Soluble stroma‐related biomarkers of pancreatic cancer
2018; Springer Nature; Volume: 10; Issue: 8 Linguagem: Inglês
10.15252/emmm.201708741
ISSN1757-4684
AutoresAndrea Resovi, Maria Rosa Bani, Luca Porcu, Alessia Anastasia, Lucia Minoli, Paola Allavena, Paola Cappello, Francesco Novelli, Aldo Scarpa, Eugenio Morandi, Anna Falanga, Valter Torri, Giulia Taraboletti, Dorina Belotti, Raffaella Giavazzi,
Tópico(s)Neuroendocrine Tumor Research Advances
ResumoResearch Article25 June 2018Open Access Transparent process Soluble stroma-related biomarkers of pancreatic cancer Andrea Resovi Andrea Resovi Laboratory of Biology and Treatment of Metastasis, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Bergamo and Milan, Italy Search for more papers by this author Maria Rosa Bani Maria Rosa Bani Laboratory of Biology and Treatment of Metastasis, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Bergamo and Milan, Italy Search for more papers by this author Luca Porcu Luca Porcu Laboratory of Methodology for Clinical Research, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy Search for more papers by this author Alessia Anastasia Alessia Anastasia Laboratory of Biology and Treatment of Metastasis, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Bergamo and Milan, Italy Search for more papers by this author Lucia Minoli Lucia Minoli Mouse and Animal Pathology Lab, Fondazione Filarete and Department of Veterinary Pathology, University of Milan, Milan, Italy Search for more papers by this author Paola Allavena Paola Allavena Department of Immunology and Inflammation, IRCCS-Humanitas Clinical and Research Center, Rozzano, Italy Search for more papers by this author Paola Cappello Paola Cappello orcid.org/0000-0002-5321-7794 CERMS, AOU Città della Salute e della Scienza, Turin, Italy Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy Molecular Biotechnology Center, Turin, Italy Search for more papers by this author Francesco Novelli Francesco Novelli CERMS, AOU Città della Salute e della Scienza, Turin, Italy Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy Molecular Biotechnology Center, Turin, Italy Search for more papers by this author Aldo Scarpa Aldo Scarpa Department of Pathology and Diagnostic, University and Hospital Trust of Verona, Verona, Italy Search for more papers by this author Eugenio Morandi Eugenio Morandi Chirurgia IV, Presidio Ospedaliero di Rho, ASST Rhodense, Milano, Italy Search for more papers by this author Anna Falanga Anna Falanga Department of Immunohematology and Transfusion Medicine, Thrombosis and Hemostasis Center, Hospital Papa Giovanni XXIII, Bergamo, Italy Search for more papers by this author Valter Torri Valter Torri Laboratory of Methodology for Clinical Research, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy Search for more papers by this author Giulia Taraboletti Giulia Taraboletti Laboratory of Biology and Treatment of Metastasis, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Bergamo and Milan, Italy Search for more papers by this author Dorina Belotti Corresponding Author Dorina Belotti [email protected] orcid.org/0000-0002-3868-9144 Laboratory of Biology and Treatment of Metastasis, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Bergamo and Milan, Italy Search for more papers by this author Raffaella Giavazzi Corresponding Author Raffaella Giavazzi [email protected] orcid.org/0000-0001-5249-8208 Laboratory of Biology and Treatment of Metastasis, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Bergamo and Milan, Italy Search for more papers by this author Andrea Resovi Andrea Resovi Laboratory of Biology and Treatment of Metastasis, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Bergamo and Milan, Italy Search for more papers by this author Maria Rosa Bani Maria Rosa Bani Laboratory of Biology and Treatment of Metastasis, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Bergamo and Milan, Italy Search for more papers by this author Luca Porcu Luca Porcu Laboratory of Methodology for Clinical Research, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy Search for more