Tran, Nhan L.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is characterized by KRAS- and autophagy-dependent tumorigenic growth. We determined the role of mutationally activated KRAS, found in ~95% of PDAC, in supporting autophagy. Surprisingly, acute KRAS suppression, which blocks proliferation, was associated with increased rather than decreased autophagic flux. Pharmacologic inhibition of ERK MAPK phenocopied the genetic silencing of KRAS and also increased autophagic flux. We speculated that the loss of ERK-driven metabolic processes may induce compensatory mechanisms to increase autophagy. Addressing a mechanism for ERK suppression-increased autophagy, we describe three mechanisms: 1) ERK inhibition-mediated increased transcription of autophagy and lysosomal genes, 2) ERK inhibitor-induced AMPK activation and suppression of mTOR signaling, and 3) ERK inhibition-facilitated decreased glycolytic flux. We then addressed whether ERK inhibition increased PDAC dependence on autophagy. Supporting this possibility, we found that cotreatment with the autophagy inhibitor chloroquine (CQ) synergistically enhanced ERK inhibitor-mediated antiproliferative activity. Similarly, genetic or pharmacologic inhibition of specific regulators of autophagy also enhanced ERK inhibitor activity. Encouraged by the synergistic relationship between ERK and autophagy inhibition, we performed a CRISPR/Cas-9 mediated genetic loss of function screen in the presence of CQ to determine additional sensitizers as well as mediators of resistance to autophagy inhibition. Top sensitizers included multiple mediators of the DNA damage response. One such sensitizing gene was CHEK1, which encodes the CHK1 serine/threonine kinase, required for checkpoint-mediated cell cycle arrest and activation of DNA repair in response to DNA damage. Accordingly, we showed that treatment with prexasertib, a clinical candidate CHK1 inhibitor, increased autophagic flux in PDAC cells and synergized with CQ to decrease PDAC cell proliferation and increase apoptosis. We conclude that concurrent suppression of multiple metabolic processes, to block compensatory rebound activities, will be needed for effective PDAC treatment. This abstract is also being presented as Poster B07. Citation Format: Kirsten L. Bryant, Jennifer E. Klomp, Ye S. Lee, Clint A. Stalnecker, Kajal R. Grover, A. Cole Edwards, Sen Peng, Mariaelena Pierobon, Emanuel F. Petricoin III, Nhan Tran, Alec C. Kimmelman, Adrienne D. Cox, Channing J. Der. Enhancing the effect of autophagy inhibition for pancreatic cancer treatment [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr PR10.

DART nanoparticles overcome critical drug delivery barriers for primary tumors and brain metastases.

Abstract BACKGROUND Glioblastomas show marked intra- and inter-tumor heterogeneity and are strongly resistant to both radio- and chemo-therapy, which are standard therapeutic modalities for this tumor. MicroRNAs (miRNAs) have the potential to serve as effective therapeutics for glioblastoma as they modulate the activity of multiple signaling pathways. METHODS Glioblastoma cultures were transfected with miR-34a or control miRNA mimics to assess biological function and therapeutic potential in vitro. miR-34a was packaged into bacterially-derived nanocells and administered intravenously for delivery to orthotopic patient-derived glioblastoma xenografts in mice. RESULTS Overexpression of miR-34a strongly reduced the activation status of the three core signaling networks that have been found to be deregulated in the vast majority of glioblastoma tumors, the receptor tyrosine kinase, p53 and Rb networks. miR-34a transfection also inhibited the survival of multiple established glioblastoma cell lines as well as primary patient-derived xenograft cultures representing the proneural, mesenchymal and classical subtypes. Transfection of miR-34a synergized with temozolomide (TMZ) in in vitro cultures of glioblastoma cells with primary TMZ sensitivity, primary TMZ resistance and acquired TMZ resistance. Intravenous administration of bacterially-derived nanocells carrying miR-34a strongly enhanced TMZ sensitivity in an orthotopic patient-derived xenograft mouse model of glioblastoma. CONCLUSIONS miR-34a strongly sensitizes a wide range of glioblastoma cell cultures to TMZ, suggesting that combination therapy of TMZ with miR-34a may serve as a novel therapeutic approach for the treatment of glioblastoma tumors. Bacterially-derived nanocells are an effective vehicle for the delivery of miR-34a to glioblastoma tumors.

