Type I collagen deletion in αSMA+ myofibroblasts enhances immune suppression and accelerates progression of pancreatic cancer

Yang Chen; Jiha Kim; Sujuan Yang; Huamin Wang; Chang-Jiun Wu; Hikaru Sugimoto; Valerie S LeBleu; Raghu Kalluri

doi:10.1016/j.ccell.2021.02.007

. Author manuscript; available in PMC: 2022 Apr 12.

Published in final edited form as: Cancer Cell. 2021 Mar 4;39(4):548–565.e6. doi: 10.1016/j.ccell.2021.02.007

Type I collagen deletion in αSMA⁺ myofibroblasts enhances immune suppression and accelerates progression of pancreatic cancer

Yang Chen ¹, Jiha Kim ¹, Sujuan Yang ¹, Huamin Wang ², Chang-Jiun Wu ³, Hikaru Sugimoto ¹, Valerie S LeBleu ¹, Raghu Kalluri ^1,⁴

PMCID: PMC8423173 NIHMSID: NIHMS1675833 PMID: 33667385

SUMMARY

Stromal desmoplastic reaction in pancreatic ductal adenocarcinoma (PDAC) involves significant accumulation of type I collagen (Col1). However, the precise molecular and mechanistic contribution of Col1 in PDAC progression remains unknown. Activated pancreatic stellate cells (PSCs)/αSMA⁺ myofibroblasts are major contributors of Col1 in the PDAC stroma. We employ a dual-recombinase genetic mouse model of spontaneous PDAC to delete Col1 specifically in myofibroblasts. This results in significant reduction of total stromal Col1 content and accelerates the emergence of PanINs and PDAC, decreasing overall survival. Mechanistic studies suggest that Col1 deletion leads to Cxcl5 upregulation in cancer cells via SOX9. Increase in Cxcl5 is associated with recruitment of myeloid-derived suppressor cells (MDSCs), and suppression of CD8⁺ T cells, which can be attenuated by combined suppression of CXCR2 and CCR2 with restrained PDAC progression. Taken together, our results unravel the fundamental role of myofibroblast-derived Col1 in regulating tumor immunity and restraining PDAC progression.

Graphical Abstract

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eTOC blurb

Chen et al. identify that the deletion of type I collagen (Col1) in αSMA⁺ myofibroblasts using dual-recombinase mouse models decreases stromal Col1 content and accelerates progression of pancreatic cancer. Col1 deletion promotes the recruitment of CD206⁺F4/80⁺ARG1⁺ myeloid-derived suppressor cells, which can be attenuated by combined inhibition of CXCR2 and CCR2.

INTRODUCTION

Type I collagen (Col1), a fibrillar collagen, is the most abundant protein in the human body and most abundantly present in bones, tendons and skin. The basic functional unit of Col1 is a heterotrimer consisting of two α1 chains and one α2 chain that come together to form a triple helical structure. Each α-chain polypeptide is synthesized in the cytosol and combines with two other α-chains to generate a triple-helical type I procollagen with N-terminal and C-terminal propeptides. Subsequently, the procollagen molecule is secreted into the extracellular space where the N-terminal and C-terminal propeptides are cleaved by propeptidases, generating the basic functional unit of Col1. The Col1 triple helical rod-like molecules interact with each other to form fibrils and undergo further crosslinking to form large bundles of fibers.

During embryogenesis, many organs express Col1 to likely facilitate cellular migration, differentiation and structural compartmentalization (Hay, 1981). Systemic deletion of Col1a1 gene (resulting in complete absence of type I collagen) leads to embryonic lethality (Lohler et al., 1984). In pathogenic conditions, such as organ fibrosis and cancer, Col1 accumulates robustly in the affected tissue. Col1 associated with tumor tissue is known to generate a biophysically ‘stiff’ microenvironment around the cancer cells facilitating cellular migration via conducive Col1 fiber ‘tracks’, and facilitate abnormal cellular interactions to induce proliferation and survival of cancer cells (Armstrong et al., 2004; Bachem et al., 2005; DuFort et al., 2016; Egeblad et al., 2010; Fujita et al., 2009; Haber et al., 1999; Hanahan and Weinberg, 2011; Levental et al., 2009).

In this regard, Col1 is a major component of the tumor stroma/microenvironment associated with pancreatic ductal adenocarcinoma (PDAC) (Mollenhauer et al., 1987; Tian et al., 2019). Alpha-smooth muscle actin (αSMA)⁺ myofibroblasts associated with PDAC are speculated to significantly contribute to the production of Col1 and proposed to impede drug delivery to cancer cells, and restrict T cell infiltration (Apte et al., 2012; Beatty et al., 2011; Ene-Obong et al., 2013; Provenzano et al., 2012; Whittle and Hingorani, 2019). Recent studies suggest that stromal fibroblasts in PDAC might exhibit context dependent functions, imparting tumor promoting and restraining influences (Biffi et al., 2019; Kalluri, 2016; Laklai et al., 2016; Lee et al., 2014; Mueller and Fusenig, 2004; Neesse et al., 2015; Ohlund et al., 2014; Ohlund et al., 2017; Olive et al., 2009; Ozdemir et al., 2014; Provenzano et al., 2012; Rhim et al., 2014; Sugimoto et al., 2006). Nevertheless, the precise function of type I collagen in the initiation and progression of PDAC remains unknown.

Current genetically engineered mouse models (GEMMs) of PDAC, such as the KPC mice (LSL-Kras^G12D/+;Trp53^R172H/+ or Trp53^loxP/loxP;Pdx1-Cre) and the PKT mice (LSL-Kras^G12D/+;Tgfbr2^loxP/loxP;P48-Cre) have been valuable in elucidating the molecular and pathological mechanisms (Chytil et al., 2002; Hingorani et al., 2005). Nevertheless, an inability to genetically manipulate genes in stromal compartments remains a limitation of these genetic models. To overcome this issue, we employed mice with dual-recombinase system (DRS), integrating the capacity to manipulate genes using both the Cre-loxP and Flp-FRT (Schonhuber et al., 2014). The dual-recombinase system allows for functional deletion of genes of interest specifically in the stromal compartment. Here, we deleted Col1 produced by specific cell types in the background of oncogenic Kras-induced spontaneous PDAC to specifically address the function of type I collagen in disease initiation and progression.

RESULTS

Dual-recombinase system mouse models develop spontaneous pancreatic cancer with opportunity for stromal cell specific gene manipulations

A dual-recombinase system mouse model for pancreatic cancer utilizes the Flippase-FRT (Flp-FRT) system to induce oncogenic Kras expression and p53 loss in cells with Pdx1-lineage (FSF-Kras^G12D/+;Trp53^frt/frt;Pdx1-Flp) (Schonhuber et al., 2014). This Flp-FRT-based model (KPPF mice) develops pancreatic intraepithelial neoplasias (PanINs) and pancreatic ductal adenocarcinoma (PDAC) in an almost identical manner to the Cre-loxP-based LSL-Kras^G12D/+;Trp53^loxP/loxP;Pdx1-Cre model (KPPC) mice (Figures S1A and S1B) (Schonhuber et al., 2014).

The KPPF and KPPC mice similarly exhibit prominent deposition of type I collagen (Col1) associated with PanINs and PDAC (Figures S1A and S1B). Employing this mouse model harboring both Cre-loxP and Flp-FRT systems and a lineage-tracing Dual-reporter, Rosa26-CAG-loxP-frt-Stop-frt-FireflyLuc-EGFP-loxP-RenillaLuc-tdTomato (Chen et al., 2018) (hereafter referred to as R26^Dual), we generated the KPPF;αSMA-Cre;R26^Dual mice (Figure 1A). In the PDAC tissues of these mice, cancer cells with Pdx1-lineage are associated with EGFP expression, while αSMA⁺ myofibroblasts exhibit tdTomato expression (Figure 1A). This dual-recombinase lineage-tracing strategy confirms the genetic recombination events mediated by Pdx1-Flp and αSMA-Cre, respectively.

Figure 1. — (A) Genetic strategy for KPPF;αSMA-Cre;R26^Dual mice, with representative images of primary tumor cryosections shown for intrinsic EGFP (in cancer cells) and tdTomato (in αSMA-lineage myofibroblasts) signals due to R26^Dual tracer. Scale bar: 20 αm.

(**B-D**) sc-RNA-seq analysis of unfractionated live cell mixture from pancreatic tumors of KPPF mice (B). Expression profile of *Col1a1*, *Acta2*, *S100a4*, and *Krt8* was shown in UMAP plot (C) and violin plot (D) with normalized expression levels of indicated genes. DCs, dendritic cells; pDCs, plasmacytoid dendritic cells; LECs, lymphatic endothelial cells.

(E) The cancer-associated fibroblast (CAF) cluster was classified into indicated subpopulations: myofibroblast (myCAF), inflammatory fibroblast (iCAF), and antigen-presenting fibroblast (apCAF). Violin plots are shown with normalized expression levels of indicated genes.

(F) The correlation between the gene expression levels of *COL1A1* and *ACTA2* (or *S100A4*) in TCGA PDAC patients (n = 179). Pearson’s correlation test was used.

See also Figure S1; Tables S1–S3.

We examined the expression of Col1, cytokeratin (CK)-19, and αSMA during PanIN and PDAC stages (Figures S1C and S1D). Normal pancreatic tissue revealed minimal/negligible presence of CK19, αSMA, or Col1. Upon the emergence of acinar-to-ductal metaplasia (ADM) or early PanIN lesions, αSMA level significantly elevated associated with emergence of myofibroblasts and pancreatic epithelial abnormalities (Figure S1C). The myofibroblast accumulation was followed by a peak level of Col1 deposition at subsequent PanIN stages (Figure S1C). As disease progressed to PDAC in these mice, the total amount of Col1 deposition further increased (Figure S1D).

