Immune and Inflammatory Cell Composition of Human Lung Cancer Stroma

Banat, GA; Tretyn, A; Pullamsetti, SS; Wilhelm, Jochen; Weigert, Andreas; Ebel, Kenny; Stiewe, Thorsten; Grimminger, F.; Seeger, W.; Fink, Lisbeth Nielsen; Rajkumar, S. Vincent

doi:10.1055/s-0035-1544728

Cited by 30 publications

(35 citation statements)

References 21 publications

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“…Second, 547our study showed that the number of immune cells in the tissue microenvironment increases 548 from normal stage to cancerous stage, which is in agreement with past studies(Banat, et al, 549 2015; Li, et al, 2016; Ruffini, et al, 2009;Seo, et al, 2018). In particular,(Banat, et al, 2015) 550comprehensively assessed the number of immune cells in lung cancer by directly counting cells 551with cell-specific biomarkers and observed an increased number of immune cells in lung cancer 552 tissues compared with healthy donor lungs.In 2018, Seo et al (2018) applied two approaches, 553 ESTIMATE and TIMER, to infer the cellular components in NSCLC (Li, et al, 2016; Seo, et 554 al., 2018; Yoshihara, et al, 2013). Their results showed a high abundance of dendritic cells 555…”

supporting

confidence: 84%

BayICE: A hierarchical Bayesian deconvolution model with stochastic search variable selection

Tai

Tseng

Hsieh

2019

Preprint

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16Gene expression deconvolution is a powerful tool for exploring the microenvironment of 17 complex tissues comprised of multiple cell groups using transcriptomic data. Characterizing 18 cell activities for a particular condition has been regarded as a primary mission against diseases. 19For example, cancer immunology aims to clarify the role of the immune system in the 20 progression and development of cancer through analyzing the immune cell components of 21 tumors. To that end, many deconvolution methods have been proposed for inferring cell 22subpopulations within tissues. Nevertheless, two problems limit the practicality of current 23approaches. First, all approaches use external purified data to preselect cell type-specific genes 24 that contribute to deconvolution. However, some types of cells cannot be found in purified 25profiles and the genes specifically over-or under-expressed in them cannot be identified. This 26is particularly a problem in cancer studies. Hence, a preselection strategy that is independent 27 from deconvolution is inappropriate. The second problem is that existing approaches do not 28 recover the expression profiles of unknown cells present in bulk tissues, which results in biased 29 estimation of unknown cell proportions. Furthermore, it causes the shift-invariant property of 30 deconvolution to fail, which then affects the estimation performance. To address these two 31 problems, we propose a novel deconvolution approach, BayICE, which employs hierarchical 32Bayesian modeling with stochastic search variable selection. We develop a comprehensive 33Markov chain Monte Carlo procedure through Gibbs sampling to estimate cell proportions, 34gene expression profiles, and signature genes. Simulation and validation studies illustrate that 35BayICE outperforms existing deconvolution approaches in estimating cell proportions. 36Subsequently, we demonstrate an application of BayICE in the RNA sequencing of patients 37with non-small cell lung cancer. The model is implemented in the R package "BayICE" and 38 the algorithm is available for download. 39 40

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confidence: 84%

BayICE: A hierarchical Bayesian deconvolution model with stochastic search variable selection

Tai

Tseng

Hsieh

2019

Preprint

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“…Components of TME comprising tumour cells, immune cells and other stromal cells along with their vital interactions contribute to treatment response and disease prognosis. Accumulating evidence in both epithelial [13][14][15][16] and neural cancers [17,18] suggest a close interplay between the tumour and niche involving non-neoplastic components to secrete signalling molecules, growth factors that contribute to increased angiogenesis, invasion, progression and metastasis [19][20][21][22][23]. The TME components are hence referred to as "partners in crime" for the tumourigenesis process [24,25].…”

