N-of-1 trials target actionable mutations, yet such approaches do not test genomically-informed therapies in patient tumor models prior to patient treatment. To address this, we developed patient-derived xenograft (PDX) models from fine needle aspiration (FNA) biopsies (FNA-PDX) obtained from primary pancreatic ductal adenocarcinoma (PDAC) at the time of diagnosis. Here, we characterize PDX models established from one primary and two metastatic sites of one patient. We identified an activating KRAS G12R mutation among other mutations in these models. In explant cells derived from these PDX tumor models with a KRAS G12R mutation, treatment with inhibitors of CDKs (including CDK9) reduced phosphorylation of a marker of CDK9 activity (phospho-RNAPII CTD Ser2/5) and reduced viability/growth of explant cells derived from PDAC PDX models. Similarly, a CDK inhibitor reduced phospho-RNAPII CTD Ser2/5, increased apoptosis, and inhibited tumor growth in FNA-PDX and patient-matched metastatic-PDX models. In summary, PDX models can be constructed from FNA biopsies of PDAC which in turn can enable genomic characterization and identification of potential therapies.
BackgroundWe have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules.ResultsWe developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM). On TCGA data, the classifier detected NF1 mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 – 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 – 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier’s NF1 score was associated with NF1 protein concentration in these samples.ConclusionsWe demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3519-7) contains supplementary material, which is available to authorized users.
Purpose Determine the roles of the phosphatidylinositol 3-kinase (PI3K) isoforms p110α and p110β in PTEN-deficient, estrogen receptor α (ER)-positive breast cancer, and the therapeutic potential of isoform-selective inhibitors. Experimental Design Anti-estrogen-sensitive and -resistant PTEN-deficient, ER+ human breast cancer cell lines, and mice bearing anti-estrogen-resistant xenografts were treated with the anti-estrogen fulvestrant, the p110α inhibitor BYL719, the p110β inhibitor GSK2636771, or combinations. Temporal response to growth factor receptor-initiated signaling, growth, apoptosis, predictive biomarkers, and tumor volumes were measured. Results p110β primed cells for response to growth factor stimulation. While p110β inhibition suppressed cell and tumor growth, dual targeting of p110α/β enhanced apoptosis and provided sustained tumor response. The growth of anti-estrogen-sensitive cells was inhibited by fulvestrant, but fulvestrant inconsistently provided additional therapeutic effects beyond PI3K inhibition alone. Treatment-induced decreases in phosphorylation of AKT and Rb were predictive of therapeutic response. Short-term drug treatment induced tumor cell apoptosis and proliferative arrest to induce tumor regression, while long-term treatment only suppressed proliferation to provide durable regression. Conclusions p110β is the dominant PI3K isoform in PTEN-deficient, ER+ breast cancer cells. Upon p110β inhibition, p110α did not induce significant reactivation of AKT, but combined targeting of p110α/β most effectively induced apoptosis in vitro and in vivo and provided durable tumor regression. Since apoptosis and tumor regression occurred early but not late in the treatment course, and proliferative arrest was maintained throughout treatment, p110α/β inhibitors may be considered short-term cytotoxic agents and long-term cytostatic agents.
Autophagy is important for a variety for virus life cycles. We sought to determine the role of autophagy in human BK polyomavirus (BKPyV) infection. The addition excess amino acids during viral infection reduced BKPyV infection. Perturbing autophagy levels using inhibitors, 3-MA, bafilomycin A1, and spautin-1, also reduced infection, while rapamycin treatment of host cells increased infection. siRNA knockdown of autophagy genes, ATG7 and Beclin-1, corresponded to a decrease in BKPyV infection. BKPyV infection not only correlated with autophagosome formation, but also virus particles localized to autophagy-specific compartments early in infection. These data support a novel role for autophagy in the promotion of BKPyV infection.
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