A conserved transcriptional signature supports metastatic colonisation of the liver by pancreatic cancer cells

doi:10.21203/rs.3.rs-1635604/v1

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A conserved transcriptional signature supports metastatic colonisation of the liver by pancreatic cancer cells

https://doi.org/10.21203/rs.3.rs-1635604/v1

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Whether pancreatic cancer cells successfully metastasise to the liver depends on complex interactions between cancer cells and the liver microenvironment, the details of which remain largely unknown. In this study, we provide evidence for a transcriptional signature, involving active MYC and repressed interferon response, that supports pancreatic cancer cells in colonising the liver. The “MYC-high/Interferon-low” signature was uncovered by comparing transcriptional profiles of human pancreatic cancer cell lines with their liver metastatic potential in mice. We confirmed the physiological relevance of this signature in two independent patient sample datasets. First, in a 187-sample study of bulk RNA sequencing of primary tumor and metastatic tissue from PDAC patients, we found an enrichment in the MYC-high/Interferon-low signature in liver metastases over primary tumor, that was not present in other metastatic sites. Second, we used a single cell-RNA sequencing dataset combined with validation experiments to show that PDAC cells are the origin of this signature in vivo and that MYC and interferon activity are negatively correlated at the cellular level. This analysis reveals a potential route to therapeutic targeting of the precursor cell populations of deadly liver metastases in pancreatic cancer.

PDAC

pancreatic ductal adenocarcinoma

metastasis

MYC

c-MYC

interferon

microenvironment

single-cell sequencing

bioinformatics

Pancreatic cancer is the third leading cause of cancer-related death in the US and has a survival rate of less than 10% [1]. Pancreatic ductal adenocarcinoma (PDAC), the most common pancreatic cancer that accounts for 90% of cases, is lethal in part due to high levels of metastatic disease on presentation. PDAC tumors can metastasise to many organs, including the lung, mesentery, and brain, but liver metastasis is the most common and has worse prognosis than other sites [2]. Despite its importance, we still have a relatively poor understanding of what factors drive PDAC metastasis to the liver, reflected in the fact that there are currently no approved therapies specifically targeting metastatic disease for PDAC [3].

A multitude of factors determine whether cancer cells successfully escape the primary tumor, survive in circulation, and establish tumors at a secondary site [4]. Genetic studies have had difficulty identifying robust mutational drivers of metastasis in PDAC [5], despite the clonal origins of metastatic tumors [6]. What is clear is that PDAC cells are highly versatile, adapting their metabolic dependencies and cellular identity throughout the metastatic journey [7, 8]. A recent study uncovered an association between increased copy number of the well-established oncogene MYC, and liver metastasis, but suggested that increased metastasis was due to escape from the primary tumor and not specific to liver colonisation [9]. The role of MYC in metastasis from PDAC is supported by experimental evidence of metastasis to the liver and diaphragm in a genetically engineered mouse model of autochthonous PDAC (Muthalagu et al. 2020).

The microenvironment of secondary organ sites is another key factor determining metastatic fate of disseminated cancer cells. Building on Paget’s “seed and soil” hypothesis, numerous studies have shown that disseminating cancer cells preferentially metastasise to specific sites for reasons including tissue architecture [10], priming of the site by primary tumor signals [11], and nutrient-metabolic compatibility [12]. Though some studies have begun to unveil the PDAC cell-liver interactions that occur during liver colonisation [13], we still have a rudimentary understanding of what genetic and transcriptional features of PDAC cells might support this. A better understanding of these traits will be important to developing therapies that can prevent or treat liver metastasis in pancreatic cancer.

To uncover features of PDAC cells that support liver metastasis formation, we utilised available data from the Broad Institute’s DepMap database on human cancer cell line encyclopedia (CCLE) cell lines [14]. Recently, data on the metastatic potential of CCLE cell lines to bone, brain, kidney, lung and liver in immunocompromised mice was reported, including 33 pancreatic cancer cell lines [12] (Fig. 1A). The metastatic potential of PDAC lines to the liver was fairly diverse as has been reported previously (Fig. 1B). Interestingly, whilst liver metastatic potential correlated with overall metastatic potential (Fig.S1A), there were several examples of lines that metastasised poorly to the liver compared to other organs (e.g. ASPC1, CFPAC1, YAPC; grey bars in Fig. 1B). In order to identify features of “liver tropic” cell lines (i.e. lines that metastasise well to the liver), lines were split into 2 groups, identified as “liver tropic” or “liver atropic” (orange and blue in Fig. 1B respectively). We then utilised existing transcriptional (RNAseq), genetic (SNP, CNV) and CRISPR screen data from DepMap in order to identify differences between these two groups.

