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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Clin Cancer Res. Author manuscript; available in PMC 2012 September 1.
Published in final edited form as:
PMCID: PMC3210576
NIHMSID: NIHMS311159

Tumor engraftment in nude mice and enrichment in stroma-related gene pathways predicts poor survival and resistance to gemcitabine in patients with pancreatic cancer

Abstract

Purpose

To evaluate prospectively the engraftment rate, factors influencing engraftment, and predictability of clinical outcome of low-passage xenografts from patients with resectable pancreatic ductal adenocarcinoma (PDA) and to establish a bank of PDA xenografts.

Experimental Design

Patients with resectable PDA scheduled for resection at the Johns Hopkins Hospital were eligible. Representative pieces of tumor were implanted in nude mice. The status of the SMAD4 gene and content of tumor-generating cells were determined by immunohistochemistry (IHC). Gene expression was performed using U133 Plus 2.0 array. Patients were followed for progression and survival.

Results

94 patients with PDA were resected, 69 tumors implanted in nude mice, and 42 (61%) engrafted. Engrafted carcinomas were more often SMAD4 mutant, had a metastatic gene expression signature and worse prognosis. Tumors from patients resistant to gemcitabine were enriched in stroma-related gene pathways. Tumors sensitive to gemcitabine were enriched in cell cycle and pyrimidine gene pathways. The time progression for patients who received treatment with gemcitabine for metastatic disease (n=7) was double in patients with xenografts sensitive to gemcitabine.

Conclusion

A successful xenograft was generated in 61% of patients attempted, generating a pool of 42 PDA xenografts with significant biological information and annotated clinical data. Patients with PDA and SMAD4 inactivation have a better engraftment rate. Engraftment is a poor prognosis factor, and engrafted tumors have a metastatic gene expression signature. Tumors from gemcitabine-resistant patients were enriched in stromal pathways.

Keywords: Pancreatic cancer, freshly generated xenografts, gemcitabine, SMAD4

INTRODUCTION

Advanced pancreatic ductal adenocarcinoma (PDA) is a lethal disease.(1) Most drugs tested in PDA clinical trials were selected on the basis of their activity in preclinical models, which is not generally predictive of clinical outcome.(2) Traditional preclinical models are cell lines cultivated in monolayers or xenografts derived from them. Cells cultured in plastic may undergo substantial changes that deviate from their originator tumor, as recently shown with lung cancer cell lines.(3) In addition, the stroma is underrepresented in these models. Several lines of evidence demonstrate that stroma has a role in cancer cell survival and metastases, particularly in pancreatic cancer.(4, 5) Not surprisingly, targeting the stroma has recently been proposed as a novel strategy to improve drug delivery and chemotherapy efficacy in PDA(6, 7), and holds particular promise for this disease characterized by an intense desmoplastic reaction.(6, 8)

Other preclinical models have been tested in PDA, including genetically engineered mouse models (GEMM). These models maintain an intact immune system and many are histologically akin to human pancreatic cancer, including a dense desmoplastic stroma.(9) However, the predictive value of GEMM for assessing efficacy of novel anticancer agents in the clinic has not been extensively tested. Their dependence on a few critical genetic lesions, such as KRAS, P53 and CDKN2A/P16, might not reflect the genetic diversity that exemplifies human PDA(10) and they failed to predict response to Ras inhibitors in one clinical trial.(11) Early passages of freshly generated xenografts are probably better candidates for mimicking the heterogeneity of the disease and might be better predictors of response. We have previously established a collection of 23, extensively characterized PDA xenografts under an anonymized exempt tissue protocol that is being used as a platform for drug screening and biomarker development.(12, 13) The initial tumor bank comprised of these PDA xenograft did not include annotated clinical data such as clinical drug response.

The primary goal of this prospective clinical trial was use freshly generated xenografts from patients with resectable PDA to determine the engraftment rate and dynamics of the process, the biological characteristics and prognostic implications of tumor engrafting as well as the correlation between patient response to gemcitabine and their corresponding xenografts. A secondary objective was to generate a clinically annotated and biologically characterized collection of PDA xenografts for future studies.

