Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Lung cancer signatures in plasma based on proteome profiling of mouse tumor models

ABSTRACT: Mouse models of cancer recapitulate many of the molecular and biological features of the human disease. We sought to exploit these experimental merits in a systematic comparative proteomics search for circulating proteins associated with lung tumor development. In-depth quantitative proteomics was applied to plasmas from three mouse models of lung adenocarcinoma driven by mutant EGFR or Kras or induced by urethane exposure and a mouse model of small cell lung cancer driven by loss of Trp53 and Rb. To further refine our lung cancer-specific and broad carcinoma signatures, we intersected these lung cancer proteome profiles with those from other well-established mouse models of pancreatic, ovarian, colon, prostate and breast cancer, as well as two mouse models of inflammation. A set of proteins regulated by Titf1/Nkx2-1, a master transcription factor in cells from the peripheral airways and a known lineage-survival oncogene in lung cancer was identified in plasmas of mouse models of lung adenocarcinoma. An EGFR network of proteins was discerned in the plasma of mice with lung tumors driven by a mutant human EGFR. Levels of these proteins returned toward baseline upon treatment with a tyrosine kinase inhibitor. Moreover, a distinct plasma signature was uncovered in the Trp53/Rb mutant small cell lung cancer model that included a set of proteins associated with neuroendocrine development. Our studies have identified novel plasma protein signatures among molecularly or histopathologically defined lung cancer subtypes. siRNA transfection experiments were performed in NCI-H3255 and HCC4019 lung adenocarcinoma cell lines using ON-TARGETplus SMARTpool small interfering RNAs (siRNAs) targeting TITF1 (L-019105-01-0005) along with a negative control (ON-TARGETplus siCONTROL nontargeting siRNA pool; D-001810-10-05) obtained from Dharmacon. 400000 cells were seeded in antibiotic-free RPMI-1640 media supplemented with 10% FBS, in 6-well culture plates. The next day, cells were transfected at a final concentration of 100nM siRNA using 6ul DharmaFECT 1 (Dharmacon) according to the manufacturer's instructions. 72-hours post-transfection, RNA was harvested using Trizol (Invitrogen) and protein using RIPA buffer for microarray expression and western blotting, respectively. RNA from TITF1 knockdown and control experiments was profiled by the MSKCC Genomics Core using the Illumina Human HT-12 v3.0 array platform according to manufacturer's instructions. Two biological replicates were profiled for each condition. Resulting data files were exported using GenomeStudio software, log2 transformed, quantile-normalized and analyzed using Partek Genomics Suite (v6.5). Average values of replicates for each gene were then compared between the TITF1 knockdown and non-targeting treatments for each cell line to identify candidate TITF1 regulated genes.

ORGANISM(S): Homo sapiens

SUBMITTER: William Lockwood

PROVIDER: E-GEOD-28480 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Summary: Lung cancer is a leading cause of cancer death, where the amplification of oncogenes contributes to tumorigenesis. Genomic profiling of 128 lung cancer cell lines and tumors revealed frequent focal DNA amplification at cytoband 14q13.3, a locus not amplified in other tumor types. The smallest region of recurrent amplification spanned the homeobox transcription factor TITF1 (also known as NKX2-1), previously linked to normal lung development and function. When amplified, TITF1 exhibited increased expression at both the RNA and protein level. siRNA-mediated knockdown of TITF1 in lung cancer cell lines with amplification led to reduced cell proliferation, manifested by both decreased cell-cycle progression and increased apoptosis. Our findings indicate that TITF1 amplification and overexpression contribute to lung cancer cell proliferation rates and survival, and implicate TITF1 as a lineage-specific oncogene in lung cancer. Set of arrays organized by shared biological context, such as organism, tumors types, processes, etc. Cell Line Keywords: Logical Set cDNA microarrays from the Stanford Functional Genomics Facility were used to perform array based Comparative Genomic Hybridization (aCGH) analysis on 52 non-small cell lung cancer (NSCLC) cell lines and 76 NSCLC tumors (36 adenocarcinomas including 2 metastases, and 40 squamous cell carcinomas including 1 metastasis). In addition, this dataset includes 6 immortalized and 3 non-immortalized lung epithelial cell lines and 1 male vs. female genomic DNA for hybridization control. Map positions for arrayed cDNA clones were assigned using the NCBI genome assembly, accessed through the UCSC genome browser database (NCBI Build 36). The most frequent focal DNA amplification not associated with a previously known oncogene occurred at cytoband 14q13.3 where TITF1 resides. The sample labeled normal is a "Normal male vs.female DNA" comparison; the samples: SAEC, HBEC3-UI, HBEC5-UI, HBEC2-KT, HBEC3-KT, HBEC4-KT, HBEC5-KT, HBEC2-E, BEAS-2B are Normal lung epithelial cell lines; samples starting with L are all Lung tumor samples and all the rest are Lung cancer cell lines. Computed

Project description:Bianconi2012 - EGFR and IGF1R pathway in lung cancer EGFR and IGF1R pathways play a key role in various human cancers and are crucial for tumour transformation and survival of malignant cells. High EGFR and IGF1R expression and activity has been associated with multiple aspects of cancer progression including tumourigenesis, metastasis, resistance to chemotherapeutics and other molecularly targeted drugs. Here, the biological relationship between the proteins involved in EGFR and IGF1R pathways and the downstream MAPK and PIK3 networks has been modelled to study the time behaviour of the overall system, and the functional interdependencies among the receptors, the proteins and kinases involved. This model is described in the article: Computational model of EGFR and IGF1R pathways in lung cancer: a Systems Biology approach for Translational Oncology. Bianconi F, Baldelli E, Ludovini V, Crinò L, Flacco A, Valigi P. Biotechnol Adv. 2012 Jan-Feb;30(1):142-53. Abstract: In this paper we propose a Systems Biology approach to understand the molecular biology of the Epidermal Growth Factor Receptor (EGFR, also known as ErbB1/HER1) and type 1 Insulin-like Growth Factor (IGF1R) pathways in non-small cell lung cancer (NSCLC). This approach, combined with Translational Oncology methodologies, is used to address the experimental evidence of a close relationship among EGFR and IGF1R protein expression, by immunohistochemistry (IHC) and gene amplification, by in situ hybridization (FISH) and the corresponding ability to develop a more aggressive behavior. We develop a detailed in silico model, based on ordinary differential equations, of the pathways and study the dynamic implications of receptor alterations on the time behavior of the MAPK cascade down to ERK, which in turn governs proliferation and cell migration. In addition, an extensive sensitivity analysis of the proposed model is carried out and a simplified model is proposed which allows us to infer a similar relationship among EGFR and IGF1R activities and disease outcome. This model is hosted on BioModels Database and identified by: MODEL1209230000 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . 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.

Dataset Information

Lung cancer signatures in plasma based on proteome profiling of mouse tumor models

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets