An In vivo Tumor Model Exploiting Metabolic Response as a Biomarker for Targeted Drug Development

Abstract

In vivo models that recapitulate oncogene-dependent tumorigenesis will greatly facilitate development of molecularly targeted anticancer therapies. We have developed a model based on activating mutations in c-KIT in gastrointestinal stromal tumors (GISTs). This model comprises murine tumors of FDC-P1 cell lines expressing c-KIT mutations that render the tumors either responsive (V560G) or resistant (D816V) to the small-molecule c-KIT inhibitor, imatinib. Clinically, GIST response to imatinib is associated with rapid reduction in fluorodeoxyglucose (FDG) uptake on positron emission tomography (PET), preceding changes in conventional response criteria by several weeks. Using the FDC-P1 model in small animal PET, FDG uptake into tumors expressing the c-KIT V560G mutation was significantly reduced as early as 4 hours after imatinib treatment. In contrast, no change in FDG uptake was observed in resistant c-KIT D816V-expressing tumors after 48 hours of imatinib treatment. Consistent with the PET results, expression of the glucose transporter, GLUT1, was significantly reduced in V560G tumors at 4 hours, preceding changes in markers of proliferation by several hours. In vitro, imatinib treatment of V560G cells resulted in a reduction of glucose transporter numbers at the cell surface and decreased glucose uptake well before changes in cell viability. Notably, decreased ambient glucose concentrations enhanced the cytotoxic effect of imatinib. Taken together, these data account for the rapidity and significance of the PET response to imatinib and suggest that metabolic effects may contribute to imatinib cytotoxicity. Further, the FDC-P1 model represents a very useful paradigm for molecularly targeted drug development. (Cancer Res 2005; 65(21): 9633-6)

Preclinical and Clinical Evidence that Deoxy-2-[18F]fluoro-D-glucose Positron Emission Tomography with Computed Tomography Is a Reliable Tool for the Detection of Early Molecular Responses to Erlotinib in Head and Neck Cancer

“…We acknowledge that the method used in this study may be less accurate than the one used by Daisne et al (26) or Boucek et al (25), as the influence of the different noise-to-signal ratios is not taken into account for threshold determination (21,22). Nevertheless, the method used in this study is available on commercialized clinical software, and according to Krak et al (27), it seemed to be a good compromise between simplicity, user independence, reproducibility, and accuracy.…”

Preclinical Efficacy of the c-Met Inhibitor CE-355621 in a U87 MG Mouse Xenograft Model Evaluated by18F-FDG Small-Animal PET

“…With the development of microPET scanners for small animals (5), assessment of tumor xenograft mouse models with 18 F-FDG became possible for preclinical oncology research. Several authors have used 18 F-FDG microPET to assess various therapies in mouse tumor xenograft models (6)(7)(8)(9). In addition, several studies have compared 18 F-FDG microPET with an alternative proliferation tracer, 39-deoxy-39- 18 F-fluorothymidine ( 18 F-FLT), to assess tumor xenograft response to a variety of therapies (10)(11)(12)(13).…”

PET Imaging for Treatment Response in Cancer

“…37,38 Because of this high level of specificity and tumor responsiveness to therapy, early response detection also has been described. 39,40 In this study, after a decrease in FDG uptake by 35% or more and assessment after 14 days of therapy, histopathologic response after 3 months of therapy was identified correctly in 77% of the patients. As in lymphoma early treatment response, identification may facilitate the use of FDG PET as a response biomarker for new therapy combinations aimed at disease cure.…”

“…28 Tumor biology specific imaging agent combinations will be significant contributions when they identify patients who can benefit from newer treatment combinations such as those that incorporate antivascular therapy. 40,55 Additionally, these imaging results will provide a means for understanding treatment response mechanisms in clinical trials.…”