We started by analysing RNA sequencing data of cell lines in vitro (Fig. 1C-E). As gene-level variability between different cell lines is large, gene set-based approaches provide a way of identifying changes in broad transcriptional programs between conditions. Gene set variation analysis (GSVA) using the hallmark gene sets uncovered several differences between liver tropic and atropic lines (Fig. 1C). We were particularly struck by the strong enrichment in MYC targets (MYC targets V1: logFC = 0.38, p = 0.02; MYC targets V2: logFC = 0.47, p = 0.02) and downregulation of interferon response pathways (Interferon alpha response: logFC=-0.39, p = 0.02; Interferon gamma response: logFC=-0.33, p = 0.02) [15]. This transcriptional profile was confirmed using the VIPER analysis package to estimate transcription factor (TF) activity based on the sequencing data (Fig. 1D) [16]. Liver tropic cell lines were characterised by increased activity of transcription factors MYC (logFC = 3.18, p = 0.04) and E2F2/4, and a strong downregulation of STAT1/2 (STAT1: logFC=-2.8, p = 0.04; STAT2: logFC=-0.37, p = 0.04) – key TFs in the interferon response pathway [17] whose promoters were recently shown to be bound by repressive MYC complexes (Muthalagu et al. 2020). Looking at the gene set activities of individual cell lines, we noticed that the majority of liver tropic lines had high MYC and low interferon response activities (Fig. 1E). Interestingly, several cell lines identified previously as being highly metastatic but not to the liver (YAPC, CFPAC1, BXPC3) had the opposite signature to liver tropic lines (see black boxes). To confirm that this profile was liver-specific and not indicative of general metastatic potential, we conducted a similar analysis looking for signatures enriched in brain-metastatic lines (Fig.S1B). Transcriptional signatures in this case were very different. Finally, we classified the cell lines according to two published systems used for molecular subtyping of clinical samples but found no clear association for a particular subtype (Fig.S1C). Thus, cells exhibiting a MYC^high/INF^low transcriptional signature have a specific advantage in colonising the liver (Fig. 1F).

Previous studies have shown that genetic duplications of MYC occur preferentially in liver metastatic clones over primary PDAC tumors [9]. To ask if increased MYC activity had a genetic basis in our analysis, we performed linear regression analysis on single nucleotide polymorphism (SNP), gene- and cytoband-level copy number variation (CNV) data from PDAC cell lines (Fig. 1G). Although a number of potential correlations were found, no SNPs or CNVs in the MYC gene or gene locus were associated with liver tropism, suggesting that the enhanced MYC activity of these cell lines does not have a genetic origin. Finally, analysis of CRISPR KO dependency data identified two genes, EFR3A and TMRT12, which were specifically associated with liver metastatic potential over other organ sites (Fig. 1H, I and S1D). Liver metastatic cells were particularly insensitive to EFR3A KO compared to other PDAC cells. EFR3A has recently been identified as a driver of KRAS signalling in PDAC cells [18] and suggests that liver metastatic cells may be less reliant on KRAS, supported by the downregulation of the “HALLMARK_KRAS_SIGNALLING_UP” gene set in Fig. 1C. TMRT12 is a tRNA methyltransferase whose silencing is associated with ribosome frame shift events that could impair MYC-driven cell division [19]. Taken together, these data further support the dependence of liver metastatic PDAC cells on MYC activity but do not find evidence that this is related directly to genetic alterations in MYC.

To look for additional evidence for a MYC^high/INF^low transcriptional signature in PDAC liver metastases, we looked to published data of human patient samples. In 2015 Moffitt et al. published a cohort of 187 PDAC samples including 25 liver, 11 lung, 9 lymph metastases, and 145 primary tumors (Fig. 2A) [20]. GSVA analysis of metastasis samples over primary tissue confirmed a MYC^high/INF^low transcriptional signature in liver metastases (Fig. 2B; MYC targets v2: logFC = 0.23, p < 0.001; Interferon alpha response: logFC=-0.31, p < 0.001; Interferon gamma response: logFC=-0.30, p < 0.001). Interestingly, this signature was much less pronounced and non-significant in lung or lymph metastases, supporting the idea of a liver-specific feature of PDAC cells. Further illustrating this, hierarchical clustering of samples using genes from MYC target and Interferon response gene sets showed that liver metastasis samples largely grouped together and were characterised by strong MYC target and low Interferon expression (Fig. 2C). Samples from primary tumor and other metastatic sites were more evenly distributed across this clustering. In agreement with the CCLE cell line data, samples in this region did not appear to be overrepresented in a particular PDAC or stromal subtype (Fig. 2C, top panel).

