Spatial transcriptomic analysis drives PET imaging of tight junction protein expression in pancreatic cancer theranostics

Wang, James; Seo, Jai Woong; Kare, Aris J.; Schneider, Martin; Pandrala, Mallesh; Tumbale, Spencer K.; Raie, Marina N.; Engudar, Gokce; Zhang, Nisi; Guo, Yutong; Zhong, Xiaoxu; Ferreira, Sofia; Wu, Bo; Attardi, Laura D.; Pratx, Guillem; Iagaru, Andrei; Brunsing, Ryan L.; Charville, Gregory W.; Park, Walter G.; Ferrara, Katherine W.

doi:10.1038/s41467-024-54761-6

Download PDF

Article
Open access
Published: 30 December 2024

Spatial transcriptomic analysis drives PET imaging of tight junction protein expression in pancreatic cancer theranostics

Nature Communications volume 15, Article number: 10751 (2024) Cite this article

8040 Accesses
4 Citations
3 Altmetric
Metrics details

Subjects

Abstract

Molecular imaging using positron emission tomography (PET) provides sensitive detection and mapping of molecular targets. While cancer-associated fibroblasts and integrins have been proposed as targets for imaging of pancreatic ductal adenocarcinoma (PDAC), herein, spatial transcriptomics and proteomics of human surgical samples are applied to select PDAC targets. We find that selected cancer cell surface markers are spatially correlated and provide specific cancer localization, whereas the spatial correlation between cancer markers and immune-related or fibroblast markers is low. Claudin-4 expression increases ~16 fold in cancer as compared with normal pancreas, and tight junction localization confers low background for imaging in normal tissue. We develop a peptide-based molecular imaging agent targeted to claudin-4 with accumulation to ~25% injected activity per cubic centimeter (IA/cc) in metastases and ~18% IA/cc in tumors. Our work motivates a data-driven approach to selection of molecular targets.

Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment

Article 28 July 2022

Spatial transcriptomic analysis of primary and metastatic pancreatic cancers highlights tumor microenvironmental heterogeneity

Article 18 September 2024

Evaluation of tumor targets selected from public genomic databases for imaging of pancreatic ductal adenocarcinoma

Article Open access 16 May 2025

Introduction

Molecular Imaging (MI) is a growing biomedical discipline that enables the non-invasive visualization, characterization, and quantification of chemical and biological processes at the cellular and subcellular levels across time in living systems. Molecular imaging can detect small cancerous lesions and provides an opportunity to assess therapeutic accumulation across a wide range of molecular therapies. Positron emission tomography (PET) uniquely quantifies molecular targets by employing sub-pharmacological amounts of molecularly-specific radiolabeled agents (radiopharmaceuticals). The short radioactive half-lives used in PET imaging facilitate repeated imaging, and the latest hardware innovations enable dynamic, functional imaging of these biomarkers in situ.

Improved imaging methods are needed to map pancreatic ductal adenocarcinoma (PDAC) lesions and guide therapy. Characterizing pancreatic nodules with imaging is significant as 30-50% of the adult population may harbor precancerous lesions^1,2,3,4. Invasive cancer can be present within intraductal papillary mucinous neoplasms (IPMNs), and similar mutational profiles to PDAC are found in incidentally-discovered pancreatic intraepithelial neoplasms (PanINs)². Further, PDAC is the third leading cause of cancer-related deaths as of 2024⁵. PET-CT imaging has been applied using ¹⁸F-FDG imaging of metabolism^6,7,8; however, ¹⁸F-FDG has low sensitivity and specificity in the diagnosis of PDAC with variable detection of metastatic lymph nodes and false-positive findings in inflammation^9,10.

Cell surface receptors are preferred for imaging and therapy due to the relative efficiency of delivery, where an ideal agent will safely accumulate within minutes while excess agent is rapidly cleared from the body, facilitating imaging and minimally-toxic therapies. PDAC markers currently under study for PET imaging, including integrins and fibroblast markers, are expressed by only a fraction of tumor cells (e.g. ITGB6, in 49% of PDAC cells¹¹) and have substantial background in many cases¹¹.

Claudin-4 (CLDN4) is a cell surface protein that is buried within cell-cell junctions in healthy tissue and has been suggested as a cancer biomarker¹². CLDN4 expression increases in PDAC, and enhanced CLDN4 expression disrupts other claudin family molecules in forming polymeric strands constituting the tight junction¹³. Increased CLDN4 expression thus results in the loss of tight junction integrity¹⁴, and we hypothesized that the junction protein would be exposed to a radioligand in the presence of disease due to the loss of junctional integrity in metastatic cells^12,15,16. Previous literature indicates that 1) CLDN4 expression is also enhanced in prostate¹⁷, breast¹⁸ and ovarian¹⁹ cancers and 2) as pancreatic precancers advance, the expression level of CLDN4 increases (p = 0.001)¹⁵. Intense positive CLDN4 immunolabeling has been noted within virtually all primary (71/72 [99%]) and metastatic (49/49 [100%]) PDAC tissue samples and in 10 of 11 precancerous PanIN specimens²⁰. Additionally, CLDN4 is a surface protein that has been applied preclinically for optical tumor imaging²¹. Although Claudin-6 and -18.2 have been the basis for development of therapeutics for ovarian and esophageal/gastric cancers²², Claudin 6 expression is not enhanced in PDAC, and Claudin-18.2 expression is lower in PDAC than that of CLDN4^11,20.

In our work, a multi-omics data-driven approach is shown to distill target receptors suitable for imaging in pancreatic cancer, improving upon past methods that lacked a data-driven basis for the comparative evaluation of target receptors. To compare and contrast the abundance and spatial distribution of potential molecular imaging targets, we first apply spatial transcriptomic methods in human surgical specimens to assess mRNA expression across cell surface markers upregulated in PDAC. While single-cell sequencing data sets can validate abundance, spatial transcriptomics provides the opportunity to determine whether the target is consistently upregulated in cells with a cancerous phenotype across the tumor. We further evaluate the pseudotime evolution of the precancers and cancer to prioritize markers that are enhanced in invasive rather than precancerous lesions. Next, immune and fibroblast populations are profiled using spatial proteomics (codetection by indexing, CODEX) and immunohistochemistry (IHC) due to the need for single-cell resolution when exploring such targets. These analyses confirm that the epithelial tumor marker abundance and consistency are higher than immune and fibroblast markers across patients. As a result of the spatial multi-omic analyses, Claudin-4 (CLDN4) is identified as a particularly attractive imaging target. Based on our analyses of human tissue, we design a peptide target radiopharmaceutical and image CLDN4 expression in multiple models of murine PDAC.

Results

Spatial sequencing of PDAC samples elucidates spatially-correlated cancer surfaceome markers

Our discovery cohort includes 40 human surgical samples of surrounding pancreas, IPMNs²³ or PDAC²⁴ acquired at Stanford (n = 12 Visium and associated CODEX and IHC data for these samples) or from publicly-available databases (n = 28 Visium)^23,24 (Supplementary Table 1, Fig. 1A) and annotated single-cell RNA sequencing results from reference databases^24,25,26. All human data collection and analysis was approved by the Stanford Institutional Review Board. The spatial transcriptomics samples span precancers, naïve untreated PDAC, and patients treated with chemotherapy or chemo-radiation or mixed treatment. We first integrated data from treatment-naïve PDAC samples and performed unsupervised clustering (Supplementary Fig. 1, Fig. 1A, B). We leveraged Seurat’s integration method, which uses canonical correlation analysis and alignment based on mutual nearest neighbors to address batch differences²⁷. Top genes in all spatial clusters are summarized in Supplementary Fig. 1B, C, whereas Fig. 1B–H focuses on the top surfaceome atlas markers in the cancer cluster. Since we seek to develop an imaging ligand that binds to PDAC cells, we filtered the cancer surfaceome to identify genes with enhanced expression in more than 80% of PDAC cells (Supplementary Table 2) as our cancer surfaceome gene list. Given that Visium transcriptomic spots contain multiple cells, we then performed hypergeometric t tests (Supplementary Fig. 1D) to determine the significance and overlap of differentially expressed genes and single-cell RNA cell signatures^24,25. Cluster 1 is primarily enriched in tumor cell signature genes, with secondary enrichment in fibroblasts and ductal cells, underscoring the tumor environment consisting of ductal cells and fibroblasts (Fig. 1C)²⁸. In particular, Cluster 1 differentially expressed S100P, MUC5AC, TFF1 and CLDN4 as compared with other clusters and surrounding normal pancreas (Fig. 1D).

**Fig. 1: Spatial transcriptomic characterization of human PDAC tissue slices.**

Since cancer development is associated with increased copy number variation (CNV), we computed CNV from our transcriptomic data across the clusters and found that Cluster 1 had greater CNV gains compared to other clusters (Supplementary Fig. 2A). Single-cell RNA tumor signatures based on^24,25 and applied across clusters also confirmed that tumor signature genes are upregulated in Cluster 1 from PDAC samples (Supplementary Fig. 2B).

