Jeff J Subleski1, Anthony J Scarzello1, W Gregory Alvord2, Qun Jiang1, Jimmy K Stauffer1, Anthony Kronfli1, Bahara Saleh1, Timothy Back1, Jonathan M Weiss1, Robert H Wiltrout3. 1. Cancer and Inflammation Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, United States. 2. Statistical Consulting, Data Management Services, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, United States. 3. Cancer and Inflammation Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, United States. Electronic address: wiltrour@mail.nih.gov.
Abstract
BACKGROUND & AIMS: Liver inflammatory diseases associated with cancer promoting somatic oncogene mutations are increasing in frequency. Preclinical cancer models that allow for the study of early tumor progression are often protracted, which limits the experimental study parameters due to time and expense. Here we report a robust inexpensive approach using Sleeping Beauty transposition (SBT) delivery of oncogenes along with Gaussia Luciferase expression vector GLuc, to assess de novo liver tumor progression, as well as the detection of innate immune responses or responses induced by therapeutic intervention. METHODS: Tracking de novo liver tumor progression with GLuc was demonstrated in models of hepatocellular carcinoma (HCC) or adenoma (HCA) initiated by hydrodynamic delivery of SBT oncogenes. RESULTS: Rising serum luciferase levels correlated directly with increasing liver tumor burden and eventual morbidity. Early detection of hepatocyte apoptosis from mice with MET+CAT transfected hepatocytes was associated with a transient delay in HCC growth mediated by a CD8(+) T-cell response against transformed hepatocytes. Furthermore, mice that lack B cells or macrophages had an increase in TUNEL(+) hepatocytes following liver MET transfection demonstrating that these cells provide protection from MET-induced hepatocyte apoptosis. Treatment with IL-18+IL-12 of mice displaying established HCC decreased tumor burden which was associated with decreased levels of serum luciferase. CONCLUSIONS: Hydrodynamic delivery of the SBT vector GLuc to hepatocytes serves as a simple blood-based approach for real-time tracking of pathologically distinct types of liver cancer. This revealed tumor-induced immunologic responses and was beneficial in monitoring the efficacy of therapeutic interventions. Published by Elsevier B.V.
BACKGROUND & AIMS: Liver inflammatory diseases associated with cancer promoting somatic oncogene mutations are increasing in frequency. Preclinical cancer models that allow for the study of early tumor progression are often protracted, which limits the experimental study parameters due to time and expense. Here we report a robust inexpensive approach using Sleeping Beauty transposition (SBT) delivery of oncogenes along with Gaussia Luciferase expression vector GLuc, to assess de novo liver tumor progression, as well as the detection of innate immune responses or responses induced by therapeutic intervention. METHODS: Tracking de novo liver tumor progression with GLuc was demonstrated in models of hepatocellular carcinoma (HCC) or adenoma (HCA) initiated by hydrodynamic delivery of SBT oncogenes. RESULTS: Rising serum luciferase levels correlated directly with increasing liver tumor burden and eventual morbidity. Early detection of hepatocyte apoptosis from mice with MET+CAT transfected hepatocytes was associated with a transient delay in HCC growth mediated by a CD8(+) T-cell response against transformed hepatocytes. Furthermore, mice that lack B cells or macrophages had an increase in TUNEL(+) hepatocytes following liver MET transfection demonstrating that these cells provide protection from MET-induced hepatocyte apoptosis. Treatment with IL-18+IL-12 of mice displaying established HCC decreased tumor burden which was associated with decreased levels of serum luciferase. CONCLUSIONS: Hydrodynamic delivery of the SBT vector GLuc to hepatocytes serves as a simple blood-based approach for real-time tracking of pathologically distinct types of liver cancer. This revealed tumor-induced immunologic responses and was beneficial in monitoring the efficacy of therapeutic interventions. Published by Elsevier B.V.
Authors: S Shimizu; H Shirato; B Xo; K Kagei; T Nishioka; S Hashimoto; K Tsuchiya; H Aoyama; K Miyasaka Journal: Radiother Oncol Date: 1999-03 Impact factor: 6.280
Authors: Esra A Akbay; Shohei Koyama; Julian Carretero; Abigail Altabef; Jeremy H Tchaicha; Camilla L Christensen; Oliver R Mikse; Andrew D Cherniack; Ellen M Beauchamp; Trevor J Pugh; Matthew D Wilkerson; Peter E Fecci; Mohit Butaney; Jacob B Reibel; Margaret Soucheray; Travis J Cohoon; Pasi A Janne; Matthew Meyerson; D Neil Hayes; Geoffrey I Shapiro; Takeshi Shimamura; Lynette M Sholl; Scott J Rodig; Gordon J Freeman; Peter S Hammerman; Glenn Dranoff; Kwok-Kin Wong Journal: Cancer Discov Date: 2013-09-27 Impact factor: 39.397
Authors: Tae-Won Kang; Tetyana Yevsa; Norman Woller; Lisa Hoenicke; Torsten Wuestefeld; Daniel Dauch; Anja Hohmeyer; Marcus Gereke; Ramona Rudalska; Anna Potapova; Marcus Iken; Mihael Vucur; Siegfried Weiss; Mathias Heikenwalder; Sadaf Khan; Jesus Gil; Dunja Bruder; Michael Manns; Peter Schirmacher; Frank Tacke; Michael Ott; Tom Luedde; Thomas Longerich; Stefan Kubicka; Lars Zender Journal: Nature Date: 2011-11-09 Impact factor: 49.962
Authors: Kerstin Schag; Susanne M Schmidt; Martin R Müller; Toni Weinschenk; Silke Appel; Markus M Weck; Frank Grünebach; Stefan Stevanovic; Hans-Georg Rammensee; Peter Brossart Journal: Clin Cancer Res Date: 2004-06-01 Impact factor: 12.531
Authors: C Bonini; G Ferrari; S Verzeletti; P Servida; E Zappone; L Ruggieri; M Ponzoni; S Rossini; F Mavilio; C Traversari; C Bordignon Journal: Science Date: 1997-06-13 Impact factor: 47.728
Authors: Rami S Kantar; Ghazal Lashgari; Elie I Tabet; Grant K Lewandrowski; Litia A Carvalho; Bakhos A Tannous Journal: Sci Rep Date: 2016-05-20 Impact factor: 4.379