MATERIAL AND METHODS

Patients eligibility

Patients with pancreatic masses suspicious of a diagnosis of PDA were enrolled, prior to surgery, in J0507, a Johns Hopkins University clinical trial (NCT00276744). Tissue samples of PDA not needed for diagnosis were xenografted into nude mice.

Eligibility criteria also included ECOG performance status 0-1, age older than 18 years, expected survival longer than 12 weeks, no prior treatment for PDA, and adequate liver, renal and bone marrow function (absolute neutrophil count ≥ 1500/uL, platelets ≥ 100000/uL, hemoglobin ≥ 9 g/dL, serum creatinin ≤2 mg/dL, bilirubin ≤2 mg/dL, ALT, AST and alkaline phosphatase ≤5 times the upper limit of normal). The study was approved by the Johns Hopkins University institutional review board.

Xenografts

PDA tumor specimens from resected patients were implanted subcutaneously on each flank of five- to six-week old athymic nude mice and expanded as previously described.(12)

Treatment

When tumors reached ≈200mm3, mice were randomized to treatment and control arms. Gemcitabine, was administered at 100mg/kg intraperitoneally to third-generation passages (F3) three times per week for four weeks. Tumor size was evaluated two times a week by caliper measurements using the following formula: Tumor volume = [length × width2]/2. Relative tumor growth inhibition/regression was calculated as T/C = (Ti-T0/Ci-C0), Ti and Ci represent tumor size of treatment and control group at the end of experiments respectively; T0 and C0 represent tumor size at initiation of experiments respectively. T/C>0 represent growth inhibition, T/C<0 represents tumor regression. The research protocol was approved by the Johns Hopkins University Animal Care and Use Committee, and animals were maintained in accordance to guidelines of the American Association of Laboratory Animal Care.

SMAD4 and ALDH staining

Immunolabeling was performed on Bond-Leica autostainer (Leica Microsystems, Bannockburn, IL.) using a standard immunohistochemistry protocol (supplementary Methods).

Microarray Gene Expression

Primary tumors were profiled when possible using Affymetrix U133 Plus 2.0 GeneChip arrays (Affymetrix, Santa Clara, CA) in duplicates. In four patients primary tumors (F0) and paired samples from F5 (passage 5) and F10 (passage 10) were profiled. Sample preparation and processing were performed as described in the Affymetrix GeneChip Expression Analysis Manual (Affymetrix, Inc.). The gene expression data will be deposited at the NCBI GEO.

GSEA

Gene set enrichment analysis was performed using GSEA software, Version 2.0.1, obtained from the Broad Institute.(14) Gene set permutations were done 1000 times for each analysis. The nominal P value and normalized enrichment score (NES) were used to rank the pathways enriched in each phenotype. We used 199 pathways defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) database as the gene set in this study. One hundred fifty-seven gene sets passed the gene set size filter criteria (min=10, max=500).

Statistical analysis

Overall survival (OS) was calculated using the Kaplan-Meier method.(15) OS was computed from the date of surgery to the date of death or last follow-up. Median OS was reported in days with 95% CIs. Survival analyses were conducted in patients who survived more than 30 days to exclude perioperative mortality. Univariate and multivariate Cox proportional hazards models were fit to assess the associations between patient characteristics and clinical outcomes. The following variables were used for univariate analysis: tumor size, grade, margin status, perineural invasion, SMAD4 status, engraftment rate and adjuvant therapy. Variables that were marginally significant in the univariate analysis were included in the Cox model multivariate analysis. The results of the Cox model are reported with hazard ratios and 95% CI. A p value <0.05 was considered significant for all statistical analysis. Statistical analyses were performed using the statistical analysis package SPSS version 17 (SPSS, Chicago IL).