To quantify how top cancer surfaceome atlas markers change across clusters (Supplementary Table 3), we created a cancer surfaceome score based on the summed average of normalized and scaled expression of cancer cell surfaceome genes in each cluster. The cancer cell surfaceome expression levels differed among clusters and were upregulated in Cluster 1 (Fig. 1E). The correlation of a set of the cancer surfaceome epithelial markers with one another (CLDN4, GRPC5A, S100P, TSPAN8, MUC1, TFF1) was > 0.6 averaged across all spatial locations in the naïve PDAC cohort (Fig. 1F). CLDN4 is involved in tight junction formation, which is compromised during cancer progression. Positive correlation among cancer surface protein encoding genes indicates that CLDN4 expression changes are spatially correlated with other cancer-associated genes. Merging scaled and normalized data across transcriptomic sets from Stanford and HTAN, the fraction of spots in Cluster 1 overexpressing CLDN4, S100P, TFF1, CEACAM5, MUC5AC and fibroblast activation protein (FAP) was 89, 78, 57, 46, 60 and 27%, respectively. With integration of batch-corrected data from Stanford and HTAN²⁴ (rather than merging the datasets across sites), the fraction of spots with CLDN4 overexpression increased to 97.5%, and differences across key genes were consistent. We performed differential analysis comparing Cluster 1 with the remaining clusters and explored cancer surfaceome genes with an average log₂ fold change greater than 2 and a p value less than 0.005. Based on the integrated gene expression, CLDN4 was upregulated to a greater extent than other genes in Cluster 1 with an average log₂ fold change of 13.9 and a p value less than 7.6 × 10⁻³⁰⁰ (Supplementary Table 3).

S100P, a largely cytoplasmic protein, was used for comparison of the spatial distribution and expression of surfaceome markers as S100P expression is absent in normal pancreas, and a specific increase in S100P expression occurs only in the PDAC tumor epithelium²⁹. S100P expression level in PDAC and IPMN is significantly higher than in nontumor pancreatic tissues, and its expression level increases with the grade of PanIN³⁰. The Pearson’s coefficient between cancer surfaceome genes and S100P was consistent within all spatial regions, with CLDN4 demonstrating the highest average coefficient (Fig. 1G). The Pearson’s coefficient between S100P and various integrin markers or FAP ranged between −0.3 and 0.4 (highest scoring integrins are shown in Fig. 1G) and was 0.37 and 0.01 for ITGB6 and FAP, respectively.

We then performed enrichment analysis (Fig. 1H) for these genes with respect to KEGG and REACTOME databases and found that Cluster 1 was enriched in tight junction (KEGG), IL-17 signaling (KEGG), and degradation of extracellular matrix (REACTOME) pathways, which implies significant stromal restructuring associated with Cluster 1.

High-resolution IHC indicated that CLDN4 protein expression was enhanced on hyperproliferative cells with a morphology consistent with cancer (Fig. 2A) (small to medium-sized irregular glands with nuclear atypia lacking tight junctions and embedded in a desmoplastic stroma). Proliferation (increased Ki67 expression) was enhanced in high-grade PanINs and cancerous ducts (Fig. 2B). Alignment of hematoxylin and eosin (H&E) images with the mRNA expression of four key surface markers (Fig. 2C) confirms enhanced CLDN4 expression in high-grade PanINs. While mRNA expression across S100P, CLDN4, TFF1 and CEACAM5 was highly correlated, expression on low-grade PanINs varied with phenotype (Fig. 2C). As shown in Fig. 2A and D, in addition to the upregulation of CLDN4 in the cancer-associated cluster, molecular imaging of CLDN4 is attractive as the protein is more accessible on diseased cells (Fig. 2D). Taken together, the results suggest that CLDN4 is consistently elevated in PDAC and is highly spatially correlated with cancer-associated markers. Alternatively, fibroblast, integrin and immune receptor markers were not spatially correlated with S100P or cancer atlas markers.

**Fig. 2: High-resolution protein imaging confirms selective expression of CLDN4 on high-grade PanINs and cancerous ducts.**

Key tumor epithelial marker expression is spatially correlated

We next visualized the spatial correlation between the top epithelial markers in PDAC and IPMNs and compared these maps with those relevant for islets and fibroblasts, where rows represent individual fields from a single patient sample (Fig. 3A). On the 100-µm spatial scale of spatial transcriptomic slides, we found that S100P, MUC5AC, TFF1 and CLDN4 were spatially correlated and moreover, in regions where ducts were filled with large numbers of highly-proliferative cancer cells (yellow arrow), TFF1 and CLDN4 were highly expressed. ITGB6 and FAP expression was elevated to a lesser extent in the tumor, yet on the scale of spatial transcriptomic imaging was not enhanced near the ducts (Fig. 3A). Mapping of insulin (INS) expression provides an alternative view of the functional islets with INS expression and was typically enhanced in spots that do not express the cancer surface markers.

**Fig. 3: Spatial gene expression maps in PDAC and IPMN.**

In IPMN samples, CLDN4 expression was enhanced in high-grade lesions

By comparison, within the IPMNs (Fig. 3B), expression of MUC5AC and TFF1 was greatly enhanced with a variable level of expression of CLDN4 and ITGB6. MUC5AC expression was extensive in IPMNs, whereas CLDN4 was preferentially enhanced in PDAC (Fig. 3A). Immunohistochemistry (IHC) for MUC5AC and CLDN4 confirmed that their protein expression patterns were closely correlated with their mRNA expression (Fig. 3C).

In IPMN lesions (Fig. 4A–D), cancer surfaceome genes were elevated in Clusters 6, 9, 12 and 16, with a greater fraction enhanced in Cluster 12 (Fig. 4E–F). NKX6-2²³ mRNA counts averaged 0.002 across all Stanford IPMN samples except for STIPMN31 (average of 1.7). When compared with underlying histology, Clusters 6 and 12 were spatially correlated with elongated ducts and hyperplastic islets. We further explored Clusters 6 and 12 by differential expression analysis. Cluster 12 included elevated expression of CLDN4 and GPRC5A (Fig. 4F–G) whereas in Cluster 6, mucin producing genes, such as MUC5AC, were elevated. Hypergeometric t tests of differentially-expressed genes with respect to single-cell RNA cell type signatures^24,25 indicated that Clusters 6, 9, 12 and 16 were enriched in tumor signature genes with p values of less than 5 × 10⁻¹¹.

**Fig. 4: Spatial transcriptomic characterization of human IPMN tissue slices.**

CNV analysis indicated that Clusters 6 and 12 had higher CNV gains (Supplementary Fig. 3A). Single-cell RNA tumor signatures in IPMN samples confirmed that tumor signature gene clusters were upregulated in Cluster 6 and 12 (Supplementary Fig. 3B). The cancer surfaceome score increased gradually across the clusters (Fig. 4H), reaching a mean score in Cluster 12 that is similar to the score observed in PDAC clusters.

In the IPMN lesions, the spatial correlation of the surfaceome markers included two groups with high inter-marker correlation, including TSPAN8, MUC5AC, S100P and TFF1 in one group ( > 0.9), and CLDN4, CD9 and APP in a second group ( > 0.6) (Fig. 4I). TFF1 has the highest correlation with S100P of all markers in the IPMNs (Fig. 4J), with greater variability in the PDAC markers from Fig. 1, resulting from the range of phenotypes from normal tissue to invasive cancer observed within the cluster. CLDN4 expression correlates spatially with cancer markers such as S100P and TFF1. Across the IPMN fields, Cluster 6 and 12 were both enriched in tight junction and infection-related pathways on KEGG enrichment analysis (Fig. 4K).

Standard medical imaging of these cases with computed tomography (CT) or magnetic resonance imaging (MRI) was also obtained and adds support for the need for molecular imaging to characterize lesions (Supplementary Fig. 4A–F). For example, retrospective analysis of contrast-enhanced MRI of the CLDN4⁺ region in lesion STIPMN34 corresponding to Cluster 12 suggested that the lesion was suspicious for cancer based on contrast enhancement (Supplementary Fig. 4G–J). This suggests that molecular imaging can enhance the detection of small regions with cancerous phenotypes.

Network analysis

We performed network analysis using the Ingenuity Pathway Analysis program (Qiagen, CA) to assess the expression of genes that are known to be upstream of S100P and CLDN4 in PDAC and IPMN lesions using our transcriptomic data (Supplementary Fig. 5A). In each case, STAT3 is known to be a part of the expression network, and STAT3 expression is enhanced. In addition, we found that enhanced BMP4, NR3C1, ESR1, PGR and CDK5 expression impact the expression of S100P, whereas ERBB2, TGFB1, WNT5A and KRAS impact the expression of CLDN4. The relative expression of these genes in Cluster 1 of PDAC samples versus normal tissue and the IPMN Cluster 6 and 12 versus all clusters within the IPMN samples are summarized (Supplementary Fig. 5A). Therefore, while the spatial distribution of enhanced expression is similar for these genes, there are differences in pathway expression, particularly in regions in which the expression level remains low. MUC5AC expression was also enhanced, particularly in early IPMN lesions. An important upstream gene involved in MUC5AC expression is IL17RA, where the IL17 pathway is known to be an important component of pancreatic cancer development. (Supplementary Fig. 5B)³¹. Evaluation of IL-17 signaling in early pancreatic disease could be further probed in future work.

CLDN4 increased at the pseudotime value associated with invasive disease on pseudotime analysis

Through pseudotime analysis of an epithelial-only dataset including normal pancreas and IPMN and PDAC regions, we then explored two trajectories which outline IPMN and PDAC sample types. In IPMN, the pseudotime trajectory involved an acinar-ductal transition that progressed to either IPMN or PDAC (Fig. 5A, B). By contrast, in the PDAC clusters, acinar-ductal metaplastic clusters rapidly progress to PDAC. When we further remapped the pseudotime trajectory into the spatial dimension on a unified scale, regions with a cancer-like or normal phenotype were segmented in IPMN (Fig. 5C–D). In PDAC, we found that ductal-originated cancerous cells were scattered throughout the specimen (Fig. 5E).

**Fig. 5: Pseudotime analysis of PDAC and IPMN human tissue spatial transcriptomes.**

The IPMNs highly expressed MUC5AC and TFF1 at earlier pseudotime values, with MUC5AC expression decreasing over time (Fig. 5F-G). Alternatively, CLDN4 increased at the pseudotime value associated with invasive disease (Fig. 5F-G), confirming the results from Fig. 4G-H, which also demonstrated that CLDN4 was enhanced in the IPMN region with the highest cancer surfaceome score.

The enhanced expression of CLDN4, TSPAN8 and TFF1 on cancer cells was not significantly reduced after treatment with chemotherapy with or without radiation (Fig. 5H-I). To explore whether CLDN4 can differentiate PDAC from normal pancreas and precancer, we combined samples from our site with refs. ^24,25,26, which included normal pancreas, and IPMN and PDAC samples. Regions were constructed from multiple spots with similar expression levels (e.g. lower 20% or upper 20% of expression levels) and classified. The area under the curve (AUC) for the differentiation of PDAC from normal or precancerous tissue for CLDN4, S100P, MUC5AC, TFF1, CEACAM5, and TSPAN8 was 0.9, 0.5, 0.3, 0.5, 0.8, and 0.4, respectively (Fig. 5J).

Finally, we analyzed subclusters of the cancer-associated Cluster 1 in Fig. 1 (Supplementary Fig. 6) in order to characterize the phenotype of spots without CLDN4 overexpression. Unsupervised clustering of cluster 1 from PDAC samples distinguished 10 subclusters (Supplementary Fig. 6A), and CLDN4 expression was not enhanced in Subcluster 10 (Supplementary Fig. 6B). Differential expression analysis demonstrated enhanced expression of TFF2, MUC6, LYZ, NKX6-2, PGC, GKN2, VSIG1, CXCL17, CLDN18 and GOLM1 in this subcluster (Supplementary Table 5 and Supplementary Fig. 6C). A fraction of these spots had a high Pearson spatial correlation between TFF2, MUC6 and LYZ overexpression, corresponding to mucous neck cells or antral gland cells³² (Supplementary Table 5 and Supplementary Fig. 6D). In addition, in a fraction of spots, NKX6-2, PGC, GKN2, VSIG1, CXCL17 and GOLM1 expression was spatially correlated with a Pearson correlation greater than 0.4 (Supplementary Fig. 6D). Overexpressed genes in each of these correlated regions were enhanced earlier in pseudotime as compared with CLDN4 (Fig. 5F, Supplementary Fig. 6E), suggesting that these spots correspond to a lower-grade phenotype.

Activated fibroblasts were organized in thin layers surrounding high-grade lesions

We then performed 51-plex CODEX and IHC (Fig. 6A). With enhanced single cell resolution, ductal lobules on the order of tens of microns can be visualized and the phenotype determined (Supplementary Fig. 7). In regions harboring high numbers of high-grade PanINs (large CD66⁺ ductal features) (Fig. 6B), CD45⁺ immune infiltration, acinar-to-ductal metaplasia (ADM, indicated by small Keratin8/18^hiEpCAM⁺ ductal features), and overall loss of acinar structure (EpCAM⁺ epithelial cells) was observed. Importantly, thin bands of fibroblasts (a-SMA⁺Podoplanin⁺ cancer associated myofibroblasts (myCAFs)) selectively stratified high-grade PanINs (Fig. 6B).

**Fig. 6: Mapping of protein at single-cell level in PDAC.**

To further classify individual cell phenotypes, we performed unsupervised clustering to generate 18 independent populations which were then manually annotated based on canonical expression profiles (Fig. 6C). Fibroblasts were segmented into a-SMA⁺Podoplanin⁺ myCAFs and a second population of heterogenous fibroblasts (a-SMA^loPodoplanin^lo) (Fig. 6C). As a result, we further explored CAF phenotype signatures in the transcriptomic data within PDAC and IPMN samples. We removed tumor and immune cells with low VIM and high EPCAM expression or high PTPRC expression. Using myCAF and iCAF signatures compiled from^33,34,35, key myCAF genes were expressed in PDAC samples to a greater extent than iCAF genes (Supplementary Fig. 8A-B). In the IPMN samples, myCAF genes were expressed in all clusters and to a greater extent in Cluster 9 and 17 (Supplementary Fig. 8C). Expression of iCAF gene signatures was low (Supplementary Fig. 8D).

By extracting Acinar, myCAF, High-Grade PanIN, and Low-Grade PanIN/ADM populations as annotated in Fig. 6C, we found across all CODEX regions that ducts surrounded by myCAFs contained highly-proliferative tumor cells (Fig. 6D). These findings were validated by calculating the likelihood, or strength, of cell-cell pairwise interactions for all regions, showing that acinar cells were likely to interact with ADM, and myCAFs, with high-grade PanINs (Fig. 6E). We further visualized these cell-cell contacts using chord diagrams, emphasizing the connection of myCAFs and high-grade PanINs (Fig. 6F).

High-grade PanIN cells and myCAFs each comprised unique neighborhoods that were well-defined and co-localized spatially (Fig. 6G, Neighborhoods 9 and 4, respectively). Early lesion cells, ADM, and acinar cells were grouped together into neighborhood 5, supporting the cell-cell interaction results from Fig. 6H and likely representing areas of tissue where early pancreatic oncogenesis is present. Neighborhood 8 was built primarily of CD31⁺Caveolin1^hi endothelial cells and a few heterogeneous CAFs (stellate cells are likely found in this cluster), reflecting angiogenic neo vessels distributed across the tumor landscape.

As previously shown in Fig. 2B, high-grade PanINs expressed elevated CLDN4 across and within the duct. CODEX results indicate that myCAFs were organized in thin layers surrounding the high-grade lesions. The spatial localization of these neighborhoods further illustrates the cellular components that promote PDAC oncogenesis and motivates the selection of CLDN4 as an imaging and therapy target³⁶.

TLOs and sparse immune infiltrates reveal leukocyte heterogeneity

As we considered possible molecular-imaging strategies, we also assessed the distribution of immune cells within PDAC and precancers. In both PDAC and IPMN transcriptomic samples, the spatial correlation (Pearson’s coefficient) between epithelial cell markers and typical immune receptors (CD3, CD8, CD64 (FCGR1A), CD14) was nearly zero (Supplementary Fig. 9A, B). Both proliferation (Ki67) and immune cell density were enhanced in the IPMN center (Supplementary Fig. 10A-D). Immune cell density was heterogeneous due to the presence of both TLOs and scattered individual leukocytes. TLOs were distributed throughout IPMNs/PDACs and detectable on spatial transcriptomics due to their millimeter-scale, spherical/oblong morphology and intense expression of immune-related genes (Supplementary Fig. 11A, B). TLO differentially-expressed genes (DEGs) and associated pathways were enriched for B/T cells, B-cell receptor/T-cell receptor (BCR/TCR) signaling, chemokine/cytokine interactions, immune cell activation, proliferation and differentiation (Supplementary Fig. 11C-F). The spatial organization of these structures was well-defined, as T cells (CD4 and CD8), B cells (CD20 and CD21), dendritic cells (CD141), plasma cells (CD38) and high endothelial vessels (CD31) were shown to occupy specialized zones within TLOs (Supplementary Fig. 11G). Upon filtering the CODEX TLO data for T and B cells, we also found that CD45RO and CD44 were highly co-expressed on both CD4 and CD8 T cells, indicative of an activated memory phenotype (Supplementary Fig. 11H, I). Furthermore, Ki67 was primarily found on TLO-core B cells (Supplementary Fig. 11H, I).

