RON is not a prognostic marker for resectable pancreatic cancer

  • Carole M Tactacan1,

    Affiliated with

    • David K Chang1, 2, 3, 8,

      Affiliated with

      • Mark J Cowley1, 8,

        Affiliated with

        • Emily S Humphrey1,

          Affiliated with

          • Jianmin Wu1, 8,

            Affiliated with

            • Anthony J Gill1, 4, 5, 8,

              Affiliated with

              • Angela Chou1, 6, 8,

                Affiliated with

                • Katia Nones7, 8,

                  Affiliated with

                  • Sean M Grimmond7, 8,

                    Affiliated with

                    • Robert L Sutherland1, 8,

                      Affiliated with

                      • Andrew V Biankin1, 2, 3, 8,

                        Affiliated with

                        • Roger J Daly1, 8Email author and

                          Affiliated with

                          • and Australian Pancreratic Genome Initiative

                            Affiliated with

                            BMC Cancer201212:395

                            DOI: 10.1186/1471-2407-12-395

                            Received: 8 February 2012

                            Accepted: 18 July 2012

                            Published: 7 September 2012

                            Abstract

                            Background

                            The receptor tyrosine kinase RON exhibits increased expression during pancreatic cancer progression and promotes migration, invasion and gemcitabine resistance of pancreatic cancer cells in experimental models. However, the prognostic significance of RON expression in pancreatic cancer is unknown.

                            Methods

                            RON expression was characterized in several large cohorts, including a prospective study, totaling 492 pancreatic cancer patients and relationships with patient outcome and clinico-pathologic variables were assessed.

                            Results

                            RON expression was associated with outcome in a training set, but this was not recapitulated in the validation set, nor was there any association with therapeutic responsiveness in the validation set or the prospective study.

                            Conclusions

                            Although RON is implicated in pancreatic cancer progression in experimental models, and may constitute a therapeutic target, RON expression is not associated with prognosis or therapeutic responsiveness in resected pancreatic cancer.

                            Keywords

                            Receptor tyrosine kinase Biomarker Gemcitabine Chemotherapy

                            Background

                            Pancreatic cancer (PC) still remains one of the most aggressive and lethal of human cancers, with a lack of prognostic biomarkers and effective treatments contributing to its poor prognosis and high mortality. Around 15% of PC patients are eligible to undergo potentially curative pancreatic surgery at the time of presentation. However, those with operable PC still only have an 18–23% 5-year survival [1, 2], with high incidences of local recurrence and hepatic metastases occurring within 1–2 years after surgery [3]. This emphasizes the need for adjuvant intervention. Gemcitabine is the current standard for post-operative chemotherapy and delays the development of recurrent disease in some PC patients [4]. One explanation for why only a subset of PC patients respond favourably to adjuvant treatment is the molecular heterogeneity of PC [5]. The concept of stratifying patients based on their molecular signatures has been successful in breast cancer and is the basis of modern clinical oncology [6]. Implementation of this treatment strategy for PC may also prove beneficial. The literature is replete with biomarkers of prognosis and therapeutic responsiveness identified through small and/or single pancreatic cancer patient cohorts. However, a recent systematic review has shown that very few of these have been independently validated [7]. The calcium binding protein S100A2 is one of the few prognostic biomarkers where this is the case [7]. In PC, tumors that are negative for S100A2 have a significant survival benefit with pancreatectomy compared to tumors with moderately-high to high expression [8].

                            Recepteur d’origine nantais (RON), also referred to as macrophage stimulating 1 receptor (MST1R), is a receptor tyrosine kinase (RTK) and member of the mesenchymal epithelial transition factor (MET)-proto-oncogene family. Similar to other cell surface receptors, RON is activated upon binding of its ligand, the hepatocyte growth factor-like (HGFL) protein, also referred to as macrophage stimulating protein (MSP) and macrophage stimulating 1 (MST1). HGFL is synthesized from hepatocytes and secreted as an endocrine mediator in an inactive precursor form called pro-MSP. Subsequently, the type II transmembrane proteinase MT-SP1, also known as suppression of tumorigenicity 14 (ST14), processes pro-MSP to its active form near the cell surface, where it activates RON, leading to the initiation of multiple signaling cascades that impact upon cellular motility and survival [9].

                            RON is overexpressed in PC relative to the ductal epithelium of non-malignant pancreas, and its signaling enhances migration, invasion, and survival of PC cells and promotes resistance to gemcitabine in experimental models, thus making it a potential therapeutic target and a possible marker of prognosis [1012]. Although RON is associated with poor survival in gastroesophageal cancer [13] and a three-gene signature involving RON, MSP, MT-SP1 is a strong indicator for metastasis and poor prognosis in breast cancer [14], the prognostic significance of RON in PC remains unknown. The goal of this study was to use a comprehensive cohort of PC patients to assess RON as a biomarker of prognosis and therapeutic responsiveness.