Single-cell RNA-sequencing identifies fibroblasts as the predominant producers of type I collagen in KPPF mice with PDAC

Recent studies used single-cell RNA-sequencing analysis (sc-RNA-seq) on mouse KPC tumors and human PDAC tumors to identify two major populations of cancer-associated fibroblasts (CAFs) (Biffi et al., 2019; Elyada et al., 2019). The ECM-producing CAFs were termed as myofibroblasts (myCAFs) and inflammatory associated CAFs as the iCAFs (along with a smaller subset of antigen-presenting CAFs as the apCAFs). Col1 production by activated PSCs/myofibroblasts was observed in both mouse and human tumors (Bernard et al., 2019; Biffi et al., 2019; Elyada et al., 2019; Hosein et al., 2019; Peng et al., 2019). Therefore, overwhelming evidence collected by many research groups shows that activated PSCs express αSMA and produce Col1 associated with PDAC (Biffi et al., 2019; Elyada et al., 2019; Feig et al., 2012; Korc, 2007; Mahadevan and Von Hoff, 2007; Ohlund et al., 2017; Whittle and Hingorani, 2019).

To further confirm fibroblasts as the major contributors of type I collagen in the KPPF (FSF-Kras^G12D/+;Trp53^frt/frt;Pdx1-Flp) mice, we performed similar sc-RNA-seq analysis of unfractionated live cell mixture from the pancreatic tumors of KPPF mice (Figures 1B–1D, S1E, and S1F). Representation of specific gene expression profiles in UMAP plots revealed that single cell isolation predominantly yielded immune cells (myeloid and lymphoid lineages), with smaller clusters (representations) of cancer cells, endothelial cells, and fibroblasts (Figures 1B and S1E; Table S1). The αSMA⁺ myofibroblasts were confirmed as dominant ECM-producing cells (Figures 1C, 1D, and S1F), as reported by several groups earlier (Apte et al., 1998; Apte et al., 1999; Elyada et al., 2019; Haber et al., 1999; Hosein et al., 2019; Mews et al., 2002). Another widely used marker for cancer-associated fibroblasts and other stromal cells, fibroblast specific protein 1 (S100a4, Fsp1), was expressed by only a subset of ECM-producing fibroblasts (Figure 1C) but also by many immune cell clusters. High transcriptional level of Col1a1 (as well as many other ECM genes) was observed in αSMA⁺ (‘myCAF’ cluster) myofibroblasts, and to a lesser extent in the cytokeratin-expressing cancer cells or Fsp1-expressing cells (Figures 1C–1E; Table S2). Consistent with recent studies, we can also sub-cluster CAFs into myofibroblasts (myCAFs), iCAFs, and apCAFs (Figure 1E; Table S3). We observed S100a4 (Fsp1) expression in apCAFs, in contrast to the Acta2 (αSMA), which was predominantly present in myCAFs (Figure 1E).

We further confirm that gene expression level of COL1A1 in TCGA human PDAC samples significantly correlates with ACTA2 (αSMA), but not S100A4 (Fsp1) (Figure 1F). These observations, along with published reports, suggest that myofibroblasts are major producers of Col1 in PDAC.

Specific deletion of Col1 in αSMA⁺ myofibroblasts accelerates PDAC with diminished overall survival

Guided by the sc-RNA-seq data and to test the function of myofibroblast-produced Col1 in pancreatic cancer, we next deleted Col1 in myofibroblasts employing the dual-recombinase FSF-Kras^G12D/+;Trp53^frt/frt;Pdx1-Flp;αSMA-Cre;Col1a1^loxP/loxP (KPPF;Col1^smaKO) mice (Figure 2A). Col1 deficiency was achieved by Col1a1 conditional deletion because Col1α1 is essential for Col1 triple helical trimer formation while Col1α2 alone cannot form Col1 trimers (Lees et al., 1997; Sharma et al., 20l7). Mice with Col1 deletion in αSMA⁺ myofibroblasts (αSMA-Cre;Col1a1^loxP/loxP) exhibit normal tissue throughout the body and normal size litters and life span, without any overt phenotype. In the context of PDAC, Col1 deletion in αSMA⁺ myofibroblasts led to decreased overall levels of fibrillar Col1, desmoplasia associated markers, and biophysical stiffness of PDAC tissue, as examined using staining on serial tissue sections (Figure 2B), circularly polarized light microscopy (Figure 2C), and atomic force microscopy (Figure 2D). Specific Col1 deletion was confirmed in myofibroblasts sorted from tumors of KPPF;Col1^smaKO mice (Figure 2E).

Figure 2. — (A) Genetic strategy to delete type I collagen α1 chain (*Col1a1*) specifically in αSMA⁺ cell population in the context of pancreatic cancer using the KPPF;Col1^smaKO mice. KPPF littermates served as control mice.

(B) Serial sections of pancreatic tumors from age-matching KPPF and KPPF;Col1^smaKO mice, stained with H&E, Picrosirius Red, Col1, Masson’s trichrome stain (MTS), and αSMA. Quantification of % positive area was based on at least 4 mice/group. Scale bar: 100 μm.

(C) Representative circularly polarized light microscopy images of KPPF and KPPF;Col1^smaKO tumor sections stained with Pircrosirius Red (n = 4/group). Scale bar: 100 μm.

(D) Representative atomic force microscopic (AFM) images of cryosections of KPPF and KPPF;Col1^smaKO tumors (n = 3/group).

(E) Primary cancer cells and fibroblasts were sorted from KPPF or KPPF;Col1^smaKO tumors. PCR product detection confirmed the specific deletion of *Col1a1* in fibroblasts sorted from KPPF;Col1^smaKO mice. The expression level of *Col1a1* was examined by qRT-PCR.

Data are represented as mean ± SEM. * P < 0.05, *** P < 0.001, **** P < 0.0001, NS: not significant, Student’s t test was used. See also Figure S2.

Col1 deletion in αSMA⁺ myofibroblasts significantly decreased the overall survival of mice (Figure 3A) and accelerated PDAC progression (Figure 3B). At the endpoint, KPPF;Col1^smaKO mice exhibited higher tumor weight but comparable body weight when compared to control KPPF mice (Figure 3C). Decreased Col1 level was also observed at the endpoint PDAC in the KPPF;Col1^smaKO tumors compared to KPPF tumors, associated with more undifferentiated and invasive PDAC tissue phenotype (Figure S2A). These results are consistent with previous studies showing that lower stroma content (such as αSMA or collagen) may implicate a worse prognosis of human PDAC (Bever et al., 2015; Erkan et al., 2008; Moffitt et al., 2015; Ozdemir et al., 2014; Wang et al., 2013). Col1 deletion in myofibroblasts did not impact the blood vessel density, pericyte coverage, or vascular integrity (as determined by interstitial albumin accumulation) in the tumors of KPPF;Col1^smaKO mice (Figures S2B and S2C). This seems reasonable as the vascular basement membranes associated with capillaries and blood vessels are predominantly composed of type IV collagen (Col4) but not Col1 (Figure S2D).

Next, we generated FSF-Kras^G12D/+;Pdx1-Flp;αSMA-Cre;Col1a1^loxP/loxP (KF;Col1^smaKO) mice harboring only the oncogenic Kras mutation that progress to PDAC at a much slower rate to carefully evaluate the impact of the loss of myofibroblast-derived Col1 on the emergence of early stage ADM and PanINs (Figure 3D). Age matched (6-month-old) KF;Col1^smaKO mice exhibited significantly more ADM and PanIN lesions when compared to FSF-Kras^G12D/+;Pdx1-Flp (KF) littermate control (αSMA-Cre-negative) mice (Figure 3E). Taken together, these results further suggest a tumor-restraining function of myofibroblast-derived Col1 in PDAC microenvironment.

Bulk RNA sequencing (RNA-seq) analysis of KPPF;Col1^smaKO tumors and KPPF tumors was examined by gene set enrichment analysis (GSEA), revealing upregulated proliferation-related E2F-target pathway and downregulated inflammation response pathway in KPPF;Col1^smaKO tumors (Figures 3F, 3G, and S2E; Table S4). KPPF;Col1^smaKO tumors revealed elevated cell proliferation when compared with KPPF tumors, as examined by Ki67 staining of cancer cells (Figure S2F). These results were consistent with the more advanced disease phenotype after Col1 deletion in myofibroblasts in these tumors. In addition, we also validated the decreased Col1a1 level by bulk RNA-seq from KPPF;Col1^smaKO tumors, and Col1 deletion in myofibroblasts did not lead to compensatory upregulation of other fibrillar collagen genes (Figures 3H and S2G).

Specific deletion of Col1 in Fsp1⁺ stromal cells does not impact PDAC progression

Many studies have suggested that Fsp1⁺ cells in the stroma also represent cancer-associated fibroblasts and likely other stromal/immune cells (Strutz et al., 1995; von Ahrens et al., 2017). Our sc-RNA-seq analysis demonstrates that Fsp1⁺ subpopulation of fibroblasts (particularly apCAFs) is partially associated with production of Col1 transcript (Figure 1E), albeit many Fsp1⁺ cells are also part of immune cell clusters with minimal Col1 transcript. Nevertheless, we first generated KPPF;Fsp1-Cre;R26^Dual mice harboring the Fsp1-Cre transgene, in which cancer cells with Pdx1-lineage express EGFP and cells with Fsp1-lineage express tdTomato (Figure S3A). Fsp1⁺ cells were present throughout the interstitium of PDAC stroma, in contrast to the localization of αSMA⁺ myofibroblasts (Figure S3B). The minimal co-localization between Fsp1⁺ cells and myofibroblasts was further confirmed by immunofluorescence staining using anti-αSMA antibody and anti-Fsp1 antibody (Figure S3C).