Abstract Purpose: Glioblastoma is the most frequent and lethal primary brain tumor. Development of novel therapies relies on the availability of relevant preclinical models. We have established a panel of 96 glioblastoma patient-derived xenografts (PDX) and undertaken its genomic and phenotypic characterization. Experimental Design: PDXs were established from glioblastoma, IDH-wildtype (n = 93), glioblastoma, IDH-mutant (n = 2), diffuse midline glioma, H3 K27M-mutant (n = 1), and both primary (n = 60) and recurrent (n = 34) tumors. Tumor growth rates, histopathology, and treatment response were characterized. Integrated molecular profiling was performed by whole-exome sequencing (WES, n = 83), RNA-sequencing (n = 68), and genome-wide methylation profiling (n = 76). WES data from 24 patient tumors was compared with derivative models. Results: PDXs recapitulate many key phenotypic and molecular features of patient tumors. Orthotopic PDXs show characteristic tumor morphology and invasion patterns, but largely lack microvascular proliferation and necrosis. PDXs capture common and rare molecular drivers, including alterations of TERT, EGFR, PTEN, TP53, BRAF, and IDH1, most at frequencies comparable with human glioblastoma. However, PDGFRA amplification was absent. RNA-sequencing and genome-wide methylation profiling demonstrated broad representation of glioblastoma molecular subtypes. MGMT promoter methylation correlated with increased survival in response to temozolomide. WES of 24 matched patient tumors showed preservation of most genetic driver alterations, including EGFR amplification. However, in four patient–PDX pairs, driver alterations were gained or lost on engraftment, consistent with clonal selection. Conclusions: Our PDX panel captures the molecular heterogeneity of glioblastoma and recapitulates many salient genetic and phenotypic features. All models and genomic data are openly available to investigators.

Abstract Glioblastoma (GBM) is increasingly recognized as a disease involving dysfunctional cellular metabolism. GBMs are known to be complex heterogeneous systems containing multiple distinct cell populations and are supported by an aberrant network of blood vessels. A better understanding of GBM metabolism, its variation with respect to the tumor microenvironment, and resulting regional changes in chemical composition is required. This may shed light on the observed heterogeneous drug distribution, which cannot be fully described by limited or uneven disruption of the blood–brain barrier. In this work, we used mass spectrometry imaging (MSI) to map metabolites and lipids in patient-derived xenograft models of GBM. A data analysis workflow revealed that distinctive spectral signatures were detected from different regions of the intracranial tumor model. A series of long-chain acylcarnitines were identified and detected with increased intensity at the tumor edge. A 3D MSI dataset demonstrated that these molecules were observed throughout the entire tumor/normal interface and were not confined to a single plane. mRNA sequencing demonstrated that hallmark genes related to fatty acid metabolism were highly expressed in samples with higher acylcarnitine content. These data suggest that cells in the core and the edge of the tumor undergo different fatty acid metabolism, resulting in different chemical environments within the tumor. This may influence drug distribution through changes in tissue drug affinity or transport and constitute an important consideration for therapeutic strategies in the treatment of GBM. Significance: GBM tumors exhibit a metabolic gradient that should be taken into consideration when designing therapeutic strategies for treatment. See related commentary by Tan and Weljie, p. 1231

