Dataset Information

E-MTAB-2706

ABSTRACT: RNA-seq of 675 commonly used human cancer cell lines

TISSUE(S): Testis, Brain, Tongue, Prostate Gland, Nasal Septum, Zone Of Skin, Lung, Liver, Lymph Node, Uterine Cervix, Blood, Kidney, Ovary, Chordate Pharynx, Uterus, Colon, Pancreas, Bone Marrow, Pleura, Stomach, Skeletal Muscle Tissue, Bone Tissue, Ileum, Esophagus, Caecum, Cartilage Tissue, Oral Cavity, Rectum, Breast, Urinary Bladder, Adrenal Gland

DISEASE(S): Prostate Adenocarcinoma,Ovarian Serous Cystadenocarcinoma,Metastatic Melanoma,Myelodysplasia,Non-hodgkin's Lymphoma,Ovarian Papillary Serous Adenocarcinoma,Squamous Cell Carcinoma,Erythroleukemia,Squamous Cell Lung Carcinoma,Ovarian Teratoma,Breast Ductal Carcinoma In Situ,Chronic Myelogenic Leukemia,Unknown Adrenal Cancer,Gastric Tubular Adenocarcinoma,Renal Clear Cell Adenocarcinoma,Pancreatic Islet Cell Carcinoma,Normal At The Time Of Derivation,Breast Adenocarcinoma,Breast Anaplastic Carcinoma,Leukemia,Bronchoalveolar Adenocarcinoma,Testicular Carcinoma,Tongue Dysplasia,Cecum Carcinoma,Pancreatic Carcinoma,Gastric Adenosquamous Carcinoma,Breast Squamous Cell Carcinoma,Pancreatic Adenosquamous Carcinoma,Skin Squamous Cell Carcinoma,Lung Papillary Adenocarcinoma,Lung Carcinoma,Non-small Cell Lung Carcinoma,Hepatocellular Carcinoma,Ovarian Adenocarcinoma,Ovarian Clear Cell Adenocarcinoma,Cecum Adenocarcinoma,Acute T Cell Leukemia,Breast Medullary Carcinoma,Granulosa Cell Tumor,Nasal Septum Squamous Cell Carcinoma,Renal Leiomyoma,Breast Synovial Sarcoma,Cervical Squamous Cell Carcinoma,Pancreatic In Situ Ductal Carcinoma,Acute Myelogenic Leukemia,Chondrosarcoma,Breast Carcinoma,Malignant Burkitt's Lymphoma,Mesothelioma,Colon Carcinoma,Urinary Bladder Transitional Cell Carcinoma,Pancreatic Ductal Adenocarcinoma,Large Cell Lung Carcinoma,Lung Carcinoid Tumor,Colon Adenocarcinoma,Renal Adenocarcinoma,Renal Cell Carcinoma,Ileum Adenocarcinoma,Multiple Myeloma,Cervical Adenocarcinoma,Nasopharyngeal Carcinoma,Tongue Squamous Cell Carcinoma,Ewing's Sarcoma,Melanoma,Small Cell Lung Carcinoma,Breast Ductal Adenocarcinoma,Lung Mucoepidermoid Carcinoma,Pancreatic Tubular Adenocarcinoma,Lung Adenosquamous Carcinoma,Lung Fibrosarcoma,Lung Anaplastic Carcinoma,Pancreatic Adenocarcinoma,Gastric Adenocarcinoma,B-cell Lymphoma,Breast Fibrocystic Disease,Rhabdomyosarcoma,Glioblastoma,Chronic Myelogenous Leukemia,Cervical Carcinoma,Neuroblastoma,Immunoblastic Large Cell Lymphoma,Renal Carcinoma,Acute Myeloid Leukemia,Gastric Carcinoma,Osteosarcoma,Ovarian Carcinoma,Acute Lymphoblastic Leukemia,Hodgkin's Lymphoma,Plasmacytoma,Breast Metaplastic Carcinoma,Lung Adenocarcinoma,Diffuse Mixed Lymphoma,Oral Cavity Squamous Cell Carcinoma,Endometrioid Adenocarcinoma,Acute Monocytic Leukemia,Transitional Cell Carcinoma Of Kidney,Rectal Adenocarcinoma,Diffuse Large B-cell Lymphoma,Lymphoma,Nasopharyngeal Squamous Cell Carcinoma,Pleural B-cell Lymphoma,Uterine Adenocarcinoma,Anaplastic Large Cell Lymphoma,Glioma,Prostate Carcinoma,Ovarian Leiomyosarcoma

PROVIDER: E-MTAB-2706 | ExpressionAtlas | 2016-06-24

REPOSITORIES: ExpressionAtlas

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Dataset's files

Source:

			Action	DRS
	E-MTAB-2706-analysis-methods.tsv	Tabular
	E-MTAB-2706-atlasExperimentSummary.Rdata	Rdata
	E-MTAB-2706-coexpressions.tsv.gz	Tabular
	E-MTAB-2706-configuration.xml	Xml
	E-MTAB-2706-configuration.xml.orig	Xml

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Project description:Bordel2018 - GSMM for Human Metabolic Reactions (HMR database) This model is described in the article: Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows Sergio Bordel Oncotarget. 2018; 9:19716-19729 Abstract: In order to choose optimal personalized anticancer treatments, transcriptomic data should be analyzed within the frame of biological networks. The best known human biological network (in terms of the interactions between its different components) is metabolism. Cancer cells have been known to have specific metabolic features for a long time and currently there is a growing interest in characterizing new cancer specific metabolic hallmarks. In this article it is presented a method to find personalized therapeutic windows using RNA-seq data and Genome Scale Metabolic Models. This method is implemented in the python library, pyTARG. Our predictions showed that the most anticancer selective (affecting 27 out of 34 considered cancer cell lines and only 1 out of 6 healthy mesenchymal stem cell lines) single metabolic reactions are those involved in cholesterol biosynthesis. Excluding cholesterol biosynthesis, all the considered cell lines can be selectively affected by targeting different combinations (from 1 to 5 reactions) of only 18 metabolic reactions, which suggests that a small subset of drugs or siRNAs combined in patient specific manners could be at the core of metabolism based personalized treatments. This model is hosted on BioModels Database and identified by: MODEL1707250000. To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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E-MTAB-2706

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