Padma Kadiyala

Pancreatic ductal adenocarcinoma (PDAC), the most common subtype of pancreatic cancer, is a deadly disease with a complex tumor microenvironment (TME). Cytotoxic combination chemotherapy treatments are the mainstay of PDAC therapy, but patients rapidly develop chemoresistance, highlighting the need to elucidate how chemotherapy alters the TME and whether these changes drive chemoresistance. In this study, we examined matched pre- and posttreatment tissue specimens using single-cell RNA sequencing (scRNA-seq) and found near-universal enrichment of axonal guidance genes in cancer-associated fibroblasts (CAF) after treatment. These neural CAFs were enriched near sites of perineural invasion (PNI), coinciding with regions of increased tumor cell proliferation, and were enriched in tumor areas distant from nerves after chemotherapy. Metastatic recurrent lesions had the highest prevalence of neural CAFs versus primary tumors and untreated metastases. Neural CAFs showed elevated noncanonical WNT mediators and axonal guidance genes, which complemented matching cognate binding partners in tumor epithelial cells, suggesting a role in tumor–stroma cross-talk. Additionally, in vitro studies revealed enrichment of axonal guidance genes in patient-derived CAF lines upon exposure to chemotherapy or tumor cells. Altogether, these findings implicate fibroblast-derived axonal guidance genes in promoting PDAC invasion and point to a promising therapeutic target for this disease. Significance: Chemotherapy induces changes in the tumor microenvironment and induces fibroblasts with high expression of axonal-guidance genes that may drive tumor progression, uncovering a potential avenue for targeting pancreatic cancer.

Supplementary Figure S1. Healthy Donor Pancreas (Validation Dataset). Supplementary Figure S2. A. Dot plot showing marker genes for each cell type in scRNAseq data. Supplementary Figure S3. A. UMAP representation of scRNAseq data of PDAC individual samples colored by Numbat joint CNV probability based on gene expression and heterozygous SNPs beta-allele frequency (BAF) ratio. Supplementary Figure S4. A. Hematoxylin and Eosin (H&E) of healthy and PDAC samples profiled with 10x Genomics CytAssist Visium platform, with overlaid Visium spots. Supplementary Figure S5. A. Dot plot showing marker genes for each spatial domain in spatial transcriptomics data. Supplementary Figure S6. A. Heatmap showing pairwise Pearson correlation between different spatial domains in different tissue types annotated by spatial domain information and tissue type. Supplementary Figure S7. A. Heatmap showing scaled expression of leading edge genes from GSEA of OXIDATIVE_PHOSPHORYLATION, FATTY_ACID_METABOLISM and BILE_ACID_METABOLISM. Supplementary Figure S8. A. Heatmap showing scaled expression of leading edge genes from GSEA of TNFA_SIGNALING_VIA_NFKB, EPITHELIAL_MESENCHYMAL_TRANSITION, INTERFERON_ ALPHA_RESPONSE, HYPOXIA, KRAS_SIGANLING_UP and IL6_JAK_STAT3_SIGNALING. Supplementary Figure S9. A. Spatial map showing the distribution of plasma cells and macrophages around neoplastic domains (PanIN, GLTumor and PDTumor) in healthy, adjacent normal and PDAC samples. Supplementary Figure S10. A. Spatial map showing the abundance and distribution of tertiary lymphoid structures around neoplastic domains in healthy, adjacent normal and PDAC tissue samples. Colors represent annotated spatial domains.

Supplementary Figure S11. A. High magnification hematoxylin and eosin (H&E) image with the Visium spots overlaid, showing adjacency of Fibro1 and Adipose domains, colored by the annotated spatial domain. Supplementary Figure S12. A. Spatially-informed clustering results from donor samples validation dataset, showing 9 different spatial domains based on gene expression from all cells in the spot. Supplementary Figure S13. A. Violin plot showing the Fibro2 and Fibro3 signature score in the fibroblast spatial domain of the donor samples validation dataset. Supplementary Figure S14. A. UMAP representation of integrated Xenium samples from Fiorini et al. (81) dataset, colored by annotated cell type. Supplementary Figure S15. A. Dot plot showing the expression of WNT ligands, receptors and downstream targets in different spatial domains from spatial transcriptomics data. Supplementary Figure S16. A. UMAP representation of scRNAseq data from the mono- and co-culture experiment.

Supp Table 1 from Asynchronous Evolution of Epithelium and Stroma Differentiates Precursor Lesions from Pancreatic Cancer

Supp Table 10 from Asynchronous Evolution of Epithelium and Stroma Differentiates Precursor Lesions from Pancreatic Cancer

ScRNAseq Macrophages Subset Markers

ScRNAseq Fibroblasts Subset Markers

Fibro2 and Fibro3 Genesets for Xenium Projection

Supp Table 14 from Asynchronous Evolution of Epithelium and Stroma Differentiates Precursor Lesions from Pancreatic Cancer

Supp Table 2 from Asynchronous Evolution of Epithelium and Stroma Differentiates Precursor Lesions from Pancreatic Cancer