                            Methods

                            Optimization of the RON antibody for immunohistochemistry

                            Anti-RON β (C-20) antibody (Santa Cruz Biotechnology) was used to immunoblot for RON expression across a panel of PC cell lines; this antibody detects full-length pro-RON (190 kDa), mature RON β-chain (150 kDa), and a short form sf-RON isoform (55 kDa) [15]. MIA PaCa-2 and Panc 10.05 were selected as low-expressing and high-expressing controls for RON expression, respectively, and were processed as formalin-fixed, paraffin-embedded blocks and subsequently sectioned at 4 μm onto positively charged slides (Superfrost plus; Menzel-Glaser, Braunschweig, Germany). Antigen was retrieved using DAKO S1699 solution in a pressure cooker for 1 min. Immunostaining was performed using a DAKO Auto-stainer. The slides were treated with 3% Peroxidase Block (DAKO, K4011) for 5 min then Protein Block (DAKO, X0909) for 10 min and stained with primary anti-RON β (C-20) antibody at a dilution of 1:100 for 60 min. EnVision + System anti-rabbit (DAKO, K4003) was used as secondary antibody then 3,3'-diaminobenzidine (DAKO, K3468) was used as a substrate. The slides were then counterstained with Mayer’s haematoxyline.

                            Patients, tissue microarrays, immunohistochemistry and statistical analysis

                            Clinico-pathologic and outcome data for 492 consecutive patients with a diagnosis of pancreatic ductal adenocarcinoma who underwent pancreatic resection were obtained from teaching hospitals associated with the Australian Pancreatic Cancer Network [16] (Table 1). This cohort consisted of a training set of 76 patients, a validation set of 316 patients and a further cohort of 100 patients accrued prospectively for the International Cancer Genome Consortium (ICGC) [17]. Detailed methods for tissue microarray construction, the assessment of immunostaining and statistical analysis were described previously [8].
                            Table 1

                            Clinico-pathological and outcome data corresponding to the pancreatic cancer tissue microarray training and validation sets, and the prospective ICGC cohort gene expression array

                             

                            Training Cohort

                            Validation Cohort

                            ICGC Cohort

                            Variables

                            n = 76 No. (%)

                            Median DSS (months)

                            P value (Logrank)

                            n = 316 No. (%)

                            Median DSS (months)

                            P value (Logrank)

                            n = 100 No. (%)

                            Median DSS (months)

                            P value (Logrank)

                            Sex

                                     

                              Male

                            45 (59.2)

                            16.3

                             

                            157 (49.7)

                            18.3

                             

                            61 (61.0)

                            18.4

                             

                              Female

                            31 (40.8)

                            8.5

                            0.0340

                            159 (50.3)

                            16.9

                            0.5792

                            39 (39.0)

                            18.3

                            0.5467

                            Age (years)

                                     

                              Mean

                            62.1

                              

                            66.7

                              

                            66.9

                              

                              Median

                            64.5

                              

                            69.0

                              

                            68.0

                              

                              Range

                            35.0 – 83.0

                              

                            28.0 – 87.0

                              

                            34.0 – 90.0

                              

                            Outcome

                                     

                              Follow-up (months)

                            0.3 – 158.0

                              

                            0.1 – 195.8

                              

                            0.1 – 29.8

                              

                              Median follow-up

                            158.0

                              

                            68.7

                              

                            14.1

                              

                              Death PC

                            68 (89.5)

                              

                            259 (82.0)

                              

                            33 (33.0)

                              

                              Death other

                            5 (6.6)

                              

                            15 (4.7)

                              

                            9 (9.0)

                              

                              Death Unknown

                            0 (0.0)

                              

                            3 (0.9)

                              

                            3 (3.0)

                              

                              Alive

                            1 (1.3)

                              

                            38 (12.0)

                              

                            55 (55.0)

                              

                              Lost to FU

                            2 (2.6)

                              

                            1 (0.3)

                              

                            0 (0.0)

                              

                            Stage a

                                     

                              I

                            16 (21.1)

                            19.6

                             

                            23 (7.3)

                            41.0

                             

                            8 (8.0)

                            17.4

                             

                              II

                            59 (77.6)

                            11.5

                             

                            282 (89.2)

                            17.8

                             

                            87 (87.0)

                            18.8

                             

                              III

                            0 (0.0)

                              

                            0 (0.0)

                              

                            1 (1.0)

                            ----b

                             

                              IV

                            1 (1.3)

                            22.0

                            0.2828

                            11 (3.5)

                            7.6

                            <0.0001

                            4 (4.0)

                            12.0

                            ****c

                            T Stage

                                     

                              T1

                            12 (15.8)

                              

                            16 (5.1)

                              

                            3 (3.0)

                              

                              T2

                            29 (38.2)

                            13.6

                             

                            33 (10.4)

                            26.6

                             

                            12 (12.0)

                            17.4

                             

                              T3

                            35 (46.1)

                            14.7

                            0.4857

                            267 (84.5)

                            16.8

                            0.0084

                            84 (84.0)

                              

                              T4

                            0 (0.0)

                              

                            0 (0.0)

                              

                            1 (1.0)

                            18.4

                            0.4297

                            N Stage

                                     

                              N0

                            37 (48.7)

                            19.8

                             

                            119 (37.7)

                            21.2

                             

                            24 (24.2)

                            17.4

                             

