HGF/c-Met Inhibition as Adjuvant Therapy Improves Outcomes in an Orthotopic Mouse Model of Pancreatic Cancer

Simple Summary

Pancreatic cancer (PC) has a poor prognosis. Even though surgical resection and adjuvant chemotherapy is the most effective therapy, recurrence remains common. In this paper, we investigate the effectiveness of dual inhibition of hepatocyte growth factor (HGF) and c-MET when used as treatment after surgical resection of PC in mice. The HGF/c-Met pathway is a major mediator of pancreatic stellate cell (stromal cell)—PC cell interactions. Using single-cell RNA sequencing, we also investigated the existence of co-metastasising cells, circulating pancreatic stellate cells (cPSCs), as facilitators of PC metastasis. We found that HGF/c-Met inhibition reduced both the risk and rate of disease progression after resection and that this effect was associated with reduced cPSC counts. In conclusion, this study is the first to demonstrate the efficacy of adjuvant HGF/c-Met inhibition and is also the first to confirm the existence of cPSCs in PC.

Abstract

Background: Inhibition of hepatocyte growth factor (HGF)/c-MET pathway, a major mediator of pancreatic stellate cell (PSC)−PC cell interactions, retards local and distant cancer progression. This study examines the use of this treatment in preventing PC progression after resection. We further investigate the postulated existence of circulating PSCs (cPSCs) as a mediator of metastatic PC. Methods: Two orthotopic PC mouse models, produced by implantation of a mixture of luciferase-tagged human pancreatic cancer cells (AsPC-1), and human PSCs were used. Model 1 mice underwent distal pancreatectomy 3-weeks post-implantation (n = 62). One-week post-resection, mice were randomised to four treatments of 8 weeks: (i) IgG, (ii) gemcitabine (G), (iii) HGF/c-MET inhibition (HiCi) and (iv) HiCi + G. Tumour burden was assessed longitudinally by bioluminescence. Circulating tumour cells and cPSCs were enriched by filtration. Tumours of Model 2 mice progressed for 8 weeks prior to the collection of primary tumour, metastases and blood for single-cell RNA-sequencing (scRNA-seq). Results: HiCi treatments: (1) reduced both the risk and rate of disease progression after resection; (2) demonstrated an anti-angiogenic effect on immunohistochemistry; (3) reduced cPSC counts. cPSCs were identified using immunocytochemistry (α-smooth muscle actin+, pan-cytokeratin−, CD45−), and by specific PSC markers. scRNA-seq confirmed the existence of cPSCs and identified potential genes associated with development into cPSCs. Conclusions: This study is the first to demonstrate the efficacy of adjuvant HGF/c-Met inhibition for PC and provides the first confirmation of the existence of circulating PSCs.

1. Introduction

Pancreatic cancer (pancreatic adenocarcinoma, PC) is a poor-prognosis cancer, with an overall 5-year survival rate of around 9% [1,2]. In patients with non-metastatic PC, surgical resection combined with modern systemic chemotherapy therapy remains the only potentially curative treatment modality [3]. Unfortunately, recurrence of disease, even for patients who undergo resection and achieve macroscopic clearance of the tumour, is common. Indeed, in patients who do not receive adjuvant systemic therapy, median disease-free survival after surgery is reported to be less than 7 months [4].

Consequently, post-resection (adjuvant) therapy is currently recommended for all patients undergoing surgery, regardless of primary tumour staging [3,5]. However, recurrence rates with current adjuvant therapies remain high, indicating a critical need for more effective systemic treatment.

Interactions between cancer cells and pancreatic stellate cells (PSCs, which produce the collagenous stroma of cancer) are now acknowledged as key drivers of cancer progression. One of the important signalling pathways that mediates cancer cell–PSC interactions is the hepatocyte growth factor (HGF)/c-Met pathway [6,7,8]. Using an orthotopic model of pancreatic cancer, wherein tumours were produced by implantation of a mixture of cancer cells and pancreatic stellate cells, we previously demonstrated that inhibition of both the ligand HGF and its receptor c-MET in combination with gemcitabine (cytotoxic agent) results in the virtual elimination of metastasis in both early and advanced-stage unresected pancreatic cancer [8,9,10]. Our hypothesis is that this triple therapy approach to inhibit metastases is also effective in the post-resection (adjuvant) setting, given that metastatic disease is a major cause of recurrence after curative resection [4,11].

Using our orthotopic model of pancreatic cancer noted above, we have also previously reported that PSCs not only facilitate local tumour growth but can move from the primary tumour to distant metastatic sites, where they likely facilitate cancer cell seeding and growth [12]. As a logical extension of this observation, we have postulated the existence of circulating PSCs (cPSCs), analogous to circulating tumour cells (CTCs) [13].

The aims of this study were (i) to assess the effects of adjuvant treatment (HGF/c-Met inhibition, with or without gemcitabine) on disease progression and on circulating cells (tumour cells and PSCs) in a post-resectional orthotopic model of pancreatic cancer and (ii) to use single-cell RNA-sequencing (scRNA-seq) to characterise circulating PSCs in the metastatic setting of an unresected model of pancreatic cancer.

2. Materials and Methods

A more detailed description of the methods is provided in Appendix A.

2.1. Orthotopic Mouse Models

Two orthotopic mouse models were used in this study: Model 1 (adjuvant treatment model) and Model 2 (cPSC characterisation model). The aim of Model 1 was to test the effectiveness of HGF/c-Met inhibition treatment in the post-resection setting. This model thus involved tumour implantation, tumour resection, drug treatments and disease analysis (tumour burden, circulating cells, immunohistochemistry). Model 2 was an observational model designed to identify and characterise circulating PSCs by single-cell RNA-sequencing (scRNA-seq). Thus, this model involved only tumour implantation, the passage of time for tumour progression and then collection of blood and tissue for scRNA-seq analysis. Summary flowcharts for each model are shown in Figure A1 and Figure A2.