Within the IPMNs (Supplementary Fig. 12A–D), lymphoid structures were frequent; however, these structures often lacked the organization demonstrated in mature TLOs (Supplementary Fig. 12A, B). We then quantified the leukocyte frequency and phenotype across the IPMN and treatment-naïve PDAC regions (Supplementary Fig. 12C, D). Leukocyte frequency increased in both IPMN and PDAC tissues compared with the surrounding normal pancreas (Supplementary Fig. 12C). The high immune cell variability in the IPMN results from a wide range of immune infiltration (20 to 60% of total cells) within each acquired CODEX region (Supplementary Fig. 12C).

Results point to biologically-driven molecular imaging of CLDN4

Based on our spatial multi-omic analyses, CLDN4 emerged as an imaging and potential therapeutic target due to its high differential expression between high-grade lesions and normal tissues, consistent correlation across mRNA expression with other cancer markers such as S100P and late-stage expression in pseudotime. Small peptides are superior for targeting due to the ease of synthesis, comparable potential affinity and specificity, improved pharmacokinetic profiles, and low immunogenicity. Clostridium perfringens enterotoxin (Cpe), a natural ligand to CLDN4, is 35 kDa. The C-terminus of Cpe (cCpe), a truncated form of Cpe, binds to the extracellular segments of CLDN4 with a reported nanomolar binding affinity constant (K_D)³⁷. Several short peptides that bind to the ECL4 loop in CLDN4 have been identified³⁸. Our ligand validation through a docking simulation with human CLDN4 (Supplementary Fig. 13A) suggests that Cpe30 MT2 (NSSYSGNYYSIL, with a reported K_D of 1.97 nM, sequence will be abbreviated hereafter to C4BP (claudin-4 binding peptide)) is promising for human imaging. We therefore synthesized DOTA-PEG₂-C4BP on a rink amide resin using a microwave-assisted peptide synthesizer. DOTA, a ⁶⁴Cu chelator, which also incorporates various therapeutic and diagnostic metal isotopes, was attached after one PEG spacer unit to avoid steric hindrance (Supplementary Fig. 13B). High-performance liquid chromatography (HPLC) purification afforded DOTA-PEG₂-C4BP in >95% purity with exact mass (M + H⁺, calculated 1897.9 Da, found 1898.0 Da) as confirmed with matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (Supplementary methods and Fig. 13C). The radiolabeling of DOTA-PEG₂-C4BP (2 nmol) with ⁶⁴CuCl₂ (70 MBq) conferred ⁶⁴Cu-DOTA-PEG₂-C4BP (28 MBq/nmol, ⁶⁴Cu labelled DOTA-PEG₂-C4BP will be abbreviated to ⁶⁴Cu-C4BP hereafter) in 80% non-decay corrected yield with >99% radiochemical purity (Supplementary Fig. 13D).

Imaging of Kras^LSL-G12D/+;Trp53^fl/fl;Rosa26^{LSL-tdTomato/LSL-tdTomato};Ptf1a^CreER (KPTC) mice, in which PDAC is driven from acinar cells in adult mice after tamoxifen treatment, was then performed at 49, 84, and 120 days after tamoxifen injection (Fig. 7A), which was near the average survival duration³⁹. Importantly, to mimic human PDAC development which occurs in adults, mice were aged to adulthood (8 – 10 weeks) before tamoxifen introduction. Pancreatic tumors and multi-organ metastases occur after tamoxifen treatment. In this model, substantial CLDN4 protein expression was detected in pancreatic tumors and metastases (Fig. 7B). At 49 days post tamoxifen treatment, a dynamic PET/CT scan was simultaneously acquired with tail vein administration of ⁶⁴Cu-C4BP (n = 4, ~0.1 μg/mouse), and imaging in the 25-30 minute window displayed a significant retention of radiolabeled ligand at the tumor sites in this transgenic mouse model (white arrowheads, Fig. 7C). Tumors and metastases were observed in three-dimensional maximum intensity projection (MIP) images (Supplementary Movies 1-4, white and yellow arrows indicate tumors) and slice images in cancer-burdened mice and not in wild-type animals (Fig. 7C-D). H&E and CLDN4 IHC stains of the intestine and liver showed metastases that aligned with the PET imaging results (Supplementary Fig. 14A-C).

**Fig. 7: Molecular imaging of claudin-4 (CLDN4) in a PDAC mouse model.**

Image-based quantification assessed the accumulation of injected activity per cc (%IA/cc) in the tumor and blood, peaking at ~15%IA/cc (2 min) and clearing to < 5%IA/cc (30 min), respectively, in tumor-burdened mice 49 days after tamoxifen administration (Fig. 7E). At later tamoxifen time points, accumulation had increased to ~25%IA/cc in metastases and was retained at ~18%IA/cc in the pancreas within 30 minutes after ⁶⁴Cu-C4BP injection (Fig. 7F). Since MRI provides more detailed soft tissue anatomical reference than CT and the KPTC mouse model has been shown to have increased signal intensity at the tumor location in T2-weighted MRI, we performed multi-modal imaging including T2-weighted MRI, PET/CT, and bright field imaging to validate that the region retaining ⁶⁴Cu-C4BP signal was consistent with pancreatic cancer (Fig. 7G). Ex vivo PET imaging confirmed the localized accumulation of ⁶⁴Cu-C4BP within the colon, pancreas and liver in transgenic mice (Fig. 7H). Biodistribution showed 6.6 ± 3.9%IA/g in the kidneys and 7.7 ± 5.4%IA/g in the liver at 22 h post injection (Supplementary Table 4). Additionally, the pancreas-to-muscle ratio in transgenic mice (3.71 ± 0.56%IA/g, p = 0.003) was significantly higher than age-matched wild-type mice (1.53 ± 0.1%IA/g) (Supplementary Table 4).

We then repeated the imaging study in the Capan-1 xenograft mouse model, once tumors reached 5-10 mm (Supplementary Fig. 15). The Capan-1 mouse model expresses human CLDN4 (Supplementary Fig. 15A, B), as human CLDN4 is highly conserved as compared to mouse protein. Accumulation was verified by PET MIP and slice imaging at 30-60 minutes after injection (Supplementary Fig. 15C, D), with retention of ⁶⁴Cu-C4BP detected in the tumor (indicated by white arrowheads). The spatial map of the radioactivity and CLDN4 expression obtained from PET and IHC tumor images confirmed colocalization of CLDN4 and ⁶⁴Cu-C4BP (Supplementary Fig. 15E). Maximum tumor accumulation reached ~12%IA/cc at 2 minutes and was retained at 6% after 60 minutes (Supplementary Fig. 15F). Blood activity was reduced over 60 min with the radiopharmaceutical clearing through the kidney (Supplementary Fig. 15G, H). A blocking study achieved by co-injection of a 500-fold excess of the cold compound reduced ⁶⁴Cu-C4BP accumulation in the tumor by 35% (p = 0.009) (Supplementary Fig. 15G). Biodistribution of ⁶⁴Cu-C4BP acquired at 2.5 h post injection showed a 48% decrease in radioactivity in the blocked group (1.21 ± 0.64%IA/g, p < 0.001) over the unblocked group (2.31 ± 0.23%IA/g) (Supplementary Fig. 15I).

Lastly, we explored the spatial extent of a therapeutic radiation dose delivered to the human surgical sample lesions, assuming that a therapeutic radiopharmaceutical successfully bound in a manner proportional to the local mRNA concentration (Supplementary Fig. 16). We compared the distribution of CLDN4 and MUC5AC mRNA to that of S100P as the gold standard (Supplementary Fig. 16A), where red spots would represent regions with S100P above baseline but in the absence of the CLDN4 or MUC5AC targeted drug. We found that nearly all of the 100 µm spots which express S100P also express CLDN4 (Supplementary Fig. 16A, B). Furthermore, given the effective therapeutic range of alpha or beta emitters (Supplementary Fig. 16C, D), few voxels, if any, would be excluded from a therapeutic dose using a CLDN4-targeted radiopharmaceutical. This finding will be assessed in vivo in future work.

Discussion

The development of biologically-inspired strategies to image and treat cancers with precise molecular targeting has high significance. With the emergence of spatial methods and data sets, there is an opportunity to use molecular imaging both to detect cancer and to design and implement therapeutic strategies. Here, we applied spatial multi-omics techniques to characterize the distribution of cancer cell surface markers in order to design an imaging and theranostic strategy. We created a cancer surfaceome score and found that high scores were spatially correlated with a cellular morphology consistent with cancer.