Next, we generated the FSF-Kras^G12D/+;Trp53^frt/frt;Pdx1-Flp;Fsp1-Cre;Col1a1^loxP/loxP (KPPF;Col1^fspKO) mice (Figure S3D). KPPF;Col1^fspKO mice exhibited similar disease progression, Col1 deposition, and overall survival as compared with KPPF mice (Figures S3E and S3F). We also isolated primary Fsp1⁺ fibroblasts from KPPF;Col1^fspKO mice and confirmed the loss of Col1a1 transcript (Figure S3G). These results suggest that while some Fsp1⁺ fibroblasts may produce Col1, their contribution to overall Col1 content in PDAC stroma is not rate limiting for PDAC progression and/or potentially compensated by other cells.

Col1 deletion in αSMA⁺ myofibroblasts impacts immune response

To examine whether Col1 deletion in myofibroblasts impacts composition of other tumor stromal cells, we further evaluated the sc-RNA-seq analysis of unfractionated live cell mixture from KPPF;Col1^smaKO tumors in comparison with KPPF tumors (Figures 4A and 4B). When compared with KPPF tumors, KPPF;Col1^smaKO tumors revealed largely unaltered cell number and cluster distribution of several cell populations, such as fibroblasts, endothelial cells, granulocytes, and cancer cells (Figures 4C, S4, and S5A). Col1 deletion in myofibroblasts did not significantly alter the composition or the gene expression profile of recently identified CAF subpopulations including myCAFs, iCAFs, and apCAFs (Figures S5B and S5C). Consistent results were also observed by 3D distribution plot of fibroblast clusters employing fibroblast marker gene sets (Figure S5D).

Figure 4. — (**A-D**) sc-RNA-seq analysis of unfractionated live cell mixture from pancreatic tumors of KPPF and KPPF;Col1^smaKO mice. Functional clusters of cells were shown in (A). The distribution of defined cell clusters comparing KPPF tumors and KPPF;Col1^smaKO tumors was shown in UMAP plots (B) and pie chart plots (C). (D) Normalized expression levels of marker genes of Myeloid-2/MDSCs, T cells, and B cells within KPPF and KPPF;Col1^smaKO tumors shown in UMAP plot.

(E) Representative immunohistochemistry staining images and quantification of CD206 (MRC1) for MDSCs, CD3 for T cells, and CD45R for B cells on KPPF and KPPF;Col1^smaKO tumors (n = 5/group). Scale bar: 100 μm.

(F) The percentages of CD11b⁺CD206⁺ myeloid cells, CD3⁺ T cells, and CD19⁺ B cells among CD45⁺ immune cells in KPPF and KPPF;Col1^smaKO tumors (n = 4/group), as examined by flow cytometry.

Data are represented as mean ± SEM. * P < 0.05, ** P < 0.01, *** P < 0.001, Student’s t test was used. See also Figures S4–S7; Tables S5 **and** S6.

We next examined whether Col1 deletion in myofibroblasts could alter the phenotype of cancer cells. Cancer cells were classified based on the genes related to the classical and basal-like subtypes (Hayashi et al., 2020; Moffitt et al., 2015) of PDAC (Figure S6A; Table S5). KPPF tumors and KPPF;Col1^smaKO tumors revealed similar cancer cell clustering and gene expression profiles of classical and basal-like subtypes (Figures S6B and S6C). The bulk RNA-seq data of KPPF and KPPF;Col1^smaKO tumors also did not reveal any obvious alteration of gene expression profiles related to the classical and basal-like subtypes (Figure S6D).

In addition, the TCGA PDAC cases were ranked according to high-to-low COL1A1 expression levels, and then plotted for the expression levels of signature genes related to basal-like or classical subtype (Figure S6E). No trend for cancer subtype alteration was observed between Col1-high and Col1-low patient groups. Taken together, we did not observe cancer subtype (classical and basal-like) alteration directly correlated with Col1 levels in mouse and human PDAC.

In contrast, KPPF;Col1^smaKO tumors revealed significantly altered profile of myeloid cells, T cells, and B cells (Figures 4B, 4C, and S7A). Myeloid cells separated into two different clusters based on gene expression profiles, namely Myeloid-1 and Myeloid-2 (Figures 4B–D and S7B; Table S6). The Myeloid-2 cluster shares molecular features with tumor-associated macrophages and related myeloid-derived suppressor cells (MDSCs) (Biswas and Mantovani, 2010). KPPF;Col1^smaKO tumors exhibited increased number of Myeloid-2 cells and decreased T/B lymphocytes (Figures 4B–4D), which correlated with the accelerated progression of KPPF;Col1^smaKO tumors. Such alterations in myeloid cell and T/B cell content increased the myeloid/T cell ratio (especially the Myeloid-2/T cell ratio) in KPPF;Col1^smaKO tumors (Figure 4C). Specifically, the increased Myeloid-2 cells in KPPF;Col1^smaKO tumors were CD206(Mrc1)⁺ F4/80(Adgre1)⁺ Arginase-1(Arg1)₊ MDSCs (Figures 4D, S7A, and S7B) (Cheng et al., 2020; Ma et al., 2011).

The increased MDSCs and decreased T/B lymphocytes by Col1 deletion were further confirmed by immunohistochemistry staining of CD206, CD3, and CD45R respectively, on both late-stage PDAC sections from KPPF;Col1^smaKO mice (Figure 4E), and early-stage PanIN/PDAC sections from KF;Col1^smaKO mice (Figure S7C). Consistent results were also observed in the flow cytometry analysis of immune cell composition (increased CD206⁺ myeloid cells and decreased T/B cells) in KPPF;Col1^smaKO tumors when compared to KPPF tumors (Figure 4F).

Col1 deletion in αSMA⁺ myofibroblasts is associated with recruitment and polarization of CD206⁺F4/80⁺Arg1⁺ MDSCs

Our previous results demonstrated an increased number of CD206(Mrc1)⁺ F4/80(Adgre1)⁺ Arginase-1(Arg1)⁺ MDSCs (Myeloid-2 cluster) in KPPF;Col1^smaKO tumors (Figures 4B–4F). The sc-RNA-seq data also revealed an enrichment of MDSCs in KPPF;Col1^smaKO tumors associated with increased expression of Mrc1 (CD206/MRC1), Arg1 (argninase-1), Adgre1 (F4/80), Ms4a8a, and Chil3 (Figures 5A–5C and S7D). These MDSCs have been shown to suppress T and B lymphocyte function via arginase-1 (Biswas and Mantovani, 2010; Bronte et al., 2003; Bronte and Zanovello, 2005; Liu et al., 2003). Trem2, recently shown to be expressed in immunosuppressive myeloid cells (Katzenelenbogen et al., 2020; Molgora et al., 2020), was also enriched in Myeloid-2 cluster (Figures 5B, 5C, and S7B). CD206⁺F4/80⁺Arg1⁺ MDSCs specifically expressed high level of interleukin-18/IL-18 (encoded by Il18, Figures 5A–5C and S7B), a potent cytokine that contributes to an immunosuppressive tumor microenvironment by augmenting MDSC activity (Lim et al., 2014; Nakamura et al., 2018). Higher IL18 expression level also correlates with significantly poor overall survival of PDAC patients (Figure 5D). In addition, CD206⁺F4/80⁺Arg1⁺ MDSCs robustly express CCL2 (monocyte chemoattractant protein-1/MCP-1, encoded by Ccl2), a critical chemoattractant of MDSCs (Figures 5B and 5E). CCL2 released by Myeloid-2 (CD206⁺F4/80⁺Arg1⁺ MDSC) subpopulation could then act on the CCR2-expressing Myeloid-1 subpopulation (Figures 5B and 5E), potentially resulting in enhanced recruitment and polarization of Myeloid-2-MDSCs from Myeloid-1-MDSCs via a mechanism of positive feedback loop (Biswas and Mantovani, 2010; Roca et al., 2009).

Figure 5. — (**A-C**) Differentially expressed genes related to Myeloid-2/MDSC function between KPPF and KPPF;Col1^smaKO tumors shown in UMAP plot and violin plot showing the normalized expression levels of indicated genes (A). Heat maps of signature genes of Myeloid-1 and Myeloid-2 clusters, comparing KPPF and KPPF;Col1^smaKO tumors, were shown in (B). Representative upregulated genes in Myeloid-2 cluster were listed in (C).

(D) Overall survival of TCGA PDAC patients correlated with *IL18* expression level. Log-rank (Mantel-Cox) test was used.

(E) Normalized expression levels of *Ccl2* and *Ccr2* genes in KPPF and KPPF;Col1^smaKO tumors shown in UMAP plot.

See also Figure S7; Table S6.