RESULTS

Overall Patient and Xenograft Characteristics

Figure 1 depicts the flow of patients. A total of 94 patients with PDA were operated on and 85 were eligible to have their tumors xenografted into nude mice. These were patients with resected PDA who had not received neoadjuvant treatment. Of these 85, 69 were xenografted. The flow chart describes the reasons why patients could not be xenografted. Forty-two of the 69 implanted cancers engrafted for an engraftment rate of 61%. Table 1 summarizes the principal clinical characteristics of patients and supplementary Table 1 lists detailed information regarding tumor stage, treatment, the xenograft generated from these patients and the principal biological information available from these tumors. This collection of well annotated PDA xenografts can form the basis of drug screening and biomarker development.

Figure 1
Patient flow chart. 94 patients with resected PDA were included in this study. 85 patients were eligible for xenografting. Patients who received neoadjuvant therapy or had stage IV resected PDA were excluded. Out of the 69 xenografted tumors, 42 were ...
Table 1
Characteristics of 69 Xenografted patients

Biological Characteristics of Engrafted Tumors

To determine biological features associated with a higher rate of engraftment, we first estimated whether the proportion of tumor initiating cells (TICs), determined by the expression of ALDH, was related to a higher engraftment rate. Our group recently showed a correlation between the tumor initiating compartment and PDA and the expression of this intracellular enzyme.(16) However, we found no differences in the expression of ALDH in carcinomas that engrafted in mice compared to those that did not (data not shown).

We examined SMAD4 alterations to see if they were associated with a higher take rate in the mouse. The primary cancers from 58 of the 69 xenografted patients were analyzed and SMAD4 status was determined by Smad4 immunolabeling patterns, a strong marker of SMAD4 genetic status.(17, 18) The incidence of Smad4 protein loss was statistically higher in engrafted than in non-engrafted patients (67 vs. 36%, p=0.024) (Figure 2A). We also show that Smad4 loss was not a marker of tumor grade given the fact that Smad4 might be deleted in low-grade tumors while preserved in poorly differentiated ones (Figure 2B).

Figure 2Figure 2
A. Engraftment rate was higher in patients with SMAD4 deletions

To explore further previous work from our group showing that SMAD4-mutant PDAs have a higher metastatic potential(18), we examined the presence of a metastasis-associated gene signature developed by Ramaswamy et al. (19) This gene-signature contains seventeen genes that were identified by comparing adenocarcinoma metastases from multiple tumor types to unmatched primary adenocarcinomas. In this analysis, we used the gene expression profiles from four primary tumors and two different passages of their matching xenografts. We found that five out of eight genes from the gene signature of metastatic adenocarcinoma (SNRPF, EIF4E2, HNRPAB, DHPS, and PTTG1) were also upregulated in the xenografts compared to their counterpart primary tumors (Figure 3). Additionally, four out of the nine genes that were downregulated in this gene signature were also downregulated in xenografts versus in primary tumors (F0).

Figure 3
Xenografts show a metastatic signature of adenocarcinoma. We collected the gene expression profiles of primary tumors (F0) and corresponding xenografts (F5 and F10). For each xenograft the first column corresponds to primary tumor (F0). Second and third ...

Engrafted patients had poorer overall survival

Because cancers that successfully engrafted putatively have a higher metastatic potential, we investigated the prognostic role of engraftment in addition to other well known prognostic factors. We conducted a univariate analysis to determine impact on survival (supplementary table 2) and found that Smad4 status, tumor size ≥ 3.5cm, positive margins, no adjuvant treatment and ability to engraft were associated with shorter survival. In contrast, only tumor engraftment (p < 0.006) and adjuvant therapy (p<0.008) were significant predictors of shorter survival in the multivariate analysis. The median survival for engrafted patients was 299 days (95% CI 230 to 369 days), whereas the median survival for patients whose cancers failed to engraft has not been reached (>800 days). Patients whose cancers failed to engraft had a 81% reduction in the risk of death [HR=0.19, 95% CI (0.060, 0.632)] (supplementary Table 3 and Figure 4). As PDA patients typically die of metastatic disease, this finding is consistent with the Smad4 status and metastatic signature of engrafted cancers.

Figure 4
Kaplan-Meier survival curves as a function of engraftment. Non engrafted patients had a decrease in the risk of death of 81%

Gene set enrichment analysis (GSEA) identified upregulation of stromal and Notch pathways in patients resistant to gemcitabine

GSEA was used to investigate gene pathways that might be involved in resistance to gemcitabine using the gene expression profiles from primary tumors from patients treated with gemcitabine (supplementary Table 1). GSEA provides statistical measurements for the gene pathways that are enriched when gene expression profiles from two different phenotypes are compared (i.e., sensitive versus resistance to gemcitabine). At baseline, 79 and 76 pathways were upregulated in patients sensitive and resistant to gemcitabine, respectively. Gemcitabine–resistant tumors showed enrichment in gene pathways related to stroma (ECM receptor interaction, focal adhesion, cell communication, GAP junction, cell adhesion molecules) and stem cells (Notch signaling pathways) (supplementary Table 4). Gemcitabine-sensitive tumors demonstrated upregulation of cell cycle and pyrimidine pathways (supplementary Table 5). Overall, these results suggest that coordinated overexpression of genes in stromal and Notch signaling pathways may confer resistance to gemcitabine.

Prediction of Clinical Outcome with Gemcitabine

A xenograft was successfully developed in 23 patients who were clinically treated with gemcitabine. In the 7 of 23 patients for whom gemcitabine was used to treat metastatic disease, the response of the xenografted cancers to gemcitabine correctly predicted longer time to progression. In patients whose xenografts were sensitive to gemcitabine, the median time to progression was 80 vs. 46 days (log-rank p=0.037). Gemcitabine was administered in the adjuvant setting to 16 patients as part of varied complex multimodality regimens, precluding correlation between activity in the preclinical model with clinical outcome. Anecdotally, however, one patient with a T3N1M0 cancer had a rather long disease-free survival after surgery and 6 cycles of conventional gemcitabine (1,203 days) but that patient’s xenograft was resistant to gemcitabine. To gain further insight into this finding we performed an orthotopic implantation of the patient’s tumor and observed that, in contrast to the subcutaneously implanted tumor, the orthotopic model was exquisitely sensitive to gemcitabine. In fact, the primary patient tumor and orthotopically implanted tumor had a similar gemcitabine response signature but diverse from the subcutaneously implanted tumor (supplementary Figure 1). Moreover, while a desmoplastic reaction was present in the orthotopic model, it was significantly decreased in the subcutaneous xenograft (supplementary Figure 2). Although this discrepancy was only observed in one patient, the evidence above suggest that orthotopic models may be better predictors of response in this disease.

DISCUSSION

The goal of this clinical trial was to prospectively generate fresh PDA xenografts with annotated clinical information and significant biological data. The main results of the study indicate that patients whose tumors engrafted have a poor outcome, which is likely related to loss of Smad4 and increased metastatic potential. Due to the inherent nature of complex adjuvant protocols, we were not able to correlate response to gemcitabine in the xenograft model and time to progression given that few patients received gemcitabine alone adjuvantly. Nonetheless, the data confirm that response to gemcitabine in the xenograft model did correlate with response to gemcitabine in the advanced metastatic setting. However, an important limitation of this study was that only seven patients received single agent gemcitabine, abrogating definitive statistical correlations.

We demonstrate that tumor engraftment is an independent predictor of survival in resected PDA. Patients whose tumors failed to engraft had an 81% reduced risk of death. To further investigate this issue we tested two complementary hypotheses. One is that engrafting tumor contains higher proportion of tumor initiating cells (TICs) as these cells have been linked to treatment resistance and development of metastasis.(20) No differences were observed in the number of TICs as assessed by IHC for ALDH.(16) However, other markers that have been associated with TICs in pancreatic cancer were not tested in this study.(21) The second hypothesis was that engrafting tumors lack Smad4 based on previous work from our group, which showed that Smad4-defective tumors have a worse prognosis and a higher metastatic potential.(18, 22, 23) Indeed, engrafted tumors more frequently had Smad4 loss (67% vs. 36%, p=0.024). Development of metastatic disease is the ultimate determinant of survival in cancer patients. This is further supported by the finding that the gene expression of xenografted tumors is similar to metastatic adenocarcinomas, suggesting that passage of the tumor in mice selects a clonal population of cells with a predilection to colonize new microenvironments. Altogether, the higher prevalence of Smad4 loss and the presence of a metastatic signature of adenocarcinoma in engrafted patients suggest that patients whose tumors engraft are more likely to have metastatic disease. This is consistent with evidence from different disease types showing that engraftment is a marker of poor prognosis.(24, 25)

Several lines of evidence suggest that the stroma plays an important role in the pathophysiology of cancer. Enrichment of stroma-related genes was observed in the progression from preinvasive disease to invasive gastrointestinal cancers.(26) This mechanism is not disease specific as mesenchymal stem cells within the stroma promote breast cancer growth and metastases.(27) Moreover, shifting towards a stromal phenotype is an intrinsic property of chemotherapy-resistant tumors.(28) An additional finding in our work was that stromal pathways were enriched in gemcitabine-resistant patients. We acknowledge that the number of samples included in the gene expression analysis was low to draw definitive conclusions (supplementary Table 1), however, this finding needs to be examined in the context of earlier works showing that stromal depletion may increase tumor permeability to gemcitabine and result in increased drug activity.(6, 7) A phase III trial is testing this “stromal collapse” strategy based on promising activity in the phase I setting.(29)

The work presented here also augments information from previous preclinical models by developing tumors from clinically well-annotated cases. For many of these tumors, there is significant information available from “omics” technologies, including global exonic sequencing.(10) This is a unique platform for performing additional research in PDA, although the development of these platforms has not been limited to PDA. Recently, several papers have reported similar efforts in other diseases such as lung cancer, melanoma and breast cancer.(24, 30, 31) It is anticipated that these models will progressively become more utilized in early drug development discovery.

A fundamental question that must be answered to justify using freshly generated xenograft platforms for drug development is whether findings in the model correlate with clinical findings. Our first attempt to see if such a correlation existed was analyzing the results of adjuvant treatment. However, most patients included in this series had been treated with complex multimodality treatments that were impossible to replicate in a murine model (supplementary Table 1). In retrospect, it became apparent that the ability to appropriately assess correlations between the model and clinical responses in patients rested upon protocol-guided treatments. In the group of patients who received gemcitabine in a setting of advanced cancer, the time to treatment failure was longer in patients whose xenografts responded better to gemcitabine. A difficulty we also encountered was the lack of efficacy of gemcitabine in these xenografts and, indeed, as borne out by applying RECIST criteria to the T/C data and finding that not a single case reached the 30% partial response mark. This lack of response highlights the major issue in PDA treatment, which is the lack of minimally effective treatments. While overall these data are encouraging, additional work is needed to properly determine the predictive capabilities of these models. This point is illustrated by the potentially better predictive power of orthotopic implantation versus subcutaneous implantation.

In summary, we have developed and characterized a set of 42 PDA engrafted xenografts with annotated clinical information. Interestingly, successfully engrafted tumors are more likely to lack Smad4 and show a gene expression profile similar to cancer metastasis, which might explain the finding that these patients have a poorer prognosis. The stromal compartment has a putative role in resistance to gemcitabine. Despite the limitations in the number of patients and the overall lack of efficacy of gemcitabine treatment, the model seems to be predictive of response to gemcitabine in the metastatic setting. Given the complexities of generating these models and testing drugs in real time, it is unlikely that this approach will be broadly applicable to personalized cancer treatment, at least in PDA with its rapid clinical course and very few effective agents to address this refractory tumor. Moreover, our findings suggest that host microenviroments are better mimicked by orthotopic models. Although our data is limited to one patient, it suggest that orthotopic xenografts may be better predictors of response. Additional work is needed to continue developing and exploring the role of freshly generated xenografts in cancer research.

Figure 5
Waterfall plot with gemcitabine activity in xenografts. We applied RECIST criteria to T/C to define sensitivity/resistance. We reduced the threshold of sensitivity to −15% in order to have sensitive xenografts, since none of them had a T/C <−30%. ...