Most importantly, we found that the expression of a small number of tumor surface markers, including CLDN4, was consistent across the patients studied, with less variability than integrin, fibroblast or immune cell markers. In the cancer cluster, the expression of genes encoding surface markers CLDN4, GPRC5A, TSPAN8, and CEACAM5 was enhanced more than 13-fold compared with the normal pancreas, whereas FAP and ITGB6 were differentially expressed by less than 4-fold. CLDN4 was expressed on ~88.8% of PDAC cells in ref. ¹¹, and here we found that ~89% of spots in the cancer-associated cluster expressed elevated CLDN4. The fraction of spots overexpressing CLDN4 increased to 97.5% upon integration (as compared with merging) of the data, reflecting differences in the sequencing depth at different sites. Such differences should be further analyzed in the future. Receiver operating characteristic analysis of PDAC diagnosis based on the top surface markers indicated that the area under the curve (AUC) was greatest for CLDN4 ( ~ 0.9).

Enhanced CLDN4 expression and localization in regions of invasive cancer were consistent throughout PDAC lesions whether treatment naïve or post therapy. CLDN4 expression increased later in the transition to invasive cancer than MUC5AC or TFF1, suggesting that this marker may differentiate between premalignant and invasive lesions. A retrospective evaluation of contrast MRI imaging suggested that molecular imaging of CLDN4 could have been useful in detecting a region with a high cancer surfaceome score. Based on the analysis of IPMN lesions in ref. ²³, in spots with minimal NKX6-2 expression, CLDN4, CXCL2, CD74, LY6E, and SPP1 expression was greater and was associated with PDAC progression and neoplastic progression of IPMN^23,40. NKX6-2 expression was low in our cancer cluster, and the results support our pseudotime analysis and solidify CLDN4 as a marker to differentiate high- and low-grade lesions.

The resulting PET imaging approach demonstrated exquisite sensitivity for the detection and mapping of small tumors and metastases in mouse models with molecularly-specific accumulation. While CLND4 is expressed in the normal colon, here, we found that the resulting radiopharmaceutical accumulation in normal tissue was low, likely due to the expression of this target in cell-cell junctions. Accumulation of the CLDN4-targeted peptide reached ~25%ID/cc in metastases and ~18% IA/cc in the pancreas at 30 minutes after ⁶⁴Cu-C4BP injection. Preclinical single photon emission computed tomography (SPECT) imaging of CLDN4 has been explored with an antibody⁴¹ and other peptides in the past⁴²; however, our ligand enhanced accumulation and improved signal-to-noise ratio. For imaging of integrins, α_vβ₆ has been the most promising with affinity of < 55 nM and uptake of ~2–4%ID/cc in a pancreatic cancer xenograft⁴³ or affinity of 0.047 nM and ~8% ID/cc accumulation in lung or breast cancer models⁴⁴ reported. However, our results suggest that the expression and localization of integrins on PDAC cells are lower than that of CLDN4 and are reduced by chemotherapy or chemo-radiation therapy. Due to the high specificity and accumulation in tumors and metastases, we are optimistic that this CLDN4-binding peptide will successfully detect pancreatic cancer in human studies. While small animal imaging PET scanners have high sensitivity, with the advent of extended field-of-view scanners, the sensitivity of clinical PET imaging is increased 10-fold from earlier work and thus the feasibility of detecting small metastases will improve. Further, in human imaging, the distance between the clearance organ (kidney) and the pancreas is increased, further improving visualization.

A single cell-based ‘omic analysis is required to fully characterize the interaction of immune and fibroblast components with epithelial targets. The immune cell distribution within pancreatic lesions was complex and will likely prove challenging to harness as a cancer-specific diagnostic feature. Without adding CODEX, we would have missed the presence of thin layers of activated fibroblasts surrounding cancerous ducts due to the relative spatial resolution and sensitivity of current spatial transcriptomic methods. Still, the numbers of myCAFs near the cancerous ducts was small and targeting these cells may not be efficient for a theranostic strategy.

Peptide targeted radionuclide therapy (PTRT) is a promising strategy for PDAC and the CLDN4-targeted peptide described here shows promise to be incorporated in PTRT. We found that a beta radiopharmaceutical field generated from a CLDN4-targeted therapy would generate an approximately uniform field for the entire cancerous specimen. Systemic administration of ¹⁷⁷Lu-targeted radiopharmaceuticals can result in specific targeting of cancer cells throughout the body and induces the cytotoxic effects of ionizing radiation⁴⁵. PTRT has improved the overall survival of neuroendocrine tumors⁴⁶ and metastatic castration-resistant prostate cancer⁴⁷, and is approved by the FDA. A future goal of this work is to incorporate CLDN4 targeting in the treatment of PDAC. Thus, the results suggest that a data-driven target selection would result in the selection of a target such as CLDN4, and we are encouraged to find that promising affinity and accumulation are feasible.

In summary, we find that spatial transcriptomics is highly useful for the selection of molecular imaging agents. While the imaging of integrins or fibroblasts can be useful, the development of epithelial-based molecular imaging and theranostic agents is desirable. CLDN4 is one such target, and imaging of this target with high sensitivity and specificity is feasible.

Methods

Ethics statement

All experiments and methods were performed in accordance with relevant guidelines and regulations. All animal experiments were conducted with approval from the Stanford University Administrative Panel on Laboratory Animal Care (APLAC). All studies involving human subjects were approved by the Institutional Review Board (IRB) of Stanford University and were deidentified and collected with written and signed informed consent.

Spatial transcriptomic processing and analysis

Formalin-fixed, paraffin-embedded (FFPE) human pancreatic cancer tissue sections were placed on spatial transcriptomic slides for spatial transcriptomic sequencing based on the 10x FFPE workflow (10x Genomics, Pleasanton, CA). Individually indexed libraries were sequenced on the Novaseq 6000 (Illumina Inc., San Diego, CA). Raw sequencing counts were processed through the SpaceRanger pipeline (10x Genomics) and aligned with a human reference (GRCh38). Downstream transcriptomic analysis was performed based on the Seurat framework with modifications. Spatial transcriptomic samples were merged or integrated using R function for merging and the Seurat integration function based on canonical correlation analysis. Normalization was performed with SCTransform, which minimizes technical noise such as difference in sequencing depth. Stanford-acquired data detected an average of 3706 spots per sample and 17943 aligned gene features, while PDAC datasets from HTAN²⁴ detected 4992 spots per sample with 36601 aligned gene features. IPMN datasets from NCBI GEO (GSE233254) detected an average of 941 spots with 36601 aligned gene features²³. Principal component analysis was performed, and the elbow plot indicated that the first 75 principal components were sufficient to describe over 99% of the data.

To optimize for subsequent Leiden clustering parameters such as number of principal components, resolution, and number of k nearest neighbors, the average silhouette score of all clusters was used as a tuning metric to design our hyperparameter tuning grid. Based on our optimized parameters, we performed Leiden clustering and projected the clustering results on the Uniform Manifold Approximation and Projection (UMAP) dimensions. We then computed the top 10 differentially-expressed genes of each cluster compared to all the remaining clusters using Seurat’s built-in functions. Since in spatial transcriptomics, each spot is not a single cell, and annotation based on cell types may not be appropriate, we performed statistical overlap calculations comparing clusters to cell signatures. Using the PDAC cell signature gene sets from single cell RNA sequencing^24,25, we performed one-sided hypergeometric t tests using differentially-expressed genes from each cluster. To plot cancer cell surfaceome gene expression levels throughout the clustered spots, we selected for surfaceome genes specific to PDAC and that are at least 80% upregulated compared to normal tissue. We further developed a cancer cell surfaceome score based on the total sum average normalized and scaled gene expression of cancer cell surfaceome in each cluster:

$${{{\rm{cscore}}}}={\sum}_{j=1}^{S}\frac{{\sum }_{i=1}^{N}({g}_{i}-\bar{g})}{\sigma }$$

(1)

Where S is number of cancer cell surfaceome genes, N is total number of spots, g_i is normalized and scaled count of each gene, $\bar{g}$ is average nomalized and scaled count and $\sigma$ is variance. Pearson’s correlation coefficients for each spot and each sample were calculated based on the standard Pearson’s correlation equation. Gene set analysis was performed with clusterProfiler enricher function in KEGG, GO, and REACTOME databases with the top 10 most significant pathways plotted⁴⁸. Differential expression fold change values mapped onto KEGG pathway plots were further calculated with pathview function in R and then simplified to key pathways. Pseudotime trajectory analysis was performed with monocle3 on a merged dataset containing two surrounding normal tissue slices, two PDAC slices, and two IPMN slices. From our population annotations, we have identified two origins which we defined as acinar ductal transitions. Acinar-ductal cell transitions and metaplasia have been demonstrated by other groups as an important component in the development of PDAC². To focus on the development from acinar-ductal transition to full cancer, we removed cancer associated fibroblasts, plasma cells, and immune cells in our trajectory. The calculated trajectories were analyzed based on their corresponding partitions in, respectively, IPMN and PDAC. To explore the spatiotemporal relationship between PDAC phenotypes, multiple pseudotime partitions were connected sequentially, and then pseudotime values were plotted with respect to their x-y spatial coordinates.

