Correction to: Determining the effects of trastuzumab, cetuximab and afatinib by phosphoprotein, gene expression and phenotypic analysis in gastric cancer cell lines.

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Determining the effects of trastuzumab, cetuximab and afatinib by phosphoprotein, gene expression and phenotypic analysis in gastric cancer cell lines.

BACKGROUND: Gastric cancer is the fifth most frequently diagnosed cancer and the third leading cause of cancer death worldwide. The molecular mechanisms of action for anti-HER-family drugs in gastric cancer cells are incompletely understood. We compared the molecular effects of trastuzumab and the other HER-family targeting drugs cetuximab and afatinib on phosphoprotein and gene expression level to gain insights into the regulated pathways. Moreover, we intended to identify genes involved in phenotypic effects of anti-HER therapies. METHODS: A time-resolved analysis of downstream intracellular kinases following EGF, cetuximab, trastuzumab and afatinib treatment was performed by Luminex analysis in the gastric cancer cell lines Hs746T, MKN1, MKN7 and NCI-N87. The changes in gene expression after treatment of the gastric cancer cell lines with EGF, cetuximab, trastuzumab or afatinib for 4 or 24 h were analyzed by RNA sequencing. Significantly enriched pathways and gene ontology terms were identified by functional enrichment analysis. Furthermore, effects of trastuzumab and afatinib on cell motility and apoptosis were analyzed by time-lapse microscopy and western blot for cleaved caspase 3. RESULTS: The Luminex analysis of kinase activity revealed no effects of trastuzumab, while alterations of AKT1, MAPK3, MEK1 and p70S6K1 activations were observed under cetuximab and afatinib treatment. On gene expression level, cetuximab mainly affected the signaling pathways, whereas afatinib had an effect on both signaling and cell cycle pathways. In contrast, trastuzumab had little effects on gene expression. Afatinib reduced average speed in MKN1 and MKN7 cells and induced apoptosis in NCI-N87 cells. Following treatment with afatinib, a list of 14 genes that might be involved in the decrease of cell motility and a list of 44 genes that might have a potential role in induction of apoptosis was suggested. The importance of one of these genes (HBEGF) as regulator of motility was confirmed by knockdown experiments. CONCLUSIONS: Taken together, we described the different molecular effects of trastuzumab, cetuximab and afatinib on kinase activity and gene expression. The phenotypic changes following afatinib treatment were reflected by altered biological functions indicated by overrepresentation of gene ontology terms. The importance of identified genes for cell motility was validated in case of HBEGF.

MET as resistance factor for afatinib therapy and motility driver in gastric cancer cells.

The therapeutic options for advanced gastric cancer are still limited. Several drugs targeting the epidermal growth factor receptor family have been developed. So far, the HER2 antibody trastuzumab is the only drug targeting the HER-family that is available to gastric cancer patients. The pan-HER inhibitor afatinib is currently investigated in clinical trials and shows promising results in cell culture experiments and patient-derived xenograft (PDX) models. However, some cell lines do not respond to afatinib treatment. The determination of resistance factors in these cell lines can help to find the best treatment option for gastric cancer patients. In this study, we analyzed the role of MET as a resistance factor for afatinib therapy in a gastric cancer cell line. MET expression in afatinib-resistant MET-amplified Hs746T cells was reduced by means of siRNA transfection. The effects of MET knockdown on signal transduction, cell proliferation and motility were examined. In addition to the manual assessment of cell motility, a computational motility analysis involving parameters such as (approximate) average speed, displacement entropy or radial effectiveness was realized. Moreover, the impact of afatinib was compared between MET knockdown cells and control cells. MET knockdown in Hs746T cells resulted in impaired signal transduction and reduced cell proliferation and motility. Moreover, the afatinib resistance of Hs746T cells was reversed after MET knockdown. Therefore, the amplification of MET is confirmed as a resistance factor in gastric cancer cells. Whether MET is a useful resistance marker for afatinib therapy or other HER-targeting drugs in patients should be investigated in clinical trials.

Evaluation of epidermal growth factor receptor signaling effects in gastric cancer cell lines by detailed motility-focused phenotypic characterization linked with molecular analysis.

