model coefficient estimate; y axis: −log10 adjusted p value). (C–K) Spatial visualization of select genes with transcriptional profiles strongly associated with tumor depth, i.e., upregulated expression in either the periphery or center of either B16F10 or MC38 tumors (mouse model and gene indicated in panels). Scale bars represent SCT-normalized gene expression levels (STAR Methods).

Figure 5

Comparison of polyA- and probe-based chemistries for matched fresh frozen and FFPE samples from a single PDAC donor (A) Quality measurement (DV200) of RNA from human tumor FFPE blocks relative to time post-excision and preservation (dark green). DV200 for fresh frozen (FF) tissue relative to time post-excision and preservation (light green). (B) Composition of donor A-FF (left) and -FFPE (right) tumor blocks. H&E-stained pathology sections were characterized using digital pathology, with respective classifications displayed. (C–F) Concordance of transcriptome capture across ST profiled sections as assessed by pseudo-bulk correlation for FF-polyA-ST versus FF-polyA-ST on the same slide (C), FFPE-polyA-ST versus FFPE-polyA-ST on the same slide (D), FFPE-polyA-ST versus FFPE-polyA-ST on different slides (E), and FF-polyA-ST versus FFPE-polyA-ST on different slides (F). (G) Gene set enrichment analysis of ST expression from donor A-FFPE and -FF tumor blocks. Network diagram displays genes upregulated in FFPE block, which is enriched for tumor-adjacent normal exocrine and endocrine tissue. (H) Concordance of transcriptome capture across ST profiled sections as assessed by pseudo-bulk correlation for FFPE-polyA-ST versus FFPE-probes-ST. (I) Correlation of gene expression from spatially co-registered FFPE-polyA-ST and FFPE-probes-ST sections. H&E images of sections and co-registration (left). Correlation of expression of sufficiently expressed genes across all co-registered spots (center) between two protocols. Representative spatial gene expression of concordant biomarker, CTRC (right).

Figure 6

Digital pathology augmentation validates transcriptomics capture of macroscopic heterogeneity in PDAC tumors (A–D) Spatial visualization of PDAC tumor signature (left) and de novo expression clusters (right) for representative sections from donor A-FF FF-polyA-ST (A), donor A-FFPE FFPE-probes-ST (B), donor B FFPE-probes-ST (C), and donor C FFPE-probes-ST (D) sections. Tumor signature is the classical PDAC signature from Collison et al. and highlights tumor regions in red. Scale bars represent average SCT-normalized expression levels of gene signatures (STAR Methods). (E–H) De-novo-derived biomarkers for each PDAC tissue and cluster in (A)–(D) (E: donor A-FF FF-polyA-ST; F: donor A-FFPE FFPE-probes-ST; G: donor B FFPE-probes-ST; H: donor C FFPE-probes-ST) (dot size: fraction of spots expressing each biomarker; dot color: mean expression level in expressing spots). (I) Digital pathology framework (left) and representative segmentation by texture and nuclei (right) for donor A-FFPE FFPE-probes-ST. (J–M) Overlay of transcriptional clusters and digital pathology textures. Clusters are numbered and depicted along the y axis. Digital pathology composition of each cluster is represented by the corresponding bar graph (J: donor A-FF FF-polyA-ST; K: donor A-FFPE FFPE-probes-ST; L: donor-B FFPE-probes-ST; M: donor C FFPE-probes-ST).

Figure 7

Derivation of tumor compartment biomarkers and putative targets from PDAC donor samples (A) Augmentation of transcriptome-based tumor compartment identification with digital pathology. Data from two cohorts are shown: donor A-FF FF-polyA-ST (left) and FFPE-probes-ST (right). Each de novo expression cluster is characterized by its average score of the classical PDAC signature from Collison et al. (y axis) and epithelium density (x axis). (B) Categorizing each spot in PDAC tumor samples into tumor (gray) and non-tumor (blue) using both pathology and expression data: representative characterization for donor A-FF FF-polyA-ST (left) and FFPE-probes-ST (right). (C) Differentially expressed genes in tumor areas (n = 3 donors, 12 sections) compared with non-tumor areas (x axis: expression level in tumor versus non-tumor; y axis: percentage of tumor-expressing spots). Genes are colored by presence or absence in established PDAC classical subtype signatures (Collison et al., Moffit et al., and Bailey et al.³⁰). (D–G) Representative receptor-ligand pairs mapped to PDAC tumors for donor B (D–F) and donor C (G). Receptor-ligand pairs indicated in panels. (H) Spatial gene expression in donor C of CSF1R (left), CD163 (middle), and MSR1 (right). Scale bars represent SCT-normalized gene expression levels (STAR Methods).