papers by this author Alessia Anastasia Alessia Anastasia Laboratory of Biology and Treatment of Metastasis, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Bergamo and Milan, Italy Search for more papers by this author Lucia Minoli Lucia Minoli Mouse and Animal Pathology Lab, Fondazione Filarete and Department of Veterinary Pathology, University of Milan, Milan, Italy Search for more papers by this author Paola Allavena Paola Allavena Department of Immunology and Inflammation, IRCCS-Humanitas Clinical and Research Center, Rozzano, Italy Search for more papers by this author Paola Cappello Paola Cappello orcid.org/0000-0002-5321-7794 CERMS, AOU Città della Salute e della Scienza, Turin, Italy Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy Molecular Biotechnology Center, Turin, Italy Search for more papers by this author Francesco Novelli Francesco Novelli CERMS, AOU Città della Salute e della Scienza, Turin, Italy Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy Molecular Biotechnology Center, Turin, Italy Search for more papers by this author Aldo Scarpa Aldo Scarpa Department of Pathology and Diagnostic, University and Hospital Trust of Verona, Verona, Italy Search for more papers by this author Eugenio Morandi Eugenio Morandi Chirurgia IV, Presidio Ospedaliero di Rho, ASST Rhodense, Milano, Italy Search for more papers by this author Anna Falanga Anna Falanga Department of Immunohematology and Transfusion Medicine, Thrombosis and Hemostasis Center, Hospital Papa Giovanni XXIII, Bergamo, Italy Search for more papers by this author Valter Torri Valter Torri Laboratory of Methodology for Clinical Research, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy Search for more papers by this author Giulia Taraboletti Giulia Taraboletti Laboratory of Biology and Treatment of Metastasis, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Bergamo and Milan, Italy Search for more papers by this author Dorina Belotti Corresponding Author Dorina Belotti [email protected] orcid.org/0000-0002-3868-9144 Laboratory of Biology and Treatment of Metastasis, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Bergamo and Milan, Italy Search for more papers by this author Raffaella Giavazzi Corresponding Author Raffaella Giavazzi [email protected] orcid.org/0000-0001-5249-8208 Laboratory of Biology and Treatment of Metastasis, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Bergamo and Milan, Italy Search for more papers by this author Author Information Andrea Resovi1,‡, Maria Rosa Bani1,‡, Luca Porcu2, Alessia Anastasia1, Lucia Minoli3, Paola Allavena4, Paola Cappello5,6,7, Francesco Novelli5,6,7, Aldo Scarpa8, Eugenio Morandi9, Anna Falanga10, Valter Torri2, Giulia Taraboletti1,‡, Dorina Belotti *,1,‡ and Raffaella Giavazzi *,1,‡ 1Laboratory of Biology and Treatment of Metastasis, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Bergamo and Milan, Italy 2Laboratory of Methodology for Clinical Research, Department of Oncology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy 3Mouse and Animal Pathology Lab, Fondazione Filarete and Department of Veterinary Pathology, University of Milan, Milan, Italy 4Department of Immunology and Inflammation, IRCCS-Humanitas Clinical and Research Center, Rozzano, Italy 5CERMS, AOU Città della Salute e della Scienza, Turin, Italy 6Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy 7Molecular Biotechnology Center, Turin, Italy 8Department of Pathology and Diagnostic, University and Hospital Trust of Verona, Verona, Italy 9Chirurgia IV, Presidio Ospedaliero di Rho, ASST Rhodense, Milano, Italy 10Department of Immunohematology and Transfusion Medicine, Thrombosis and Hemostasis Center, Hospital Papa Giovanni XXIII, Bergamo, Italy ‡These authors contributed equally to this work as first authors ‡These authors contributed equally to this work as senior authors *Corresponding author. Tel: +39 035 42131; Fax: +39 035 319331; E-mail: [email protected] *Corresponding author. Tel: +39 02 39014732; Fax: +39 02 39014734; E-mail: [email protected] EMBO Mol Med (2018)10:e8741https://doi.org/10.15252/emmm.201708741 