Image based identification and targeting of cancer stem cells in pancreatic adenocarcinoma (PDAC)
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE75797 | GEO | 2016/06/15
SECONDARY ACCESSION(S): PRJNA305397
REPOSITORIES: GEO
Similar Datasets
Project description:Purpose: The goals of this study were to identify quantitative gene expression differences between wild type, Musashi1 null, Msuashi2 null and Musashi1/Musashi2 null MIAPaCa2 pancreatic cancer cells mRNA profiles of MIA PaCa-2 cancer cells were generated by deep sequencing, in triplicate, using Illumina HiSeq2500.
2016-06-15 | E-GEOD-75797 | biostudies-arrayexpress
Project description:Pancreatic intraepithelial neoplasia (PanIN) is a premalignant lesion that can progress to pancreatic ductal adenocarcinoma, a highly lethal malignancy marked by its late stage at clinical presentation and profound drug resistance. Here we developed novel fluorescent reporter mice that show that the stem cell determinant, Musashi (Msi) is a critical element of pancreatic cancer progression. These reporters allowed functional and image based tracking of stem cell signals within cancers in vivo, revealing that Msi expression rises as PanINs progress to adenocarcinoma, and that Msi reporter+ tumor cells are the key drivers of pancreatic cancer. To visualize and track the function of live Msi-expressing cells in vivo, we developed Msi knock-in reporters in which fluorescent signals reflected endogenous Msi expression. This dataset represents pancreatic cancer_wt and pancreatic cancer_KO cells. Three wt reps and three KO reps.
2016-06-15 | E-GEOD-73312 | biostudies-arrayexpress
Project description:Pancreatic intraepithelial neoplasia (PanIN) is a premalignant lesion that can progress to pancreatic ductal adenocarcinoma, a highly lethal malignancy marked by its late stage at clinical presentation and profound drug resistance. Here we developed novel fluorescent reporter mice that show that the stem cell determinant, Musashi (Msi) is a critical element of pancreatic cancer progression. These reporters allowed functional and image based tracking of stem cell signals within cancers in vivo, revealing that Msi expression rises as PanINs progress to adenocarcinoma, and that Msi reporter+ tumor cells are the key drivers of pancreatic cancer.
2016-06-15 | GSE73312 | GEO
Project description:Pancreatic intraepithelial neoplasia is a pre-malignant lesion that can progress to pancreatic ductal adenocarcinoma, a highly lethal malignancy marked by its late stage at clinical presentation and profound drug resistance. The genomic alterations that commonly occur in pancreatic cancer include activation of KRAS2 and inactivation of p53 and SMAD4 (refs 2-4). So far, however, it has been challenging to target these pathways therapeutically; thus the search for other key mediators of pancreatic cancer growth remains an important endeavour. Here we show that the stem cell determinant Musashi (Msi) is a critical element of pancreatic cancer progression both in genetic models and in patient-derived xenografts. Specifically, we developed Msi reporter mice that allowed image-based tracking of stem cell signals within cancers, revealing that Msi expression rises as pancreatic intraepithelial neoplasia progresses to adenocarcinoma, and that Msi-expressing cells are key drivers of pancreatic cancer: they preferentially harbour the capacity to propagate adenocarcinoma, are enriched in circulating tumour cells, and are markedly drug resistant. This population could be effectively targeted by deletion of either Msi1 or Msi2, which led to a striking defect in the progression of pancreatic intraepithelial neoplasia to adenocarcinoma and an improvement in overall survival. Msi inhibition also blocked the growth of primary patient-derived tumours, suggesting that this signal is required for human disease. To define the translational potential of this work we developed antisense oligonucleotides against Msi; these showed reliable tumour penetration, uptake and target inhibition, and effectively blocked pancreatic cancer growth. Collectively, these studies highlight Msi reporters as a unique tool to identify therapy resistance, and define Msi signalling as a central regulator of pancreatic cancer.
| S-EPMC4998062 | biostudies-literature
Project description:Pancreatic ductal adenocarcinoma (PDAC) has one of the worst survival rates of all cancers. ANO1 (TMEM16A) is a recently identified Ca(2+)-activated Cl(-) channel (CaCC) that is upregulated in several tumors. Although ANO1 was subject to extensive studies in the recent years, its pathophysiological function has only been poorly understood. The aim of the present study is to establish the significance of ANO1 in PDAC behavior and demarcate its roles in PDAC from those of the volume-regulated anion channel (VRAC). We performed qPCR and Western blot measurements on different PDAC cell lines (Panc-1, Mia PaCa 2, Capan-1, AsPC-1, BxPC-3) and compared the results to those obtained in a human pancreatic ductal epithelium (HPDE) cell line. All cancer cell lines showed an upregulation of ANO1 on mRNA and protein levels. Whole-cell patch-clamp recordings identified large Ca(2+) and voltage-dependent Cl(-) currents in PDAC cells. Using siRNA knockdown of ANO1 and three ANO1 inhibitors (T16Ainh-A01, CaCCinh-A01, and NS3728), we found that ANO1 is the main constituent of CaCC current in PDAC cells. We further characterized these three inhibitors and found that they had unspecific effects on the free intracellular calcium concentration. Functional studies on PDAC behavior showed that surprisingly inhibition of ANO1 did not influence cellular proliferation. On the other hand, we found ANO1 channel to be pivotal in PDAC cell migration as assessed in wound healing experiments.