A limitation of bulk sequencing data is a difficulty in discerning which cell populations contribute to molecular signatures. To look for evidence for a MYC^high/INF^low in PDAC cells of liver metastases, we utilised a recent single cell RNA sequencing (scRNAseq) dataset that contained 14,926 cells from 10 primary PDAC and 5 liver metastatic samples (Fig. 3A) [21]. PDAC cells were identified from samples based on scoring of a “epithelial tumor cell” (ETC) gene set identified by the authors (Fig.S2A; Supp Table 1), isolating 9888 tumor cells (6313 from liver metastases, 3575 from primary tumors). Interestingly, t-distributed stochastic neighbor embedding (tSNE) plots of ETCs showed primary tumor samples from all patients clustered together, whereas liver metastases from different patients formed distinct clusters (Fig. 3B, S2B). Gene set enrichment analysis (GSEA) of individual patient metastases revealed a MYC^high/INF^low transcriptional signature in 4/5 liver samples compared to grouped primary tumor cells (Fig. 3C). Plots of individual genes showed that genes including members of the major histocompatibility complex (HLA-DRB1) and interferon response factors (IRF3) were broadly expressed in primary tumor compared to liver metastases (Fig. 3D, S2C-E). In comparison, key MYC targets such as RPS3 were enriched in liver samples. To further validate this, TF activities were estimated for all cells and compared between all liver metastases and primary tumor samples (Fig. 3E). Interestingly, MYC was the most strongly upregulated TF in liver metastases (LogFC = 1.85, p < 0.001). In comparison, key interferon TFs RFXAP (LogFC=-1.69, FDR < 0.001), STAT1 (LogFC=-0.87, p < 0.001) and STAT2 (LogFC=-0.66, p < 0.001) were all downregulated.

scRNAseq data enables us to ask whether or not MYC and Inteferon response pathways are negatively correlated within individual cells. To test this, VIPER TF estimates of MYC, STAT1 and STAT2 activity were calculated for PDAC cells in liver metastases and primary tumor samples. Activity of STAT TFs were highly correlated (Fig.S2F), whereas MYC activity was negatively correlated with STAT1 expression (Fig. 3F). Taken together, these analyses provide support from clinical samples that the MYC^high/INF^low transcriptional signature is a consistent feature of PDAC cells that have metastasised to the liver. MYC activity suppressing interferon signalling in PDAC cells was recently reported in primary tumors in the KMC (Lsl-Kras^G12D;Pdx1^CreER;Rosa26^{DM − lsl−MYC}) mouse model of PDAC, driven by activated KRAS^G12D combined with conditionally expressed MYC driven from the Rosa26 locus [22]. To evidence this relationship, we conducted in vitro experiments using the liver-tropic cell line PSN1 from the DepMap study. Inhibition of ERK1/2 activity leads to rapid loss of MYC protein expression as has been reported previously (Fig. 4A) [23]. To see if loss of MYC altered expression of interferon response genes, qPCR of PSN1 cells was conducted with samples treated with 100nM trametinib (Fig. 4B). Trametinib treatment led to ~ 4 fold change (FC) in interferon regulators, IRF5 (FC = 3.47, p = 0.07), IRF7 (FC = 3.1, p = 0.03) and IFNB1 (FC = 3.2, p = 0.37), whilst MYC expression was drastically reduced (FC = 0.04, p < 0.001). Thus, loss of MYC expression correlates with increased interferon regulatory gene expression.

Uncovering features of cancer cells that facilitate colonisation of secondary organs is critical in improving our understanding of metastatic disease and developing effective therapies against it. In this study, we provide evidence for a MYC^high/INF^low transcriptional signature that supports PDAC cell colonisation of the liver. The role of MYC in PDAC progression and metastasis has become well-established over the last few years. Activation of MYC in premalignant pancreatic epithelium initiates and sustains remodelling of the stromal environment [24] and suppresses antitumor immunity [25]. Gene amplifications of MYC are frequent in PDAC [26] and dramatically accelerate progression of PDAC [22]. A recent study by Maddipati et al. provided direct evidence for MYC amplifications determining metastatic burden in PDAC, but suggested that this way due to increased intravasation into the bloodstream and was not specific to the liver [9].