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The clinical management of pancreatic ductal adenocarcinoma (PDAC) is hampered by the lack of reliable biomarkers. This study investigated the value of soluble stroma-related molecules as PDAC biomarkers. In the first exploratory phase, 12 out of 38 molecules were associated with PDAC in a cohort of 25 PDAC patients and 16 healthy subjects. A second confirmatory phase on an independent cohort of 131 PDAC patients, 30 chronic pancreatitis patients, and 131 healthy subjects confirmed the PDAC association for MMP7, CCN2, IGFBP2, TSP2, sICAM1, TIMP1, and PLG. Multivariable logistic regression model identified biomarker panels discriminating respectively PDAC versus healthy subjects (MMP7 + CA19.9, AUC = 0.99, 99% CI = 0.98–1.00) (CCN2 + CA19.9, AUC = 0.96, 99% CI = 0.92–0.99) and PDAC versus chronic pancreatitis (CCN2 + PLG+FN+Col4 + CA19.9, AUC = 0.94, 99% CI = 0.88–0.99). Five molecules were associated with PanIN development in two GEM models of PDAC (PdxCre/LSL-KrasG12D and PdxCre/LSL-KrasG12D/+/LSL-Trp53R172H/+), suggesting their potential for detecting early disease. These markers were also elevated in patient-derived orthotopic PDAC xenografts and associated with response to chemotherapy. The identified stroma-related soluble biomarkers represent potential tools for PDAC diagnosis and for monitoring treatment response of PDAC patients. Synopsis Seven stroma-related circulating biomarkers that discriminate between healthy subjects and pancreatic ductal adenocarcinoma (PDAC) in two different cohorts of patients have been identified and suggested as a potential diagnosis tool to monitor treatment efficacy in patients. Panels of biomarkers that improve the performance of CA19.9 in distinguishing i) PDAC from healthy subjects and ii) PDAC from chronic pancreatitis have been developed. Multivariable models confirmed their predictive accuracy at early stages of disease. The association of these biomarkers with the development of pre-invasive disease in GEM models confirmed their potential to detect early stage lesions. The correlation of biomarker levels with tumor burden and drug response in patient-derived PDAC xenograft models indicate their value to monitor treatment response and efficacy. Introduction Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive epithelial malignancies, with a 5-year survival rate of 6% (Tempero et al, 2013). Although progression from tumor initiation to advanced invasive cancer may take up to about 10 years (Yachida et al, 2010), PDAC is often diagnosed at an advanced stage, because of non-specific symptomatology, the absence of effective imaging tests to identify early disease, and the lack of specific and sensitive diagnostic circulating biomarkers (Korc, 2007). Late diagnosis of PDAC leads to a limited therapeutic time window during which serological markers capable of monitoring treatment effectiveness could change the fate of PDAC patients. The biomarker CA19.9, currently used to detect and monitor PDAC, is not sufficiently sensitive and specific to have reliable diagnostic value. In addition, it is not expressed in approximately 20% of the Lewis antigen-negative population. There are also racial and sex variations in CA19.9 expression with the highest levels in Caucasians (Tempero et al, 2013). Recent proteomic studies have identified circulating molecules or autoantibodies that are up-regulated in PDAC, but few have been investigated further as a serological diagnostic or prognostic biomarker for the disease (Brand et al, 2011; Capello et al, 2013, 2017; Chan et al, 2014; Shaw et al, 2014; Zhang et al, 2014; Balasenthil et al, 2017). PDAC is notable for its desmoplastic stromal reaction and prominent extracellular matrix (ECM) deposition (Whatcott et al, 2015). Stromal elements and extracellular matrix remodeling have a role in PDAC progression and, ultimately, chemotherapy delivery and activity (Feig et al, 2012). The microenvironment changes from normal to a tumor-supportive state, favoring tumor growth and invasion. It has been suggested that desmoplasia