STATEMENT OF TRANSLATIONAL RELEVANCE

Current preclinical models of pancreatic cancer based on cell lines or xenografts derived from them are not predictive of drug activity in the clinic and do not represent the heterogeneity and complexity of this disease. We have established a platform of freshly generated pancreatic cancer xenografts annotated with clinical information and show that patients whose tumors engrafted had a significantly worse prognosis. The data show that these models predict response to gemcitabine in the metastatic setting and that the stromal compartment has a critical role in gemcitabine resistance. This collection of freshly generated pancreatic ductal adenocarcinoma models are a useful platform for preclinical drug screening, biomarker discovery, and delineating the biology of pancreatic cancer.

Supplementary Material

Acknowledgement

Joann Aaron for scientific editing

Grant support: Fundación de Caja Madrid Fellowship 2008-2010 (IGL), SEOM Young Investigator Grant 2007 (IGL), CA113669 and The Sol Goldman Pancreatic Cancer Research Center (AM), CA116554 and CA129963 (MH),

Footnotes

Presented at: The 2008 ASCO Annual Meeting “A prospective validation of a direct tumor xenograft model in pancreatic ductal adenocarcinoma (PDA)” and the 2009 ASCO Annual Meeting “Activity of Gemcitabine in direct patient-derived xenografts and clinical outcome: validation of an in vivo model for drug development”.

Contributors: Conception and design: AM, BRV, AJ and MH. Financial support: MH and AM.

Administrative support: MH, GT. Provision of study materials or patients: IGL, MU, GT, DL, AM, RH, AJ, MH. Experiments, collection and assembly of data: IGL, MU, NVR, ACT, EO, CK, MCV, AS and RS. Data analysis and interpretation: IGL, MU, RH, AM, MH. Manuscript writing: IGL, MU, NVR, RH, AM, MH.