Col1 deletion in αSMA⁺ myofibroblasts alters chemokine release by cancer cells and contributes to MDSC recruitment

Next, we examined myeloid cell recruiting cytokines/chemokines that might be responsible for the increased CD206⁺F4/80⁺Arg1⁺ MDSCs in KPPF;Col1^smaKO tumors. RNA-seq data revealed significantly increased expression of Cxcl5 (encoding CXCL5) in KPPF;Col1^smaKO tumors (Figure 6A). CXCL5 has been extensively studied as a potent MDSC chemoattractant (Cheng et al., 2020; Steele et al., 2016; Wang et al., 2016), and shown as transcriptionally regulated by SOX9 (Bezzi et al., 2018). sc-RNA-seq data revealed that cancer cells are the prevalent producers of CXCL5 in PDAC (Figure S7E). Higher expression level of human CXCL6 (the human homolog of mouse Cxcl5) correlates with poor overall survival of pancreatic adenocarcinoma patients (Figure 6B). RNA-seq data revealed an upregulation of Cxcl5 along with a number of other SOX9-targeted genes in KPPF;Col1^smaKO tumors (Figure 6C). Increased percentage of SOX9⁺ cancer cells was confirmed by immunohistochemistry in both late-stage PDAC in KPPF;Col1^smaKO mice and early-stage PanIN/PDAC in KF;Col1^smaKO mice (Figure 6D). SOX9 expression reveals reverse correlation with COL1A1, based on the RNA-seq data from TCGA PDAC cohort (Figure 6E).

Figure 6. — (A) Expression levels of genes encoding indicated chemokines related to myeloid cell recruitment, based on bulk RNA-seq data of KPPF and KPPF;Col1^smaKO tumors.

(B) Overall survival of TCGA PDAC patients correlated with *CXCL6* (the human homolog of mouse *Cxcl5*) expression level. Log-rank (Mantel-Cox) test was used.

(C) Heat map of SOX9-regulated genes, including *Cxcl5*, based on RNA-seq of KPPF and KPPF;Col1^smaKO tumors. Expression alterations of these genes were also shown with Log2-fold change and P values (Wald Test).

(D) Representative images of SOX9 immunohistochemistry staining and positivity quantification in KPPF and KPPF;Col1^smaKO tumors, or KF and KF;Col1^smaKO tumors (n = 5/group). Scale bar: 100 μm.

(E) Correlation between the gene expression levels of *COL1A1* and *SOX9* in TCGA PDAC patients (n = 179). Pearson’s correlation test was used.

(F) The expression levels of *Sox9* and *Cxcl5* examined by qRT-PCR in primary KPPF;Col1^smaKO cancer cells treated with purified Col1 (n = 3 biological replicates). Cancer cell morphology and proliferation were also shown.

(G) Survival of KPPF (n = 7) and KPPF;Col1^smaKO (n = 9) mice treated with CXCR2 inhibitor SB-225002 and CCR2 inhibitor RS-504393, as compared to the survival of untreated KPPF (n = 20) and KPPF;Col1^smaKO (n = 18) mice from Figure 3A. Log-rank (Mantel-Cox) test was used.

(H) The percentages of CD11b⁺CD206⁺ myeloid cells, CD3⁺ T cells, and CD19⁺ B cells among CD45⁺ immune cells in KPPF and KPPF;Col1^smaKO tumors (n = 4/group) after CXCR2 inhibitor and CCR2 inhibitor treatment, as compared to untreated KPPF and KPPF;Col1^smaKO tumors from Figure 4F.

Data are represented as mean ± SEM. * P < 0.05, ** P < 0.01, *** P < 0.001, Student’s t test was used unless otherwise indicated. See also Figure S7.

Given previous studies showing regulation of SOX9 activity by extracellular matrix content (Allen et al., 2012; Kim et al., 2015), we next investigated whether the increased SOX9 could be associated with the decreased stromal Col1 content in the KPPF;Col1^smaKO tumors. Primary PDAC cells were established from KPPF;Col1^smaKO tumors and treated with purified Col1 at different concentrations. Col1 treatment dose-dependently decreased the expression levels of Sox9 and Cxcl5, along with inhibition of the proliferation of PDAC cells, suggesting a direct impact of Col1 levels on Sox9 and Cxcl5 expression in cancer cells (Figure 6F). These results demonstrate that the decreased Col1 content in KPPF;Col1^smaKO tumors could upregulate Sox9 and Cxcl5, potentially contributing to the recruitment of MDSCs.

To investigate the functional contribution of CCL2 (Figures 5B and 5E) and CXCL5 (Figures 6A–6C) to the recruitment of MDSC and acceleration of KPPF;Col1^smaKO tumor progression, we treated KPPF and KPPF;Col1^smaKO mice with inhibitors against CXCL5-CXCR2 axis (CXCR2 inhibitor, SB-225002) and CCL2-CCR2 axis (CCR2 inhibitor, RS-504393). The inhibitor combination of CXCL5-CXCR2 and CCL2-CCR2 axes significantly improved KPPF;Col1^smaKO mouse survival, bringing it back to the similar levels observed in the control KPPF mice. This result underscores the essential role of enriched CD206⁺Arg1⁺CCL2⁺ tumor-promoting MDSCs in the accelerated tumor progression of KPPF;Col1^smaKO mice (Figure 6G). The survival of KPPF mice was minimally improved by the treatment of inhibitors, in comparison with untreated KPPF mice (Figure 6G). In addition, the inhibitor combination treatment also largely reversed the Col1-deletion-induced immune profile alterations (including increased CD11b⁺CD206⁺ MDSCs, and decreased T/B cells) in KPPF;Col1^smaKO tumors (Figure 6H).

Col1 deletion in αSMA⁺ myofibroblasts is associated with impaired recruitment and activation of T and B lymphocytes

Guided by our previous data showing decreased T/B lymphocytes in KPPF;Col1^smaKO tumors (Figures 4B–4F), we further investigated the differentially expressed T/B lymphocyte-related genes between KPPF;Col1^smaKO and KPPF tumors (Figures 7A, 7B, and S8A). T and B lymphocytes in KPPF;Col1^smaKO tumors revealed not only decreased total cell numbers (Figure 4C), but also significantly suppressed expression of multiple key proteins associated with lymphocyte homing and activation (Figures 7A and 7B), such as L-selectin (CD62L, encoded by Sell) and CD69 (encoded by Cd69). T-cell-specific activation markers, Cd5 (Biancone et al., 1996) and B-cell-specific activation marker gene Pou2af1 (Gstaiger et al., 1995; Strubin et al., 1995) were also significantly downregulated in T cells and B cells, respectively (Figures 7A and 7B). In contrast, T and B cells in KPPF;Col1^smaKO tumors revealed significantly increased expression of PIK3IP1 (encoded by Pik3ip1), an inhibitory marker of lymphocytes (Chen et al., 2019; Uche et al., 2018).

Figure 7. — (**A and B**) Differentially expressed T/B lymphocyte related genes between KPPF and KPPF;Col1^smaKO tumors shown in UMAP plot (A) and violin plot (B) showing the normalized expression levels of indicated genes.

(C) Significantly downregulated signature genes related to T cells and B cells in KPPF;Col1^smaKO tumors, based on bulk RNA-seq data of KPPF and KPPF;Col1^smaKO tumors, were shown in heat map or listed with Log2-fold change and P values (Wald Test).

(D) Heat map of genes encoding extracellular matrix (ECM) components based on RNA-seq of KPPF and KPPF;Col1^smaKO tumors.

(E) GSEA revealing significantly downregulated interferon-γ/α response pathways in KPPF;Col1^smaKO tumors compared to KPPF tumors. NES, normalized enrichment score. FDR, false discovery rate (q value).

(F) Heat map of interferon (IFN) regulatory factor genes and IFN-induced genes based on RNA-seq of KPPF and KPPF;Col1^smaKO tumors.

See also Figure S8.

Additionally, bulk RNA-seq data confirmed significantly downregulated expression of T/B lymphocyte associated genes, such as Sell, Cd3g, Cd3e, Cd19, Il2rg, Cd5, and Cd69, in the KPPF;Col1^smaKO tumors (Figure 7C). The RNA-seq data also revealed the downregulation of Cxcl16 (CXCL16, a T cell chemoattractant) (Matsumura et al., 2008; Slaney et al., 2014) in KPPF;Col1^smaKO tumors (Figure S8B).

Next, we examined the genes related to lymphocyte recruitment in KPPF and KPPF;Col1^smaKO tumors. Sell, Madcam1, and Cd34 (encoding L-selectin, MADCAM-1, and CD34) were downregulated in KPPF;Col1^smaKO tumors (Figures 7D and S8C). In addition, the RNA-seq data demonstrated that type I interferon (IFN) pathway and IFN related genes were significantly suppressed in KPPF;Col1^smaKO tumors (Figures 7E and 7F), which could also contribute to suppressed T/B lymphocyte response (Fuertes et al., 2011; Muthalagu et al., 2020). Taken together, our data suggest that the hampered recruitment and activation of T/B cells in KPPF;Col1^smaKO tumors could be due to the suppression of key molecules (such as L-selectin, CD69, and CXCL16) and type I IFN pathway.