2.2. Tumour Implantation

The basic schema for these mouse models has been described previously [14,15]. Briefly, athymic nude mice (BALB/c-Fox1^nu/Ausb), aged 8−10 weeks and weighing 16−18 g, were injected in the distal pancreas with a mixture of 10⁶ luciferase-tagged AsPC-1 cells (kindly provided by Professor Takashi Murakami, Saitama University, Saitama, Japan) and 10⁶ cancer-associated human PSCs (Pancreatic Research Group PSC bank) in 50 μL of phosphate-buffered saline (PBS). Humane endpoints included loss of body weight > 20%, features of untreatable distress and tumour size > 1 cm³. Mortality from tumour progression was not considered as an acceptable humane endpoint; therefore, no survival analysis was possible.

2.3. Tumour Resection

Tumour resection was only performed in the adjuvant treatment model (Model 1). The period between implantation and resection was 3 weeks (determined by empiric optimisation). Bioluminescence imaging, described below, was performed the day prior to planned resection to exclude mice with clearly disseminated disease.

The tumour resection procedure has been described previously [15]. Briefly, the mouse was anaesthetised, the suture line excised and distal pancreatectomy/splenectomy performed. The pancreatic transection margin was sealed with a titanium ligation clip. The entire procedure is summarised in Figure A3.

2.4. Treatments

In Model 1, mice were treated with gemcitabine and/or combination rilotumumab and Compound A (c-Met small molecule inhibitor) in a 2 × 2 factorial design. The four groups (n = 8 mice/group) were as follows:

• Control (Ctrl): IgG, 300 μg/mouse, by intraperitoneal (i.p.) injection daily (Amgen Inc., Thousand Oaks, CA, USA) and soybean oil, 10 μL/g by daily oral gavage (Sigma-Aldrich Pty Ltd., Castle Hill, NSW, Australia);
• Gemcitabine (G): Gemcitabine, 75 mg/kg i.p. daily (Hospira, Mulgrave, VIC, Australia) alone;
• HGF/c-Met inhibition (HCI): combination of rilotumumab, 300 μg/mouse i.p. daily (Amgen Inc.) and Compound A, suspended in soybean oil, 60 mg/kg by daily oral gavage (Amgen Inc.);
• Combination of Gemcitabine and HGF/c-Met inhibition (G+HiCi): Gemcitabine, rilotumumab and Compound A.

Treatments were commenced 1-week post-resection and continued for 8 weeks (Figure A1).

2.5. Assessment of Effects of Treatment

For the adjuvant treatment model (Model 1), bioluminescence imaging was performed weekly after the implantation of the tumour until the end of the treatment period, so as to non-invasively track tumour progression. D-luciferin (15 mg/mL. PerkinElmer Inc., Waltham, MA, USA) was administered by intraperitoneal injection and imaging was performed using IVIS Lumina II (Caliper Life Sciences, Hopkinton, MA, USA), 18−26 min after injection. Image data analysis was performed using Living Image version 4.5.5 (64-bit) for Windows (Caliper Life Sciences). Total tumour burden was semi-quantitatively estimated by the ventral bioluminescent flux of the mouse.

The final tumour burden was assessed at necropsy. The abdominal and chest cavities were then examined for evidence of metastasis, which was recorded on a standard chart. The primary tumour was excised and the tumour volume estimated according the formula (½ × length × width × height) [16].

For Model 2, primary and metastatic nodules were excised at necropsy. These samples were processed separately into single-cell suspensions using enzymatic and physical dissociation using the Tumour Dissociation Kit and GentleMACS Octo (Miltenyi Biotec, Macquarie Park, NSW, Australia) as per the manufacturer’s instructions. Dead cells were removed from the resulting single-cell suspension by the use of the Dead Cell Removal Kit and the MACS magnetic cell separator system (Miltenyi Biotec, Macquarie Park, NSW, Australia)

2.6. Circulating Tumour Cells and Circulating Pancreatic Stellate Cells

At necropsy, 20 mL/kg of blood was collected from the portal vein and immediately placed into a CellSave tube (for Model 1; Menarini-Silicon Biosystems) or an EDTA tube (for Model 2; Interpath Service Pty Ltd., West Heidelberg, Victoria, Australia).

Circulating cell enrichment in Model 1 was performed using a filtration-based system, CellSieve platform (Creatv Micro Tech, Rockville, MD, USA), according to the manufacturer’s instructions. Circulating cell enrichment in Model 2 was performed using a negative immunomagnetic technique using the AutoMACS Pro cell separator and Mouse Cell Depletion Microbead cocktail kit (Miltenyi Biotec, Macquarie Park, NSW, Australia), as per the manufacturer’s instructions.

2.7. Immunocytochemistry

Circulating tumour cells and circulating stellate cells were fixed with 4% paraformaldehyde, permeabilised with 0.3% Triton x-100 (Sigma-Aldrich Pty Ltd., Castle Hill, NSW, Australia) and blocked with normal donkey serum (Jackson ImmunoResearch Laboratories Inc., West Grove, PA, USA). They were then incubated with rabbit polyclonal anti-CD45 primary antibody (1:200. Abcam plc, Cambridge, UK), and donkey anti-rabbit IgG conjugated with AlexaFluor 647 (1:800. Jackson ImmunoResearch Laboratoraties Inc.). After further blocking with normal mouse serum (Jackson ImmunoResearch Laboratories Inc.), the cells were incubated with monoclonal mouse anti-human α-smooth muscle actin (clone 1A4) antibody conjugated with Cyanine 3 (1:150. Sigma-Aldrich Pty Ltd.) and monoclonal mouse anti-human pan-cytokeratin (clone C-11) antibody conjugated with fluorescein isothiocyanate (FITC) (1:450. Sigma-Aldrich Pty Ltd.). This was mounted and nuclear stained with Fluoromount-G with DAPI (4′,6-diamidino-2-phenylindole) (Life Technologies Corporation, Tullamarine, VIC, Australia).

