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Non–small cell lung cancer (NSCLC) is characterized by early metastasis and has the highest mortality rate among all solid tumors, with the majority of patients diagnosed at an advanced stage where curative therapeutic options are lacking. In this study, we identify a targetable mechanism involving TGFβ elevation that orchestrates tumor progression in this disease. Substantial activation of this pathway was detected in human lung cancer tissues with concomitant downregulation of BAMBI, a negative regulator of the TGFβ signaling pathway. Alterations of epithelial-to-mesenchymal transition (EMT) marker expression were observed in lung cancer samples compared with tumor-free tissues. Distinct alterations in the DNA methylation of the gene regions encoding TGFβ pathway components were detected in NSCLC samples compared with tumor-free lung tissues. In particular, epigenetic silencing of BAMBI was identified as a hallmark of NSCLC. Reconstitution of BAMBI expression in NSCLC cells resulted in a marked reduction of TGFβ-induced EMT, migration, and invasion in vitro, along with reduced tumor burden and tumor growth in vivo. In conclusion, our results demonstrate how BAMBI downregulation drives the invasiveness of NSCLC, highlighting TGFβ signaling as a candidate therapeutic target in this setting. Cancer Res; 76(13); 3785–801. ©2016 AACR.

Description of additional methods and procedures used in the study.

Activity of the TGF-beta pathway in tumor-free lung and lung cancer (S1); Validation of the microarray data for individual targets with qRT-PCR assay (S2); Expression of EMT-related proteins in human lung tumor tissues and tumor-free lung (S3); Secretion of TGF-beta and expression of the pathway components in NSCLC cell lines (S4); Activation of TGF-beta pathway members and expression of EMT markers in lung cancer cells with BAMBI reconstitution (S5); Verification of DNA methylation values determined by HumanMethylation450K BeadChip analysis by performing bisulfite pyrosequencing (S6); siRNA-mediated knockdown of BAMBI expression in A549 cell line enhances TGF-beta-induced signaling (S7); siRNA-mediated knockdown of BAMBI expression in the squamous cell carcinoma cell line SK-MES1 enhances TGF-beta-induced signaling (S8); Human alveolar epithelial cells type II (AECII) cells express low amount of SMAD3 (S9); GFP- and BAMBI-GFP-positive A549 cells are present in the mice lungs at the time of sacrifice (S10).

Immunohistochemical analysis of patient material (S1); IHC score for TGF-beta pathway member from matched patient samples (S2); List of TGF-beta regulated genes (S3); List of EMT-related genes (S4); Results of paired t-tests for Microarray (S5); Patient information (S6); List of specific primers used for qRT-PCR gene expression analysis (S7).

Non–small cell lung cancer (NSCLC) is characterized by early metastasis and has the highest mortality rate among all solid tumors, with the majority of patients diagnosed at an advanced stage where curative therapeutic options are lacking. In this study, we identify a targetable mechanism involving TGFβ elevation that orchestrates tumor progression in this disease. Substantial activation of this pathway was detected in human lung cancer tissues with concomitant downregulation of BAMBI, a negative regulator of the TGFβ signaling pathway. Alterations of epithelial-to-mesenchymal transition (EMT) marker expression were observed in lung cancer samples compared with tumor-free tissues. Distinct alterations in the DNA methylation of the gene regions encoding TGFβ pathway components were detected in NSCLC samples compared with tumor-free lung tissues. In particular, epigenetic silencing of BAMBI was identified as a hallmark of NSCLC. Reconstitution of BAMBI expression in NSCLC cells resulted in a marked reduction of TGFβ-induced EMT, migration, and invasion in vitro, along with reduced tumor burden and tumor growth in vivo. In conclusion, our results demonstrate how BAMBI downregulation drives the invasiveness of NSCLC, highlighting TGFβ signaling as a candidate therapeutic target in this setting. Cancer Res; 76(13); 3785–801. ©2016 AACR.

Background The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. Results The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Conclusion Here we describe a new, efficient method for the analysis of sparse and heterogeneous time course data with high detection sensitivity and transparency. It is implemented as R package TTCA (transcript time course analysis) and can be installed from the Comprehensive R Archive Network, CRAN. The source code is provided with the Additional file 1. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1440-8) contains supplementary material, which is available to authorized users.

Lung cancer, with its most prevalent form non-small-cell lung carcinoma (NSCLC), is one of the leading causes of cancer-related deaths worldwide, and is commonly treated with chemotherapeutic drugs such as cisplatin. Lung cancer patients frequently suffer from chemotherapy-induced anemia, which can be treated with erythropoietin (EPO). However, studies have indicated that EPO not only promotes erythropoiesis in hematopoietic cells, but may also enhance survival of NSCLC cells. Here, we verified that the NSCLC cell line H838 expresses functional erythropoietin receptors (EPOR) and that treatment with EPO reduces cisplatin-induced apoptosis. To pinpoint differences in EPO-induced survival signaling in erythroid progenitor cells (CFU-E, colony forming unit-erythroid) and H838 cells, we combined mathematical modeling with a method for feature selection, the L1 regularization. Utilizing an example model and simulated data, we demonstrated that this approach enables the accurate identification and quantification of cell type-specific parameters. We applied our strategy to quantitative time-resolved data of EPO-induced JAK/STAT signaling generated by quantitative immunoblotting, mass spectrometry and quantitative real-time PCR (qRT-PCR) in CFU-E and H838 cells as well as H838 cells overexpressing human EPOR (H838-HA-hEPOR). The established parsimonious mathematical model was able to simultaneously describe the data sets of CFU-E, H838 and H838-HA-hEPOR cells. Seven cell type-specific parameters were identified that included for example parameters for nuclear translocation of STAT5 and target gene induction. Cell type-specific differences in target gene induction were experimentally validated by qRT-PCR experiments. The systematic identification of pathway differences and sensitivities of EPOR signaling in CFU-E and H838 cells revealed potential targets for intervention to selectively inhibit EPO-induced signaling in the tumor cells but leave the responses in erythroid progenitor cells unaffected. Thus, the proposed modeling strategy can be employed as a general procedure to identify cell type-specific parameters and to recommend treatment strategies for the selective targeting of specific cell types., Author Summary A major challenge in the development of therapeutic interventions is the selective inhibition of a signal transduction pathway in one cell type such as a cancer cell leaving the other cell type such as a healthy cell as unaffected as possible. Here, we propose a new approach that combines mathematical modeling based on quantitative experimental data with statistical methods. We demonstrate based on simulated data that our approach can determine which parameters are the same and which parameters differ in two exemplary cell types. We compare a lung cancer cell line to the precursor cells of red blood cells. We show that the same signal transduction network induced by erythropoietin (EPO), a hormone that is frequently employed to treat anemia in cancer patients, regulates survival of both cell types. Based on our experimental data in combination with our computational approach, we identify seven cell type-specific differences in this signaling pathway. Our strategy allows predicting therapeutic targets that could be inhibited to interfere with survival of lung cancer cells while leaving production of red blood cells unaffected.

Theranostics 5(8), 905-918 (2015). doi:10.7150/thno.11304, Published by Ivyspring, Wyoming, NSW