Lower Col1 level correlates with less T cells in mouse and human PDAC

Consistent with our previous results, the decreased T cells in KPPF;Col1^smaKO tumors was also validated by TSA multispectral imaging (Figure 8A). Next we sought to investigate the correlation between stromal Col1 level and T cell recruitment in human PDAC samples (n = 108) by further analyzing TSA multispectral imaging quantitative results (Carstens et al., 2017). We observed a positive correlation between Col1 level with the several T cell subpopulations (Figure 8B), including CD8⁺ T cells, CD4⁺ T cells, and CD4⁺ FoxP3⁻ T effector cells (Teffs), but not CD4⁺ FoxP3⁺ regulatory T cells (Tregs). Next, the patient cohort was stratified into Col1-high and Col1-low groups. The Col1-high group was significantly associated with more CD8⁺ T cells, CD4⁺ T cells, and CD4⁺ FoxP3⁻ Teffs, but not CD4⁺ FoxP3⁺ regulatory Tregs, when compared to Col1-low group (Figures 8B and S8D; Table S7). In addition, Col1-high group also revealed a trend towards less lymph node metastases and better long-term survival (Figures 8C and 8D). Col1-high group and Col1-low group exhibited similar tumor stage distribution (as indicated by AJCC stage scores) and similar tumor differentiation status (Figure 8E). Also, the average Col1 area levels were similar among various groups classified by either disease stage or differentiation status (Figure 8E). Consistent results were observed in TCGA PDAC dataset, showing similar tumor stage distribution between the COL1A1-high and COL1A1-low groups, as well as similar COL1A1 expression levels in various disease stage groups (Figure 8F). These results suggest that the difference in Col1 level (or T cell presence) between Col1-high group and Col1-low group is not a reflection of potential difference in tumor stage or tumor differentiation status.

Taken together, these results demonstrate that Col1 deficiency in PDAC increases immunosuppressive CD206⁺ F4/80⁺ arginase-1⁺ MDSCs, associated with decreased T/B lymphocytes and accelerated tumor progression (Figure S8E).

DISCUSSION

For several decades, type I collagen (Col1) has been reported by many investigators as a significant component of the deposited extracellular matrix (ECM) in PDAC stroma (Imamura et al., 1995; Mollenhauer et al., 1987; Tian et al., 2019). Employing different concentrations of Col1 to embed cancer cells, it has been suggested that ECM rigidity/stiffness plays a role in accelerating cancer progression (Bonnans et al., 2014; Laklai et al., 2016; Miroshnikova et al., 2016; Mouw et al., 2014; Northcott et al., 2018; Paszek et al., 2005). Crosslinking capacity of Col1 has also been shown to play a role in accelerating cancer progression, and suppression of crosslinking enzymes such as Lysl oxidases inhibits this property of crosslinked Col1 (Levental et al., 2009; Pickup et al., 2013). Despite many such studies, direct assessment of the specific function of Col1 in PDAC remained undetermined.

This study employed complex mouse models to unravel the functional role of Col1 in the context of spontaneous PDAC. The study strategy exploited in vivo manipulation of Col1 to demonstrate that loss of Col1 in αSMA⁺ myofibroblasts leads to acceleration of PDAC, without compensatory increase in the production of other collagens in the tumor microenvironment. Our results further suggest a tumor-restraining function of myofibroblast-derived Col1 in PDAC microenvironment. Our results also validate previous findings that myofibroblasts can function to restrain PDAC progression (Bissell and Hines, 2011; Lee et al., 2014; Ozdemir et al., 2014; Rhim et al., 2014). The current study lends itself to an appreciation that Col1 produced by myofibroblasts contributes significantly to total Col1 content of PDAC, and its deletion leads to ~50% drop in PDAC stromal Col1 content. Moreover, such loss leads to acceleration of PDAC, in association with softer tumor tissue. Complete deletion of Col1 was not achieved in this mouse model and therefore it is possible that the complete deletion of Col1 might result in a different PDAC phenotype. In this regard, Hynes group recently showed that the total extracellular matrix content of tumors is a reflection of specific contributions by stromal fibroblasts and cancer cells (Tian et al., 2019; Tian et al., 2020). In this study, we show a global downregulation of genes encoding ECM proteins produced by fibroblasts in the KPPF;Col1^smaKO tumors, with upregulation of genes encoding cancer cell-derived ECM proteins.

Col1 deletion in myofibroblasts is associated with increased number of MDSCs, which specifically express high levels of CD206, F4/80, argninase-1, CCL2, and interleukin-18, and likely contribute to the immunosuppressive microenvironment in PDAC. These MDSCs can further suppress T and B lymphocyte function via arginase-1 and CD206. Mechanistic studies indicate that the increased CD206⁺F4/80⁺Arg1⁺ MDSC recruitment could result from the decreased stromal Col1 content, which subsequently induces the SOX9-regulated expression of Cxcl5 in cancer cells. Our study clearly suggests that Col1 made by fibroblasts can functionally impact immune profile of PDAC and suppression of CD206⁺F4/80⁺Arg1⁺ MDSC recruitment can restrain PDAC progression.

In summary, our results unravel the fundamental role of myofibroblast-derived Co1l in regulating immune response and restraining PDAC progression. This study provides insights into the complex stromal biology of PDAC microenvironment and potential therapeutic interventions.

STAR METHODS

RESOURCE AVAILABILITY

Lead Contact

Further information and requests for resources, reagents and samples should be directed to and will be fulfilled by the Lead Contact, Raghu Kalluri (rkalluri@mdanderson.org).

Materials Availability

Materials and reagents used in this study are listed in the Key Resources Table. Reagents generated in our laboratory in this study or previous studies are available upon request.

KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Goat Albumin	Bethyl	Cat# A90-134, RRID:AB_67120
Mouse αSMA	DAKO	Cat# M0851, RRID:AB_2223500
Rabbit CD206 (MRC1)	Abcam	Cat# ab64693, RRID:AB_1523910
Rabbit CD45R	Abcam	Cat# ab64100, RRID:AB_1140036
Rabbit CK19	Abcam	Cat# ab52625, RRID:AB_2281020
Goat Collagen I	SouthernBiotech	Cat# 1310-01, RRID:AB_2753206
Rabbit Ki67	Abcam	Cat# ab15580, RRID:AB_443209
Rabbit SOX9	Abcam	Cat# ab185966, RRID:AB_2728660
Rabbit NG2	Chemicon/Millipore	Cat# AB5320, RRID:AB_11213678
Rat CD31	Dianova	Cat# DIA-310, RRID:AB_2631039
Goat Collagen IV	Abcam	Cat# ab6585, RRID:AB_305583
Rabbit FSP1	DAKO	Cat# A5114, RRID:AB_2335679
Rabbit CD3	Abcam	Cat# ab16669, RRID:AB_443425
Rabbit CD4	Cell Signaling	Cat# 25229, RRID:AB_2798898
Rabbit CD8a	Cell Signaling	Cat# 98941, RRID:AB_2756376
Rat Cytokeratin 8	DSHB	Cat# TROMA-I, RRID:AB_531826
Rat PDGFRα(CD140a)-PE	BioLegend	Cat# 135905, RRID:AB_1953268
Rat EpCAM(CD326)-AF488	BioLegend	Cat# 118210, RRID:AB_1134099
Rat CD45-Pacific Blue	BioLegend	Cat# 103126, RRID:AB_493535
Rat CD3-AF700	eBioscience	Cat# 56-0032-82, RRID:AB_529507
Rat CD11b-BV711	BD	Cat# 563168, RRID:AB_2716860
Rat CD206-FITC	BioLegend	Cat# 141703, RRID:AB_10900988
Rat CD19-APC	eBioscience	Cat# 17-0193-82, RRID:AB_1659676
Bacterial and Virus Strains

Biological Samples
Tissue microarray sections of human PDAC	MDACC	IRB LAB05-0854
Chemicals, Peptides, and Recombinant Proteins
RS 504393	Tocris	Cat# 2517/10
SB 225002	Tocris	Cat# 2725/10
Collagenase IV	Gibco	Cat# 17104019
Dispase II	Gibco	Cat# 17105041
Liberase	Roche	Cat# 5401020001
DNase I	Roche	Cat# 10104159001
Type I collagen solution from rat tail	Sigma-Aldrich	Cat# C3867
Critical Commercial Assays
Direct-zol RNA Kit	Zymo Research	Cat# R2050
Reverse Transcription Kit	Applied Biosystems	Cat# 4368814
SYBR Green Master Mix	Applied Biosystems	Cat# 4367659
Cell Counting Kit-8	Abcam	Cat# ab228554
Live-Dead-eFluor780	eBioscience	Cat# 65-0865-14
Foxp3/Transcription Factor Staining Buffer Set	eBioscience	Cat# 00-5523-00
Gomori’s Trichrome Stain Kit	Leica Biosystems	Cat# 38016SS2
Chromium Single Cell 3’ Reagent Kits (v2)	10x Genomics	Cat# PN-120237
ABC-Kit	Vector	Cat# PK-6100
Stable DAB	Invitrogen	Cat# 750118
Direct Red 80	Sigma-Aldrich	Cat# 365548
TruSeq Stranded mRNA Sample Prep Kit	Illumina	Cat# 20020594
Deposited Data
KPPF and KPPF;Col1smaKO tumor RNA-sequencing	This paper	GEO: GSE131500
Single-cell RNA-sequencing	This paper	GEO: GSE166298
TCGA pancreatic adenocarcinoma cohort survival and gene expression data (GDAC Firehose PAAD)	Broad Institute	http://gdac.broadinstitute.org/runs/stddata__2016_01_28/data/PAAD/20160128/
Experimental Models: Cell Lines
Primary mouse KPPF;Col1smaKO cancer cells	This paper	N/A
Experimental Models: Organisms/Strains
Mouse: FSF-Kras^G12D/+;Pdx1-Flp	Schonhuber et al., 2014	N/A
Mouse: Trp53^frt/+	Lee et al., 2012	N/A
Mouse: αSMA-Cre	LeBleu et al., 2013	N/A
Mouse: Fsp1-Cre	Xue et al., 2003; Bhowmick et al., 2004	N/A
Mouse: Rosa26-CAG-loxP-frt-Stop-frt-FireflyLuc-EGFP-loxP-RenillaLuc-tdTomato	Chen et al., 2018	N/A
Mouse: Col1a1^{tm1a(EUCOMM)Wtsi}	EuMMCR	https://www.mousephenotype.org/data/alleles/MGI:88467/tm1a(EUCOMM)Wtsi
Mouse: Col1a1^loxP/loxP	This study	N/A
Oligonucleotides
Mouse Gapdh qRT-PCR F AGGTCGGTGTGAACGGATTTG	This paper	N/A
Mouse Gapdh qRT-PCR R TGTAGACCATGTAGTTGAGGTCA	This paper	N/A
Mouse Col1a1 qRT-PCR F GCTCCTCTTAGGGGCCACT	This paper	N/A
Mouse Col1a1 qRT-PCR R CCACGTCTCACCATTGGGG	This paper	N/A
Mouse Cxcl5 qRT-PCR F GTTCCATCTCGCCATTCATGC	This paper	N/A
Mouse Cxcl5 qRT-PCR R GCGGCTATGACTGAGGAAGG	This paper	N/A
Mouse Sox9 qRT-PCR F GAGCCGGATCTGAAGAGGGA	This paper	N/A
Mouse Sox9 qRT-PCR R GCTTGACGTGTGGCTTGTTC	This paper	N/A
Recombinant DNA