Abstract Glioblastoma (GBM) is the most common and deadly malignancy of the central nervous system in adults with a median survival of about 15 months after diagnosis. Its aggressive and highly infiltrative nature renders the current standard treatment of maximal surgical resection, radiation, and chemotherapy relatively ineffective. Identifying the molecular events that regulate GBM migration/invasion is required to develop more effective therapeutic regimens to treat GBM. Expression of TROY, an orphan receptor of the TNFR superfamily, increases with increasing glial tumor grade, inversely correlates with patient survival, stimulates GBM cell invasion in vitro and in vivo, and increases resistance to temozolomide and radiation therapy. Conversely, silencing TROY expression inhibits GBM cell invasion, increases sensitivity to temozolomide, and prolongs survival in a preclinical intracranial xenograft model. Thus, the TROY signaling pathway represents an attractive therapeutic target. We previously reported that increased TROY expression strongly activates NF-kB. We have identified for the first time that TROY forms a unique signaling complex with JAK1. Interaction with TROY stimulates phosphorylation of JAK1 and significantly increases the activation of STAT3. TROY specifically associates with JAK1 as it does not associate with other JAK family members. Co-immunoprecipitation analysis demonstrated that TROY interacts with JAK1 through its cytoplasmic domain as a TROY variant without its extracellular domain associates with JAK1 while a TROY variant without a cytoplasmic domain fails to bind JAK1. Luciferase reporter assay confirmed that full length TROY or a variant lacking the extracellular domain are able to induce STAT3 activation, but not a TROY variant lacking the cytoplasmic domain. Pharmacological inhibition of JAK1 by the FDA approved inhibitor ruxolitinib or knockdown of JAK1 by siRNAs significantly inhibited TROY-induced STAT3 activation and GBM cell migration. Together, our data indicate that the TROY-JAK1 complex represents an unappreciated therapeutic target to inhibit glioma invasion and decrease therapeutic resistance.

Abstract BACKGROUND Conventional magnetic resonance imaging (MR) guides patient care in GBM. However, there is mounting awareness that MR enhancement is non-specific reflecting either tumor progression or non-tumoral inflammatory changes. Histological evaluation of GBM is held as the gold standard for disease assessment. However, the invasiveness of this methodology and the sample sparsity limit its usefulness. Methods to infer histological underpinnings of MRI are needed to improve clinical care. METHODS A transfer learning mixed effects model based on T1Gd and FLAIR MR voxel based image features trained and cross-validated to predict FPKM values of CD68, CASP3, CD8A and IL13RA2. Training data included RNAseq from 38 image-localized biopsies from 15 newly-diagnosed GBM patients. These models were then applied to two independent patients, chosen based on therapy and the quality of image registration, at three different time points, just prior to receiving IL13RA2 targeted CAR-T therapy for rGBM, after 2 cycles and after 4 cycles, resulting in a voxel-based prediction of the relative expression levels of these genes. RESULTS Cross-validation of the proposed machine learning models demonstrated a Pearson Correlation coefficient between predicted and observed FPKM values of 0.89 (CD68), 0.92 (CASP3), 0.93 (CD8A), and 0.88 (IL13RA2). When the models were applied to the two CAR-T patients, CD68-modeled expression levels were seen to increase throughout therapy for one patient, while remaining stable for the other. Survival from first CAR-T infusion was, respectively, 412 and 83 days suggesting that the increased CD68 was indicative of therapeutic effectivity. Further, the spatial activity predictions were consistent with expected therapeutic action as the correlation coefficient between CASP3 and the product of IL13R2 and CD8A expression was 0.81, suggesting the T-cells targeting IL13RA2 were inducing cell death. CONCLUSIONS Preliminary results with our model highlights the potential of spatially resolved radiomic maps to provide insight into regional therapeutic effectivity.

Abstract INTRODUCTION The quantification of intratumoral heterogeneity – through radiomics-based approaches - can help resolve the regionally distinct genetic drug targets that may co-exist within a single Glioblastoma (GBM) tumor. While this offers potential diagnostic value under the paradigm of individualized oncology, clinical decision-making must also consider the degree of uncertainty associated with each model. In this study, we evaluate the performance of a novel machine-learning (ML) algorithm, called Gaussian Process (GP) modeling, that can quantify the impact of multiple sources of uncertainty in ML model development and prediction accuracy, including variabilities in the copy number measurement, radiomics features, training sample characteristics, and training sample size. METHOD We collected 95 image-localized biopsies from 25 primary GBM patients. We coregistered stereotactic locations with preoperative multi-parametric MRI features (conventional MRI, DSC perfusion, Diffusion Tensor Imaging) to generate spatially matched pairs of MRI and copy number variants (CNV) for for each biopsy. We developed a Gaussian Process (GP) model to predict CNV for Epidermal Growth Factor Receptor (EGFR) based on MRI radiomic features in each patient. We used leave-one-patient-out cross validation to quantify prediction accuracy and model uncertainty. Spatial prediction and uncertainty (p-value) maps were overlaid to help visualize regional genetic variation of EGFR and uncertainty of the radiomic predictions. RESULT: The initial GP radiomics model for EGFR amplification (CNV > 3.5) produced a sensitivity of 0.8 and specificity of 0.8. Samples/regions associated with high uncertainty (p-value >0.05) correlated with either 1) extrapolation of radiomic features from the training set-defined feature space or 2) insufficient training samples in the feature space. CONCLUSION We present a ML-based model that quantifies spatial genetic heterogeneity in GBM, while also estimating model uncertainties that result from multi-source data variabilities. This approach lays the groundwork for prospective clinical integration of modeling-based diagnostic approaches in the paradigm of individualized medicine.

