Dataset Information

ProteoMixture: A Cell Deconvolution Tool for Bulk Tissue Proteomics Data

ABSTRACT: Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome (and transcriptome data) generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms ESTIMATE and ConsensusTME (r ≥ 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

INSTRUMENT(S): Q Exactive HF-X

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): T Cell, Ovary Stroma, Ovarian Serous Carcinoma Cell, Ovarian Surface Epithelial Cell, Fibroblast

DISEASE(S): Ovarian Serous Carcinoma

SUBMITTER: Thomas Conrads

LAB HEAD: Thomas P. Conrads

PROVIDER: PXD044157 | Pride | 2024-01-12

REPOSITORIES: Pride

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Project description:Epithelial ovarian carcinoma (EOC) is an aggressive tumor often diagnosed at an advanced stage, when there is little prospect for cure. Despite some advances in surgical and chemotherapeutic strategies, only marginal improvements in patient outcome have been obtained. Hence, understanding the biological mechanisms underpinning EOC progression is critical for its treatment and to ameliorate patients survival. Recently, we reported that CD157 is expressed in EOC and controls tumor cell migration and invasion. Using stable overexpression and knockdown in OVCAR-3 and OV-90 ovarian cancer cell lines, we demonstrated that CD157 overexpression promotes morphological and functional changes, characterized by downregulation of epithelial marker and upregulation of mesenchymal ones. These are mediated at the transcriptional level by altering the expression of Snail and Zeb1 transcriptional repressors. The effects of CD157 overexpression on ovarian cancer phenotype translate into increased tumor cell motility and mesotelial invasion, while its knockdown significantly reduces the migratory potential, implying a direct correlation between CD157 expression levels and EOC aggressiveness. The analysis of the transcriptomic profiling highlighted 378 significantly differentially expressed genes, representing the signature of CD157-overexpressing EOC cells. The overall picture deduced from the analysis of these modulated transcripts indicated that high CD157 expression results in strengthening of a number of biological functions that favour tumor progression (including cell differentiation, cell motility and migration), and weakening of selected biological processes that hinder the tumor progression (such as apoptosis, cell death and response to stress). Collectively, these data support a causal role of CD157 in the control of ovarian cancer progression motivating the existence of a direct correlation between the expression levels of CD157 and the adverse clinical outcome in EOC patients, and suggest that CD157 may represent a valuable therapeutic target. Gene expression analysisi of control cell lines (OVCAR-3/mock and OV-90/mock) and testing cell lines (OVCAR-3/CD157 and OV-90/CD157), with two replicates, with dye swap, performed for each sample.

Project description:We previously demonstrated that the osteopontin-c (OPNc) splice variant activates several aspects of the progression of ovarian cancers. The goal of the present study was to develop cell line model to determine the impact of OPNc overexpression on main cancer signaling pathways and thus obtain insights into the mechanisms of OPNc pro-tumorigenic roles. Methods: OvCar-3 cells were stably transfected to overexpress OPNc. Transcriptomic profiling was performed on these cells and compared to controls, to identify OPNc overexpression-dependent changes in gene expression levels and pathways by qRT-PCR analyses; Results: Of 84 genes tested by using a multiplex real-time PCR Cancer Pathway Array approach, 34, were differentially expressed between OvCar-3 OPNc-overexpressing cells in relation to control clones. Differentially expressed genes are included in all main hallmarks of cancer, and several interacting proteins have been identified using an interactome network analysis. Conclusions: Overall, the present study elucidated transcriptional changes of ovarian cancer cells in response to OPNc overexpression, which provides an assessment for predicting the molecular mechanisms by which this splice variant promotes tumor progression features. Cell Culture, OPN Plasmids and Transfection; As a model to examine the signaling pathways modulated by OPNc overexpression in ovarian carcinoma, we used OvCar-3 cell line, which were provided by ATCC. The open reading frame of OPNc was cloned into the pCR3.1 mammalian expression vector as previously described [Tilli et al., 2012]. Transfections were performed using LipofectamineTM 2000 (Invitrogen, CA). Our previous data demonstrated that OvCar-3 stably transfected cells contain higher levels of protein and the mRNA OPNc splicing isoform in relation to their endogenous levels in empty vector-transfected cells. OvCar-3 transfected with empty vector (EV) were used as a negative control in these assays. Human Cancer Pathway Finder PCR Array The Human Cancer Pathway Finder SuperArray (PAHS-033A; Qiagen) was used to determine changes in the specific genes encoding proteins related to the main hallmarks of cancer in response to OPNc overexpression. The assay design criteria ensure that each qPCR reaction will generate single, gene-specific amplicons and prevent the co-amplification of non-specific products. The qPCR Assays used in PCR Arrays are optimized to work under standard conditions enabling a large number of genes to be assayed simultaneously. Their specificity is guaranteed by SABiosciences when RT2 SYBR Green qPCR Master Mixes are used as part of the complete PCR Array System protocol. Simultaneous gene expression analyses require similar qPCR efficiencies for accurate comparison among genes. RT2 qPCR Primer Assays are designed with an amplicon size ranging from 100 to 250 bp and with PCR efficiencies uniformly greater than 90%. Overall, more than 10 thermodynamic criteria are included in the design of each RT2 qPCR Primer Assay to ensure the most reliable and accurate results for pathway-based gene expression analysis in the PCR Array System. We analyzed mRNA levels of 84 genes related to cell cycle control, apoptosis and cell senescence, signal transduction molecules and transcription factors, adhesion, angiogenesis, invasion and metastasis; and also 5 housekeeping genes and genomic DNA contamination controls. The PCR plates were run using the CFX96 Real-Time System cycler (BioRad, Hercules, CA), following a superarray two-step cycling PCR protocol, in which each plate ran one cycle for 10 minutes at 95M-BM-0C, as well as 40 cycles of 95M-BM-0C for 15 seconds and 60M-BM-0C for 1 minute. After the superarray protocol was run for each plate, RT-PCR data analysis was performed using the website:http://www.SABiosciences.com/pcrarraydataanalysis.php, in order to compare gene expression of OPNc-overexpressing cells and empty vector transfected cells. Total RNA quality control, cDNA synthesis and the quantitative real-time RT-PCR (qRT-PCR) array were performed as recommended by the manufacturer (Qiagen). Data analysis of gene expression was performed using Excel-based PCR Array Data Analysis Software provided by manufacturer (Qiagen). Fold-changes in gene expression were calculated using the M-NM-^TM-NM-^TCT method, and five stably expressed housekeeping genes (M-NM-22 microglobulin, hypoxanthine phosphoribosyltransferase 1, ribosomal protein L13a, GAPDH and M-NM-2-actin) were used to normalize the level of expression. The statistical analysis was performed to compare the gene expression values for the OPNc-overexpressing cells and those transfected with empty vector. P<0.05 was considered statistically significant. Only genes showing a 1.5-fold or greater change were considered for further analysis.

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ProteoMixture: A Cell Deconvolution Tool for Bulk Tissue Proteomics Data

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