Software and Algorithms
Flowjo v10.7.1	Flowjo, L.L.C.	RRID: SCR_008520
Prism v8.0.0	GraphPad Software Inc.	RRID: SCR_002798
Fiji v2.0.0	ImageJ	RRID: SCR_002285
cBioportal v2.2.0	MSK Center for Mol Onc	https://www.cbioportal.org/
Seurat R package (3.5.3)	Satija et al., 2015	https://satijalab.org/seurat/
DESeq2	Anders and Huber, 2010	https://bioconductor.org/packages/release/bioc/html/DESeq2.html
STAR aligner	Dobin et al., 2013	https://github.com/alexdobin/STAR
Gene Set Enrichment Analysis (GSEA)	Broad Institute	http://software.broadinstitute.org/gsea/index.jsp
Other

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Data and Code Availability

The accession numbers for the data reported in this paper are GEO: GSE131500 and GSE166298. The survival and gene expression data of TCGA pancreatic adenocarcinoma cohort were based on the GDAC Firehose PAAD dataset (previously known as the TCGA Provisional dataset).

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Mice

FSF-Kras^G12D/+ (Schonhuber et al., 2014), Pdx1-Flp (Schonhuber et al., 2014), Trp53^frt/+ (Lee et al., 2012), αSMA-Cre (LeBleu et al., 2013), and Fsp1-Cre (Bhowmick et al., 2004; Xue et al., 2003) mouse strains were previously documented. Col1a1^loxP/loxP mouse strain (with loxP-flanked exons 2-5) was established in the Genetically Engineered Mouse Facility at MD Anderson Cancer Center (MDACC) using the Col1a1^{tm1a(EUCOMM)Wtsi} embryonic stem cells that were obtained from the European Mouse Mutant Cell Repository (EuMMCR). Rosa26-CAG-loxP-frt-Stop-frt-FireflyLuc-EGFP-loxP-RenillaLuc-tdTomato (referred to as R26^Dual) mouse strain contains the R26^Dual dual-fluorescence reporter allele, which allows the EGFP expression under the control of Pdx1-Flp transgene, or the tdTomato expression under the control of αSMA-Cre and Fsp1-Cre transgenes (Chen et al., 2018). Characterization of genotyping and disease phenotypes for the FSF-Kras^G12D/+;Pdx1-Flp (referred to as KF) or FSF-Kras^G12D/+;Trp53^frt/frt;Pdx1-Flp (referred to as KPPF) mice was performed as previously described by Saur and colleagues (Schonhuber et al., 2014). We crossed the KF and KPPF mice with the αSMA-Cre, Fsp1-Cre, Col1a1^loxP/loxP, or R26^Dual mouse strains, resulting in the generation of the KF;αSMA-Cre;Col1a1^loxP/loxP (referred to as KF;Col1^smaKO), KPPF;αSMA-Cre;Col1a1^loxP/loxP (referred to as KPPF;Col1^smaKO), KPPF;Fsp1-Cre;Col1a1^loxP/loxP (referred to as KPPF;Col1^fspKO) mice. These mice allow the Col1a1 deletion in either αSMA⁺ myofibroblasts or Fsp1⁺ cell population in the context of spontaneous PDAC. The aforementioned experimental mice with desired genotypes were monitored and analyzed with no randomization or blinding. Both female and male mice with desired genotype(s) for PDAC were used for experimental mice. CXCR2 inhibitor and CCR2 inhibitor were used for the treatment of KPPF and KPPF;Col1^smaKO mice. CXCR2 inhibitor SB-225002 (Tocris) was intraperitoneally administered at 10 mg/kg daily. CCR2 inhibitor RS-504393 (Tocris) was subcutaneously administered at 5 mg/kg twice daily. All mice were housed under standard housing conditions at MDACC animal facilities, and all animal procedures were reviewed and approved by the MDACC Institutional Animal Care and Use Committee.

Cell culture

Isolation of primary PDAC cells from mouse pancreatic tumors was performed as previously described with minor modifications (Chen et al., 2018; Zheng et al., 2015). Fresh tumor tissues were minced with sterilized lancets, digested with collagenase IV (17104019, Gibco, 4 mg/mL)/dispase II (17105041, Gibco, 4 mg/mL)/RPMI at 37 °C for 0.5 hour, filtered by 70 μm cell strainers to generate single cell suspension and resuspended in RPMI/20%FBS. Cells were cultured in RPMI medium containing 20% FBS and 1% penicillin-streptomycin-amphotericin B (PSA) antibiotic mixture. Primary KPPF;Col1^smaKO cancer cells established from KPPF;Col1^smaKO tumors were treated with purified Col1 (Sigma-Aldrich C3867) at indicated concentrations for 48 hours (n = 3 biological replicates). The morphology and proliferation of cancer cells under Col1 treatment were examined by phase-contrast microscopy and CCK-8 cell viability assay, respectively.

Patient cohort for PDAC tissue microarray

All human PDAC sections were fixed on tissue microarray slides, which contain three representative 1 mm cores from each patient (two representative cores of tumor and one core of matched benign pancreatic tissue). The tissue microarrays were constructed from FFPE blocks of archived PDAC specimens using a previously described methodology (Wang et al., 2002). This study was approved by the Institutional Review Board of MDACC (IRB LAB05-0854). Informed consent was obtained from all patients. Those patients received no neoadjuvant therapy. Cases and clinical information were retrieved from the surgical pathology files of the Department of Pathology, MDACC (Table S7).

METHOD DETAILS

Histology and immunohistochemistry

For paraffin-fixed samples, mouse tissues were fixed in 10% neutral buffered formalin, embedded in paraffin, and sectioned at 5 μm thickness. Sections were processed for hematoxylin and eosin (H&E) staining. Masson’s trichrome stain (MTS) was conducted using Gomori’s Trichrome Stain Kit (38016SS2, Leica Biosystems). Picrosirius red staining for collagen was conducted using 0.1% Picrosirius Red (Direct Red80; Sigma-Aldrich) and counterstained with Weigert’s haematoxylin. Images were captured with a Leica DM 1000 LED microscope and an MC120 HD Microscope Camera with LAS V4.4 Software (Leica). To quantify Masson’s Trichrome staining, the hue value for strong positive blue pixel (collagen) was set as 0.6 with a hue width of 0.1. To quantify Picrosirius Red staining, the hue value for strong positive red pixel (collagen) was set at 0.95 with a hue width of 0.1. To quantify immunohistochemical staining, the strong positive brown pixels were quantified using hue value of 0.1 and hue width of 0.4. Circularly polarized light microscopy of tissue sections stained with Picrosirius red was conducted according to previous studies (Levental et al., 2009; Ozdemir et al., 2014), using an Axio Observer.Z1 Fluorescence Microscope equipped with Rotatable Polarizer D and Analyzer Slider D/A Rot 360 (Zeiss). Formalin-fixed, paraffin-embedded sections were processed for immunohistochemical staining as previously documented (Chen et al., 2018; Zheng et al., 2015). Sections were incubated with primary antibodies: albumin (A90-134A-8, Bethyl, 1:200), αSMA (M0851, Dako, 1:100), CD3 (ab16669, Abcam, 1:200), CD206 (ab64693, Abcam, 1:200), CD45R (ab64100, Abcam, 1:200), CK19 (ab52625, Abcam, 1:200), type I collagen (Col1; ab34710, Abcam, 1:200), Ki67 (ab15580, Abcam, 1:200), SOX9 (ab185966, Abcam, 1:200), followed by biotinylated secondary antibodies, and streptavidin HRP (Biocare Medical). For all immunolabeling experiments, sections were developed by DAB and counterstained with hematoxylin. All the aforementioned procedures for staining, imaging, and quantification were performed blinded to the sample identity and phenotype.

Immunofluorescence

Tumor tissues from the mice with R26^Dual lineage tracing were fixed in 4% paraformaldehyde overnight at 4 °C and equilibrated in 30% sucrose overnight at 4 °C. Tissues were then embedded in O.C.T. compound (TissueTek) and processed for 5-μm-thick cryosections. Sections were blocked for 1 h with 4% cold water fish gelatin (Aurion) and immunostained overnight at 4 °C with αSMA antibody (M0851, Dako, 1:100) or Fsp1 antibody (A5114, Dako, 1:100), followed by AlexaFluor647 secondary antibodies (Invitrogen). Staining for αSMA was performed with Mouse-on-Mouse (M.O.M.) kit (Vector Laboratories) following the manufacturer’s instructions. Slides were then mounted with DAPI-containing Vectashield Mounting Medium (Vector Laboratories), visualized under the LSM800 confocal laser scanning microscope, and analyzed with ZEN software (Zeiss). Immunohistochemical staining of formalin-fixed, paraffin-embedded sections were conducted using the following primary antibodies: CD31 (DIA310, Dianova, 1:100), Collagen IV (AB6586, Abcam, 1:100), and NG2 (AB5320, Chemicon/Millipore, 1:150), followed by fluorescence-labeled secondary antibodies (Jackson ImmunoResearch).

