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Proteotranscriptomics Reveal Signaling Networks in the Ovarian Cancer Microenvironment.

ABSTRACT: Ovarian cancer is characterized by early transcoelomic metastatic spread via the peritoneal fluid, where tumor cell spheroids (TU), tumor-associated T cells (TAT), and macrophages (TAM) create a unique microenvironment promoting cancer progression, chemoresistance, and immunosuppression. However, the underlying signaling mechanisms remain largely obscure. To chart these signaling networks, we performed comprehensive proteomic and transcriptomic analyses of TU, TAT, and TAM from ascites of ovarian cancer patients. We identify multiple intercellular signaling pathways driven by protein or lipid mediators that are associated with clinical outcome. Beyond cytokines, chemokines and growth factors, these include proteins of the extracellular matrix, immune checkpoint regulators, complement factors, and a prominent network of axon guidance molecules of the ephrin, semaphorin, and slit families. Intriguingly, both TU and TAM from patients with a predicted short survival selectively produce mediators supporting prometastatic events, including matrix remodeling, stemness, invasion, angiogenesis, and immunosuppression, whereas TAM associated with a longer survival express cytokines linked to effector T-cell chemoattraction and activation. In summary, our study uncovers previously unrecognized signaling networks in the ovarian cancer microenvironment that are of potential clinical relevance.

SUBMITTER: Worzfeld T

PROVIDER: S-EPMC5795391 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Proteotranscriptomics Reveal Signaling Networks in the Ovarian Cancer Microenvironment.

Worzfeld Thomas T Finkernagel Florian F Reinartz Silke S Konzer Anne A Adhikary Till T Nist Andrea A Stiewe Thorsten T Wagner Uwe U Looso Mario M Graumann Johannes J Müller Rolf R

Molecular & cellular proteomics : MCP 20171115 2

Ovarian cancer is characterized by early transcoelomic metastatic spread via the peritoneal fluid, where tumor cell spheroids (TU), tumor-associated T cells (TAT), and macrophages (TAM) create a unique microenvironment promoting cancer progression, chemoresistance, and immunosuppression. However, the underlying signaling mechanisms remain largely obscure. To chart these signaling networks, we performed comprehensive proteomic and transcriptomic analyses of TU, TAT, and TAM from ascites of ovaria ...[more]

PMID: 29141914

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Project description:MotivationProtein phosphorylation is a ubiquitous mechanism of post-translational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as downregulation of tumor suppressors and upregulation of oncogenes by the dysregulation of associated kinase and phosphatase networks are shown to have key roles in tumor growth and progression. Despite recent advances that enable large-scale monitoring of protein phosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers.ResultsWe develop a network-based algorithm, CoPPNet, to enable unsupervised subtyping of cancers using phosphorylation data. For this purpose, we integrate prior knowledge on evolutionary, structural and functional association of phosphosites, kinase-substrate associations and protein-protein interactions with the correlation of phosphorylation of phosphosites across different tumor samples (a.k.a co-phosphorylation) to construct a context-specific-weighted network of phosphosites. We then mine these networks to identify subnetworks with correlated phosphorylation patterns. We apply the proposed framework to two mass-spectrometry-based phosphorylation datasets for breast cancer (BC), and observe that (i) the phosphorylation pattern of the identified subnetworks are highly correlated with clinically identified subtypes, and (ii) the identified subnetworks are highly reproducible across datasets that are derived from different studies. Our results show that integration of quantitative phosphorylation data with network frameworks can provide mechanistic insights into the differences between the signaling mechanisms that drive BC subtypes. Furthermore, the reproducibility of the identified subnetworks suggests that phosphorylation can provide robust classification of disease response and markers.Availability and implementationCoPPNet is available at http://compbio.case.edu/coppnet/.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:BACKGROUND:Bulk tumor tissue samples are used for generating gene expression profiles in most research studies, making it difficult to decipher the stroma-cancer crosstalk networks. In the present study, we describe the use of microdissected transcriptome profiles for the identification of cancer-stroma crosstalk networks with prognostic value, which presents a unique opportunity for developing new treatment strategies for ovarian cancer. METHODS:Transcriptome profiles from microdissected ovarian cancer-associated fibroblasts (CAFs) and ovarian cancer cells from patients with high-grade serous ovarian cancer (n = 70) were used as input data for the computational systems biology program CCCExplorer to uncover crosstalk networks between various cell types within the tumor microenvironment. The crosstalk analysis results were subsequently used for discovery of new indications for old drugs in ovarian cancer by computational ranking of candidate agents. Survival analysis was performed on ovarian tumor-bearing Dicer/Pten double-knockout mice treated with calcitriol, a US Food and Drug Administration-approved agent that suppresses the Smad signaling cascade, or vehicle control (9-11 mice per group). All statistical tests were two-sided. RESULTS:Activation of TGF-β-dependent and TGF-β-independent Smad signaling was identified in a particular subtype of CAFs and was associated with poor patient survival (patients with higher levels of Smad-regulated gene expression by CAFs: median overall survival = 15 months, 95% confidence interval [CI] = 12.7 to 17.3 months; vs patients with lower levels of Smad-regulated gene expression: median overall survival = 26 months, 95% CI = 15.9 to 36.1 months, P = .02). In addition, the activated Smad signaling identified in CAFs was found to be targeted by repositioning calcitriol. Calcitriol suppressed Smad signaling in CAFs, inhibited tumor progression in mice, and prolonged the median survival duration of ovarian cancer-bearing mice from 36 to 48 weeks (P = .04). CONCLUSIONS:Our findings suggest the feasibility of using novel multicellular systems biology modeling to identify and repurpose known drugs targeting cancer-stroma crosstalk networks, potentially leading to faster and more effective cures for cancers.