The copy number variation (CNV) calculation was performed with the infercnv package⁴⁹ with the default settings by comparing each cluster to the average of all clusters. Single-cell RNA (scRNA) tumor signatures were obtained from²⁴.

CODEX processing and analysis

FFPE pancreas slices were stained with a 51-plexed antibody panel as in ref. ⁵⁰. After imaging processing, DAPI-based cell segmentation was performed with the DeepCell algorithm through the EnableMedicine portal. We further filtered the segmentation results based on size of the cell, total biomarker intensity, DNA channel intensity and signal coefficient of variation using EnableMedicine’s SpatialMap package on R. The filtered segmentation results were then normalized and scaled for principal component analysis. Unsupervised Leiden clustering was then executed to identify cell populations of interest. Populations were manually annotated using expression intensity patterns and spatial organization. Cell-cell interactions were calculated using a k nearest neighbors algorithm to create a graph network of individual cells (10 nearest neighbors were used), and then pairwise adjacency was calculated using a hypergeometric method. Neighborhoods were constructed using the previous calculated nearest neighbors graph (knn = 10), and k = 11 neighborhoods were built based on an optimal silhouette score. Segmented regions were also exported to the cloud-based cytometry platform OMIQ for additional visualization.

Therapeutic simulations

To model peptide binding to a given spatial transcriptomic region, we first labelled spatial transcriptomic spots using an expression threshold for MUC5AC/CLDN4 and S100P. The thresholds were assigned by first selecting spots that had expression greater than 0 for MUC5AC/CLDN4 or S100P, and then calculating the first quartile of expression for those genes in the corresponding positive expressing spots. Then, all spots for a spatial transcriptomic region were assigned into four groups (Gene < Threshold and S100P < Threshold, Gene <Threshold and S100P > Threshold, Gene > Threshold and S100P > Threshold, Gene > Threshold and S100P < Threshold) using the previously calculated thresholds. Dose point kernels (DPKs) were acquired for alpha/beta particle emitters (²¹¹At from⁵¹ and ¹⁷⁷Lu from⁵²). Spatial transcriptomic expression maps were scaled with a 1:1 micron to pixel conversion and smoothed for continuous expression. DPKs were then convolved with the expression maps to hypothesize radiation density for genes of interest. mRNA expression was assumed to have a 1:1 correlation with cell-surface protein expression. Furthermore, it was assumed that a targeted radiotherapy binds specifically only to the target and remains on the target until it has completely decayed.

Transgenic mouse model³⁹

To deconstruct the requirements for pancreatic cancer development from adult pancreatic cells, the Attardi laboratory has developed a Kras^LSL-G12D/+;Rosa26^{LSL-tdTomato/LSL-tdTomato};Ptf1a^CreER mouse model³⁹ by activating oncogenic Kras^G12D and deleting Trp53 in mouse pancreatic cells with a tamoxifen-inducible knock-in Ptf1a^CreER allele in mice on a C57BL/6 background with both genders. Tamoxifen was prescribed at 8–10 weeks of age. All mice are housed at a temperature range of 20–22 ^oC, humidity of 30–70%, with a diurnal light/dark cycle that switches at 7 am and 7 pm. Since the tumors are orthotopically induced in the body cavity with potential for metastasis, and tumor volume cannot be readily measured, body weight, signs of muscle atrophy and emaciation (body condition score), and general behavior was monitored 3 times per week. A weight loss of 20% or 15% for 72 h with a body condition score of < 2 was an endpoint for euthanasia and was within the guidelines of APLAC.

Radiolabeling

All radiolabeling experiments were conducted under the Controlled Radiation Authorization (CRA) approved by Stanford University. For the radiolabeling of DOTA-PEG₂-C4BP with ⁶⁴Cu (t_1/2 = 12.7 h), ⁶⁴CuCl₂ (74 MBq, 2 mCi; 0.1 M HCl) from Washington University was mixed with freshly prepared labeling buffer (200 μL, 0.1 M ammonium citrate/0.1 M ascorbic acid, pH 6.0). The peptide (2 nmol) was added to reach molar activity (74 MBq/nmol, 2 mCi/nmol). The reaction cocktail was then mixed at 600 rpm, 60 °C for 15 minutes, and the reaction progress checked by radio-TLC. The solid phase extraction cartridge (C18 SepPak plus light column) was pre-activated with 5 mL 99.9% ethanol followed by 10 mL water. The radiolabeled peptides were eluted from the cartridge with ~0.5–1 mL solution prepared with 1:1 volume ratio of 99.9 % EtOH:water. The eluted solution was diluted with 0.9% sterile NaCl with 10 mg/mL ascorbic acid (pH 7) until a final ethanol concentration reached less than 10%. Radiolabeling yield and radiochemical purity were analyzed with a TLC scanner (Bioscan) and HPLC (C18 column 4.6 × 250 mm, Phenomenex) connected to radiodetector, respectively. Non-decay corrected isolated yield, radio-TLC yield, and radiochemical purity in preliminary studies were ( > 68%, >99%, >95%), respectively.

PET/CT, MR imaging and biodistribution

For PET/CT imaging, mice were anesthetized using 3.0% isoflurane in oxygen, maintained at 1.5–2.0% isoflurane, and subjected to ⁶⁴Cu-C4BP injections (126 ± 22 mCi (4.6 ± 0.8 MBq)/mouse). Images of KPTC:Kras^LSL-G12D/+;Trp53^fl/fl;Rosa26^{LSL-tdTomato/LSL-tdTomato};Ptf1a^CreER mouse model (n = 6) and age-matched wild type C57BL/6 mice (n = 6) were acquired at 0, 4, and 21 h post-injection. PET/CT (Inveon, Siemens) scanning for the 0 h time point started on the PET scanner bed with a simultaneous tail-vein injection of ⁶⁴Cu-C4BP for 30 minutes to obtain dynamic temporal images, then subsequently, CT imaging was acquired for 10 minutes for the anatomical reference. The following time-point scans were performed at 4 and 21 h. All 12 mice were scanned at 49 and 84 days after tamoxifen treatment to induce tumors. At 84 days, 6 mice (n = 3: KPTC, n = 3 wild-type control mice) were euthanized after PET/CT scans, and ex vivo PET imaging of organs such as liver, spleen, pancreas, colon and small intestines was performed. After collecting all organs of interest (blood, heart, lungs, spleen, stomach, feces, kidneys, muscle, bone, skin, and brain), radioactivity in each organ was measured using a gamma counter. The biodistribution of ⁶⁴Cu-C4BP is presented as percent injected activity per g (%IA/g) in organs. At 120 days, the remaining mice were imaged with PET/CT, and based on morbidity, were euthanized. One mouse showing significant weight loss was scanned under PET/CT and T2-weighted MRI, and a bright field image of the abdominal area was taken. T2-weighted MR images were acquired with an 11.7 T Agilent MR scanner with Bruker interface. Mice were oriented similar to PET/CT and scanned with a TR of 2500 msec and a TE of 3 msec with standard fat suppression T2-weighted fast spin-echo sequence with a slice thickness of 0.8 mm to capture the pancreas. Organs showing peritoneal metastasis were further analyzed with IHC and H&E.

Quantitative region of interest (ROI) analysis

PET image analysis was performed with Inveon Research Workplace (IRW) software after the co-registration of PET and CT images. Images were quantified by manually drawing regions-of-interest (ROI) in the hot spots adjacent to abdominal organs such as stomach, liver and kidney. Radioactivity in organs was normalized to the percent injected activity per cubic centimeter volume (%IA/cc).

Software

Rstudio with R version 4.2.2 was used for computation environment and language. Seurat v4 was used for spatial transcriptomic analysis and clusterProfiler v4 was used for enrichment analysis. Monocle3 was used for pseudotime analysis. Ggplot was used for plotting analysis results in R. Custom scripts were created for data wrangling between packages, MA plots, surfaceome score calculations, and mapping of pseudotime onto spatial dimension. Enable Medicine’s online proprietary software and R package, Spatialmap, were used for multiplexed IHC analysis.