Atomic force microscopy (AFM)

Fresh tissues were embedded and frozen in O.C.T. compound (TissueTek) Cryosections were obtained at a thickness of 50 μm and attached to poly-L-lysine-coated glass microscope slides (Sigma-Aldrich). Cryosections were rinsed carefully with water to remove the O.C.T. compound and air-dried prior to imaging. Images were obtained at the AFM core facility of UT Health Science Center, using a BioScope II atomic force microscope (Bruker Corporation) integrated with a TE2000 inverted optical microscope (Nikon). Quantification of the average Young’s Elastic Modulus (MPa) value was based on 3 mice for each group. Each mouse was examined for 5 to 8 randomly selected tumor areas across the tissue section, while each area was probed for at least 3 randomly selected points.

qRT-PCR

Total RNA was extracted from cells using Direct-zol RNA Kit (Zymo Research), processed for cDNA synthesis using the Reverse Transcription Kit (Applied Biosystems), and subjected to the qRT-PCR using SYBR Green Master Mix (Applied Biosystems). The expression level of indicated genes was normalized to the expression of Gapdh as housekeeping gene. The qRT-PCR primers are listed in Key Resources Table.

Total mRNA sequencing

For mRNA sequencing (RNA-seq) analysis of tumor tissues, freshly dissected tumor samples were frozen in RNase-free tubes with liquid nitrogen and preserved at −80°C. Samples were homogenized using bead tubes with ceramic beads on Fisherbrand Bead Mill 24 homogenizer (Fisher Scientific). Total RNA was extracted using Direct-zol RNA Kit (Zymo Research). Quality control analysis was conducted using RNA 6000 Nano Kit on Bioanalyzer 2100 (Agilent). Total mRNA sequencing was performed using Illumina TruSeq stranded mRNAseq Library and High-Output sequencing PE 75x75 nt on NextSeq 500 (Illumina) by MDACC Sequencing and ncRNA Program core facility. Raw sequencing data from the Illumina platform were converted into Fastq files and aligned to the reference genome mm10 using the Spliced Transcripts Alignment to a Reference (STAR) algorithm (Dobin et al., 2013). HTSeq-count was then utilized to generate the raw counts for each gene. Raw counts were then analyzed by DESeq2 (Anders and Huber, 2010) for data processing, normalization, and differential expression analysis according to standard procedures. Unsupervised hierarchical clustering heat map was based on the differentially expressed genes (defined by DESeq2 P-value < 0.05 and fold change > 1.5) between KPPF and KPPF;Col1^smaKO tumors. Color scheme of the heat map represents row Z-score distribution. Heat maps of differentially expressed genes were plotted with log2-fold change of indicated genes. Functional categorization and pathway reconstitution from the RNA-seq data were conducted using gene set enrichment analysis (GSEA; Broad Institute). NOM. p. val refers to nominal P value. NES refers to normalized enrichment score. FDR refers to false discovery rate (q value). All analyses were implemented in R.

Single-cell RNA-sequencing (sc-RNA-seq)

Fresh tumor tissue was minced with sterilized lancets, digested with collagenase IV (17104019, Gibco, 4 mg/mL)/dispase II (17105041, Gibco, 4 mg/mL)/RPMI at 37 °C for 0.5 hour, filtered by 70 μm cell strainers, and resuspended in PBS/2%FBS as single cell suspension. The single cell suspension was stained with Live/Dead viability dye eFluor 780 (65-0865-14, eBioscience), filtered through a 40 μm mesh, and then sorted for live cells with Aria II sorter (BD Biosciences) at the South Campus Flow Cytometry Core Laboratory of MDACC. The median gene reads per cell were around 2300. The percentage of total mapped sequence reads with the QC30 threshold in RNA read scores were above 70%. The Fraction Reads in Cells scores were around 80 %. sc-RNA-seq on these samples was conducted at the Sequencing and Microarray Facility of MDACC. Single cell Gel Bead-In-Emulsions (GEMs) generation and barcoding, post GEM-RT cleanup and cDNA amplification, library construction and Illumina-ready sequencing library generation were prepared by following the manufacturer’s guidelines. High Sensitivity dsDNA Qubit kit was used to estimate the cDNA and Library concentration. HS DNA Bioanalyzer was used for the quantification of cDNA. DNA 1000 Bioanalyzer was used for the quantification of libraries. The “c-loupe” files were generated by using Cell Ranger software pipelines following manufacturer’s guidelines. Cells from unfractionated KPPF tumors (n = 3) or KPPF;Col1^smaKO tumors (n = 2) were encapsulated using 10X Genomics’ Chromium controller and Single Cell 3’ Reagent Kits v2. Following capture and lysis, cDNA was synthesized and amplified to construct Illumina sequencing libraries. The libraries from about 1,000 cells per sample were sequenced with Illumina Nextseq 500. The run format was 26 cycles for read 1, 8 cycles index 1, and 124 cycles for read 2. sc-RNA-seq data were processed by the Sequencing and Microarray Facility in MD Anderson Cancer Center. Library Seurat version 3.5.3 (Satija et al., 2015), dplyr and cowplot were loaded into R to explore QC metrics, filter cells, normalize data, cluster cells, and identify cluster biomarkers. To filter out low-quality cells, a threshold with a minimum of 200 and a maximum of 7000 genes per cell was used. Cells with more than 10% of the mitochondrial genome were also removed for further analysis. “RunUMAP” function was used for clustering the cells. Based on the “JackStrawPlot” and “ElbowPlot” functions, the first 27 principal components were used for UMAP projection and clustering analysis. “FindAllMarkers” function was used to identify the specific markers for each cell cluster. “DoHeatmap” function was used to show the top 10 genes in each cluster. “VlnPlot” function was used to show expression probability distributions across cell clusters of the genes we selected to assign the cell type identity, and the genes that we were interested in. The signature gene expression lists for all major cell clusters and their subpopulations were provided in the spreadsheet files as Tables S1, S3, S5, and S6.

Flow cytometry for cell sorting

Immune cell population analysis by flow cytometry

Fresh tumor tissue was digested with 0.1 mg/mL Liberase TL (Roche, 05401020001) and 0.2 mg/mL DNase-I (Roche, 10104159001) in RPMI for 30 min at 37 °C for 0.5 hour with gentle mixing, followed by further dissociation using gentleMACS C-tubes on the gentleMACS Dissociator (Miltenyi Biotec). The digestion was stopped with RPMI with 10% FBS and 10 mM EDTA. Cells were filtered by 70 μm cell strainers, washed, resuspended, and subjected to a gradient centrifugation using Histopaque-1119 (Sigma-Aldrich, 11191). Cells were stained in the dark on ice for 30 min with 100 μL antibody cocktail for membrane markers (Key Resources Table), diluted in FACS buffer containing 20% Brilliant Stain Buffer (BD Bioscience, 566349) and 50 μg/mL anti-mouse CD16/CD32 (2.4G2) FC block (BD Biosciences, 553142). Cells were washed twice with PBS/2%FBS, fixed-permeabilized with Foxp3/Transcription Factor Staining Buffer Set (eBioscience, 00-5523-00), washed twice with 1x Permeabilization buffer, and stained in the dark on ice for 30 min with antibody cocktail (for intracellular markers) diluted in 1x Permeabilization buffer. Cells were washed twice with 1x Permeabilization buffer and washed with PBS/2%FBS. Data were acquired using the Fortessa-X20 (BD Bioscience) and analyzed using FlowJo Ver 10. Immune populations were gated on single live CD45⁺ cells.

Multispectral imaging of multiplex stained tissue microarray sections

Human PDAC tissue microarray sections were stained by multi-plex staining and multispectral imaging. The multiplex staining procedures, spectral unmixing and cell segmentation using the Nuance and inForm imaging softwares were described previously (Carstens et al., 2017). Antibody concentrations used for the multiplex staining (of Col1, cytokeratin-8, CD3, CD4, CD8, FoxP3) can be found in Key Resources Table. Multiplex stained slides were imaged with the Vectra Multispectral Imaging System version 2, with Vectra software version 3.0.3 (Perkin Elmer). Each tissue section was scanned in its entirety using a 4x objective. Up to 80 regions (at 20x) per section were selected for multispectral imaging using the Phenochart software (Perkin Elmer). Each multiplex field was scanned every 10 nm of the emission light spectrum across the range of each emission filter cube. Filter cubes used for multispectral imaging were DAPI (440-600 nm), FITC (520 nm-680 nm), Cy3 (570-690 nm), Texas Red (580-700 nm) and Cy5 (680-720 nm). Multispectral images from single marker stained slides with the corresponding fluorophores were used to generate a spectral library using the Nuance Image Analysis software (Perkin Elmer). The library contained the emitting spectral peaks of all fluorophores and was used to unmix each multispectral image (spectral unmixing) to its individual 6 components by using the inForm 2.2 image analysis software. Thresholds of detection for the different markers were adjusted across different cohorts in order to ensure consistent capture of positive signal across all controls. All images in each cohort were processed using the same thresholds of staining positivity. After spectral unmixing and cell segmentation, the images were then subjected to a proprietary inForm active learning phenotyping algorithm. This algorithm enables the individual identification of each DAPI-stained cell based on the pattern of fluorescence staining signal and nuclear/cell morphological features. Cells were classified into the following classes according to our markers of interest: cytokeratin-8⁺ (CK8⁺) cancer cells, total T cells (CD3⁺), CD8⁺ T cells (CD3⁺CD8⁺), CD4⁺ Teff cells (CD3⁺CD4⁺Foxp3⁻), CD4⁺ Treg cells (CD3⁺CD4⁺FoxP3⁺), and other (CK8⁻CD3⁻). All the aforementioned procedures for staining, imaging, image processing, and quantifications were performed blinded to the sample identity and clinical outcomes.