REFERENCES

1. Hidalgo M. Pancreatic cancer. N Engl J Med. 2010;362:1605–17. [PubMed]
2. Johnson JI, Decker S, Zaharevitz D, et al. Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trials. Br J Cancer. 2001;84:1424–31. [PMC free article] [PubMed]
3. Daniel VC, Marchionni L, Hierman JS, et al. A primary xenograft model of small-cell lung cancer reveals irreversible changes in gene expression imposed by culture in vitro. Cancer Res. 2009;69:3364–73. [PMC free article] [PubMed]
4. Hwang RF, Moore T, Arumugam T, et al. Cancer-associated stromal fibroblasts promote pancreatic tumor progression. Cancer Res. 2008;68:918–26. [PMC free article] [PubMed]
5. Vonlaufen A, Joshi S, Qu C, et al. Pancreatic stellate cells: partners in crime with pancreatic cancer cells. Cancer Res. 2008;68:2085–93. [PubMed]
6. Olive KP, Jacobetz MA, Davidson CJ, et al. Inhibition of Hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer. Science. 2009;324:1457–61. [PMC free article] [PubMed]
7. Maitra A, Rajeshkumar N, Rudek M. nab-paclitaxel targets tumor stroma and results, combined with gemcitabine, in high efficacy against pancreatic cancer models. AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; Boston. 2009. al. e. 2009.
8. Mahadevan D, Von Hoff DD. Tumor-stroma interactions in pancreatic ductal adenocarcinoma. Mol Cancer Ther. 2007;6:1186–97. [PubMed]
9. Olive KP, Tuveson DA. The use of targeted mouse models for preclinical testing of novel cancer therapeutics. Clin Cancer Res. 2006;12:5277–87. [PubMed]
10. Jones S, Zhang X, Parsons DW, et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. 2008;321:1801–6. [PMC free article] [PubMed]
11. Kohl NE, Omer CA, Conner MW, et al. Inhibition of farnesyltransferase induces regression of mammary and salivary carcinomas in ras transgenic mice. Nat Med. 1995;1:792–7. [PubMed]
12. Rubio-Viqueira B, Jimeno A, Cusatis G, et al. An in vivo platform for translational drug development in pancreatic cancer. Clin Cancer Res. 2006;12:4652–61. [PubMed]
13. Rubio-Viqueira B, Hidalgo M. Direct in vivo xenograft tumor model for predicting chemotherapeutic drug response in cancer patients. Clin Pharmacol Ther. 2009;85:217–21. [PubMed]
14. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–50. [PubMed]
15. Kaplan EL, Meier P. Nonparametric estimator from incomplete observations. JAmerican Statistical Association. 1958;53:457–81.
16. Rasheed ZA, Yang J, Wang Q, et al. Prognostic significance of tumorigenic cells with mesenchymal features in pancreatic adenocarcinoma. J Natl Cancer Inst. 2010;102:340–51. [PMC free article] [PubMed]
17. Wilentz RE, Su GH, Dai JL, et al. Immunohistochemical labeling for dpc4 mirrors genetic status in pancreatic adenocarcinomas : a new marker of DPC4 inactivation. Am J Pathol. 2000;156:37–43. [PubMed]
18. Iacobuzio-Donahue CA, Fu B, Yachida S, et al. DPC4 gene status of the primary carcinoma correlates with patterns of failure in patients with pancreatic cancer. J Clin Oncol. 2009;27:1806–13. [PMC free article] [PubMed]
19. Ramaswamy S, Ross KN, Lander ES, Golub TR. A molecular signature of metastasis in primary solid tumors. Nat Genet. 2003;33:49–54. [PubMed]
20. Charafe-Jauffret E, Ginestier C, Iovino F, et al. Aldehyde dehydrogenase 1-positive cancer stem cells mediate metastasis and poor clinical outcome in inflammatory breast cancer. Clin Cancer Res. 2010;16:45–55. [PMC free article] [PubMed]
21. Li C, Heidt DG, Dalerba P, et al. Identification of pancreatic cancer stem cells. Cancer Res. 2007;67:1030–7. [PubMed]
22. Blackford A, Serrano OK, Wolfgang CL, et al. SMAD4 gene mutations are associated with poor prognosis in pancreatic cancer. Clin Cancer Res. 2009;15:4674–9. [PMC free article] [PubMed]
23. Tascilar M, Skinner HG, Rosty C, et al. The SMAD4 protein and prognosis of pancreatic ductal adenocarcinoma. Clin Cancer Res. 2001;7:4115–21. [PubMed]
24. Nemati F, Sastre-Garau X, Laurent C, et al. Establishment and characterization of a panel of human uveal melanoma xenografts derived from primary and/or metastatic tumors. Clin Cancer Res. 2010;16:2352–62. [PubMed]
25. John T, Kohler D, Pintilie M, et al. The ability to form primary tumor xenografts is predictive of increased risk of disease recurrence in early-stage non-small cell lung cancer. Clin Cancer Res. 2011;17:134–41. [PubMed]
26. Saadi A, Shannon NB, Lao-Sirieix P, et al. Stromal genes discriminate preinvasive from invasive disease, predict outcome, and highlight inflammatory pathways in digestive cancers. Proc Natl Acad Sci U S A. 2010;107:2177–82. [PubMed]
27. Karnoub AE, Dash AB, Vo AP, et al. Mesenchymal stem cells within tumour stroma promote breast cancer metastasis. Nature. 2007;449:557–63. [PubMed]
28. Marchini C, Montani M, Konstantinidou G, et al. Mesenchymal/stromal gene expression signature relates to basal-like breast cancers, identifies bone metastasis and predicts resistance to therapies. PLoS One. 2010;5:e14131. [PMC free article] [PubMed]
29. Von Hoff DD, Rmanathan R, Borad M, et al. SPARC correlation with response to gemcitabine (G) plus nab-paclitaxel (nab-P) in patients with advanced metastatic pancreatic cancer: A phase I/II study. J Clin Oncol. 2009;27:15s.
30. Dong X, Guan J, English JC, et al. Patient-derived first generation xenografts of non-small cell lung cancers: promising tools for predicting drug responses for personalized chemotherapy. Clin Cancer Res. 2010;16:1442–51. [PubMed]
31. Marangoni E, Vincent-Salomon A, Auger N, et al. A new model of patient tumor-derived breast cancer xenografts for preclinical assays. Clin Cancer Res. 2007;13:3989–98. [PubMed]