                              N1

                            39 (51.3)

                            9.7

                            <0.0001

                            197 (62.3)

                            16.7

                            0.0267

                            75 (75.8)

                            18.4

                            0.4714

                            Grade

                                     

                              I

                            7 (9.2)

                              

                            26 (8.2)

                              

                            4 (4.0)

                              

                              II

                            43 (56.6)

                            15.0

                             

                            209 (66.1)

                            17.7

                             

                            61 (61.0)

                            ----

                             

                              III

                            26 (34.2)

                            11.2

                            0.0283

                            81 (25.6)

                            18.3

                            0.5971

                            33 (33.0)

                              

                              IV

                                  

                            2 (2.0)

                            15.1

                            0.0011

                            Tumor size

                                     

                              ≤ 20 mm

                            15 (19.7)

                            17.1

                             

                            77 (24.6)

                            32.0

                             

                            14 (14.0)

                            18.3

                             

                              > 20 mm

                            61 (80.3)

                            11.9

                            0.1232

                            236 (75.4)

                            16.0

                            <0.0001

                            86 (86.0)

                            18.4

                            0.8056

                            Margins

                                     

                              Clear

                            40 (52.6)

                            18.6

                             

                            195 (61.7)

                            22.4

                             

                            66 (66.0)

                            ----

                             

                              Involved

                            36 (47.4)

                            9.7

                            0.0004

                            121 (38.3)

                            13.3

                            <0.0001

                            34 (34.0)

                            13.9

                            0.0335

                            Tumor Location

                                     

                              Head

                            62 (81.6)

                            15.6

                             

                            258 (81.6)

                            18.8

                             

                            85 (85.0)

                            18.4

                             

                              Others

                            14 (18.4)

                            7.4

                            0.0004

                            58 (18.4)

                            13.0

                            0.0312

                            15 (15.0)

                            13.6

                            0.0488

                            Perineural Invasion

                                     

                              Negative

                            24 (32.0)

                            15.6

                             

                            82 (26.6)

                            25.6

                             

                            20 (20.6)

                            ----

                             

                              Positive

                            51 (68.0)

                            13.6

                            0.1909

                            226 (73.4)

                            17.4

                            0.1180

                            77 (79.4)

                            17.4

                            0.0211

                            Vascular Invasion

                                     

                              Negative

                            45 (60.0)

                            15.0

                             

                            161 (53.5)

                            21.2

                             

                            39 (40.6)

                            ----

                             

                              Positive

                            30 (40.0)

                            10.1

                            0.0141

                            140 (46.5)

                            16.2

                            0.0070

                            57 (59.4)

                            15.9

                            0.0348

                            Adj Chemotherapy

                                     

                              Yes

                            13 (17.1)

                            13.6

                             

                            98 (31.0)

                            22.4

                             

                            65 (68.4)

                            21.4

                             

                              No

                            63 (82.9)

                            14.1

                            0.7737

                            218 (69.0)

                            16.5

                            0.0451

                            30 (31.6)

                            12.0

                            0.0007

                            RON Expression d

                                     

                              Low or absent

                            51 (79.7)

                            15.0

                             

                            265 (94.3)

                            17.1

                             

                            42 (52.5)

                            9.8

                             

                              High

                            13 (20.3)

                            6.4

                            0.0409

                            16 (5.7)

                            18.3

                            0.2799

                            38 (47.5)

                            8.0

                            0.3830

                            Patient Cohorts Characteristics.

                            a AJCC 7th Edition.

                            b Median survival has not been reached yet.

                            c Rank test cannot be tested as one or more groups contained no censored observations.

                            d High RON expression was defined as H > 210 for immunohistochemistry or greater than 8.74 on normalized Log2 score for gene expression array.

                            Immunostaining was performed on the training and validation set using the anti-RON β (C-20) antibody as described above. Positive RON expression was defined as a modified histoscore (intensity x %) >210 as this was the most discriminant cut-off point in the training set. Median survival was estimated using the Kaplan-Meier method and the difference was tested using the log-rank Test. P values of less than 0.05 were considered statistically significant. Statistical analysis was performed using StatView 5.0 Software (Abacus Systems, Berkeley, CA, USA). Disease-specific survival was used as the primary endpoint.

                            Expression array analysis

                            The pancreatic ICGC cohort consists of prospectively acquired, primary operable, non-pretreated pancreatic ductal adenocarcinoma samples [18]. For the ICGC cohort, tumour cells were enriched by macro-dissection, then RNA was extracted from tumors using Qiagen Allprep® (Qiagen, Valencia, CA) in accordance with the manufacturer's instructions, assayed for quality on an Agilent Bioanalyzer 2100 (Agilent Technologies, Palo Alto, CA), and hybridized to Illumina Human HT-12 V4 microarrays. mRNA expression data were available for 88 of 100 patients. Raw iDAT files were processed using IlluminaGeneExpressionIdatReader (Cowley et al. manuscript in preparation). Following array quality control, data were vst transformed and robust spline normalized, using the lumi R/Bioconductor package [19]. To confirm the microarray probe quality, we: aligned the probe sequences to the genome using UCSC BLAT [20]; and also used an Illumina reannotation pipeline [21]. Both methods confirmed that the probes for RON, MSP, MT-SP1 perfectly and uniquely match the 3’ end of the intended gene. The RON probe binds both full length RON, and sf-RON. Expression levels of single-gene (RON, MSP, MT-SP1), two-gene (RON + MSP) and three-gene (RON + MSP + MT-SP1) combinations were used to separate patients into two groups: high for those patients with above-mean expression in all genes in the signature, or low for all other patients. For RON, we also chose an 80% low : 20% high cutoff to match that proportion of RON high patients in the training cohort. Survival analysis was performed using the Cox proportional hazards model, using the survival package (version 2.36-9) in R (version 2.13.1). Expression levels for all signatures were also analyzed in a subset of 65 patients from the ICGC cohort, which omitted patients with advanced disease.