Since α-SMA is also expressed by other mesenchymal cells such as fibroblasts, the identity of true cPSCs was confirmed by chemically quenching selected filtration membranes and re-staining the membranes for PSC-specific markers, desmin and glial fibrillary acid protein (GFAP). Quenching of fluorescent signal (in preparation for re-staining) was performed chemically with sodium borohydride (1 mg/mL. Sigma-Aldrich Pty Ltd., Castle Hill, NSW, Australia) based on the method of Adams et al. [17]. Re-staining was then performed by 5% bovine serum albumin (Roche Diagnostics, Indianapolis, IN, USA), followed by incubation with monoclonal mouse anti-human glial fibrillary acidic protein (clone GA5, conjugated with Alexafluor 488) (1:100. Novus Biologicals USA, Centennial, CO, USA) and monoclonal rabbit anti-human desmin (clone Y66, conjugated with Alexafluor 594) (1:100, Abcam plc, Cambridge, UK). Mounting was again with Fluoromount-G with DAPI.

Cells were enumerated using a semi-automated technique using ImageJ version 1.52i (National Institutes of Health, Bethesda, MD, USA). The process is summarised in Figure A4.

2.8. Immunohistochemistry

Immunohistochemistry of paraffin-embedded sections of local recurrent tumour of the adjuvant treatment model (Model 1) was performed in standard fashion. Primary antibodies used included rabbit polyclonal anti-DCLK1 (1:800. Sigma-Aldrich Pty Ltd., Castle Hill, NSW, Australia), mouse monoclonal anti-human cytokeratin (clones AE1/AE3) (1:100. Agilent Technologies Pathology, Mulgrave, VIC, Australia), rabbit polyclonal anti-Ki67 (1:300. Abcam plc, Cambridge, UK) and rabbit polyclonal anti-CD31 (1:50. Abcam plc). This was followed by incubation with relevant secondary antibodies—Goat anti-rabbit immunoglobulin (Agilent Technologies Pathology) and goat anti-mouse immunoglobulin (Agilent Technologies Pathology)—and DAB visualisation. TUNEL (terminal deoxynucleotidyl transferase dUTP nick-end labelling) staining was performed using an in situ cell death detection kit (Roche Diagnostics, Indianapolis, IN, USA). Morphometric analysis and cell counting was performed using ImageJ version 1.52i.

2.9. Single-Cell RNA Sequencing

This was performed only for Model 2. Cell partitioning was performed using the 10x Genomics Chromium platform (10x Genomics, Pleasanton, CA, USA) according to the manufacturer’s instructions. Sequencing was performed at the Ramaciotti Centre for Genomics (UNSW) using the Illumina NovaSeq 6000 platform (Illumina, Inc., San Diego, CA, USA) with the SP 100 cycle flow cell with read lengths 28, 8 and 91 bp.

2.10. Bioinformatics

Generation of expression matrices was performed using the CellRanger pipeline, version 3.1.0 (10× Genomics). A combined reference genome was based on GRCh38 human assembly (Ensemble release 93), GRCm38 mouse assembly (release 93), modified firefly (Photinus pyralis) luciferase transgene, luc+, from the pGL3 Basic plasmid (Promega Corporation, Madison, WI, USA). The basic sequencing and alignment metrics are shown in

Table A1. Basic sequencing and alignment metrics for the four samples.

	Mouse Primary (MsPrim)	Mouse Secondary (MsSec)	Mouse Blood (MsBlood)	Cultured Cells (Cult)
Reference genome used for alignment	Human/luc++ Mouse	Human/luc++ Mouse	Human/luc++ Mouse	Human/luc+
Cell metrics
Estimated number of cells	1952	3850	6764	5131
Estimated human cells	260	84	1513	5131
Estimated mouse cells	1696	3767	5262	-
Fraction GEMs with >1 cell (%) (95%CI) *	2.3 (0.9−3.8)	1.3 (0.0−3.5)	0.8 (0.5−1.2)	-
Sequencing depth metrics
Mean reads per cell	131,113	68,694	35,043	48,214
Median genes per cell (human)	2449	664	94	3393
Median genes per cell (mouse)	1662	1752	1028	-
Sequence saturation	83.6%	72.5%	71.1%	27.9%
Sequencing metrics
Q30 bases in RNA read †	93.4%	93.6%	93.6%	94.1%
Alignment metrics
Reads mapped confidently to genome	89.3%	88.0%	84.4%	93.8%
Human	23.7%	8.7%	0.3%	93.8%
Mouse	65.6%	79.2%	84.1%	-
Reads mapped confidently to transcriptome	55.3%	56.1%	55.1%	69.9%
Human	14.1%	5.5%	0.2%	93.8%
Mouse	41.3%	50.5%	54.9%	-
Human cells left after filtration	100	40	75	2791

Notes: * This estimates the fraction of multiplets in the sample. It is calculated from the fraction of GEMs with significant number of reads mapped to both human and mouse genomes, which is then extrapolated to all GEMs. This is therefore not estimable when only the human reference genome was used (hence the absence of a value in the cultured cell sample). ^† Fraction of RNA read bases with Q-score ≥ 30, excluding very low quality/no-call (Q ≤ 2) bases from the denominator.

.

Processing of the expression matrix data was performed using Seurat (version 3.1.4) [18] and Monocle (version 2.14.0) [19] packages in R (version 3.6.2). Cell filtering, gene expression normalisation, scaling and centering were performed. Linear and non-linear dimensional reduction was performed and visualised by principal component analysis and UMAP/t-SNE (uniform manifold approximation and projection/t-stochastic neighbour embedding) plots, respectively.