might have a prognostic role since fibrosis, stromal abundance, and reactivity have been correlated with shorter survival in patients with resected PDAC (Erkan, 2013). The abundant stroma is one of the main reasons for the limited drug response of PDAC (Neesse et al, 2011). Based on these observations, we hypothesized that the tumor microenvironment might be a source of circulating molecules exploitable as diagnostic biomarkers and as endpoints of target therapies. Since stromal modifications occur early in tumorigenesis and persist in advanced tumors, stroma-related circulating molecules might have great potential for the diagnosis of PDAC identified at a stage in which the disease is still operable. In this study, we combined multiple approaches to investigate circulating stroma-related molecules as PDAC diagnostic biomarkers and as endpoints for assessing the effectiveness of treatment. Thirty-eight stroma-associated potential circulating biomarkers, including extracellular matrix proteins and proteolytic fragments, matrix-degrading enzymes and their inhibitors, growth factors, antiangiogenic factors, and adhesion molecules, were selected from previous proteomic analyses (Yu et al, 2005; Bloomston et al, 2006; Faca et al, 2008; Kojima et al, 2008; Fiedler et al, 2009; Rong et al, 2010; Xue et al, 2010; Pan et al, 2011) and measured in the plasma of PDAC patients, healthy controls, and chronic pancreatitis patients. Selected candidate molecules were further validated in genetically engineered mouse models of PDAC with mutated Kras (KC mice) or with mutated Kras and TP53 (KPC mice) and associated with PDAC initiation and progression (PanIN-PDAC) (Hingorani et al, 2003, 2005; Capello et al, 2013), as well as in patient-derived PDAC xenografts (PDAC-PDX), where their levels correlated with tumor burden and response to treatment. Results Selection of candidate PDAC stroma-related biomarkers Tumor-stroma-associated PDAC biomarkers were selected from eight proteomic studies on circulating proteins that are differentially expressed in PDAC and healthy subjects (Yu et al, 2005; Bloomston et al, 2006; Faca et al, 2008; Kojima et al, 2008; Fiedler et al, 2009; Rong et al, 2010; Xue et al, 2010; Pan et al, 2011). Thirty-eight candidates were selected because they were found in at least two independent analyses and/or were related to tumor/stroma interaction by Gene Ontology. These data were integrated with manually curated additional information from the literature. The selected molecules are listed in Table EV1 and include extracellular matrix proteins and proteolytic fragments, matrix-degrading enzymes and their inhibitors, growth factors, angiogenesis regulatory factors, and adhesion molecules. Analysis of circulating PDAC stroma-related biomarkers in patients First exploratory phase The levels of the 38 selected candidate biomarkers were analyzed in the plasma of patients with histologically verified PDAC (n = 25) and in healthy controls (n = 16) (cohort no. 1 in Table 1). The concentrations are shown in Table EV2. Table 1. Clinical characteristics of the study population Number of cases Gender (%) Age median (Range) PDAC stage (%) Cohort 1 (Exploratory phase) Healthy n = 16 M (62.5) F (37.5) 59 (54–65) Stage IA (16) Stage IIA (24) Stage IIB (56) ND (4) PDAC n = 25 M (40) F (60) 50 (47–82) Cohort 2 (Confirmatory phase) Healthy n = 131 M (49.6) F (50.4) 55 (44–66) Stage IA (1.5) Stage IB (0.8) Stage IIA (16.8) Stage IIB (66.4) Stage III (0.8) Stage IV (2.3) ND (11.4) Chronic pancreatitis n = 30 M (73.3) F (26.7) 53 (34–79) PDAC n = 131 M (49.6) F (50.4) 70 (38–88) ND, not determined; M, males; F, females. We identified six clusters of biomarkers that are as correlated as possible with each other and as uncorrelated as possible with biomarkers in other clusters (Table 2). The plasma levels of the molecules in these clusters are shown in Fig 1. At univariate logistic regression, three clusters (clusters 3, 4, and 6) were significantly associated with the presence of PDAC (respectively P = 0.007, P = 0.005, and P = 0.07). Table 2. Clusters of biomarkers