Section: Doi: 101159/000488709mentioning

confidence: 99%

Evidence of Tumour Microenvironment and Stromal Cellular Components in Retinoblastoma

et al. 2018

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Background: The tumour microenvironment (TME) consisting of tumour cells and multiple stromal cell types regulate tumour growth, invasion and metastasis. While the concept of TME and presence of stromal cellular components is widely established in cancers, its significance in the paediatric intraocular malignancy, retinoblastoma (RB), remains unknown. Methods: The study qualitatively identified the presence of multiple stromal cellular subtypes in RB TME by immunohistochemistry. Results: Results of the study identified the presence of stromal cell types such as endothelial cells, tumour-associated macrophages, fibroblasts, cancer-associated fibroblasts, retinal astrocytes and glia in RB TME. The extent of stromal marker positivity, however, did not correlate with histopathological features of RB. Conclusions: The findings of the study convincingly suggest the presence of a stromal component in RB tumours. The interactions between stromal cells and tumour cells might be of profound importance in RB progression.

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“…The study protocol for tissue donation was approved by the ethics committee (Ethik Kommission am Fachbereich Humanmedizin der Justus Liebig Universität Giessen) of the University Hospital Giessen (Giessen, Germany) and is in accordance with national law and with Good Clinical Practice/International Conference on Harmonisation guidelines. Written informed consent was obtained from each patient or the patient's next of kin (AZ 31/93) (25)(26)(27).…”

Section: Lung Tumor Tissuementioning

confidence: 99%

miR‐574‐5p as RNA decoy for CUGBP1 stimulates human lung tumor growth by mPGES‐1 induction

et al. 2019

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MicroRNAs (miRs) are important posttranscriptional regulators of gene expression. Besides their well‐characterized inhibitory effects on mRNA stability and translation, miRs can also activate gene expression. In this study, we identified a novel noncanonical function of miR‐574‐5p. We found that miR‐574‐5p acts as an RNA decoy to CUG RNA‐binding protein 1 (CUGBP1) and antagonizes its function. MiR‐574‐5p induces microsomal prostaglandin E synthase‐1 (mPGES‐1) expression by preventing CUGBP1 binding to its 3′UTR, leading to an enhanced alternative splicing and generation of an mPGES‐1 3′UTR isoform, increased mPGES‐1 protein expression, PGE2 formation, and tumor growth in vivo. miR‐574‐5p–induced tumor growth in mice could be completely inhibited with the mPGES‐1 inhibitor OIL Moreover, miR‐574‐5p is induced by IL‐1β and is strongly overexpressed in human nonsmall cell lung cancer where high mPGES‐1 expression correlates with a low survival rate. The discovered function of miR‐574‐5p as a CUGBP1 decoy opens up new therapeutic opportunities. It might serve as a stratification marker to select lung tumor patients who respond to the pharmacological inhibition of PGE2 formation.—Saul, M. J., Baumann, I., Bruno, A., Emmerich, A. C., Wellstein, J., Ottinger, S. M., Contursi, A., Dovizio, M., Donnini, S., Tacconelli, S., Raouf, J., Idborg, H., Stein, S., Korotkova, M., Savai, R., Terzuoli, E., Sala, G., Seeger, W., Jakobsson, P.‐J., Patrignani, P., Suess, B., Steinhilber, D. miR‐574‐5p as RNA decoy for CUGBP1 stimulates human lung tumor growth by mPGES‐1 induction. FASEB J. 33, 6933–6947 (2019). http://www.fasebj.org

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Immune and Inflammatory Cell Composition of Human Lung Cancer Stroma

Cited by 30 publications

References 21 publications

BayICE: A hierarchical Bayesian deconvolution model with stochastic search variable selection

BayICE: A hierarchical Bayesian deconvolution model with stochastic search variable selection

Evidence of Tumour Microenvironment and Stromal Cellular Components in Retinoblastoma

miR‐574‐5p as RNA decoy for CUGBP1 stimulates human lung tumor growth by mPGES‐1 induction

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