Circulating pancreatic stellate cells were identified using three different methods, although the broad strategy involved first distinguishing human (xenografted) from mouse (native) cells, followed by distinguishing PSCs from cancer cells (See Section 3.2.1 or Appendix A.11.1). Single-cell trajectories were constructed using the reversed graph embedding technique implemented by Monocle 2 [19]. A more detailed description of the bioinformatics techniques used may be found in the Appendix A.

2.11. Non-Bioinformatics Statistical Analysis

Descriptive statistics for parametric, non-parametric and categorical variables were presented as mean ± SE, median (interquartile range, IQR) and n (%), respectively. Raw circulating rare cell counts were adjusted to account for the sample collection volume and reported as counts per 200 μL of sampled blood. Basic inferential tests, including ANOVA/t-test, Kruskall–Wallis test and Fisher’s exact/chi-squared tests, were first performed to explore the relationship between variables. If the global test demonstrated a near-significant result (p-value < 0.1), then post-hoc Tukey’s multiple comparison test was performed.

For regression modelling, treatments were considered as two independent treatment groups due to the 2 × 2 factorial pattern. First-order interactions were included in initial regression models, but these were dropped if the corresponding p-value exceeded 0.1 or if only one of the treatments was significant. Other independent variables were included in the model if these were found to be significant or near-significant (p < 0.15) on univariate analysis. Regression models used included multilevel mixed-effects models (to account for animal set level clustering and for longitudinal analysis of tumour burden), Poisson regression (with robust standard errors) (for CTCs and cPSC counts) and logistic regression analysis (for probability of progressive disease). Further details of these statistical models are described in the Appendix A.

All non-bioinformatics statistical analysis and data visualisation were performed using either GraphPad Prism version 8.3.0 for Windows (GraphPad Software LLC, San Diego, CA, USA) or Stata 15.1 IC for Windows (StataCorp LLC, College Station, TX, USA).

3. Results

The 64 mice that underwent resection with macroscopically clear margins were randomised to treatments. Two mice died early and received less than 1 week of drug treatment. As this study aims to evaluate the anti-tumoural effects of treatments, these two mice were excluded from further analysis. The baseline characteristics of these mice were not significantly different amongst the four treatment groups (

Table 1. Characteristics of the mice at the commencement of treatment.

Treatments	Ctrl	G	HiCi	G+HiCi	p-Value
	n (%) or mean ± SE
n (%)	14 (23%)	15 (24%)	18 (29%)	15 (24%)
Resection Characteristics:
Resected tumour vol (mm3)	156 ± 22	217 ± 31	175 ± 20	205 ± 19	0.269
Macroscopically clear margins	14 (100%)	15 (100%)	18 (100%)	15 (100%)
Extra-pancreatic involvement	3 (21%)	4 (27%)	8 (44%)	4 (27%)	0.560
Tumour Burden at Commencement:
Ventral radiant flux at treatment start (base 10 log units) (10x p/s)	5.8 ± 0.22	6.0 ± 0.27	5.9 ± 0.19	6.0 ± 0.31	0.925

).

3.1. Effects of HGF/c-Met Adjuvant Treatments

3.1.1. HGF/c-Met Inhibition Inhibited Tumour Progression Post-Resection

The trajectories of the tumour burden of each individual over time may be summarised using a spaghetti plot. Using the mixed-effects model for longitudinal analysis of tumour burden, we found that higher residual tumour burden post-resection significantly increased the rate of progression, while both G and HiCi treatments reduced the rate of progression of disease (Figure 1a;

Table A2. Model specification for Figure 1a: mixed-effects model for longitudinal analysis of tumour burden for all mice. (a) Multivariate model of the ventral radiant flux of mice. The coefficients represent the log10 value of the n-fold change associated with one unit change in the covariate. To make this model easier to interpret, the initial flux value was centred around 10⁶ p/s (that is, 10⁶ is 0, 10⁷ is 1, etc.). (b) As the model may be confusing for the reader, this table shows the predicted rates of progression under various conditions (according to this model). The rate of progression is in this table expressed as n-fold changes of ventral radiant flux per week.

(a) Multivariate model for disease progression, as measured by ventral radiant flux, over time.
	Coefficient (log 10 Scale)	95% CI	z	p-Value
Baseline slope:
Treatment weeks (per week)	0.130	0.101 to 0.159	8.79	< 0.001
Factors affecting slope:
Gemcitabine (G)	−0.050	−0.089 to −0.0113	−2.53	0.011
Rilotumumab + Compound A (HiCi)	−0.069	−0.106 to −0.031	−3.61	< 0.001
G × AR interaction	0.064	0.0102 to 0.117	2.33	0.02
Initial flux (per 10-fold change)	0.070	0.053 to 0.087	8.14	<0.001
Other model terms:
Initial flux (per 10-fold change)	0.98	0.82 to 1.1	12.3	< 0.001
Intercept	6.0	5.9 to 6.2	78.8	< 0.001
Random effects (intercept):
Variance	0.257	0.176 to 0.376
(b) Rate of progression of disease as predicted by the multivariate model above
	n-Fold Change per Week		95% CI
Baseline (initial flux 106 p/s, Ctrl)	1.35		1.26 to 1.44
Treatment groups:
Rilotumumab + Compound A (HiCi)	1.15		1.09 to 1.22
Gemcitabine alone (G)	1.20		1.13 to 1.28
G+AR	1.19		1.11 to 1.27
Initial tumour burden:
Flux of 10⁷ p/s, Ctrl group	1.59		1.46 to 1.72
Flux of 10⁸ p/s, Ctrl group	1.87		1.67 to 2.08

). However, G and HiCi treatments appeared to be non-additive.

