BACKGROUND: Realistic models of pancreatic cancer are necessary to develop effective drugs for the disease. More aggressive tumor models enhanced by brighter fluorescent biomarkers to follow the disease in real time would enhance the ability to predict accurately the effect of novel therapeutics on this particularly malignant human cancer. MATERIALS AND METHODS: A novel, highly fluorescent, red fluorescent protein (RFP)-expressing pancreatic cancer model was orthotopically established in nude mice. The MIA-PaCa-2 human pancreatic cancer cell line was transduced with RFP and grown subcutaneously. Fluorescent tumor fragments were then surgically transplanted onto the nude mouse pancreas. Groups treated with intraperitoneal gemcitabine or intravenous irinotecan were sequentially imaged to compare, in real time, the antimetastatic and antitumor effects of these agents compared with untreated controls. RESULTS: Rapid tumor growth and widespread metastases developed in untreated mice within 2 weeks, leading to a median survival of 21 days. In contrast, significant tumor growth suppression and consequent increase in survival (32.5 days, P = 0.009) were achieved with CPT-11. Gemcitabine highly improved survival (72 days, P = 0.004) by inducing transient tumor regression over the first 3 weeks. However, at this time, growth and dissemination occurred despite continued treatment, suggesting the development of tumor resistance. The antimetastatic efficacy of each drug was followed noninvasively in real time by imaging the RFP-expressing tumor and metastases, and was confirmed by fluorescent open imaging of autopsy specimens. CONCLUSIONS: This highly metastatic model reliably simulates the aggressive course of human pancreatic cancer. Noninvasive, sequential imaging permits quantification of tumor growth and dissemination and, thereby, real time evaluation of therapeutic efficacy. These features make this model an ideal, preclinical system with which to study novel therapeutics for pancreatic cancer.
BACKGROUND: Realistic models of pancreatic cancer are necessary to develop effective drugs for the disease. More aggressive tumor models enhanced by brighter fluorescent biomarkers to follow the disease in real time would enhance the ability to predict accurately the effect of novel therapeutics on this particularly malignant humancancer. MATERIALS AND METHODS: A novel, highly fluorescent, red fluorescent protein (RFP)-expressing pancreatic cancer model was orthotopically established in nude mice. The MIA-PaCa-2 humanpancreatic cancer cell line was transduced with RFP and grown subcutaneously. Fluorescent tumor fragments were then surgically transplanted onto the nude mouse pancreas. Groups treated with intraperitoneal gemcitabine or intravenous irinotecan were sequentially imaged to compare, in real time, the antimetastatic and antitumor effects of these agents compared with untreated controls. RESULTS: Rapid tumor growth and widespread metastases developed in untreated mice within 2 weeks, leading to a median survival of 21 days. In contrast, significant tumor growth suppression and consequent increase in survival (32.5 days, P = 0.009) were achieved with CPT-11. Gemcitabine highly improved survival (72 days, P = 0.004) by inducing transient tumor regression over the first 3 weeks. However, at this time, growth and dissemination occurred despite continued treatment, suggesting the development of tumor resistance. The antimetastatic efficacy of each drug was followed noninvasively in real time by imaging the RFP-expressing tumor and metastases, and was confirmed by fluorescent open imaging of autopsy specimens. CONCLUSIONS: This highly metastatic model reliably simulates the aggressive course of humanpancreatic cancer. Noninvasive, sequential imaging permits quantification of tumor growth and dissemination and, thereby, real time evaluation of therapeutic efficacy. These features make this model an ideal, preclinical system with which to study novel therapeutics for pancreatic cancer.
Authors: John J Grzesiak; Hop S Tran Cao; Douglas W Burton; Sharmeela Kaushal; Fabian Vargas; Paul Clopton; Cynthia S Snyder; Leonard J Deftos; Robert M Hoffman; Michael Bouvet Journal: Int J Cancer Date: 2011-04-13 Impact factor: 7.396
Authors: Ahmet Erten; Wolf Wrasidlo; Miriam Scadeng; Sadik Esener; Robert M Hoffman; Michael Bouvet; Milan Makale Journal: Nanomedicine Date: 2010-07-03 Impact factor: 5.307
Authors: S M Goicoechea; R García-Mata; J Staub; A Valdivia; L Sharek; C G McCulloch; R F Hwang; R Urrutia; J J Yeh; H J Kim; C A Otey Journal: Oncogene Date: 2013-03-25 Impact factor: 9.867
Authors: Paul Carroll; Lise J Schreuder; Julian Muwanguzi-Karugaba; Siouxsie Wiles; Brian D Robertson; Jorge Ripoll; Theresa H Ward; Gregory J Bancroft; Ulrich E Schaible; Tanya Parish Journal: PLoS One Date: 2010-03-23 Impact factor: 3.240
Authors: Cristina A Metildi; Csilla N Felsen; Elamprakash N Savariar; Quyen T Nguyen; Sharmeela Kaushal; Robert M Hoffman; Roger Y Tsien; Michael Bouvet Journal: Ann Surg Oncol Date: 2014-10-16 Impact factor: 5.344