PUMILIO competes with AUF1 to control DICER1 RNA levels and miRNA processing.

DICER1 syndrome is a cancer pre-disposition disorder caused by mutations that disrupt the function of DICER1 in miRNA processing. Studying the molecular, cellular and oncogenic effects of these mutations can reveal novel mechanisms that control cell homeostasis and tumor biology. Here, we conduct the first analysis of pathogenic DICER1 syndrome allele from the DICER1 3'UTR. We find that the DICER1 syndrome allele, rs1252940486, abolishes interaction with the PUMILIO RNA binding protein with the DICER1 3'UTR, resulting in the degradation of the DICER1 mRNA by AUF1. This single mutational event leads to diminished DICER1 mRNA and protein levels, and widespread reprogramming of miRNA networks. The in-depth characterization of the rs1252940486 DICER1 allele, reveals important post-transcriptional regulatory events that control DICER1 levels.

Exploiting mitochondrial and metabolic homeostasis as a vulnerability in NF1 deficient cells.

Neurofibromatosis type 1 is a disease caused by mutation of neurofibromin 1 (NF1), loss of which results in hyperactive Ras signaling and a concomitant increase in cell proliferation and survival. Patients with neurofibromatosis type 1 frequently develop tumors such as plexiform neurofibromas and malignant peripheral nerve sheath tumors. Mutation of NF1 or loss of the NF1 protein is also observed in glioblastoma, lung adenocarcinoma, and ovarian cancer among other sporadic cancers. A therapy that selectively targets NF1 deficient tumors would substantially advance our ability to treat these malignancies. To address the need for these therapeutics, we developed and conducted a synthetic lethality screen to discover molecules that target yeast lacking the homolog of NF1, IRA2. One of the lead candidates that was observed to be synthetic lethal with ira2Δ yeast is Y100. Here, we describe the mechanisms by which Y100 targets ira2Δ yeast and NF1-deficient tumor cells. Y100 treatment disrupted proteostasis, metabolic homeostasis, and induced the formation of mitochondrial superoxide in NF1-deficient cancer cells. Previous studies also indicate that NF1/Ras-dysregulated tumors may be sensitive to modulators of oxidative and ER stress. We hypothesize that the use of Y100 and molecules with related mechanisms of action represent a feasible therapeutic strategy for targeting NF1 deficient cells.

A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma.

BACKGROUND: We 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. RESULTS: We 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. CONCLUSIONS: We 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.

Combined Inhibition of Both p110α and p110ß Isoforms of Phosphatidylinositol 3-Kinase Is Required for Sustained Therapeutic Effect in PTEN-Deficient, ER+ Breast Cancer.

Purpose: Determine the roles of the PI3K isoforms p110α and p110ß 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 p110ß inhibitor GSK2636771, or combinations. Temporal response to growth factor receptor-initiated signaling, growth, apoptosis, predictive biomarkers, and tumor volumes were measured.Results: p110ß primed cells for response to growth factor stimulation. Although p110ß inhibition suppressed cell and tumor growth, dual targeting of p110α/ß 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, whereas long-term treatment only suppressed proliferation to provide durable regression.Conclusions: p110ß is the dominant PI3K isoform in PTEN-deficient, ER+ breast cancer cells. Upon p110ß inhibition, p110α did not induce significant reactivation of AKT, but combined targeting of p110α/ß most effectively induced apoptosis in vitro and in vivo and provided durable tumor regression. Because apoptosis and tumor regression occurred early but not late in the treatment course, and proliferative arrest was maintained throughout treatment, p110α/ß inhibitors may be considered short-term cytotoxic agents and long-term cytostatic agents. Clin Cancer Res; 23(11); 2795-805. ©2016 AACR.

Genomic characterization of patient-derived xenograft models established from fine needle aspirate biopsies of a primary pancreatic ductal adenocarcinoma and from patient-matched metastatic sites.

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.

Host cell autophagy promotes BK virus infection.

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.