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. 2020 May 14;181(4):832-847.e18.
doi: 10.1016/j.cell.2020.03.062. Epub 2020 Apr 17.

Endocrine-Exocrine Signaling Drives Obesity-Associated Pancreatic Ductal Adenocarcinoma

Affiliations

Endocrine-Exocrine Signaling Drives Obesity-Associated Pancreatic Ductal Adenocarcinoma

Katherine Minjee Chung et al. Cell. .

Abstract

Obesity is a major modifiable risk factor for pancreatic ductal adenocarcinoma (PDAC), yet how and when obesity contributes to PDAC progression is not well understood. Leveraging an autochthonous mouse model, we demonstrate a causal and reversible role for obesity in early PDAC progression, showing that obesity markedly enhances tumorigenesis, while genetic or dietary induction of weight loss intercepts cancer development. Molecular analyses of human and murine samples define microenvironmental consequences of obesity that foster tumorigenesis rather than new driver gene mutations, including significant pancreatic islet cell adaptation in obesity-associated tumors. Specifically, we identify aberrant beta cell expression of the peptide hormone cholecystokinin (Cck) in response to obesity and show that islet Cck promotes oncogenic Kras-driven pancreatic ductal tumorigenesis. Our studies argue that PDAC progression is driven by local obesity-associated changes in the tumor microenvironment and implicate endocrine-exocrine signaling beyond insulin in PDAC development.

Keywords: beta cells; cholecystokinin; genetically engineered mouse models; leptin; obesity; pancreatic cancer; pancreatic islets; tumor microenvironment.

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Conflict of interest statement

Declaration of Interests K.J.D. is currently an employee of Sherlock Biosciences. M.D.B. acknowledges research support from ViaCyte and Dexcom and serves on the medical advisory boards for Novo Nordisk and ARIEL Precision Medicine. A.L.G. has received honoraria from Merck and Novo Nordisk and has received research funding from Novo Nordisk. M.I.M. has served on advisory panels for Pfizer, Novo Nordisk, and Zoe Global; he has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, Novo Nordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, M.I.M. is an employee of Genentech and a holder of Roche stock. R.G.K. is co-founder and a Scientific Advisory Board member of Elucidata; a Consultant/Advisory Board member for Agios, Janssen, BI-Lilly, and Pfizer; and a recipient of sponsored research agreements from Agios, AstraZeneca/BMS, Lilly, Pfizer, and Poxel. S.K. is a paid scientific advisor to AI Therapeutics. B.M.W. declares research funding from Celgene and Eli Lilly & Company, and consults for BioLineRx, Celgene, G1 Therapeutics, and GRAIL. T.J. is a Board of Directors member of Amgen and Thermo Fisher Scientific, co-founder and Scientific Advisory Board member of Dragonfly Therapeutics, co-founder of T2 Biosystems, and Scientific Advisory Board member of SQZ Biotech with equity holding in all five companies; he receives funding from the Johnson & Johnson Lung Cancer Initiative and Calico. C.S.F. reports receiving personal fees from Eli Lilly, Entrinsic Health, Pfizer, Merck, Sanofi, Roche, Genentech, Merrimack Pharma, Dicerna, Bayer, Celgene, Agios, Gilead Sciences, Five Prime Therapeutics, Taiho, KEW, and CytomX Therapeutics and receiving support from CytomX Therapeutics. M.D.M. acknowledges research support from Genentech.