BACKGROUND: Gastric cancers frequently overexpress the epidermal growth factor receptor (EGFR), which has been implicated in pathological processes including tumor cell motility, invasion and metastasis. Targeting EGFR with the inhibitory antibody cetuximab may affect the motile and invasive behavior of tumor cells. Here, we evaluated the effects of EGFR signaling in gastric cancer cell lines to link the phenotypic behavior of the cells with their molecular characteristics. METHODS: Phenotypic effects were analyzed in four gastric cancer cell lines (AGS, Hs746T, LMSU and MKN1) by time-lapse microscopy and transwell invasion assay. Effects on EGFR signaling were detected using Western blot and proteome profiler analyses. A network was constructed linking EGFR signaling to the regulation of cellular motility. RESULTS: The analysis of the effects of treatment with epidermal growth factor (EGF) and cetuximab revealed that only one cell line (MKN1) was sensitive to cetuximab treatment in all phenotypic assays, whereas the other cell lines were either not responsive (Hs746T, LMSU) or sensitive only in certain tests (AGS). Cetuximab inhibited EGFR, MAPK and AKT activity and associated components of the EGFR signaling pathway to different degrees in cetuximab-sensitive MKN1 cells. In contrast, no such changes were observed in Hs746T cells. Thus, the different phenotypic behaviors of the cells were linked to their molecular response to treatment. Genetic alterations had different associations with response to treatment: while PIK3CA mutations and KRAS mutation or amplification were not obstructive, the MET mutation was associated with non-response. CONCLUSION: These results identify components of the EGFR signaling network as important regulators of the phenotypic and molecular response to cetuximab treatment.

Associating approximate paths and temporal sequences of noisy detections: Application to the recovery of spatio-temporal cancer cell trajectories.

In this paper we address the problem of recovering spatio-temporal trajectories of cancer cells in phase contrast video-microscopy where the user provides the paths on which the cells are moving. The paths are purely spatial, without temporal information. To recover the temporal information associated to a given path we propose an approach based on automatic cell detection and on a graph-based shortest path search. The nodes in the graph consist of the projections of the cell detections onto the geometrical cell path. The edges relate nodes which correspond to different frames of the sequence and potentially to the same cell and trajectory. In this directed graph we search for the shortest path and use it to define a temporal parametrization of the corresponding geometrical cell path. An evaluation based on 286 paths of 7 phase contrast microscopy videos shows that our algorithm allows to recover 92% of trajectory points with respect to the associated ground truth. We compare our method with a state-of-the-art algorithm for semi-automated cell tracking in phase contrast microscopy which requires interactively placed starting points for the cells to track. The comparison shows that supporting geometrical paths in combination with our algorithm allow us to obtain more reliable cell trajectories.

Monitoring the cytoskeletal EGF response in live gastric carcinoma cells.

Altered cell motility is considered to be a key factor in determining tumor invasion and metastasis. Epidermal growth factor (EGF) signaling has been implicated in this process by affecting cytoskeletal organization and dynamics in multiple ways. To sort the temporal and spatial regulation of EGF-dependent cytoskeletal re-organization in relation to a cell's motile behavior time-lapse microscopy was performed on EGF-responsive gastric carcinoma-derived MKN1 cells co-expressing different fluorescently labeled cytoskeletal filaments and focal adhesion components in various combinations. The experiments showed that EGF almost instantaneously induces a considerable increase in membrane ruffling and lamellipodial activity that can be inhibited by Cetuximab EGF receptor antibodies and is not elicited in non-responsive gastric carcinoma Hs746T cells. The transient cell extensions are rich in actin but lack microtubules and keratin intermediate filaments. We show that this EGF-induced increase in membrane motility can be measured by a simple image processing routine. Microtubule plus-ends subsequently invade growing cell extensions, which start to accumulate focal complexes at the lamellipodium-lamellum junction. Such paxillin-positive complexes mature into focal adhesions by tyrosine phosphorylation and recruitment of zyxin. These adhesions then serve as nucleation sites for keratin filaments which are used to enlarge the neighboring peripheral keratin network. Focal adhesions are either disassembled or give rise to stable zyxin-rich fibrillar adhesions which disassemble in the presence of EGF to support formation of new focal adhesion sites in the cell periphery. Taken together the results serve as a basis for modeling the early cytoskeletal EGF response as a tightly coordinated and step-wise process which is relevant for the prediction of the effectiveness of anti-EGF receptor-based tumor therapy.