As the above analysis may be influenced by the relative proportions of mice that were “cured” by surgical resection (i.e., no residual disease at the commencement of adjuvant treatment), treatment effects were analysed by two further methods. First, a logistic regression model of the probability of progressive disease (defined in the Appendix A) was constructed. This demonstrated that the odds of developing progressive disease was significantly reduced with HiCi treatment (OR 0.15 (95% CI 0.0250 to 0.86). p = 0.033) but not with G treatment (OR 0.80 (95%CI 0.172 to 3.68). p = 0.771) (Figure 1b;

Table A3. Model specification for Figure 1c: Logistic regression model predicting probability of progressive disease. Progressive disease was defined by a statistically and clinically significant increase in the disease burden (see detailed definition in the text). The odds ratio represents the change in the odds of progressive disease when the relevant variable is true (compared to the baseline case). The intercept for this model represents the baseline odds of progressive disease.

	Odds Ratio	95% CI	z	p-Value
Gemcitabine alone (G)	0.80	0.172 to 3.68	−0.29	0.771
Rilotumumab + Compound A (HiCi)	0.15	0.0250 to 0.86	−2.13	0.033
Initial flux (per 10-fold change from 106 p/s)	4.7	1.95 to 11.2	3.45	0.001
Baseline odds (Ctrl, initial flux of 106 p/s)	0.45	0.136 to 1.50	−1.30	0.195

).

Second, longitudinal analysis was performed on the subset of animals that harboured residual disease and thus would potentially respond to adjuvant therapy. Statistical modelling of this subset of mice confirmed treatment effects of HiCi but not G treatment (Figure 1c;

Table A4. Model specification for Figure 1d: mixed-effects model for longitudinal analysis of tumour burden for mice with recurrent disease. (a) The Table shows the statistical model of the effect of treat-ments and initial tumour burden on the rate of progression of disease in this subgroup. The co-efficient for treatment weeks represents the baseline slope, while the coefficients for the interac-tion terms with treatment weeks (labelled in the table as “factors affecting slope”) represent fac-tors which change the slope of the disease burden curve. The units for these coefficients are log10 n-fold change of radiant flux per week. The baseline condition represents mice with an initial flux of 10⁶ p/s in the control group, although the baseline for a given animal is assumed to vary (random intercepts model). (b) To assist the reader in interpreting this model, this Table shows the rate of progression of disease, predicted by the model, of mice under various conditions. The rate of change is expressed as an n-fold change in the radiant flux per week.

(a) Multivariate model for disease progression, as measured by ventral radiant flux, over time
	Coefficient	95% CI	z	p-Value
Baseline slope:
Treatment weeks (per week)	0.199	0.153 to 0.246	8.39	<0.001
Factors affecting slope:
Gemcitabine (G)	−0.051	−0.119 to 0.0179	−1.45	0.148
Rilotumumab + Compound A (HiCi)	−0.086	−0.152 to −0.019	−2.54	0.011
G×HiCi interaction	0.092	−0.0077 to 0.192	1.81	0.070
Initial flux (per 10-fold change)	0.0242	−0.0035 to 0.052	1.71	0.087
Other model terms:
Initial flux (per 10-fold change)	1.01	0.76 to 1.26	7.91	<0.001
Intercept (Baseline)	6.06	5.8 to 6.4	37.9	<0.001
Random effects (intercept):
Variance	0.403	0.227 to 0.72
(b) Rate of progression of disease as predicted by the multivariate model above
	n-Fold Change per Week Estimated by Model		95% CI
Baseline (mouse with 106 p/s initial radiant flux, control group)	1.58		1.42 to 1.76
Treatment groups:
Rilotumumab + Compound A (HiCi)	1.30		1.16 to 1.46
Gemcitabine alone (G)	1.41		1.25 to 1.59
G+HiCi	1.43		1.22 to 1.68

). The rates of progression of disease as predicted by this multivariate model are shown in Table A4b. When reported as mean tumour burden doubling times (as measured by radiant flux), the HiCi group had the longest doubling time (18.5 days), indicating substantially slowed progression compared to the Ctrl (10.6 days), G (14.1 days) and G+HiCi (13.6 days) groups. The spaghetti plot of this subgroup analysis is shown in Figure A6.

HiCi treatment did not significantly influence body weight changes, while G treatment led to progressive mild weight loss, possibly suggesting G, but not HiCi, exhibited treatment toxicity. There were no other physiological or behavioural side effects observed, beyond those associated with progressive cancer.

3.1.2. HGF/c-Met Inhibition Is Associated with Reduced Tumour Vascularity

Immunohistochemistry performed on local recurrent disease demonstrated that the cytokeratin positive cancer cell fraction was decreased in the gemcitabine treated tumours compared to controls (p = 0.032), while Ki67 positive cells per cancer cell area was significantly increased by gemcitabine treatment, suggesting G treatment may select out a subset of cells with higher proliferative potential (Figure 2a,b). Gemcitabine treatment was also found to increase vascularity of tumours (increased CD31 per unit area) compared to controls, while HiCi treatment was associated with reduced vascularity, suggesting an anti-angiogenic effect (Figure 2c). Neither apoptosis (TUNEL staining) nor stemness (DCLK1 staining) demonstrated statistically significant differences between treatment groups.

3.1.3. Circulating Tumour Cells as a Marker of Recurrence

A mean ± SE of 340 ± 6.7 μL of portal vein blood were collected from 54 animals for analysis of circulating rare cells. CTCs were identified as CK+, CD45− and aSMA− cells. Circulating PSCs were identified as CK−, CD45− and aSMA+ cells (Figure 3a). As noted under Methods, the identities of PSCs were confirmed by positive expression of the PSC-selective markers desmin and GFAP (Figure 3b). Interestingly, we observed that cPSCs could be subcategorised morphologically (by wide-field microscopy) into four groups: stellate (St), rounded with cytoplasmic extensions (CE), ovoid (Ov) and rounded and bland (RB) (Figure 3c). These morphological types differed in size and circularity, but not staining intensity for α-SMA (Figure A7). We also noted that while most cPSCs existed as individual cells, a number of cPSCs were found in heterotypic cell clusters containing both cPSCs and CTCs (Figure 3d). The significance of such clusters was not investigated.