Statistical analyses

Statistical analyses were completed using Graphpad Prism 9 (RRID:SCR_000306), RStudio 2023.09.1, and R version 4.2.2. Significance was assessed using an unpaired t-test and one-way ANOVA as described in the figure captions. Pearson’s correlations were calculated with the correlation function in R (corr).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All data are available in Synapse.org database, and source data for the main figures is attached. A subset of IPMN spatial transcriptomic datasets (LG_1, LG_2, LG_3, LG_4, LG_5, LG_6, LG_7, HG_1, HG_2, HG_3 PDAC_1, PDAC_2, PDAC_3) were downloaded from NCBI GEO with an accession number: GSE233254 based on reference ²³ [https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE233254]. A subset of PDAC spatial transcriptomic datasets (HT264P1, HT270P1, HT284P1, HT288P1, HT224P1, HT231P1, HT231P1-2, HT232P1, HT232P1-2, HT259P1, HT306P1, HT306P1-2, HT306P1-3, HT306P1-4, HT242P1) were downloaded from the HTAN Data Coordinating Center Data Portal at the NCI (https://humantumoratlas.org/explore) based on the reference²⁴. These data are found in the atlas HTAN WUSTL as 10x Visium pancreatic cancer files. The files can be accessed directly using the shortened URL. Level 3 transcriptomic datasets from Stanford (STPDAC11, STPDAC12, STPDAC13, STPDAC14, STIPMN11, STIPMN12, STIPMN21, STIPMN22, STIPMN31, STIPMN32, STIPMN33, STIPMN34) in this study have been deposited in the Synapse.org database (created by Sage Bionetworks in partnership with scientific organizations) under accession code syn61831984. Level 3 datasets are a genomic terminology used by HTAN and others and refer to spatial transcriptomic datasets where derived biomolecular data are mapped to image positions and include barcodes, features, matrices, and corresponding histology image file. Access can be obtained by registering as a user at Synapse.org. The processed data are available as corresponding Source Data file. The processed transcriptomic data and CODEX data generated in this study are provided in the Supplementary Information/Source Data file. Source data are provided with this paper.

Code availability

Code for optimizing clustering parameters have been deposited onto Zenodo.org and can be accessed at [https://doi.org/10.5281/zenodo.13882933]⁵³.

References

Bell, A. T. F. et al. PanIN and CAF transitions in pancreatic carcinogenesis revealed with spatial data integration. Cell Systems 15.8, 753–769 (2024).
Carpenter, E. S. et al. Analysis of Donor Pancreata Defines the Transcriptomic Signature and Microenvironment of Early Neoplastic Lesions. Cancer Discov. 13, 1324–1345 (2023).
Article CAS PubMed PubMed Central MATH Google Scholar
Konstantinidis, I. T. et al. Incidentally discovered pancreatic intraepithelial neoplasia: what is its clinical significance? Ann. Surg. Oncol. 20, 3643–3647 (2013).
Article PubMed MATH Google Scholar
Brosens, L. A., Hackeng, W. M., Offerhaus, G. J., Hruban, R. H. & Wood, L. D. Pancreatic adenocarcinoma pathology: changing “landscape”. J. Gastrointest. Oncol. 6, 358 (2015).
PubMed PubMed Central Google Scholar
Siegel, R. L., Giaquinto, A. N. & Jemal, A. Cancer statistics, 2024. CA: a cancer J. clinicians 74, 12–49 (2024).
Google Scholar
Lennon, A. M. et al. Feasibility of blood testing combined with PET-CT to screen for cancer and guide intervention. Science 369, eabb9601 (2020).
Article CAS PubMed PubMed Central MATH Google Scholar
Pons-Belda, O. D., Fernandez-Uriarte, A. & Diamandis, E. P. Can circulating tumor DNA support a successful screening test for early cancer detection? the grail paradigm. Diagnostics 11, 2171 (2021).
Article CAS PubMed PubMed Central Google Scholar
Cohen, J. D. et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926–930 (2018).
Article ADS CAS PubMed PubMed Central MATH Google Scholar
Röhrich, M. et al. Impact of 68Ga-FAPI PET/CT Imaging on the therapeutic management of primary and recurrent pancreatic ductal adenocarcinomas. J. Nucl. Med. 62, 779–786 (2021).
Article PubMed PubMed Central MATH Google Scholar
Yang, T., Ma, L., Hou, H., Gao, F. & Tao, W. FAPI PET/CT in the diagnosis of abdominal and pelvic tumors. Front. Oncol. 11, 797960 (2022).
Hu, Z. et al. The Cancer Surfaceome Atlas integrates genomic, functional and drug response data to identify actionable targets. Nat. Cancer 2, 1406–1422 (2021).
Article CAS PubMed PubMed Central MATH Google Scholar
Fujiwara-Tani, R. et al. Claudin-4: a new molecular target for epithelial cancer therapy. Int. J. Mol. Sci. 24, 5494 (2023).
Shashikanth, N. et al. Tight junction channel regulation by interclaudin interference. Nat. Commun. 13, 3780 (2022).
Article ADS CAS PubMed PubMed Central MATH Google Scholar
Sasaki, T. et al. Targeting claudin‐4 enhances chemosensitivity of pancreatic ductal carcinomas. Cancer Med. 8, 6700–6708 (2019).
Article CAS PubMed PubMed Central MATH Google Scholar
Lee, J. H. et al. Immunohistochemical analysis of claudin expression in pancreatic cystic tumors. Oncol. Rep. 25, 971–978 (2011).
PubMed MATH Google Scholar
Joshi, S. et al. Genetically engineered mucin mouse models for inflammation and cancer. Cancer Metastasis Rev. 34, 593–609 (2015).
Article CAS PubMed PubMed Central MATH Google Scholar
Landers, K. A. et al. Identification of claudin-4 as a marker highly overexpressed in both primary and metastatic prostate cancer. Br. J. Cancer 99, 491–501 (2008).
Article CAS PubMed PubMed Central MATH Google Scholar
Kominsky, S. L. et al. Clostridium perfringens enterotoxin elicits rapid and specific cytolysis of breast carcinoma cells mediated through tight junction proteins claudin 3 and 4. Am. J. Pathol. 164, 1627–1633 (2004).
Article CAS PubMed PubMed Central Google Scholar
Martín de la Fuente, L. et al. Claudin-4 expression is associated with survival in ovarian cancer but not with chemotherapy response. Int J. Gynecol. Pathol. 37, 101–109 (2018).
Article PubMed MATH Google Scholar
Nichols, L. S., Ashfaq, R. & Iacobuzio-Donahue, C. A. Claudin 4 protein expression in primary and metastatic pancreatic cancer: support for use as a therapeutic target. Am. J. Clin. Pathol. 121, 226–230 (2004).
Article CAS PubMed Google Scholar
Neesse, A. et al. Claudin-4-targeted optical imaging detects pancreatic cancer and its precursor lesions. Gut. 62, 1034–1043 (2013).
Article CAS PubMed MATH Google Scholar
Zhang, D., Huang, G., Liu, J. & Wei, W. Claudin18.2-targeted cancer theranostics. Am. J. Nucl. Med Mol. Imaging 13, 64–69 (2023).
PubMed PubMed Central Google Scholar
Sans, M. et al. Spatial transcriptomics of intraductal papillary mucinous neoplasms of the pancreas identifies NKX6-2 as a driver of gastric differentiation and indolent biological potential. Cancer Discov. 13, 1844–1861 (2023).
Article CAS PubMed PubMed Central MATH Google Scholar
Cui Zhou, D. et al. Spatially restricted drivers and transitional cell populations cooperate with the microenvironment in untreated and chemo-resistant pancreatic cancer. Nat. Genet. 54, 1390–1405 (2022).
Article CAS PubMed PubMed Central MATH Google Scholar
Peng, J. et al. Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma. Cell Res. 29, 725–738 (2019).
Article CAS PubMed PubMed Central MATH Google Scholar
Jones R. C. et al. The Tabula Sapiens Consortium*. (2002). The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376, 6594 (2022).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e1821 (2019).
Article CAS PubMed PubMed Central MATH Google Scholar
Liberzon, A. et al. The molecular signatures database hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Article CAS PubMed PubMed Central MATH Google Scholar
Crnogorac-Jurcevic, T. et al. Molecular alterations in pancreatic carcinoma: expression profiling shows that dysregulated expression of S100 genes is highly prevalent. J. Pathol. 201, 63–74 (2003).
Article CAS PubMed Google Scholar
Wu, Y. et al. S100 proteins in pancreatic cancer: current knowledge and future perspectives. Front Oncol. 11, 711180 (2021).
Article CAS PubMed PubMed Central Google Scholar
Hu, F. et al. IL-17 in pancreatic disease: pathogenesis and pharmacotherapy. Am. J. Cancer Res. 10, 3551–3564 (2020).
CAS PubMed PubMed Central MATH Google Scholar
Hoffmann, W. TFF2, a MUC6-binding lectin stabilizing the gastric mucus barrier and more (Review). Int J. Oncol. 47, 806–816 (2015).
Article CAS PubMed MATH Google Scholar
Elyada, E. et al. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts. Cancer Discov. 9, 1102–1123 (2019).
Article CAS PubMed PubMed Central MATH Google Scholar
Öhlund, D. et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. J. Exp. Med. 214, 579–596 (2017).
Article PubMed PubMed Central MATH Google Scholar
Han, C., Liu, T. & Yin, R. Biomarkers for cancer-associated fibroblasts. Biomark. Res. 8, 64 (2020).
Article PubMed PubMed Central MATH Google Scholar
Zhang, T., Ren, Y., Yang, P., Wang, J. & Zhou, H. Cancer-associated fibroblasts in pancreatic ductal adenocarcinoma. Cell Death Dis. 13, 897 (2022).
Article CAS PubMed PubMed Central Google Scholar
Vecchio, A. J., Rathnayake, S. S. & Stroud, R. M. Structural basis for Clostridium perfringens enterotoxin targeting of claudins at tight junctions in mammalian gut. Proc. Natl. Acad. Sci. USA. 118, e2024651118 (2021).
Ling, J., Liao, H., Clark, R., Wong, M. S. & Lo, D. D. Structural constraints for the binding of short peptides to claudin-4 revealed by surface plasmon resonance. J. Biol. Chem. 283, 30585–30595 (2008).
Article CAS PubMed PubMed Central Google Scholar
Flowers, B. M. et al. Cell of origin influences pancreatic cancer subtype. Cancer Discov. 11, 660–677 (2021).
Article CAS PubMed PubMed Central MATH Google Scholar
Tsutsumi, K. et al. Expression of claudin-4 (CLDN4) mRNA in intraductal papillary mucinous neoplasms of the pancreas. Mod. Pathol. 24, 533–541 (2011).
Article CAS PubMed Google Scholar
Torres, J. B. et al. Imaging of claudin-4 in pancreatic ductal adenocarcinoma using a radiolabelled anti-claudin-4 monoclonal antibody. Mol. Imaging Biol. 20, 292–299 (2018).
Article CAS PubMed MATH Google Scholar
Torres, J. B. et al. Radiolabeled cCPE peptides for SPECT imaging of claudin-4 overexpression in pancreatic cancer. J. Nucl. Med. 61, 1756–1763 (2020).
Article CAS PubMed PubMed Central MATH Google Scholar
Ganguly, T. et al. Preclinical Evaluation of (68)Ga- and (177)Lu-Labeled Integrin α(v)β(6)-Targeting Radiotheranostic Peptides. J. Nucl. Med. 64, 639–644 (2023).
Article CAS PubMed Google Scholar
Quigley, N. G. et al. PET/CT imaging of head-and-neck and pancreatic cancer in humans by targeting the “cancer integrin” αvβ6 with Ga-68-Trivehexin. Eur. J. Nucl. Med Mol. Imaging 49, 1136–1147 (2022).
Article CAS PubMed Google Scholar
Dash, A., Chakraborty, S., Pillai, M. R. & Knapp, F. F. Jr. Peptide receptor radionuclide therapy: an overview. Cancer Biother Radiopharm. 30, 47–71 (2015).
CAS PubMed Google Scholar
Strosberg, J. et al. Phase 3 trial of (177)Lu-dotatate for midgut neuroendocrine tumors. N. Engl. J. Med. 376, 125–135 (2017).
Article CAS PubMed PubMed Central Google Scholar
Morris, M. J. et al. Phase III study of lutetium-177-PSMA-617 in patients with metastatic castration-resistant prostate cancer (VISION). J. Clin. Oncol. 39, LBA4–LBA4 (2021).
Article Google Scholar
Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
Article ADS CAS PubMed PubMed Central MATH Google Scholar
Schneider, M. K. et al. Combined near infrared photoacoustic imaging and ultrasound detects vulnerable atherosclerotic plaque. Biomaterials 302, 122314 (2023).
Article CAS PubMed PubMed Central Google Scholar
Peter, R. et al. Small-scale (sub-organ and cellular level) alpha-particle dosimetry methods using an iQID digital autoradiography imaging system. Sci. Rep. 12, 17934 (2022).
Article ADS CAS PubMed PubMed Central Google Scholar
Graves, S. A., Flynn, R. T. & Hyer, D. E. Dose point kernels for 2,174 radionuclides. Med. Phys. 46, 5284–5293 (2019).
Article CAS PubMed Google Scholar
Cluster tuner for Spatial transcriptomic analysis drives PET imaging of tight junction protein expression in pancreatic cancer theranostics (Zenodo, https://doi.org/10.5281/zenodo.13882933, 2024).