Abstract Glioblastomas (GBMs) are known to be complex tumors comprising multiple subpopulations of genetically-distinct cancer cells; it is thought that this genetic variation is a major factor in the lack of observed survival benefit of treatment regimes that target one of these subpopulations. The field of radiogenomics seeks to study correlations between MRI patterns and genetic features of GBM tumors. Spatial radiogenomic maps produced using machine-learning (ML) methods that are trained against information from image-localized patient biopsies identify regions where particular cancer sub-populations are predicted to occur within a GBM, thus non-invasively characterizing the regional genetic variability of these tumors. These tumor subpopulations may also interact with one another, in ways which may be of a competitive or cooperative nature to varying degrees. It is important to ascertain the nature of these interactions, as they may have implications for treatment response to targeted therapies, and characterization of the spatio-temporal dynamics of these co-evolving sub-populations will shed light on why some therapies fail. Here we combine mathematical modeling techniques and spatially-resolved radiogenomic maps to study the nature of these interactions between molecularly-distinct GBM subpopulations. We model the interactions between cell populations using a partial differential equation based formalism. The model is parameterized using radiogenomic ML maps from which we infer the nature of interactions between subpopulations. Furthermore, using maps as inputs, the model turns static maps into dynamic information, thus providing insight into how these subpopulations composing the tumor change over time and the effect this has on observed treatment response for individual patients.

Abstract Therapeutic resistance stemming from inter and intra-tumoral heterogeneity is a significant impediment towards development of effective therapeutics for glioblastoma. We hypothesized that microRNAs can potentially counteract resistance emanating from such heterogeneity as they simultaneously modulate the expression of multiple proteins. We identified microRNA-34a as a unique microRNA which modulates multiple oncoproteins in GBM using two different in silico approaches. We investigated the therapeutic effects of microRNA-34a in three primary patient-derived xenografts (PDX) representing classical (GBM6), proneural (GBM118) and mesenchymal (GBM118) subtypes; four established cell lines (T98G, U251, A172, LN229) and two cell lines with acquired resistance to temozolomide (A172-TR, LN229-TR) in vitro. Glioblastoma cell cultures showed variable responses to temozolomide but microRNA-34a inhibited proliferation in all cell cultures. Furthermore, microRNA-34a also sensitized all tested cell lines to temozolomide (combination index < 0.8, p=.03) and radiation treatment (dose enhancement factor 1.7–2.2, p=0.02). Mechanistically, microRNA 34a down-regulates at least six distinct therapeutic resistance proteins. Importantly, these resistance proteins are expressed in distinct spatial niches and are prognostic for patient survival based on our analysis of the cancer genome atlas (TCGA) data. For in vivo delivery of microRNA-34a, we utilized nanocells which are derived from genetically modified bacteria, loaded with microRNA-34a and tagged with a bispecific antibody targeting EGFR. Nanocells were injected intravenously while temozolomide was administered by oral gavage in an orthotopic PDX model. We confirmed delivery of microRNA-34a to tumor by observing down-regulation of cMet and phosphorylated Akt in treated mice. Importantly, microRNA-34a nanocells resulted in significant reduction in tumor growth (p=0.021), increased survival (p<0.001) with microRNA-34a monotherapy and synergy in combination with temozolomide in vivo. Taken together, our results suggest that delivery of miR-34a may be a powerful new adjuvant for the treatment of glioblastoma in combination with temozolomide that can mitigate both inter- and intra-tumor heterogeneity.