| S-EPMC4464647 | biostudies-literature
Project description:To bridge the translational gap between recent discoveries of distinct molecular phenotypes of pancreatic cancer and tangible improvements in patient outcome, there is an urgent need to develop strategies and tools informing and improving the clinical decision process. Radiomics and machine learning approaches can offer non-invasive whole tumor analytics for clinical imaging data-based classification. The retrospective study assessed baseline computed tomography (CT) from 207 patients with proven pancreatic ductal adenocarcinoma (PDAC). Following expert level manual annotation, Pyradiomics was used for the extraction of 1474 radiomic features. The molecular tumor subtype was defined by immunohistochemical staining for KRT81 and HNF1a as quasi-mesenchymal (QM) vs. non-quasi-mesenchymal (non-QM). A Random Forest machine learning algorithm was developed to predict the molecular subtype from the radiomic features. The algorithm was then applied to an independent cohort of histopathologically unclassifiable tumors with distinct clinical outcomes. The classification algorithm achieved a sensitivity, specificity and ROC-AUC (area under the receiver operating characteristic curve) of 0.84 ± 0.05, 0.92 ± 0.01 and 0.93 ± 0.01, respectively. The median overall survival for predicted QM and non-QM tumors was 16.1 and 20.9 months, respectively, log-rank-test p = 0.02, harzard ratio (HR) 1.59. The application of the algorithm to histopathologically unclassifiable tumors revealed two groups with significantly different survival (8.9 and 39.8 months, log-rank-test p < 0.001, HR 4.33). The machine learning-based analysis of preoperative (CT) imaging allows the prediction of molecular PDAC subtypes highly relevant for patient survival, allowing advanced pre-operative patient stratification for precision medicine applications.
| S-EPMC7141256 | biostudies-literature
Project description:Introduction: PDAC is a lethal malignancy with a clear unmet need; almost all patients fail 1st, 2nd, and 3rd line multi-agent cytotoxic chemotherapy. The mammalian target of rapamycin (mTOR) has been identified as a key signaling node enhancing tumor survival and drug resistance in PDAC; hence, it is considered a promising therapeutic target. Areas covered: We comprehensively reviewed the evidence from preclinical and phase I and II clinical trials, based on the authors'clinical experience and a PubMed, Cochrane library, Embase, and Google Scholar search everolimus + pancreatic cancer. Expert opinion: Everolimus has not demonstrated efficacy in PDAC; however, an mTOR inhibitor in combination with stroma-targeted therapies may be a promising area to explore in clinical trials.
| S-EPMC6681663 | biostudies-literature
Project description:BACKGROUND:Although the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment. METHODS:We performed a gene expression microarray meta-analysis of the tumor against normal tissues in order to identify differentially expressed genes (DEG) shared among all datasets, named core-genes (CG). We confirmed the CG protein expression in pancreatic tissue through The Human Protein Atlas. It was selected five genes with the highest area under the curve (AUC) among these proteins with expression confirmed in the tumor group to train an artificial neural network (ANN) to classify samples. RESULTS:This microarray included 461 tumor and 187 normal samples. We identified a CG composed of 40 genes, 39 upregulated, and one downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that fourteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our ANN, we selected the best genes (AHNAK2, KRT19, LAMB3, LAMC2, and S100P) to classify the samples based on AUC using mRNA expression. The network classified tumor samples with an f1-score of 0.83 for the normal samples and 0.88 for the PDAC samples, with an average of 0.86. The PDAC-ANN could classify the test samples with a sensitivity of 87.6 and specificity of 83.1. CONCLUSION:The gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on RNA expression. This software can be useful in the PDAC diagnosis.
| S-EPMC6995241 | biostudies-literature
Project description:Previously, we characterized qualitative imaging-based subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed tomography (CT) scans. Conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we developed a quantitative classification of this imaging-based subtype (quantitative delta; q-delta). Retrospectively, baseline pancreatic protocol CT scans of three cohorts (cohort#1 = 101, cohort#2 = 90 and cohort#3 = 16 [external validation]) of patients with PDAC were qualitatively classified into high and low delta. We used a voxel-based method to volumetrically quantify tumor enhancement while referencing normal-pancreatic-parenchyma and used machine learning-based analysis to build a predictive model. In addition, we quantified the stromal content using hematoxylin- and eosin-stained treatment-naïve PDAC sections. Analyses revealed that PDAC quantitative enhancement values are predictive of the qualitative delta scoring and were used to build a classification model (q-delta). Compared to high q-delta, low q-delta tumors were associated with improved outcomes, and the q-delta class was an independent prognostic factor for survival. In addition, low q-delta tumors had higher stromal content and lower cellularity compared to high q-delta tumors. Our results suggest that q-delta classification provides a clinically and biologically relevant tool that may be integrated into ongoing and future clinical trials.
| S-EPMC7762105 | biostudies-literature
Project description:siRNA of MYEOV, KCNN4, S100A16 and DDX60L treated MiaPaCa2 and PANC-1 cells were used for RNA-seq to identify the transcriptomic alterations.
2021-04-06 | GSE171486 | GEO