associated with PDAC Cluster No. Molecules Ro2a Rn2b (1 - Ro2)/(1 - Rn2) ratio P valuec 1 MMP12 0.810 0.087 0.208 0.364 MMP13 0.790 0.117 0.238 IGFBP4 0.864 0.316 0.199 IGFBP5 0.726 0.266 0.373 SPARC 0.656 0.206 0.433 2 ES 0.581 0.086 0.459 0.176 PDGF-BB 0.776 0.253 0.299 FGF-2 0.848 0.087 0.166 VEGFA 0.948 0.098 0.057 3 TIMP1 0.836 0.181 0.200 0.007 sICAM1 0.535 0.210 0.588 MMP7 0.648 0.056 0.373 PICP 0.244 0.067 0.810 PLG 0.249 0.126 0.859 TSP2 0.772 0.333 0.342 4 IGFBP2 0.573 0.232 0.556 0.005 FN 0.231 0.053 0.812 PINP 0.121 0.024 0.900 CCN1 0.587 0.294 0.585 CCN2 0.627 0.097 0.413 5 sVCAM1 0.606 0.042 0.412 0.332 NGAL 0.606 0.024 0.404 6 Col4 0.715 0.085 0.311 0.070 Lam-P1 0.715 0.063 0.304 a Squared correlation coefficient between a given biomarker and its own cluster. b The next highest squared correlation coefficient between a given biomarker and any other cluster. c P value from 1 df Wald χ2 for association with outcome. Figure 1. Phase I exploratory phase (cohort no. 1): plasma levels of molecules belonging to PDAC-associated clustersPlasma levels of the 13 biomarkers belonging to clusters 3, 4, and 6 (see Table 2) in healthy subjects (n = 16) and PDAC patients (n = 25), *P < 0.01 (Mann–Whitney). Data are expressed as a scatter plot, mean ± SEM. P-values (for each cluster) were calculated with the Wilcoxon rank-sum test and indicate a significant association between each cluster and PDAC. Download figure Download PowerPoint Molecules in these clusters (with the exception of Lam-P1 for which commercially available kits had been discontinued) were selected for further analysis. Second confirmatory phase Molecules in clusters 3, 4, and 6 (TIMP1, sICAM1, MMP7, PICP, PLG, TSP2, IGFBP2, FN, PINP, CCN1, CCN2, Col4) were further analyzed in a larger independent cohort of PDAC patients (n = 131), pancreatitis patients (n = 30), and sex-matched healthy individuals (n = 131) (cohort no. 2 in Table 1). The distribution of the twelve molecules in the second cohort of patients is shown in Table EV3. Seven (TIMP1, sICAM1, MMP7, TSP2, PLG, IGFBP2, and CCN2) of the 12 molecules were significantly up-regulated in PDAC patients compared to healthy controls (P < 0.001) (Fig 2A; Tables EV3 and EV4). The differences between PDAC and healthy subjects were excellent for MMP7 (AUC = 0.98) and good for CCN2 (AUC = 0.86), which demonstrated a discriminatory ability similar to CA19.9 (AUC = 0.87), while IGFBP2 and TIMP1 (AUC = 0.82), TSP2 (AUC = 0.78), sICAM1 (AUC = 0.77), and PLG (AUC = 0.66) had a weaker discriminatory ability (Fig EV1 and Table EV4). The seven molecules were confirmed to be significantly up-regulated also at early stages (stages IA, IB, and IIA) when tumor is confined to the pancreas and is not spread to nearby lymph nodes (N0) or to distant sites (M0). AUCs used to estimate the predictive accuracy of distributional models did not change significantly between stages (Fig EV1 and Appendix Table S2). Age difference between PDAC patients and healthy individuals did not affect the significant association between each selected biomarker and PDAC, as assessed by a multivariable logistic regression model, adjusted for age effect (data not shown). No correlation was found between these molecules and CA19.9 (Appendix Fig S1). Figure 2. Phase II confirmatory phase (cohort no. 2): plasma levels and AUC values of selected molecules A. Plasma levels of selected candidate biomarkers analyzed in healthy subjects (n = 131), pancreatitis patients (n = 30), and PDAC patients (n = 131). Data are expressed as a scatter plot, mean ± SEM, *P < 0.001 (Wilcoxon rank-sum test). B. Receiver operator characteristic (ROC) curves of the single biomarkers and of biomarker panels (indicated with All) for diagnosis of PDAC versus healthy controls and PDAC versus pancreatitis. Areas under the curve (AUC) with 99% CI are presented. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Phase II confirmatory phase: AUC values of selected molecules in plasma of PDAC patients divided by stagesReceiver operator characteristic (ROC) curves for diagnosis of PDAC versus healthy controls (not divided (All), early stages (IA, IB, and IIA), and late stages (IIB)). Areas under the curve (AUC) with 99% CI are presented for the whole population. Download figure Download PowerPoint Cohort no. 2 also included 30 patients with chronic pancreatitis (CP). The levels of TIMP1, sICAM1, MMP7, IGFBP2, CCN2, and TSP2, though lower than in PDAC, were increased in plasma from CP compared with healthy controls. Significantly lower levels of Col4 and FN were specifically associated with CP (P < 0.001). PLG in CP was significantly lower than in PDAC (P < 0.001), but not significantly different from healthy subjects (P < 0.05) (Fig 2A and Table EV4). Using a multivariable logistic regression model, we then developed biomarker panels that improved the discovery power of CA19.9 in PDAC versus healthy subjects and PDAC versus CP. In accordance with the statistical procedure adopted, the resulting panel consisting of MMP7 and CA19.9 statistically better discriminated PDAC versus healthy subjects with an AUC of 0.99 (99% CI = 0.98–1.00) compared with CA19.9 (AUC = 0.87, 99% CI = 0.81–0.93). Similarly, the panel consisting of CCN2 and CA19.9 with an AUC of 0.96 (99% CI = 0.92–0.96) discriminated PDAC versus healthy subjects better than CA19.9 (Fig 2B). Multivariable models defined in the overall population were evaluated by stage. As reported in Appendix Table S2, they confirmed their optimal predictive accuracy without significant interaction between stages. A panel consisting of CCN2, PLG, FN, Col4, and CA19.9 improved the performance of CA19.9 in distinguishing PDAC from CP. These five biomarkers, which individually (Table EV4) had an AUC of 0.56 (99% CI = 0.41–0.71, CCN2), 0.74 (99% CI = 0.61–0.86, PLG), 0.80 (99% CI = 0.65–0.94, FN), 0.74 (99% CI = 0.59–0.89, Col4), and 0.83 (99% CI = 0.75–0.92, CA19.9), when analyzed in combination showed an AUC of 0.94 (99% CI = 0.88–0.99) indicating a significantly higher capability to discriminate PDAC from CP (Fig 2B). Similar results were obtained separating PDAC by stages (Appendix Table S2). Circulating stroma-related molecules in mouse models of PDAC High TIMP1, MMP7, TSP2, CCN2, and ICAM1 in KrasG12D- and p53R172H-driven PanIN development We used KC mice expressing the mutation of Kras (KrasG12D) in pancreatic progenitor cells and progressing from a healthy condition to different grades of PanIN (PanIN-1A–1B–2–3) (Hingorani et al, 2003; Cappello et al, 2013) and KPC mice, carrying KrasG12D/+ and p53R172H/+ mutations and developing PanINs that ultimately progresses to overt carcinoma (Hingorani et al, 2005) to study the importance of the selected biomarkers during PDAC induction. Plasma was collected at 60, 120, 180, 240, and 330 days of age from KC mice and at 30, 90, and 150 days of age from KPC mice. We measured those biomarkers for which reliable ELISA was commercially available. Plasma TIMP1, MMP7, and TSP2 levels rose significantly over time in relation to PanIN development in both KC and KPC mice (Fig 3A). At death of KC mice on day 330, when histological analysis of the pancreas confirmed the presence of PanIN-1A, PanIN-1B, and PanIN-2 in, respectively, 2, 2, and 4 lesions (Appendix Fig S2A), MMP7 and TSP2 plasma levels were significantly higher than control PdxCre mice (Fig 3B; KC). Figure 3. Biomarkers in PdxCre/LSL-KrasG12D (KC) and KrasG12D/Trp53R172H (KPC) GEM models A. Levels of TIMP1, MMP7, and TSP2 in plasma of KC mice (n = 7–9) at 60, 120, 180, 240, and 330 days of age and of KPC mice (n = 3–8) at 30, 90, and 150 days of age (mean ± SEM). *P < 0.05 (Mann–Whitney). The exact n and P-values are indicated in Appendix Table S3A. B. Levels of TIMP1, MMP7, and TSP2 in plasma of healthy mice (n = 15), mice with chronic pancreatitis at 150 days of age (n = 19), KC mice at 330 days of age (control PdxCre n = 3–4; KC n = 7–8), and KPC mice at 150 days of age (control PdxCre n = 4–7; KPC n = 4). The exact n is indicated in Appendix Table S3B. Box plots extend from 25th to 75th percentiles, whiskers extend from min to max, and horizontal lines indicate median. P-values were calculated with one-way ANOVA with