ABSTRACT
BACKGROUND: The insulin-like growth factor 1 (IGF1) signaling axis plays a major role in tumorigenesis. In a previous experiment, we chronically treated mice with several agonists of the IGF1 receptor (IGF1R). We found that chronic treatment with insulin analogues with high affinity towards the IGF1R (IGF1 and X10) decreased the mammary gland tumor latency time in a p53R270H/+WAPCre mouse model. Frequent injections with insulin analogues that only mildly activated the IGF1R in vivo (glargine and insulin) did not significantly decrease the tumor latency time in this mouse model. METHODS: Here, we performed next-generation RNA sequencing (40 million, 100 bp reads) on 50 mammary gland tumors to unravel the underlying mechanisms of IGF1R-promoted tumorigenesis. Mutational profiling of the individual tumors was performed to screen for treatment-specific mutations. The transcriptomic data were used to construct a support vector machine (SVM) classifier so that the phenotypic characteristics of tumors exposed to the different insulin analogue treatments could be predicted. For translational purposes, we ran the same classifiers on transcriptomic (micro-array) data of insulin analogue-exposed human breast cancer cell lines. Genome-scale metabolic modeling was performed with iMAT. RESULTS: We found that chronic X10 and IGF1 treatment resulted in tumors with an increased and sustained proliferative and invasive transcriptomic profile. Furthermore, a Warburg-like effect with increased glycolysis was observed in tumors of the X10/IGF1 groups and, to a lesser extent, also in glargine-induced tumors. A metabolic flux analysis revealed that this enhanced glycolysis programming in X10/IGF1 tumors was associated with increased biomass production programs. Although none of the treatments induced genetic instability or enhanced mutagenesis, mutations in Ezh2 and Hras were enriched in X10/IGF1 treatment tumors. CONCLUSIONS: Overall, these data suggest that the decreased mammary gland tumor latency time caused by chronic IGF1R activation is related to modulation of tumor progression rather than increased tumor initiation.
Subject(s)
Breast Neoplasms/metabolism , Breast Neoplasms/pathology , Glucose/metabolism , Receptor, IGF Type 1/metabolism , Animals , Biomarkers , Breast Neoplasms/genetics , Breast Neoplasms/mortality , Cell Line, Tumor , Cell Movement , Enhancer of Zeste Homolog 2 Protein/genetics , Epithelial-Mesenchymal Transition/genetics , Female , Gene Expression Profiling , Glycolysis , Insulin/metabolism , Insulin-Like Growth Factor I/metabolism , Insulin-Like Growth Factor I/pharmacology , Mice , Mice, Transgenic , Mutation , Prognosis , Receptor, IGF Type 1/agonists , Signal Transduction , Transcriptome , Tumor Burden , ras Proteins/geneticsABSTRACT
UNLABELLED: Quantitative assessment of (11)C-erlotinib uptake may be useful in selecting non-small cell lung cancer (NSCLC) patients for erlotinib therapy. The purpose of this study was to find the optimal pharmacokinetic model for quantification of uptake and to evaluate various simplified methods for routine analysis of (11)C-erlotinib uptake in NSCLC patients. METHODS: Dynamic (15)O-H2O and (11)C-erlotinib scans were obtained in 17 NSCLC patients, 8 with and 9 without an activating epidermal growth factor receptor mutation (exon 19 deletion or exon 21-point mutation). Ten of these subjects also underwent a retest scan on the same day. (11)C-erlotinib data were analyzed using single-tissue and 2-tissue-irreversible and -reversible (2T4k) plasma input models. In addition, several advanced models that account for uptake of radiolabeled metabolites were evaluated, including a variation of the 2T4k model without correcting for metabolite fractions in plasma (2T4k-WP). Finally, simplified methods were evaluated-that is, SUVs and tumor-to-blood ratios (TBR)-for several scan intervals. RESULTS: Tumor kinetics were best described using the 2T4k-WP model yielding optimal fits to the data (Akaike preference, 43.6%), acceptable test-retest variability (12%), no dependence on perfusion changes, and the expected clinical group separation (P < 0.016). Volume of distribution estimated using 2T4k-WP and 2T4k were highly correlated (R(2) = 0.94). Similar test-retest variabilities and clinical group separations were found. The 2T4k model did not perform better than an uncorrected model (2T4k-WP), probably because of uncertainty in the estimation of true metabolite fractions. Investigation of simplified approaches showed that SUV curves normalized to patient weight, and injected tracer dose did not reach equilibrium within the time of the scan. In contrast, TBR normalized to whole blood (TBR-WB) appeared to be a useful outcome measure for quantitative assessment of (11)C-erlotinib scans acquired 40-60 min after injection. CONCLUSION: The optimal model for quantitative assessment of (11)C-erlotinib uptake in NSCLC was the 2T4k-WB model. The preferred simplified method was TBR-WB (40-60 min after injection) normalized using several whole-blood samples.
Subject(s)
Erlotinib Hydrochloride/metabolism , Adult , Aged , Biological Transport , Carcinoma, Non-Small-Cell Lung/diagnostic imaging , Carcinoma, Non-Small-Cell Lung/metabolism , Female , Humans , Kinetics , Lung Neoplasms/diagnostic imaging , Lung Neoplasms/metabolism , Male , Middle Aged , Models, Biological , Whole Body ImagingABSTRACT
Tumor heterogeneity plays an important role in the development of treatment-resistance, especially in the current era of targeted therapies. Although tumor heterogeneity is a widely recognized phenomenon, it is at present unclear how this knowledge should be incorporated into daily clinical practice. In this report, we describe an innovative nuclear imaging method that may play a role in detecting tumor heterogeneity in the future.