Learning cellular texture features in microscopic cancer cell images for automated cell-detection.

In this paper we present a new approach for automated cell detection in single frames of 2D microscopic phase contrast images of cancer cells which is based on learning cellular texture features. The main challenge addressed in this paper is to deal with clusters of cells where each cell has a rather complex appearance composed of sub-regions with different texture features. Our approach works on two different levels of abstraction. First, we apply statistical learning to learn 6 different types of different local cellular texture features, classify each pixel according to them and we obtain an image partition composed of 6 different pixel categories. Based on this partitioned image we decide in a second step if pre-selected seeds belong to the same cell or not. Experimental results show the high accuracy of the proposed method and especially average precision above 95%.

Tracking and quantitative analysis of dynamic movements of cells and particles.

Understanding complex cellular processes requires investigating the underlying mechanisms within a spatiotemporal context. Although cellular processes are dynamic in nature, most studies in molecular cell biology are based on fixed specimens, for example, using immunocytochemistry or fluorescence in situ hybridization (FISH). However, breakthroughs in fluorescence microscopy imaging techniques, in particular, the discovery of green fluorescent protein (GFP) and its spectral variants, have facilitated the study of a wide range of dynamic processes by allowing nondestructive labeling of target structures in living cells. In addition, the tremendous improvements in spatial and temporal resolution of light microscopes now allow cellular processes to be analyzed in unprecedented detail. These state-of-the-art imaging technologies, however, provide a huge amount of digital image data. To cope with the enormous amount of image data and to extract reproducible as well as quantitative information, computer-based image analysis is required. In this article, we describe methods for computer-based analysis of multidimensional live cell microscopy images and their application to study the dynamics of cells and particles. First, we sketch a general workflow for quantitative analysis of live cell images. Then, we detail computational methods for automatic image analysis comprising image preprocessing, segmentation, registration, tracking, and classification. We conclude with a discussion of quantitative analysis and systems biology.

A maximal mass confinement principle for rigid and locally rigid image registration.

In this paper we propose (1) to set the problem of image registration as a contour/region-template-to-image matching problem using so-called confiners--also called blobs or components--as template regions, (2) to select the confiners of one of the images by passing through the hierarchical structure which they define and registering them successively rigidly form coarse-to-fine to the other image, the target image, and (3) we propose a maximum mass confinement (MMC) principle for contour-to-image registration. This principle allows us to derive a similarity measure assessing how well the confiner fits into the target image simply by calculating the gray value mass confined by its contour. By optimizing this measure for rigid transformations we obtain our MMC algorithm registering a contour locally rigid to the target image. We illustrate that by proceeding based on (1-3) problems can be avoided which were related to previous registration algorithms based on confiners. We compare our MMC algorithm with another template matching algorithm based on normalized mutual information. Equally, we compare our hierarchical image registration strategy with B-Spline based non-rigid registration using normalized mutual information. We performed our evaluation on real and simulated images in terms of robustness, accuracy and computation speed. We show that both, MMC template matching on its own and hierarchical image registration using MMC, in most cases outperform the respective alternative method.

Quantitative motion analysis and visualization of cellular structures.

The availability of cellular markers tagged with the green fluorescent protein (GFP) has recently allowed a large number of cell biological studies to be carried out in live cells, thereby addressing the dynamic organization of cellular structures. Typically, microscopes capable of video recording are used to generate time-resolved data sets. Dynamic imaging data are complex and often difficult to interpret by pure visual inspection. Therefore, specialized image processing methods for object detection, motion estimation, visualization, and quantitation are required. In this review, we discuss concepts for automated analysis of multidimensional image data from live cell microscopy and their application to the dynamics of cell nuclear subcompartments.