Download references

Acknowledgements

The authors acknowledge the support of Stanford Medicine Catalyst, NIHR01CA250557, R01CA289924 and R01CA253316 to K.W.F. and NIGMS5T32GM007276 to A.J.K. We thank Dr. Jane Chen at the UC Davis Center for Genomic Pathology Lab for mouse tissue IHC and H&E processing. We also thank Peter Chou, Aaron Chiou, and Aya Kondo for guidance using Enable Medicine’s cloud platform. PET/CT/MRI imaging was performed at the Stanford Center for Innovation in In Vivo Imaging (SCi3) and the Canary Center of Stanford University.

Author information

These authors contributed equally: James Wang, Jai Woong Seo, Aris J. Kare.

Authors and Affiliations

Molecular Imaging Program at Stanford, Department of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA, USA
James Wang, Jai Woong Seo, Aris J. Kare, Martin Schneider, Mallesh Pandrala, Spencer K. Tumbale, Marina N. Raie, Gokce Engudar, Nisi Zhang, Yutong Guo, Bo Wu & Katherine W. Ferrara
Department of Bioengineering, Stanford University, 443 Via Ortega, Stanford, CA, USA
Aris J. Kare
Department of Radiation Oncology, Stanford University, 857 Blake Wilbur Drive, Stanford, CA, USA
Xiaoxu Zhong, Sofia Ferreira, Laura D. Attardi & Guillem Pratx
Department of Genetics, Stanford University, 291 Campus Drive, Stanford, CA, USA
Laura D. Attardi
Nuclear Medicine and Molecular Imaging Division, Department of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA, USA
Andrei Iagaru
Department of Radiology, Stanford University, 300 Pasteur Drive, Stanford, CA, USA
Ryan L. Brunsing
Department of Pathology, Stanford University, 300 Pasteur Drive, Stanford, CA, USA
Gregory W. Charville
Department of Medicine-Gastroenterology & Hepatology, Stanford University, 300 Pasteur Drive, Stanford, CA, USA
Walter G. Park

Authors

James Wang
View author publications
Search author on:PubMed Google Scholar
Jai Woong Seo
View author publications
Search author on:PubMed Google Scholar
Aris J. Kare
View author publications
Search author on:PubMed Google Scholar
Martin Schneider
View author publications
Search author on:PubMed Google Scholar
Mallesh Pandrala
View author publications
Search author on:PubMed Google Scholar
Spencer K. Tumbale
View author publications
Search author on:PubMed Google Scholar
Marina N. Raie
View author publications
Search author on:PubMed Google Scholar
Gokce Engudar
View author publications
Search author on:PubMed Google Scholar
Nisi Zhang
View author publications
Search author on:PubMed Google Scholar
Yutong Guo
View author publications
Search author on:PubMed Google Scholar
Xiaoxu Zhong
View author publications
Search author on:PubMed Google Scholar
Sofia Ferreira
View author publications
Search author on:PubMed Google Scholar
Bo Wu
View author publications
Search author on:PubMed Google Scholar
Laura D. Attardi
View author publications
Search author on:PubMed Google Scholar
Guillem Pratx
View author publications
Search author on:PubMed Google Scholar
Andrei Iagaru
View author publications
Search author on:PubMed Google Scholar
Ryan L. Brunsing
View author publications
Search author on:PubMed Google Scholar
Gregory W. Charville
View author publications
Search author on:PubMed Google Scholar
Walter G. Park
View author publications
Search author on:PubMed Google Scholar
Katherine W. Ferrara
View author publications
Search author on:PubMed Google Scholar

Contributions

J.W., A.J.K., J.W.S. and K.W.F. designed and implemented the study and wrote the paper. G.C. and M.S. provided samples and pathological interpretation. W.P., R.B. and A.I. provided clinical guidance on the study design and image interpretation. M.P., S.T., B.W. G.E., N.Z., Y.G. and M.R. performed experiments. S.F. and L.A. developed and characterized the mouse models. X.Z. and G.P. provided guidance on therapeutic simulations.

Corresponding author

Correspondence to Katherine W. Ferrara.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks Xianlu Peng, Youqiong Ye and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Peer Review File

Description of Additional Supplementary Files

Supplementary Movie 1

Supplementary Movie 2

Supplementary Movie 3

Supplementary Movie 4

Reporting Summary

Source data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Cite this article

Wang, J., Seo, J.W., Kare, A.J. et al. Spatial transcriptomic analysis drives PET imaging of tight junction protein expression in pancreatic cancer theranostics. Nat Commun 15, 10751 (2024). https://doi.org/10.1038/s41467-024-54761-6

Download citation

Received: 04 January 2024
Accepted: 19 November 2024
Published: 30 December 2024
DOI: https://doi.org/10.1038/s41467-024-54761-6

This article is cited by

Finding spatially variable ligand-receptor interactions with functional support from downstream genes
- Shiying Li
- Ruohan Wang
- Shuai Cheng Li
Nature Communications (2025)