Figures

Figure 1.
Figure 1.. Accelerated PDAC progression in KCO mice.
A) Schematic of transgenic/knock-in alleles in leptin-deficient KC mice (KC; ob/ob or KCO). Black arrows denote promoters. Kras and Lep promoters are endogenous. Blue triangles denote LoxP sites. A STOP cassette prevents oncogenic Kras (G12D) expression prior to Cre-mediated recombination. * denotes point mutation in ob gene causing premature stop. B) Body weight (mean +/− s.e.m.) of KC mice of varying ob genotype over time (n=22-46 mice/group). ***p<0.001, ****p<0.0001, KC; ob/ob compared to KC; +/+ mice, two-tailed student’s t-test at each time point. C) Representative histologic sections of pancreata from mice of designated genotypes at 3 months of age demonstrated differences in Ck19+ ductal tumor burden. Scale bar: 200 μm. D) Tumor burden (mean +/− s.e.m.) in mice of designated genotypes at 3 months of age (n=5-8 mice/group). **p<0.01, ****p<0.0001, two-tailed student’s t-test. NS = non-significant. E) Percentage of mice in (D) harboring PanINs and/or adenocarcinoma. F) Kaplan-Meier survival curves for mice of designated genotypes (n=14-106 mice/group). Log-rank test: p<0.0001 KC; +/+ vs. KC; ob/ob, p<0.0001 KC; ob/+ vs. KC; ob/ob, p>0.05 KC; +/+ vs. KC; ob/+ See also Figure S1.
Figure 2.
Figure 2.. Weight loss intercepts early tumor progression in KCO mice.
A) Schematic of AAV vectors administered to KCO mice. ITR = internal tandem repeat for AAV2. CAGGS = CMV enhancer chicken beta-actin promoter. GFP = green fluorescent protein. B) Schematic of AAV treatment for tumor interception experiment. AAV-GFP-treated mice served as controls. AAV was administered at 6 weeks of age prior to development of significant tumor burden. Mice were analyzed 6 weeks later. Percent change in body weight (mean +/− s.e.m., n=8-10 mice/group) following AAV administration is shown. C) Representative histologic sections of pancreata of mice at endpoint in (B) demonstrated a reduction in Ck19+ ductal tumors with AAV-Leptin. Quantification of tumor burden (mean +/− s.e.m., n=8-10 mice/group) is shown. D) Greater weight loss is associated with less tumor burden in mice treated with AAV-Leptin. Each point represents one mouse (n=10). Correlation coefficient (R) and p-value from simple linear regression are shown. E) Schematic for caloric restriction (CR) experiment. Mice began CR at 6 weeks of age as in (B). Controls remained on ad libitum diet (AL). Percent change in body weight (mean +/− s.e.m., n=6 mice/group) following intervention is shown. F) Representative histologic sections of pancreata of mice at endpoint in (E) demonstrated a reduction in Ck19+ ductal tumors with CR. Quantification of tumor burden (mean +/− s.e.m., n=6 mice/group) is shown. p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, two-tailed student’s t-test for all pairwise comparisons. Scale bars: 200 μm. See also Figure S2.
Figure 3.
Figure 3.. Obesity promotes tumor progression independent of new driver mutations.
A) Tumor suppressor proteins frequently mutated in human PDAC were retained in 8/8 (100%) KCO tumors analyzed by IHC. Scale bar: 50 μm. B) Exome and mRNA sequencing did not reveal new single nucleotide variants in tumor suppressor genes in KCO tumors. Average mutant allele fraction identified for each base in the coding sequence is shown. A single KPC tumor sequenced in parallel shows the presence of the expected R172H codon change. See also Tables S1-S5.
Figure 4.
Figure 4.. Enhanced inflammation and fibrosis in KCO tumors.
A) Heat map of row normalized Z-scores (of mixing weights from ICA decomposition) for gene expression signatures that separate tumors from obese (KCO) and non-obese (KC and KPC) models. See Methods for details. Rows represent individual tumors. Red corresponds to positive and blue to negative Z-scores. B) Network representation of overlapping enriched GSEA/MSigDB curated (C2) gene sets associated with the KCO signatures in (A). Cellular processes associated with related gene sets are listed. C) GSEA using the curated gene set collection (C2 in MSigDB) revealed an enrichment of genes involved in immune cell activation/signaling and extracellular matrix/fibrosis in KCO tumors See Table S6 for complete list. D) GSEA showed an enrichment of inflammation and fibrosis gene sets (hallmark gene set collection (H in MSigDB)) with KCO tumors. E) Histologic analyses revealed extensive fibrosis (Sirius red staining of collagen) and Cd45+ immune cell infiltration predominantly with F4/80+ macrophages and sparse presence of Cd3+ T and B220+ B cells. Scale bars: 100 μm F) Box and whisker plots (boxes denote 25th-75th percentile, error bars denote min/max) of standardized signature scores for TCGA tumors corresponding to each molecular subtype (Bailey et al., 2016) are shown. See Methods for details. p-values confirmed significant enrichment of TCGA tumors highly-correlated to KCO signatures with the immunogenic subtype (hypergeometric test). See also Figure S3 and Table S6.
Figure 5.
Figure 5.. Pancreatic islet adaptation in KCO tumors.
A) GSEA using the curated (C2 in MSigDB) and hallmark (H in MSigDB) gene set collections revealed an association between KCO tumors and genes expressed in pancreatic beta cells. See Table S6 for complete list. B) Relative gene expression (mean +/− s.e.m. normalized RNA-seq expression counts with non-obese KC/KPC tumors as baseline) for general neuroendocrine markers observed in pancreatic islet cells showed mild to no significant difference between KCO (n=15) and KC/KPC (n=17) tumors. **p<0.01, two-tailed Mann-Whitney test, comparing KCO to KC/KPC. NS = non-significant. C) Relative gene expression (mean +/− s.e.m. normalized RNA-seq expression counts with KC/KPC tumors as baseline) of islet genes in tumors from KCO mice compared to non-obese models is shown. * p<0.05, **p<0.01, ***p<0.001, ****p<0.0001, two-tailed Mann-Whitney test, comparing KCO to KC/KPC expression for each gene. D) IHC showed aberrant glucagon and GLP-1 expression throughout pancreatic islets in KCO mice compared to non-obese controls, consistent with upregulation of Gcg and Pcsk1/Pcsk2 observed by RNA-seq in (C). Scale bar: 50 μm. See also Figure S4 and Table S6.
Figure 6.
Figure 6.. scRNA-seq identifies beta cell expression of Cck in obesity
A) PHATE visualization plots for single hormone-expressing islet clusters identified Ins1/Ins2+ beta cells, Gcg+ alpha cells, Sst+ delta cells, and Ppy+ PP cells amongst all sequenced cells. B) PHATE plot colored by the Enhanced Experimental Signal (EES) values from MELD (Burkhardt et al., 2019) indicated the likelihood of observing each transcriptional profile in ob/ob (red) and wild-type (WT, blue). C) The distribution of EES values in the clusters in (A) showed only moderate overlap in beta and alpha clusters between genotypes. Gray dots in each column mark the mean EES value in each cluster. Single cells color-coded by genotype showed a relative increase in beta cells in ob/ob islets (mean EES>0) and a proportional decrease in alpha, delta, and PP cells (mean EES<0). D) Upregulated genes comparing ob/ob with WT beta cells showed significant overlap (hypergeometric test) with gene sets associated with protein translation and secretion. E) Mean single cell expression counts (square-root transformed library size-normalized UMI/cell +/− s.d.) for beta cells showed upregulation of hormones and secretory granule genes in ob/ob islets. Each colored dot represents a single cell. Gray circles represent median expression. F) PHATE visualization shows Cck expression exclusively in beta cells. Cck is expressed in ob/ob, but not WT, islets as shown in (E). G) Upregulated genes comparing Cck+ versus Cck- ob/ob beta cells showed significant overlap (hypergeometric test) with gene sets associated with protein translation and secretion. H) Gene-gene expression plots after MAGIC imputation (van Dijk et al., 2018) showed an inverse relationship between the expression of Ins1, Ins2, Slc30a8, and Insig1 with Cck in ob/ob beta cells. Spearman correlation coefficients (R) are listed for each plot. See also Figures S5-S6 and Table S7.
Figure 7.
Figure 7.. Islet-derived Cck promotes pancreatic ductal cancer development
A) Relative gene expression (mean +/− s.e.m. normalized RNA-seq expression counts with KC tumors as baseline) of Cck in obese (KCO and KCO mice treated with AAV-GFP) and non-obese models (KC, KPC, and KCO mice treated with AAV-Leptin) is shown (n=5-9 per group). **p<0.01, ***p<0.001, two-tailed Mann-Whitney test. B) Cck was aberrantly overexpressed in pancreatic islets of KCO compared to KC and KPC mice. Scale bar: 100 μm. C) CCK IHC on human PDAC tissues demonstrated expression specifically in islets (arrows). Scale bar: 100 μm. D) The majority of human PDAC specimens displayed islet CCK expression by IHC at all BMI levels at diagnosis. E) CCK expression in isolated pancreatic islets from human donors with high BMI (n=53, above median US BMI of 28.2 kg/m2) was significantly (*p<0.05, two-tailed Mann-Whitney test) greater than from low BMI donors (n=55, below median). Box and whisker plots (boxes denote 25th-75th percentile, error bars denote min/max) are shown. F) Representative histologic sections of pancreata demonstrated an increase in Ck19+ ductal tumorigenesis in MKC (MIP-Cck; KC) mice compared to KC mice. Quantification of tumor burden (mean +/− s.e.m., n=5-6 mice/group) at 3 months of age is shown. *p<0.05, two-tailed student’s t-test. Scale bar: 200 μm. G) Model for obesity-associated islet adaptations in PDAC development. Brown denotes Cck expression. ADM = acinar-to-ductal metaplasia. TF = transcription factor. See also Figures S3 and S7.

Comment in

References

    1. Ardito CM, Gruner BM, Takeuchi KK, Lubeseder-Martellato C, Teichmann N, Mazur PK, Delgiorno KE, Carpenter ES, Halbrook CJ, Hall JC, et al. (2012). EGF receptor is required for KRAS-induced pancreatic tumorigenesis. Cancer Cell 22, 304–317. - PMC - PubMed
    1. Babic A, Bao Y, Qian ZR, Yuan C, Giovannucci EL, Aschard H, Kraft P, Amundadottir LT, Stolzenberg-Solomon R, Morales-Oyarvide V, et al. (2016). Pancreatic Cancer Risk Associated with Prediagnostic Plasma Levels of Leptin and Leptin Receptor Genetic Polymorphisms. Cancer Res 76, 7160–7167. - PMC - PubMed
    1. Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, Colaprico A, Wendl MC, Kim J, Reardon B, et al. (2018). Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell 173, 371–385 e318. - PMC - PubMed
    1. Bailey P, Chang DK, Nones K, Johns AL, Patch AM, Gingras MC, Miller DK, Christ AN, Bruxner TJ, Quinn MC, et al. (2016). Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 531, 47–52. - PubMed
    1. Bao Y, Giovannucci EL, Kraft P, Qian ZR, Wu C, Ogino S, Gaziano JM, Stampfer MJ, Ma J, Buring JE, et al. (2013a). Inflammatory plasma markers and pancreatic cancer risk: a prospective study of five U.S. cohorts. Cancer Epidemiol Biomarkers Prev 22, 855–861. - PMC - PubMed

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