                            Results

                            Optimization of anti-RON antibody for immunohistochemistry

                            RON protein expression was determined by Western blot analysis across a panel of PC cell lines (Figure 1). All cell lines expressed RON, but at varying levels. These results were used to select appropriate low-expressing and high-expressing control cell line blocks for subsequent immunohistochemistry. MIA PaCa-2 was used as the low-expressing control (Figure 2A), and Panc 10.05 was used as the high-expressing control (Figure 2B). The anti-RON antibody gave strong staining of a subset of pancreatic cancers (Figure 2D, I & J) that was absent upon use of control rabbit IgG (Figure 2C). Examples where RON expression in pancreatic cancer was undetectable, or low, are also shown (Figure 2G and H). RON is rarely expressed in the ductal cells of non-cancerous pancreas [3], so this was used as the negative-control tissue (Figure 2E). RON is known to be overexpressed in breast cancer [14], which provided an additional positive-control tissue (Figure 2F). Staining was present in both membrane and cytoplasm, which is consistent with past literature [10, 12].
                            http://static-content.springer.com/image/art%3A10.1186%2F1471-2407-12-395/MediaObjects/12885_2012_3546_Fig1_HTML.jpg
                            Figure 1

                            Western blot of pro-RON and RON β-chain expression across a panel of pancreatic cancer cell lines. MIA PaCa-2 and Panc 10.05 are low-expressing and high-expressing controls, respectively.

                            http://static-content.springer.com/image/art%3A10.1186%2F1471-2407-12-395/MediaObjects/12885_2012_3546_Fig2_HTML.jpg
                            Figure 2

                            Optimization of RON immunohistochemical staining. Immunohistochemical staining of RON in MIA PaCa-2 and Panc 10.05 cell lines (A and B), using IgG rabbit and anti-RON primary antibodies in pancreatic cancer tissues (C and D), and in benign ductal cells of the pancreas and in breast cancer with high RON expression (E and F), at low and high magnification. Examples of absent, low, moderate to high RON staining in pancreatic cancer tissue are also shown (G-J).

                            RON expression in pancreatic cancer and its association with patient prognosis

                            Three independent patient cohorts were utilized for this study. Two of the cohorts, the training and validation sets, which were acquired retrospectively, were immunohistochemically stained for RON expression. High RON expression (H-score >210) was a biomarker of poor prognosis in the training set (Figure 3A). However, in the larger validation set RON expression was not prognostic (Figure 3B). The evaluation of RON as a predictive marker in the response to adjuvant gemcitabine therapy was not investigated in the training set as this cohort pre-dates adjuvant gemcitabine given as standard treatment. In the validation set, RON expression did not co-segregate with chemotherapy responsiveness, however a trend towards better qualitative response was seen for the RON low or absent group (Figure 4A & B).
                            http://static-content.springer.com/image/art%3A10.1186%2F1471-2407-12-395/MediaObjects/12885_2012_3546_Fig3_HTML.jpg
                            Figure 3

                            Kaplan-Meier survival curves for patients stratified based upon RON expression in the training (n = 64, with n = 51 low and n = 13 high) (A) and validation (n = 281, with n = 265 low and n = 16 high) (B) cohorts. The p-value from fitting a Cox proportional hazards model to the survival curves is indicated in the plots.

                            http://static-content.springer.com/image/art%3A10.1186%2F1471-2407-12-395/MediaObjects/12885_2012_3546_Fig4_HTML.jpg
                            Figure 4

                            Kaplan-Meier survival curves for patients stratified according to adjuvant chemotherapy status with high RON expression (n = 16) (A) and low or absent RON expression (n = 265) (B) in the validation cohort (n = 281). The p-value from fitting a Cox proportional hazards model to the survival curves is indicated in the plots.

                            Additionally, there was no association between RON expression and tumor stage (Chi-squared P = 0.123), tumor size (P = 0.629) lymph node metastases (P = 0.942), grade (P = 0.332), perineural (P = 0.335) or vascular invasion (P = 0.210).

                            RON mRNA was expressed in a continuous manner over a third, prospectively acquired cohort, so two cutoffs were used: a 50%:50% (Figure 5A), and an 80%:20% (Figure 5B) low:high cutoff to match the frequency utilized in the retrospective cohorts subjected to immunohistochemistry. At neither cutoff was RON associated with prognosis. We omitted a group of patients with more advanced disease (leaving n = 65), and again, RON was not associated with prognosis (data not shown). In addition, MSP or MT-SP1 expression (Figure 6A & B), a two-gene expression signature of RON + MSP (Figure 6C), and a three-gene expression signature of RON + MSP + MT-SP1 (Figure 6D), were also investigated, however, none of these signatures were associated with prognosis.
                            http://static-content.springer.com/image/art%3A10.1186%2F1471-2407-12-395/MediaObjects/12885_2012_3546_Fig5_HTML.jpg
                            Figure 5

                            Kaplan-Meier survival curves for patients stratified based upon RON mRNA expression in the prospectively accrued ICGC cohort of PC (n = 88). Patients were split into two groups using a 50% low : 50% high cutoff (n = 44 in both groups) (A), or an 80% low : 20% high cutoff (n = 70 and n = 18, respectively) which matches the cutoff found using protein expression in the training cohort (B). The p-value from fitting a Cox proportional hazards model to the survival curves is indicated in the plots.

                            http://static-content.springer.com/image/art%3A10.1186%2F1471-2407-12-395/MediaObjects/12885_2012_3546_Fig6_HTML.jpg
                            Figure 6

                            Kaplan-Meier survival curves for patients stratified based upon mRNA expression levels of MSP (A), MT-SP1 (B), a two-gene signature combination of RON + MSP expression (C), and a three-gene signature combination of RON + MSP + MT-SP1 expression (D) from the prospectively accrued ICGC cohort (n = 88). All genes use a 50%:50% low:high cutoff. The p-value from fitting a Cox proportional hazards model to the survival curves is indicated in the plots.

                            Discussion

                            Pre-clinical studies identified RON as a potential predictive marker for gemcitabine response in pancreatic cancer [11, 12]. This study examined RON as a prognostic and predictive biomarker in three large well-annotated cohorts of patients (total of 492) with resectable PC using immunohistochemistry for the training and validation set and gene expression arrays for the ICGC cohort. Apart from the training cohort, RON expression was not associated with survival. The validation cohort consisted of 316 patients and had a 99% power to detect a hazard ratio of 1.90 (assuming it has a HR and an expression pattern similar to the training cohort with a Type I error of 0.05). However, RON overexpression was only observed in 5.4% of patients in the validation set as compared to 22.1% in the training set, but the reason for the much lower proportion of RON over-expressers in the validation set is not known. The unexpected lower proportion of the over-expressers in the validation cohort has reduced the power to detect a difference and therefore, the inability to detect a difference may be due to a Type 2 error rather than there being truly no difference between the two groups. A post-hoc analysis of the validation set using different cut-off points for RON expression could not demonstrate any differential survival (data not shown). It is therefore likely that due to the smaller number of patients, the training cohort returned a false-positive result, which highlights the importance of independent validation in biomarker discovery and development.

                            Both the antibody used for IHC and the probe used for mRNA analysis recognized the same RON isoforms: the full-length inactive precursor pro-RON, the active RON β-chain and short form sf-RON [15], thus enabling comparisons to be made between the cohorts.

                            RON mRNA was expressed in a continuous manner in the third, prospectively acquired cohort, so two cutoffs were used: the mean (Figure 5A), and an 80%:20% low:high cutoff to match the frequency utilized in the restrospective cohorts (Figure 5B). Using both approaches, RON was not associated with prognosis. Additional to RON expression alone, a combination gene signature of RON, MSP and MT-SP1, which is prognostic for breast cancer, was also evaluated. However, in PC this three-gene signature, and even two-gene signature combinations, did not correlate with patient survival. We acknowledge that there is reduced power in the 2-gene and 3-gene signatures due to low sample numbers in each strata, but it should be noted that this is the largest prospectively accrued cohort of PC patients with mRNA data to date. Collectively, this suggests that the role of RON is cancer-type specific and that RON is unlikely to be a major metastatic driver in PC. However, the ICGC cohort is still relatively young, currently with a median follow up of 14.1 months, and exhibits relatively small numbers. Therefore, the negative result can also possibly be due to the study being under-powered.

                            Data from this study do not support RON as a prognostic or predictive biomarker in resectable PC. This study, however, did demonstrate that RON is expressed in a large proportion of PC (training set: 61 of 64, 95.3%; validation set: 253 of 281, 90.0%), which is consistent with previous published data [10]. Due to increased expression during PC progression RON may still prove to be an effective therapeutic target.

                            To improve the efficiency of gemcitabine in PC, several studies have evaluated the co-administration of gemcitabine with other cytotoxic and biological agents [22, 23]. However, the epidermal growth factor receptor (EGFR) inhibitor, erlotinib, was the only agent to show efficacy in a phase III setting [24]. The EGFR is commonly overexpressed in PC and is associated with poor prognosis and disease progression [25]. The frequent overexpression of RON in PC suggests that it could also be a target for therapy. Supporting this concept, silencing RON expression reduced growth of pancreatic cancer xenografts and increased gemcitabine-induced apoptosis [11]. In addition, function-blocking monoclonal antibodies and small molecular inhibitors directed against RON have demonstrated promising results in pre-clinical models [10, 26, 27].

                            Conclusion

                            RON is not associated with prognosis or therapeutic responsiveness in resectable PC, in the current cohorts of patients. However it may still represent a therapeutic target in this disease.

                            Abbreviations

                            EGFR: 

                            Epidermal growth factor receptor

                            HGFL: 

                            Hepatocyte growth factor-like

                            H-score: 

                            Histoscore

                            HR: 

                            Hazard ratio

                            ICGC: 

                            International Cancer Genome Consortium

                            MET: 

                            Mesenchymal epithelial transition factor

                            MSP: 

                            Macrophage stimulating protein

                            MT-SP1: 

                            Membrane-type serine protease 1

                            PC: 

                            Pancreatic cancer

                            RON: 

                            Recepteur d’origine nantais

                            RTK: 

                            Receptor tyrosine kinase.

                            Declarations

                            Acknowledgements

                            This work was funded by the National Health and Medical Research Council of Australia. CMT and ESH are recipients of Cancer Institute of NSW Research Scholar Awards.

                            Australian Pancreatic Cancer Genome Initiative consortia

                            The Kinghorn Cancer Centre, Garvan Institute of Medical Research (372 Victoria St, Darlinghurst, Sydney, NSW 2010, Australia)

                            Andrew V. Biankin, Amber L. Johns, Amanda Mawson, David K. Chang, Christopher J. Scarlett, Mary-Anne L. Brancato, Sarah J. Rowe, Skye L. Simpson, Mona Martyn-Smith, Lorraine A. Chantrill, Venessa T. Chin, Angela Chou, Mark J. Cowley, Warren Caplan, Jeremy L. Humphris, Marc D. Jones, R. Scott Mead, Adnan M. Nagrial, Marina Pajic, Jessica Pettit, Mark Pinese, Ilse Rooman, Jianmin Wu, Roger J. Daly, Elizabeth A. Musgrove, Robert L. Sutherland;

                            Queensland Center for Medical Genomics, Institute for Molecular Bioscience (University of Queensland, St Lucia QLD 4072, Australia)

                            Sean M. Grimmond, Nicola Waddell, Karin S. Kassahn, David K. Miller, Peter J. Wilson, Ann-Marie Patch, Sarah Song, Ivon Harliwong, Senel Idrisoglu, Craig Nourse, Ehsan Nourbakhsh, Suzanne Manning, Shivangi Wani, Milena Gongora, Matthew Anderson, Oliver Holmes, Conrad Leonard, Darrin Taylor, Scott Wood, Christina Xu, Katia Nones, J. Lynn Fink, Angelika Christ, Tim Bruxner, Nicole Cloonan, Felicity Newell, John V. Pearson;

                            Royal North Shore Hospital (Westbourne Street, St Leonards, NSW 2065, Australia)

                            Jaswinder S. Samra, Anthony J. Gill, NickPavlakis, Alex Guminski, Christopher Toon;

                            Bankstown Hospital (Eldridge Rd, Bankstown, NSW 2200, Australia)

                            Andrew V. Biankin, Ray Asghari, Neil D. Merrett, David K. Chang, Darren A. Pavey, Amitabha Das;

                            Liverpool Hospital (Elizabeth St, Liverpool, NSW 2170, Australia)

                            Peter H. Cosman, Kasim Ismail, Chelsie O’Connor;

                            Westmead Hospital (Cnr of Hawkesbury and Darcy Roads, Westmead NSW 2145, Australia)

                            Vincent W. Lam, Duncan McLeod, Henry C. Pleass, Virginia James;

                            Royal Prince Alfred Hospital (Missenden Rd, Camperdown, NSW 2050, Australia)

                            James G. Kench, Caroline L. Cooper, David Joseph, Charbel Sandroussi, Michael Crawford;

                            Fremantle Hospital (Alma St, Fremantle, WA 6959, Australia)

                            Michael Texler, Cindy Forrest, Andrew Laycock, Krishna P. Epari, Mo Ballal, David R. Fletcher, Sanjay Mukhedkar;

                            Sir Charles Gardiner Hospital (Hospital Ave, Nedlands, WA 6009, Australia)

                            Nigel A. Spry, Bastiaan DeBoer, Ming Chai, Kynan Feeney;

                            St John of God Healthcare (12 Salvado Rd, Subiaco WA 6008, Australia)

                            Nikolajs Zeps, Maria Beilin;

                            Royal Adelaide Hospital (North Terrace, Adelaide, SA 5000, Australia)

                            Nam Q. Nguyen, Andrew R. Ruszkiewicz, Chris Worthley, Chuan P. Tan, Tamara Debrencini;

                            Flinders Medical Center (Flinders Drive, Bedford Park, SA 5042, Australia)

                            John Chen, Mark E. Brooke-Smith, Virginia Papangelis;

                            Greenslopes Private Hospital (Newdegate St, Greenslopes, Qld 4120, Australia)

                            Henry Tang, Andrew P. Barbour;

                            Envoi Pathology (1/49 Butterfield St, Herston QLD 4006, Australia)

                            Andrew D. Clouston, Patrick Martin;

                            Princess Alexandria Hospital (Cnr of Cornwall St & Ipswich Rd, Woolloongabba QLD 4102, Australia)

                            Thomas J. O’Rourke, Amy Chiang, Jonathan W. Fawcett, Kellee Slater, Shinn Yeung, Michael Hatzifotis, Peter Hodgkinson;

                            Austin Hospital (145 Studley Rd, Heidelberg VIC 3084, Australia)

                            Christopher Christophi, Mehrdad Nikfarjam, Victorian Cancer Biobank (1 Rathdowne Street, Carlton Victoria 3053 Australia);

                            Johns Hopkins Medical Institutes (600 North Wolfe St, Baltimore, MD 21287, USA)

                            James R. Eshleman, Ralph H. Hruban, Anirban Maitra, Christine A. Iacobuzio-Donahue, Richard D. Schulick, Christopher L. Wolfgang, Richard A. Morgan;

                            ARC-NET Center for Applied Research on Cancer (University of Verona, Via dell'Artigliere, 19 37129 Verona, Province of Verona, Italy)

                            Aldo Scarpa, Rita T. Lawlor, Stefania Beghelli, Vincenzo Corbo, Maria Scardoni, Claudio Bassi;

                            University of California, San Francisco (500 Parnassus Ave, San Francisco, CA 94122, USA)

                            Margaret A. Tempero

                            Authors’ Affiliations

                            (1)
                            Cancer Research Program, Garvan Institute of Medical Research
                            (2)
                            Department of Surgery, Bankstown Hospital
                            (3)
                            South Western Sydney Clinical School, Faculty of Medicine, University of NSW
                            (4)
                            Department of Anatomical Pathology, Royal North Shore Hospital
                            (5)
                            Northern Clinical School, Faculty of Medicine, University of Sydney
                            (6)
                            Department of Anatomical Pathology, St. Vincent’s Hospital
                            (7)
                            Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland
                            (8)
                            Australian Pancreatic Cancer Genome Initiative Consortium, The Kinghorn Cancer Centre, Garvan Institute of Medical Research