All circulating rare cell (CTC/cPSC) counts reported herein represent the count per low-powered field (LPF) per 200 μL of portal vein blood. The median (IQR) number of CTCs detected was 1.5 (0.28 to 3.8) but ranged up to 45 (Figure 4a). Nine (17%) of the specimens did not yield any CTCs.

Animals with histological or bioluminescence evidence of recurrence were found to have higher CTC counts compared to animals without (median count 3.1 vs. 0.57; p = 0.0099) (Figure 4b). Receiver operator characteristic (ROC) curve analysis demonstrated an area under the curve (AUC) of 0.71 (95% CI 0.56 to 0.85), suggesting that CTC count was a fairly good test to determine the presence of tumour recurrence (Figure 4c).

3.1.4. Both G and HiCi Treatments Reduced CTC Counts

The median CTC counts were 4.2, 0.81, 2.0 and 0.76 for control, G, HiCi and G+HiCi groups, respectively, suggesting a possible treatment effect by both gemcitabine and AR. Poisson regression demonstrated that, after taking into account tumour burden, both G and HiCi treatment were indeed associated with lower numbers of CTCs (Figure 4d). High tumour burden (as represented by the last measured ventral radiant flux prior to necropsy), not surprisingly, was also associated with a higher CTC count.

3.1.5. HiCi Treatment Reduced Circulating Pancreatic Stellate Cells (cPSCs)

Putative cPSCs were identified using immunocytochemistry: α-SMA+ (α-smooth muscle actin, PSC activation marker), CK− (pan-cytokeratin, epithelial/cancer marker), CD45− (leucocyte marker) and DAPI (nuclear staining) (Figure 3a and Figure A4). The median (IQR) number of cPSCs detected was 0.13 (0 to 0.22) per LPF per 200 μL of portal vein blood but ranged up to 9.3 (Figure 4a). Seventeen (32%) of the specimens did not yield any cPSCs. Statistical modelling found that HiCi treatment was associated with reduced cPSC counts, whereas gemcitabine treatment had no significant effect (Figure 4e). Unlike CTC numbers, the cPSC count was not associated with the tumour burden, as measured by the last radiant flux measurement (Figure 4f). Tumour recurrence demonstrated a non-significant trend for increasing the cPSC count (Figure 4g).

3.2. Transcriptomic Characterisation of cPSCs

To further characterise cPSCs, single-cell transcriptomic analysis was performed (Model 2). Two mice with orthotopically implanted pancreatic cancer were allowed to progress for 8 weeks. The mice were euthanised and samples of the primary tumour (MsPrim), metastasis (MsSec) and blood (MsBlood) were collected and pooled. Cultured cancer-associated human PSCs and AsPC-1 cells (Cult)—which were the source of implanted cells—were used for comparison. Single-cell RNA sequencing was performed; the basic sequencing and alignment metrics are shown in Table A1.

3.2.1. Circulating PSC Identities Confirmed by ScRNA-seq

To identify circulating PSCs, we first distinguished human cells from mouse cells. The human cells thus identified could only have two identities, PSCs or PC cells (Figure 5a). The first step utilised relative unique molecular identifier (UMI) counts (Figure A8a). This was performed using relative UMI counts of mouse to human genes, with accuracy of this confirmed by cell classification based on gene expression of orthologous genes (Figure A8b) [20]. Mouse and human cells were further classified using canonical markers (Figure A9).

The number of human cells available for analysis in the four samples, after filtration for quality, were 100 (MsPrim), 40 (MsSec), 75 (MsBlood) and 2791 (Cult). From these cells, cPSCs and CTCs were identified using three different bioinformatics approaches (Figure A5). In the first two approaches, human cells from each of the four samples were integrated using an anchor-based method. Successful integration of gene expression data was achieved (Figure A10). The resulting UMAP (uniform manifold approximation and projection) plot indicated that these human cells clustered with the two cultured cell types (PSCs and PC cells) (Figure 5b). Those PSCs originating from the MsBlood sample were classified as cPSCs. Sixteen cPSCs were identified based on UMAP coordinates (Approach 1) and 14 cPSCs were identified using unsupervised clustering (Approach 2). A third approach, avoiding data integration altogether, was used to confirm these findings (Figure A5b). This utilised the differential gene expression between PSCs and AsPC-1 cultured cells (i.e., parental cells) to identify cells in mouse samples. This yielded 20 cPSCs and 58 CTCs (Figure 5c).

The high degree of agreement between the three independent approaches (Figure 5d) strongly supports the existence of cPSCs in our orthotopic mouse model of PC. The identification of putative mouse cPSCs provides further supportive evidence for their existence (Figure A11).

The pattern of differential gene expression between these two cell types supports their presumptive identities. Figure 5e shows the gene expression of the 62 CTCs and 16 cPSCs classified using the first approach. Circulating PSCs expressed higher levels of genes associated with extra-cellular matrix proteins (gene names in blue in the figure: COL1A1, SPARC, COL1A2, FN1, DCN) and myofibroblast cytoskeletal proteins (in brown in the figure: CALD1, TAGLN, TPM1, ACTA2, PALLD), whereas CTCs expressed epithelial and cancer markers (in purple in figure: CEACAM1, KRT19, LGALS4, MUC1). Importantly, the transgene for luciferase, only present in the implanted luciferase-tagged AsPC-1 cancer cells (luc+, in pink in the figure), was not expressed by any of the putative cPSCs, whereas a proportion of CTCs did express it at detectable levels. This non-detection of the luciferase transgene expression was statistically significantly different from the luciferase expression of all cancer cells across the samples (p = 0.0030). This strengthens the conclusion that these putative cPSCs are not cancer cells.

3.2.2. Potential Pathways Influencing cPSCs

To understand the role of PSCs and to identify potential pathways for future study, trajectory analysis was performed on PSCs across the mouse model samples (MsPrim, MsSec, MsBlood) using the Monocle package, as described further in the Appendix A. Trajectory analysis orders cells according to their relative gene expression by assuming the cells represent different states along a development pathway (trajectory). The inferred passage of time over this pathway is called pseudo-time. Figure 6a shows that the trajectory takes on a biologically plausible progression from a state dominated by primary cancer PSCs (upper left), then cPSCs (lower limb) and finally, metastasis (upper right). This is supported by the progressive peaks of cells from the primary tumour, blood and secondary tumour over pseudo-time (Figure 6b). A possible interpretation of this trajectory is shown in Figure 6c. Genes with significant changes in their expression over pseudo-time (FDR < 0.05) of this trajectory is shown in Figure A12. The gene ontology process, response to endogenous stimulus, was found to be enriched (FDR = 0.0345). This included genes IGFBP1, CLDN4 (involved in tight junction interactions) and PMEPA1 (involved in transforming growth factor β (TGF-β) receptor signalling pathways) (

Table A9. Enriched pathways amongst upregulated PSC genes over pseudo-time. This Table summarises the pathways identified as being over-represented in the protein network presented in Figure A12. The pathways explored included Gene Ontology (GO) biological processes, KEGG pathways and Reactome pathways. Abbreviations: Bkg, Background (number of genes in the pathway). FDR, false discovery rate.

Pathway Accession	Description	Genes	Bkg	FDR
GO Process
GO:0045669	Positive regulation of osteoblast differentiation	CEBPB, JUND	58	0.0020
GO:0009719	Response to endogenous stimulus	CLDN4, JUND, CEBPB, IGFBP1, PMEPA1	1353	0.0345
KEGG
hsa04657	IL-17 signalling pathway	CEBPB, JUND	92	0.0100
Reactome
HSA-8957275	Post-translational protein phosphorylation	CYR61, IGFBP1	106	0.0382
HSA-381426	Regulation of IGF transport and uptake by IGFBPs	CYR61, IGFBP1	123	0.0382

).

To explore the changes in PSCs depending on their cell fate at the major branch point where some cells become metastatic cells (i.e., localised within metastases) and some remain in the blood as circulating PSC, branch point analysis was performed. Figure 6d summarises the genes that were differentially expressed (FDR < 0.05) between cells passing down the two cell fates (cPSCs and metastatic PSCs). The centre of the heatmap represents the pre-branch state, while the left and right margins represent the two different cell fates. Changes in gene expression is represented by the heatmap and passage of pseudo-time represented by the distance from the centre. The two genes that were upregulated as cells passed into the metastatic cell fate were COL1A1 and MPC2, whereas 13 genes were upregulated as cells passed down the trajectory towards cPSCs (Figure 4c). STRING network analysis of these 13 upregulated genes related to cPSC cell fate demonstrates six interactions (p-Value = 0.00144) (Figure A13).

4. Discussion

This study is the first to demonstrate that the use of HGF/c-Met in the adjuvant setting resulted in the inhibition of PC progression post-resection. This paper also provides the first confirmation of the presence of circulating PSCs and describes their modulation by adjuvant therapy for PC.

4.1. HGF/c-Met Inhibition as Adjuvant Treatment

The major finding of this study was that concomitant inhibition of both the ligand and receptor of the HGF/c-Met pathway in an adjuvant setting significantly reduced the rate of progression of recurrence and allowed the mice to maintain a stable disease burden over the observation period. This occurred even in mice with a moderate degree of residual disease post-resection.

We previously demonstrated that HGF/c-Met pathway inhibition is effective at minimising and even eliminating metastasis in early as well as advanced models of pancreatic cancer [8,9,10]. Our novel data raise the interesting possibility that the mechanisms for this prevention of metastasis with HGF/c-MET inhibition involve reduction of circulating PSCs.

The therapeutic use of HGF/c-Met pathway inhibitors is not new. A number of phase III trials of selective HGF or c-Met inhibition have failed to demonstrate drug efficacy [21,22,23]. The development of rilotumumab, the HGF inhibitor used in this paper, has largely been abandoned since the unsuccessful RILOMET-1 trial [24]. The trial was terminated after the rate of fatal adverse events due to disease progression was observed to be higher in patients randomised to rilotumumab (in combination with ECX (epirubicin, cisplatin, capecitabine) chemotherapy) group. Importantly, however, the frequency of serious adverse events as well as fatal adverse events, not due to disease progression, was not significantly different between the treatment groups [24].

However, there are a number of reasons why these negative human trials should not preclude the use of the proposed two-pronged HGF/c-Met inhibition approach for the treatment of human PC. (i) It must be recognised that none of these previous clinical trials were performed on patients with PC. PC is well known to overexpress HGF as well as c-Met, both in patients and in cell lines [25,26,27], and this overexpression expression is known to be associated with poorer survival [28]. (ii) The treatment approach used in these previous trials differed from the treatment employed in this model. The RILOMET-1 trial inhibited the HGF/c-Met pathway by rilotumumab only. Rilotumumab alone has been shown, in our group’s work, to be less effective than combination HGF and c-Met inhibition [9,10]. (iii) The clinical trials in non-small cell lung cancer have utilised HGF or c-Met inhibition exclusively to inhibit this pathway (generally in combination with an epidermal growth factor receptor inhibitor). Dual inhibition of both the ligand and receptor of the HGF/c-Met pathway has the theoretical advantage of overcoming incomplete neutralisation of HGF. This has not yet been the subject of clinical trials in humans and there is no evidence of emerging clinical trials using this approach—no trials utilising a two-pronged approach to HGF/c-Met inhibition are currently registered with ClinicalTrials.gov.

Taken together, the findings of the current study and our previous publications strongly suggest efficacy for HGF/c-MET inhibition ± chemotherapy in early, advanced and adjuvant preclinical settings, thus presenting a strong case for clinical trials of this treatment strategy in patients with PC.