Tukey's multiple comparison test. C. Histological analysis of pancreas from PdxCre and KC mice with different grades of PanIN lesions at 330 days of age. Anti-TIMP1, anti-MMP7, anti-TSP2, and anti-CCN2 staining of a representative KC PanIN lesion (200×, scale bars: 100 μm). Download figure Download PowerPoint TIMP1, MMP7, and TSP2 levels were also significantly higher in KPC than PdxCre mice at 150 days of age (Fig 3B; KPC). None of the three molecules were elevated in the plasma of mice with caerulein-induced chronic pancreatitis after 7 weeks of treatments confirming the specific association of high levels of TIMP1, MMP7, and TSP2 with neoplastic transformation (Fig 3B and Appendix Fig S3). Pancreatic RNA expression analysis was performed for CCN2, IGFBP2, ICAM1, and PLG for which ELISA kits to measure mouse proteins were not available, and for TIMP1, MMP7, and TSP2. Real-time PCR with mouse-specific probes showed that TIMP1, MMP7, TSP2, CCN2, and ICAM1 (P < 0.05) were more expressed in KC than in control PdxCre mice at 330 days of age (Appendix Fig S2B). TIMP1, MMP7, TSP2, and CCN2 expression in PanIN lesions was confirmed by immunohistochemistry. TIMP1, MMP7, TSP2, and CCN2 staining was typically low in PdxCre pancreas, while a more intense staining was observed in PanIN lesions (Fig 3C). TIMP1, MMP7, TSP2, CCN2, and ICAM1 are elevated in mice with patient-derived PDAC xenografts We measured TIMP1, MMP7, and TSP2 using mouse-specific ELISA in three PDAC-PDX (HuPa4, HuPa8, and HuPa11) growing orthotopically in the pancreas of SCID mice. These tumors are characterized by relevant amounts of host murine stroma (Appendix Fig S4) supporting their use for stroma-derived biomarker validation. In all the PDAC-PDX models, circulating mouse TIMP1, MMP7, and TSP2 levels were significantly higher than in healthy mice (P < 0.05) (Fig 4A) paralleling the tumor growth in the pancreas as shown by the significant correlation with tumor burden measured by MRI (TIMP1, r = 0.68; MMP7, r = 0.60 and TSP2, r = 0.82) (Fig 4B). Figure 4. Biomarkers in PDAC-PDX growing orthotopically in mouse pancreas A. Levels of murine TIMP1, MMP7, and TSP2 in plasma of mice bearing PDAC-PDX (HuPa4, HuPa8, and HuPa11) growing orthotopically in the pancreas (mean ± SEM; n ≥ 3 for each group), *P < 0.05 (Mann–Whitney). The exact n and P-values are indicated in Appendix Table S4A. B. Correlations between the levels of the three selected biomarkers and the tumor volume in mice bearing HuPa8. Pearson coefficient (r). C. Expression of murine TIMP1, MMP7, TSP2, CCN2, ICAM1, IGFBP2, and PLG analyzed in tumors from pancreas (HuPa4, HuPa8, and HuPa11) by RT–PCR. The expression level of target genes was normalized to the geometric median of β-actin and GAPDH housekeeping genes and expressed as 2-ΔΔCT (mean ± SEM, *P < 0.05; Healthy n = 7; HuPa4, HuPa8, and HuPa11 n = 4) (Mann–Whitney). The exact P-values are indicated in Appendix Table S4B. D. Histological analysis of representative PDAC-PDX (HuPa4, HuPa8, and HuPa11). Hematoxylin–eosin, anti-TIMP1, anti-MMP7, anti-TSP2, and anti-CCN2 staining of PDAC-PDX tumors (200×, scale bars: 100 μm). Download figure Download PowerPoint TIMP1, TSP2, and CCN2 but not MMP7 mRNA was highly expressed in HuPa4, HuPa8, and HuPa11 (PDAC-PDX versus healthy mice P < 0.05) (Fig 4C). In agreement, immunohistochemical analysis highlighted a strong expression of TIMP1, TSP2, and CCN2 proteins in the tumor stroma (Fig 4D). ICAM1 was more expressed in all the three PDAC-PDX compared to healthy mice (P < 0.05) while IGFBP2 mRNA levels were significantly higher only in HuPa4 (P < 0.05), and PLG was not increased in PDAC-PDX (Fig 4C). TIMP1, MMP7, and TSP2 as biomarkers of treatment response in PDAC-PDX models TIMP1, MMP7, and TSP2 increased over time in plasma of mice bearing HuPa8 (Fig 5A). To analyze the relevance of the selected stroma-related molecules as markers of drug response, we treated HuPa8-bearing mice with gemcitabine monotherapy or combined with albumin-bound paclitaxel (NAB-P) to reproduce clinical studi
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