                            References

                            1. Neoptolemos JP, Dunn JA, Stocken DD, Almond J, Link K, Beger H, Bassi C, Falconi M, Pederzoli P, Dervenis C, et al.: Adjuvant chemoradiotherapy and chemotherapy in resectable pancreatic cancer: a randomised controlled trial. Lancet 2001,358(9293):1576–1585.PubMedView Article
                            2. Neoptolemos JP, Stocken DD, Friess H, Bassi C, Dunn JA, Hickey H, Beger H, Fernandez-Cruz L, Dervenis C, Lacaine F, et al.: A randomized trial of chemoradiotherapy and chemotherapy after resection of pancreatic cancer. N Engl J Med 2004,350(12):1200–1210.PubMedView Article
                            3. Sperti C, Pasquali C, Piccoli A, Pedrazzoli S: Recurrence after resection for ductal adenocarcinoma of the pancreas. World J Surg 1997,21(2):195–200.PubMedView Article
                            4. Oettle H, Post S, Neuhaus P, Gellert K, Langrehr J, Ridwelski K, Schramm H, Fahlke J, Zuelke C, Burkart C, et al.: Adjuvant chemotherapy with gemcitabine vs observation in patients undergoing curative-intent resection of pancreatic cancer: a randomized controlled trial. JAMA 2007,297(3):267–277.PubMedView Article
                            5. Samuel N, Hudson TJ: The molecular and cellular heterogeneity of pancreatic ductal adenocarcinoma. Nat Rev Gastroenterol Hepatol 2012,9(2):77–87.View Article
                            6. van de Vijver MJ, He YD, Van't Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, et al.: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002,347(25):1999–2009.PubMedView Article
                            7. Jamieson NB, Carter CR, McKay CJ, Oien KA: Tissue biomarkers for prognosis in pancreatic ductal adenocarcinoma: a systematic review and meta-analysis. Clin Cancer Res 2011,17(10):3316–3331.PubMedView Article
                            8. Biankin AV, Kench JG, Colvin EK, Segara D, Scarlett CJ, Nguyen NQ, Chang DK, Morey AL, Lee CS, Pinese M, et al.: Expression of S100A2 calcium-binding protein predicts response to pancreatectomy for pancreatic cancer. Gastroenterology 2009,137(2):558–568. 568 e551–511PubMedView Article
                            9. Wagh PK, Peace BE, Waltz SE: Met-related receptor tyrosine kinase Ron in tumor growth and metastasis. Adv Cancer Res 2008, 100:1–33.PubMedView Article
                            10. Camp ER, Yang A, Gray MJ, Fan F, Hamilton SR, Evans DB, Hooper AT, Pereira DS, Hicklin DJ, Ellis LM: Tyrosine kinase receptor RON in human pancreatic cancer: expression, function, and validation as a target. Cancer 2007,109(6):1030–1039.PubMedView Article
                            11. Logan-Collins J, Thomas RM, Yu P, Jaquish D, Mose E, French R, Stuart W, McClaine R, Aronow B, Hoffman RM, et al.: Silencing of RON receptor signaling promotes apoptosis and gemcitabine sensitivity in pancreatic cancers. Cancer Res 2010,70(3):1130–1140.PubMedView Article
                            12. Thomas RM, Toney K, Fenoglio-Preiser C, Revelo-Penafiel MP, Hingorani SR, Tuveson DA, Waltz SE, Lowy AM: The RON receptor tyrosine kinase mediates oncogenic phenotypes in pancreatic cancer cells and is increasingly expressed during pancreatic cancer progression. Cancer Res 2007,67(13):6075–6082.PubMedView Article
                            13. Catenacci DV, Cervantes G, Yala S, Nelson EA, El-Hashani E, Kanteti R, El Dinali M, Hasina R, Bragelmann J, Seiwert T, et al.: RON (MST1R) is a novel prognostic marker and therapeutic target for gastroesophageal adenocarcinoma. Cancer Biol Ther 2011,12(1):9–46.PubMedView Article
                            14. Welm AL, Sneddon JB, Taylor C, Nuyten DS, van de Vijver MJ, Hasegawa BH, Bishop JM: The macrophage-stimulating protein pathway promotes metastasis in a mouse model for breast cancer and predicts poor prognosis in humans. Proc Natl Acad Sci U S A 2007,104(18):7570–7575.PubMedView Article
                            15. Liu X, Zhao L, Derose YS, Lin YC, Bieniasz M, Eyob H, Buys SS, Neumayer L, Welm AL: Short-form ron promotes spontaneous breast cancer metastasis through Interaction with Phosphoinositide 3-Kinase. Genes Cancer 2011,2(7):753–762.PubMedView Article
                            16. Australian Pancreatic Cancer Network. ["http://​www.​pancreaticcancer​.​net.​au"] []
                            17. International Cancer Genome Consortium. ["http://​www.​icgc.​org"] []
                            18. Hudson TJ, Anderson W, Artez A, Barker AD, Bell C, Bernabe RR, Bhan MK, Calvo F, Eerola I, Gerhard DS, et al.: International network of cancer genome projects. Nature 2010,464(7291):993–998.PubMedView Article
                            19. Du P, Kibbe WA, Lin SM: lumi: a pipeline for processing Illumina microarray. Bioinformatics 2008,24(13):1547–1548.PubMedView Article
                            20. Kent WJ: BLAT–the BLAST-like alignment tool. Genome Res 2002,12(4):656–664.PubMed
                            21. Barbosa-Morais NL, Dunning MJ, Samarajiwa SA, Darot JF, Ritchie ME, Lynch AG, Tavare S: A re-annotation pipeline for Illumina BeadArrays: improving the interpretation of gene expression data. Nucleic Acids Res 2010,38(3):e17.PubMedView Article
                            22. Strimpakos A, Saif MW, Syrigos KN: Pancreatic cancer: from molecular pathogenesis to targeted therapy. Cancer Metastasis Rev 2008,27(3):495–522.PubMedView Article
                            23. O'Reilly EM, Abou-Alfa GK: Cytotoxic therapy for advanced pancreatic adenocarcinoma. Semin Oncol 2007,34(4):347–353.PubMedView Article
                            24. Moore MJ, Goldstein D, Hamm J, Figer A, Hecht JR, Gallinger S, Au HJ, Murawa P, Walde D, Wolff RA, et al.: Erlotinib plus gemcitabine compared with gemcitabine alone in patients with advanced pancreatic cancer: a phase III trial of the National Cancer Institute of Canada Clinical Trials Group. J Clin Oncol 2007,25(15):1960–1966.PubMedView Article
                            25. Ueda S, Ogata S, Tsuda H, Kawarabayashi N, Kimura M, Sugiura Y, Tamai S, Matsubara O, Hatsuse K, Mochizuki H: The correlation between cytoplasmic overexpression of epidermal growth factor receptor and tumor aggressiveness: poor prognosis in patients with pancreatic ductal adenocarcinoma. Pancreas 2004,29(1):e1–8.PubMedView Article
                            26. O'Toole JM, Rabenau KE, Burns K, Lu D, Mangalampalli V, Balderes P, Covino N, Bassi R, Prewett M, Gottfredsen KJ, et al.: Therapeutic implications of a human neutralizing antibody to the macrophage-stimulating protein receptor tyrosine kinase (RON), a c-MET family member. Cancer Res 2006,66(18):9162–9170.PubMedView Article
                            27. Zhang Y, Kaplan-Lefko PJ, Rex K, Yang Y, Moriguchi J, Osgood T, Mattson B, Coxon A, Reese M, Kim TS, et al.: Identification of a novel recepteur d'origine nantais/c-met small-molecule kinase inhibitor with antitumor activity in vivo. Cancer Res 2008,68(16):6680–6687.PubMedView Article
                            28. Pre-publication history

                              1. The pre-publication history for this paper can be accessed here:http://​www.​biomedcentral.​com/​1471-2407/​12/​395/​prepub

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                            © Tactacan et al.; licensee BioMed Central Ltd. 2012

                            This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.