Cell viability assay

Cancer cells (3 × 10³ cells per well in 100 μL RPMI with 1% FBS) were seeded in to 96-well plates and then treated with indicated conditions for 48 h. Cell viability/number in each well of 96-well plates was determined using the Cell Counting Kit-8 (CCK8; Abcam ab228554), examined at OD 450 nm on a microplate reader following the manufacturer’s instructions.

QUANTIFICATION AND STATISTICAL ANALYSIS

Statistical analyses of flow cytometry and immunostaining quantifications were performed with unpaired, two-tailed t test, one-way ANOVA with Tukey’s multiple comparison test, or Fisher’s exact test using GraphPad Prism (GraphPad Software, San Diego, CA, USA). Chi-square χ2 analyses were performed comparing the case distribution of AJCC tumor stages and tumor differentiation status among TCGA PDAC cohort and/or MDACC PDAC cohort. The expression levels of indicated genes among TCGA PDAC cohort samples were based on the RNA Seq V2 RSEM data. TCGA data were downloaded from cBioPortal (Cerami et al., 2012; Gao et al., 2013). For the survival analysis based on IL18 expression, patients with available OS data and RNA-seq data (n = 177) were stratified into IL18-high (n = 90) and IL18-low (n = 87) groups based on the median IL18 expression level. Similarly, for the survival analysis based on CXCL6 (the human homolog of mouse Cxcl5) expression, patients (n = 177) were stratified into CXCL6-high (n = 90) and CXCL6-low (n = 87) groups based on the median CXCL6 expression level. Kaplan-Meier plots were drawn for survival analysis and the log rank Mantel-Cox test was used to evaluate statistical differences. Data met the assumptions of each statistical test, where variance was not equal (determined by an F-test) Welch’s correction for unequal variances was applied. A P value < 0.05 was considered statistically significant. Error bars represented standard error of the mean (S.E.M.) when multiple visual fields were averaged to produce a single value for each animal, which was then averaged again to represent the mean bar for the group in each graph.

Supplementary Material

NIHMS1675833-supplement-1.pdf^{(18MB, pdf)}

Data S1

Source data tables. Related to Figures 1–8, S2, S3, and S6–S8.

NIHMS1675833-supplement-2.xlsx^{(19.1MB, xlsx)}

Table S1

List of the signature genes of indicated functional cell clusters from the single-cell RNA-sequencing analysis data. Related to Figure 1.

NIHMS1675833-supplement-3.xlsx^{(438.3KB, xlsx)}

Table S3

List of the signature genes of indicated cancer-associated fibroblast (CAF) subpopulations from the single-cell RNA-sequencing analysis data. Related to Figure 1.

NIHMS1675833-supplement-4.xlsx^{(118.8KB, xlsx)}

Table S5

List of the signature genes of indicated cancer cell subpopulations from the single-cell RNA-sequencing analysis data. Related to Figure 4.

NIHMS1675833-supplement-5.xlsx^{(99.9KB, xlsx)}

Table S6

List of the signature genes of indicated myeloid cell subpopulations from the single-cell RNA-sequencing analysis data. Related to Figures 4 and 5.

NIHMS1675833-supplement-6.xlsx^{(61.2KB, xlsx)}

Table S7

Clinical information of the patient cohort for PDAC tissue microarray. Related to Figure 8.

NIHMS1675833-supplement-7.xlsx^{(79.3KB, xlsx)}

Highlights.

Col1 deletion in myofibroblasts decreases stromal Col1 content in pancreatic tumors

Col1 deletion aggravates pancreatic tumor progression and immunosuppression

Col1 deletion promotes recruitment of CD206⁺ARG1⁺myeloid-derived suppressor cells

CXCR2 and CCR2 inhibition reverses tumor progression promoted by Col1 deletion

ACKNOWLEDGEMENTS

We thank D. Saur for providing the KPPF and R26^Dual mouse strain, C.G. Liu at the MDACC Sequencing and ncRNA Program for RNA-seq analysis, K.M. Ramirez and R. Jewell at the Advanced Cytometry & Sorting Facility at South Campus of MDACC for flow cytometry (in part supported by NCI P30CA16672), E.J. Thompson, D.P. Pollock and Y. Chen at the Advanced Technology Genomics Core for their assistance for the single-cell RNA-sequencing analysis in part supported by NCI CA016672 (ATGC), the Genetically Engineered Mouse Facility team of MDACC for the generation of Col1a1^loxP/loxP mouse strain. We would like to thank Y. Fan and P.E. Phillips for their technical help with flow cytometry and immunohistochemistry/multispectral imaging, K.M. McAndrews for flow cytometry data analysis, J. Zhang, X. Song, and X. Mao for RNA-seq data processing, J.L. Carstens for TSA multispectral imaging, A.M. Zaske at the AFM core facility of UT Health Science Center for atomic force microscopy. This work was primarily supported by the Cancer Prevention and Research Institute of Texas (CPRIT). Pancreatic cancer research work in the Kalluri laboratory is also supported by NCI PO1CA95616, and was supported by CPRIT Award RP150231. The current address of Jiha Kim is Department of Biological Sciences, North Dakota State University, Fargo, ND, 58102, USA.

Footnotes

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DECLARATOIN OF INTERESTS

The authors declare no competing interests.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

NIHMS1675833-supplement-1.pdf^{(18MB, pdf)}

Data S1

Source data tables. Related to Figures 1–8, S2, S3, and S6–S8.

NIHMS1675833-supplement-2.xlsx^{(19.1MB, xlsx)}

Table S1

List of the signature genes of indicated functional cell clusters from the single-cell RNA-sequencing analysis data. Related to Figure 1.

NIHMS1675833-supplement-3.xlsx^{(438.3KB, xlsx)}

Table S3

List of the signature genes of indicated cancer-associated fibroblast (CAF) subpopulations from the single-cell RNA-sequencing analysis data. Related to Figure 1.

NIHMS1675833-supplement-4.xlsx^{(118.8KB, xlsx)}

Table S5

List of the signature genes of indicated cancer cell subpopulations from the single-cell RNA-sequencing analysis data. Related to Figure 4.

NIHMS1675833-supplement-5.xlsx^{(99.9KB, xlsx)}

Table S6

List of the signature genes of indicated myeloid cell subpopulations from the single-cell RNA-sequencing analysis data. Related to Figures 4 and 5.

NIHMS1675833-supplement-6.xlsx^{(61.2KB, xlsx)}

Table S7

Clinical information of the patient cohort for PDAC tissue microarray. Related to Figure 8.

NIHMS1675833-supplement-7.xlsx^{(79.3KB, xlsx)}

Data Availability Statement

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PERMALINK

Type I collagen deletion in αSMA+ myofibroblasts enhances immune suppression and accelerates progression of pancreatic cancer

Yang Chen

Jiha Kim

Sujuan Yang

Huamin Wang

Chang-Jiun Wu

Hikaru Sugimoto

Valerie S LeBleu

Raghu Kalluri

SUMMARY

Graphical Abstract

eTOC blurb

INTRODUCTION

RESULTS

Dual-recombinase system mouse models develop spontaneous pancreatic cancer with opportunity for stromal cell specific gene manipulations

Figure 1. Single-cell RNA-sequencing (sc-RNA-seq) identifies fibroblasts as the predominant producers of type I collagen in KPPF mice with PDAC.

Single-cell RNA-sequencing identifies fibroblasts as the predominant producers of type I collagen in KPPF mice with PDAC

Specific deletion of Col1 in αSMA+ myofibroblasts accelerates PDAC with diminished overall survival

Figure 2. Specific deletion of Col1 in αSMA+ myofibroblasts decreases Col1 protein in autochthonous model of PDAC.

Figure 3. Genetic deletion of Col1 in αSMA+ myofibroblasts accelerates PDAC progression with decreased survival.

Specific deletion of Col1 in Fsp1+ stromal cells does not impact PDAC progression

Col1 deletion in αSMA+ myofibroblasts impacts immune response

Figure 4. Col1 deletion in αSMA+ myofibroblasts impacts immune response in PDAC.

Col1 deletion in αSMA+ myofibroblasts is associated with recruitment and polarization of CD206+F4/80+Arg1+ MDSCs

Figure 5. Col1 deletion increases CD206+F4/80+Arg1+ MDSCs (Myeloid-2 cluster).

Col1 deletion in αSMA+ myofibroblasts alters chemokine release by cancer cells and contributes to MDSC recruitment

Figure 6. Col1 deletion alters chemokine release by cancer cells and promotes MDSC recruitment.

Col1 deletion in αSMA+ myofibroblasts is associated with impaired recruitment and activation of T and B lymphocytes

Figure 7. Col1 deletion inhibits T and B lymphocytes in PDAC.