Modeling targeted inhibition of MEK and PI3 kinase in human pancreatic cancer.

Activating mutations in the KRAS oncogene occur in approximately 90% of pancreatic cancers, resulting in aberrant activation of the MAPK and the PI3K pathways, driving malignant progression. Significant efforts to develop targeted inhibitors of nodes within these pathways are underway and several are currently in clinical trials for patients with KRAS-mutant tumors, including patients with pancreatic cancer. To model MEK and PI3K inhibition in late-stage pancreatic cancer, we conducted preclinical trials with a mutant Kras-driven genetically engineered mouse model that faithfully recapitulates human pancreatic ductal adenocarcinoma development. Treatment of advanced disease with either a MEK (GDC-0973) or PI3K inhibitor (GDC-0941) alone showed modest tumor growth inhibition and did not significantly enhance overall survival. However, combination of the two agents resulted in a significant survival advantage as compared with control tumor-bearing mice. To model the clinical scenario, we also evaluated the combination of these targeted agents with gemcitabine, the current standard-of-care chemotherapy for pancreatic cancer. The addition of MEK or PI3K inhibition to gemcitabine, or the triple combination regimen, incrementally enhanced overall survival as compared with gemcitabine alone. These results are reminiscent of the survival advantage conferred in this model and in patients by the combination of gemcitabine and erlotinib, an approved therapeutic regimen for advanced nonresectable pancreatic cancer. Taken together, these data indicate that inhibition of MEK and PI3K alone or in combination with chemotherapy do not confer a dramatic improvement as compared with currently available therapies for patients with pancreatic cancer.

Identification and analysis of in vivo VEGF downstream markers link VEGF pathway activity with efficacy of anti-VEGF therapies.

PURPOSE: The aim of this study was to identify conserved pharmacodynamic and potential predictive biomarkers of response to anti-VEGF therapy using gene expression profiling in preclinical tumor models and in patients. EXPERIMENTAL DESIGN: Surrogate markers of VEGF inhibition [VEGF-dependent genes or VEGF-dependent vasculature (VDV)] were identified by profiling gene expression changes induced in response to VEGF blockade in preclinical tumor models and in human biopsies from patients treated with anti-VEGF monoclonal antibodies. The potential value of VDV genes as candidate predictive biomarkers was tested by correlating high or low VDV gene expression levels in pretreatment clinical samples with the subsequent clinical efficacy of bevacizumab (anti-VEGF)-containing therapy. RESULTS: We show that VDV genes, including direct and more distal VEGF downstream endothelial targets, enable detection of VEGF signaling inhibition in mouse tumor models and human tumor biopsies. Retrospective analyses of clinical trial data indicate that patients with higher VDV expression in pretreatment tumor samples exhibited improved clinical outcome when treated with bevacizumab-containing therapies. CONCLUSIONS: In this work, we identified surrogate markers (VDV genes) for in vivo VEGF signaling in tumors and showed clinical data supporting a correlation between pretreatment VEGF bioactivity and the subsequent efficacy of anti-VEGF therapy. We propose that VDV genes are candidate biomarkers with the potential to aid the selection of novel indications as well as patients likely to respond to anti-VEGF therapy. The data presented here define a diagnostic biomarker hypothesis based on translational research that warrants further evaluation in additional retrospective and prospective trials.

Assessing therapeutic responses in Kras mutant cancers using genetically engineered mouse models.

The low rate of approval of novel anti-cancer agents underscores the need for better preclinical models of therapeutic response as neither xenografts nor early-generation genetically engineered mouse models (GEMMs) reliably predict human clinical outcomes. Whereas recent, sporadic GEMMs emulate many aspects of their human disease counterpart more closely, their ability to predict clinical therapeutic responses has never been tested systematically. We evaluated the utility of two state-of-the-art, mutant Kras-driven GEMMs--one of non-small-cell lung carcinoma and another of pancreatic adenocarcinoma--by assessing responses to existing standard-of-care chemotherapeutics, and subsequently in combination with EGFR and VEGF inhibitors. Standard clinical endpoints were modeled to evaluate efficacy, including overall survival and progression-free survival using noninvasive imaging modalities. Comparisons with corresponding clinical trials indicate that these GEMMs model human responses well, and lay the foundation for the use of validated GEMMs in predicting outcome and interrogating mechanisms of therapeutic response and resistance.