4.2. Circulating PSCs—A Novel cellular Intermediary for PC Metastasis?

The second major finding of this paper is the discovery of cPSCs. This has the potential of providing a new perspective in our understanding of the metastatic progression of pancreatic cancer. PSCs are known to play an important role in the development of PC [29]. While there have been some reports suggesting a “protective” effect of PSCs, the weight of available evidence points to a facilitatory function for these cells in cancer progression [30,31,32]. The pro-metastatic effect of PSCs, as demonstrated in co-injection mouse models, suggests a role beyond just local primary tumour effects [14,33]. It is also in these co-injection models, using human cancer cells and human PSCs, that the first evidence of possible PSC migration was found, as evidenced by co-localisation of human nuclear antigen and α-SMA in metastatic deposits [14]. Subsequently, the definitive evidence for the capacity of PSCs to migrate was reported by our group through the use of a gender mismatch study [12].

The existence of these PSCs at distant sites must imply the existence of a route of dissemination. Possible routes include the classical pathways of spread of malignancy (haematogenous, lymphatic, transcoelomic). The disparate sites of metastasis, especially beyond the peritoneal cavity (for instance, lung), suggest the former two, thus first hinting at the existence of cPSCs.

In this study, circulating PSCs were identified using a range of well-established PSC-selective markers and by confirming that these cells did not exhibit cancer cell or leucocyte markers. Strong evidence that our findings are not “chance” observations is provided by the following observations: (i) cPSCs could be reliably identified in two separate mouse models of PC; (ii) cPSCs were identified using both immunohistochemistry and single-cell RNA sequencing; (iii) cPSC numbers were found to vary in a way that is consistent with their postulated pathophysiology; (iv) cPSCs were modulated by drugs which strongly inhibit metastatic disease (HGF/c-Met inhibition [8,9,10]); and (v) cPSCs tend to be more commonly found in mice with recurrent disease.

An alternative explanation of the observed cPSCs is the presence of CTCs which have undergone EMT (epithelial to mesenchymal transition). Such EMT CTCs, reported in a variety of cancers, are characterised by the loss of epithelial markers (such as EpCAM) and have been known to evade capture by classical EpCAM-based CTC isolation systems [34,35,36]. However, in the context of PC, EMT CTCs are less well-described and there is evidence, in both humans and mouse models, to suggest that the expression of CK in these cells may remain intact, despite the gain of mesenchymal marker expression [37,38]. Furthermore, immunocytochemistry performed on cPSCs demonstrated that they stained for PSC-specific markers such as GFAP. Finally, in our scRNA-seq data, we found that none of the putative cPSCs identified expressed the luciferase transgene, thus strongly suggesting that these cells are distinct from the implanted luciferase-transfected cancer cells.

Characterisation of these rare circulating cells is challenging. We have presented single-cell transcriptomic characterisation data for cPSCs, which allowed the identification of potentially important cPSC genes involved in the metastatic process. Some of the most promising genes worthy of further investigations include insulin-like growth factor-binding protein 1 (IGFBP1), claudins and hyaluronan mediated motility receptor (HMMR).

IGFBP are a family of proteins which bind IGFs (insulin-like growth factors) to regulate their bioavailability and half-life. Insulin-like growth factor 1 (IGF-1) signalling has been known to influence the growth of PC cells in vitro, promote PC cell and PSC migration in vitro and is associated with poorer survival in patients with PC [39,40,41,42,43]. More recently, it has also been found to be an important part of tumour−stromal cross-talk [44].

Unlike IGFBP1, the role of claudins in PSC physiology has not previously been described. These form a component of tight junctions, classically associated with epithelial rather than mesenchymal cells. However, it should be noted that mesenchymal cells are not completely devoid of tight junctions or tight junction-like intercellular connections. For instance, junctional adhesion molecules (JAMs), components of tight junctions, are found to be expressed in fibroblasts and claudins have been found to be expressed in a variety of sarcomas [45,46]. Thus, PSC-expressed claudins may potentially play a role in the pathophysiology of PC [47]. Furthermore, this raises the possibility that intercellular junctions, such as tight junctions, may be another medium through which PSC−PC cross-talk can occur [48].

Hyaluronan mediated motility receptor (HMMR) is important in mesenchymal cell migration in wound repair, including fibroblast migration and differentiation into myofibroblasts [49]. In PC, strong expression of this protein by immunohistochemistry is associated with poorer survival in PC [50]. Surprisingly, its role in PSCs has not been well studied. However, HMMR (in combination with CD44) has been hypothesised to play an important role in cancer-associated fibroblast migration [51]. Most interestingly, fibroblasts transfected with HMMR injected subcutaneously in mice generated tumours which spontaneously metastasised to the lungs [52]. It is therefore conceivable that this may be an important factor in the generation (migration and intravasation) of cPSCs.

It is acknowledged that the transcriptomic analysis of cPSC physiology reported in this study will require protein- and functional-level validation. However, our findings provide novel insights to better understand how cPSCs support metastasis.

5. Implications and Conclusions

In summary, this paper describes the use of an effective adjuvant mouse model of pancreatic cancer developed in our laboratory [15] to demonstrate, for the first time, that combined HGF/c-Met inhibition is effective in retarding the rate of progression of disease after surgical resection of the primary tumour. There is now a strong argument to test this treatment strategy in a randomised controlled trial for PC patients.

We also describe, again for the first time, the identification and characterisation of circulating PSCs in the setting of pancreatic cancer. The trend for higher cPSC counts to be associated with recurrent disease after tumour resection, and their modulation with HGF/c-Met inhibition suggests that these cells play a significant role in the metastatic process. These cells highlight a potential gap in the current state of understanding of the pathogenesis of PC metastasis and may have wider implications for understanding the pathogenesis of cancer metastasis in general, given that PSCs in PC play a role analogous to that of cancer-associated fibroblasts in many other cancers [53].