That we identify a strong MYC^high/INF^low signature in liver tropic cell lines from the MetMap study has several important implications. Firstly, in this study cells were delivered into mice via intracardiac injection and therefore changes in intravasation observed by Maddipati et al. cannot explain the differing metastatic potential reported here, though they may occur via a similar mechanism [9]. Maddipati et al. show that intravasation is driven by recruitment of macrophages in primary tumor. Early macrophage recruitment is important for forming a supportive metastatic niche [13] and thus it is possible that MYC^high/INF^low PDAC cells interact differently with resident Kupffer cells or invading monocytes to support liver colonisation. Secondly, the MetMap study used immunocompromised mice that lacked B cells and almost all NK cells [12]. Thus, it is unlikely that increased liver colonisation by cell lines with suppressed interferon responses was due to the same immune suppression mechanisms shown by Muthalagu et al. to be important for primary tumor progression [22]. This is supported by the observation that interferon activity has been shown to reduce colorectal cancer liver metastasis in nude mice [27] due to specific interactions with the liver metastatic niche [28]. Precisely how the MYC^high/INF^low signature supports liver colonisation is currently unclear, but is a clinically important question for future study.

We report a specific upregulation of MYC^high/INF^low signature in liver metastases compared to primary tumor in two separate human tissue datasets. Though this alone is not direct evidence of a role of this signature in supporting liver colonisation, that we find it enriched in liver metastases over lung and lymph nodes metastases does support a degree of liver-specificity. Our analysis of Lin et al.’s single cell dataset additionally demonstrates that (1) MYC^high/INF^low signature in patients arises from differences in PDAC cell expression and (2) MYC and Interferon signatures are negatively correlated at the single-cell level. This is supported by both our trametinib treatment of PSN1 cells, and work by Muthalagu et al. that detailed the relationship between MYC and interferon response in PDAC primary tumors. Looking forward, it would be interesting to know whether increased MYC expression or repression of interferon response in metastatic cell lines that do not colonise the liver (e.g. CFPAC1, YAPC, ASPC1) enhances liver metastasis formation. We hope that this study adds weight to the hypothesis that the MYC-INF axis may be a potential therapeutic target for treatment of metastatic PDAC.

Cell culture

Cell lines used in this study were cultured at 37˚C under 5% CO₂ in a humidified incubator and were tested regularly for mycoplasma contamination. Human PSN1 cells were purchased from ATCC and cultured in RPMI-1640 medium supplemented with FCS (10%), glutamine (2mM), sodium pyruvate (0.11 g L^− 1), penicillin (10,000 units ml^− 1) and streptomycin (10,000 units ml^− 1).

Western blotting

Protein lysates were prepared in radioimmunoprecipitation (RIPA) lysis buffer (150mM NaCl, 10mM Tris-HCl pH7.5, 1mM ethylenediaminetetraacetic acid (EDTA), 1% Triton X-100, 0.1% SDS, 1X protease and phosphatase inhibitors (Roche)). Equal volumes of lysate were resolved on 8–12% SDS-PAGE gels and transferred onto nitrocellulose membranes. Membranes were blocked (5%BSA / TBST) and incubated for 16h in 4°C with one of the following antibodies: anti-MYC (1:1000; ab32072, Abcam), anti-pERK1/2 (1:1000; #9101, Cell Signaling Technology), anti-ERK1/2 (1:1000; #9102, Cell Signaling Technology), and anti-GAPDH (1:1000; MAB374, Millipore). Protein detection was achieved using Alexa-Fluor conjugated secondary antibodies and signal was imaged using the LI-COR Odyssey CLx (LI-COR Biosciences) system. All images were analysed using Image Studio Lite software, version 5.2.5. Experiments were repeated at least 3 times and used a single technical replicate per repeat.

qPCR

Total RNA extraction and purification from cells was performed using the RNeasy Mini Kit (Qiagen) combined with RNaseFree DNase (QIAGEN) treatment, according to the manufacturer’s instructions. RNA concentration and purity were assessed using NanoDrop2000C (Thermo Fisher Scientific). cDNA was synthesised with either the DyNAmo cDNA synthesis kit (F-470L, Thermo Fisher Scientific) or Maxima First Strand cDNA synthesis kit (K1641, Thermo Fisher Scientific). Then qRT-PCR was performed using the DyNAmo HS SYBR Green qPCR kit (F410L, Thermo Fisher Scientific). PCR was performed on a C1000 Thermal Cycler (CFX96 Real time system, BioRad) as follows: 3min at 95ºC, 40-cycles of 20s 95°C, 30s 60°C, 30s 72°C and final 5min 72°C. Relative mRNA quantification was performed using the 2^-ddCT method for multiple genes. The following primer sets were used to detect indicated mRNA transcripts: Actin Fw: CCAACCGCGAGAAGATGA, Rv: CCAGAGGCGTACAGGGATAG; IRF5 F: CACACTCCAGCCCACTTTC, R: GCATCTGCACCTGGTAAGG; IRF7 Fw: CTGCAGTCACACCTGTAGCC, Rv: GTGGACTGAGGGCTTGTAGC; IFNB1 Fw: GACGCCGCATTGACCATCTA, Rv: AGCCAGGAGGTTCTCAACAAT; MYC Fw: GCCCCTGGTGCTCCATGA, Rv: CAACATCGATTTCTTCCTCATCTTCT. Experiments were repeated at least 3 times and used a single technical replicate per repeat.

Analysis of PDAC CCLE datasets

All data from PDAC CCLE lines were downloaded from the DepMap (https://depmap.org/portal) and MetMap (https://depmap.org/metmap/data/) portals. All differential comparisons between liver tropic and atropic cell lines were performed using voom-limma [29]. Expression analysis used log2(count + 1). For gene set analysis, single samples GSEA signature projections were generated using the GSVA package [15] and compared between liver tropic and atropic lines. Transcription factor (TF) activity was estimated from expression data using the viper package [16]. Human regulons were taken from the Dorothea package with only high confidence (A and B scores) regulons used [30]. Single nucleotide polymorphisms coding missense mutations were extracted from the CCLE mutations dataset and used for downstream analysis. Copy number and cytoband data were binarized using a cutoff of ≤ − 1 (loss) and ≥ 1 (gain). CRISPR KO dependency analysis did not include PATU8988T, QGP1, HPAC, PANC0327, CAPAN2, SW1990, PK59, PANC0813 PDAC cell lines as these data was not available. PDAC subtype classification used a score-based method using the mean expression of marker genes (see Supplementary Table 4).

Analysis of patient single-cell expression data

Raw read count matrix was downloaded from GEO omnibus and analysed in R using the Seurat package [31]. Cells were filtered using the following criteria: nFeature_RNA > 200 & nFeature_RNA < 5000 & percent.mt < 1.5, and log(1p) normalised. Epithelial tumor cells were isolated based on an average expression of 20 top marker genes for ETCs by the original authors [21]. Clustering was done using the findNeighbours and findClusters commands and visualised as t-SNE plots using RunTSNE command in Seurat. TF activity estimates in single cells was conducted using a single-cell implementation of VIPER [32] using the same regulons as used previously. Linear models calculating TF activity differences between metastatic and primary tumors were conducted using limma and visualised with the EnhancedVolcano package. GSEA of scRNAseq data was done using the fgsea package (Korotkevich et al. 2019). All analyses were done using the Broad Institute’s hallmark gene sets taken from MSigDB (https://www.gsea-msigdb.org/).

Analysis of Moffitt dataset

Gene matrix of log2(Cy5) data was downloaded from gene omnibus GSE71729. Subtype classification and stromal identity of samples was taken from the original authors metadata. GSVA analysis was conducted as before, using sample tissue origin as a differentiating factor. Unsupervised hierarchical clustering based on expression of genes in the MYC_TARGETS_V2, INTERFERON_ALPHA_RESPONSE and INTERFERON_GAMMA_RESPONSE genesets was used to define sample ordering in Fig. 2C.

Ethical approval and consent to participate

Consent for publication

Not applicable

Availability of data and materials

Publicly available datasets were downloaded from the GEO Omnibus under accession numbers GSE154778 (Lin et al. 2020), GSE71729 (Moffitt et al. 2015). RNA sequencing, genomic, and CRISPR KO data from PDAC cell lines were downloaded from the DepMap download portal (https://depmap.org/portal/download/). Data on PDAC CCLE cell line metastatic potentials were taken from the MetMap download portal (https://depmap.org/metmap/data/). All analysis was conducted with RStudio and markdown files of all analysis and plots can be found at https://github.com/jamesdrew-work/PdacLiverMetastasis.

Competing interests

The authors declare no competing interests.

Funding

Research was funding by Cancer Research UK core funding to LMM (A24452) and for core funding to the CRUK Beatson Institute (A17196 and A31287).

Author contributions

Study was conceived by JD and LMM. All analysis and experiments were conducted by JD. Manuscript was written by JD and LMM.

Acknowledgements

We thank Jen Morton, Daniel Murphy and Ed Roberts for their invaluable advice and discussion.

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A conserved transcriptional signature supports metastatic colonisation of the liver by pancreatic cancer cells

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Cell culture

Western blotting

qPCR

Analysis of PDAC CCLE datasets

Analysis of patient single-cell expression data

Analysis of Moffitt dataset

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