Project description:BackgroundUnderstanding the tumor microenvironment (TME) is essential for the advancement of immunotherapy for ovarian cancer (OC). Nonetheless, predicting transcription factor (TF) regulation from the TME using single-cell RNA sequencing (scRNA-seq) data is challenging.MethodsThe OC scRNA-seq data were analyzed with a specialized scRNA-seq transcriptome analysis application. The OC TME was utilized to instruct the SCENIC procedure for TF regulation. We built a risk model using Lasso regression and identified immunological subgroups using ConsensusClusterPlus. To analyze the percentage of invading immune cells, the algorithms CIBERSORT, ESTIMATE, and xCell were used. We computed the stromal score, immunological score, estimate score, and tumor purity to evaluate the risk model's capacity to predict the tumor immune microenvironment. Additionally, the expression of immunological checkpoints was examined, and for pertinent evaluation, the imvigor 210 dataset of the immunotherapy cohort was used. pRophetic predicted the sensitivity of 138 GDSC database drugs. In addition, we examined the expression of unproven risk model genes using qPCR and immunohistochemistry (JCHAIN, UBD, and RARRES1). Cell proliferation was assessed by colony formation assays. Transwell experiments were used to examine the invasion and migration ability of OC cells.ResultsSix immunologically malignant cell subpopulations have been identified within the cancer immune microenvironment (referred to as TC0-6). Unique in its immunological profile, TC0 demonstrates the most intimate interactions with immune cells. Following a meta gene screen in the TC0 subpopulation using the top 30 targets of 14 transcription factor (TF) factors, two distinct immunological molecular subtypes-the C1 and C2 subtypes-with notable survival differences were discovered. On the basis of nine genes whose expression differs between the C1 and C2 subtypes, a risk model was constructed. The risk model is an accurate method for forecasting the effectiveness of immunotherapies, clinicopathological characteristics, and survival. JCHAIN and UBD expression in OC tissues was found to be low according to qPCR and IHC analyses, whereas RARRES1 expression was found to be high. The functional experiment results indicated that downregulation of JCHAIN and UBD and overexpression of RARRES1 could suppress the proliferation, migration, and invasion of OC cells in vitro.ConclusionBased on TF regulatory networks in the tumor microenvironment, this study developed a 9-gene risk model for the prognosis of ovarian cancer. This model may aid in the future promotion of personalized OC immunotherapy. In addition, JCHAIN, UBD, and RARRES1 were identified as three novel immune-related biomarkers for OC.

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Proteotranscriptomics Reveal Signaling Networks in the Ovarian Cancer Microenvironment.

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Proteotranscriptomics Reveal Signaling Networks in the Ovarian Cancer Microenvironment.

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