Negative feedback regulation as a means to constrain the oncogenic potential of mutant Egfr in NSCLC

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2011.Cataloged from PDF version of thesis. Vita.Includes bibliographical references.The discovery of EGFR kinase domain mutations in NSCLC patients who responded to tyrosine kinase inhibitors (TKIs) represented the first example of a targeted therapy for lung cancer. The dependence of these human tumors on sustained mutant receptor expression for survival, together with the discovery that ectopic expression of the receptor resulted in transformation, suggested that these mutations are causal events, and as such would be sufficient to induce tumor formation in the lung. To investigate this, and to further our understanding of how deregulated signaling through the mutant receptor could initiate tumor formation, we generated both a conditional and constitutive knock-in allele of one such mutation, L858R, at the endogenous murine Egfr locus. Expression of mutant Egfr failed to induce lung tumors in these mice; further analysis of the germline mutant mice revealed significant downregulation of the mutant receptor, and this was predominantly at the post-transcriptional level. These data suggest that normal cells can respond to an oncogenic lesion by upregulating negative feedback pathways to counteract the induction of aberrant signaling, and disabling these feedback mechanisms may be an essential component of the progression of EGFR mutant tumors. A multitude of positive and negative feedback loops converge on signaling pathways to ensure the appropriate output in response to a given stimulus. The role of oncogenes is typically thought of in terms of increasing the output of a signaling pathway, and while the contribution of the associated negative feedback loops is no doubt important, they have been afforded little attention. Recent studies have highlighted the existence of negative feedback mechanisms in established tumors and the integral role they play in shaping the signaling network, with a corresponding appreciation for how such feedback loops can profoundly influence therapeutic response. The capacity of negative regulators to modulate the oncogenic potential of a mutant protein in the context of tumor initiation has rarely been examined. Using a doxycyclineinducible system we recapitulate the aforementioned downregulation of mutant Egfr, initially observed in both tissues and MEFs derived from EgfrL858R mutant mice, in an ectopic cell culture expression system. We establish a role for ERK pathway signaling, and specifically DUSP6, in receptor degradation, and solidify the role of the E3 ligase, CULLIN5, in the downregulation of the mutant receptor. The existence of these negative feedback loops may explain the observation that mutation of EGFR is often coincident with gene amplification in NSCLC, and suggest that such amplification may primarily serve as a means to counteract the downregulation. The amplification of oncogenes is a recurring feature of many human tumors, but the contribution of gene amplification to particular stages of tumor development, or the molecular requirements for amplification to occur are unknown. EGFR is mutated and coincidentally amplified in NSCLC, but the relative contribution of mutation and amplification, both to tumor phenotype and therapeutic sensitivity, is not clear. The inability to model amplification in the mouse has contributed significantly to our limited mechanistic understanding of how gene amplification occurs in tumors. Using a yeast endonuclease, -Scel, and an allele that contains target sites for this enzyme engineered telomeric to mutant Egfr on chromosome 11, we attempted to initiate breakage-fusion-bridge (BFB) cycles in the lung, as these are thought to be a precursor to gene amplification. Our inability to elicit tumor formation using this strategy highlights the limitations in our understanding of how amplicons form in human tumors or the particular context required. While it would provide tremendous insight into mutant EGFR tumor development, a model of targeted gene amplification has thus far eluded us, and remains one of the significant challenges facing the mouse modeling community.by Keara M. Lane.Ph.D

Measuring Signaling and RNA-Seq in the Same Cell Links Gene Expression to Dynamic Patterns of NF-κB Activation

Signaling proteins display remarkable cell-to-cell heterogeneity in their dynamic responses to stimuli, but the consequences of this heterogeneity remain largely unknown. For instance, the contribution of the dynamics of the innate immune transcription factor nuclear factor κB (NF-κB) to gene expression output is disputed. Here we explore these questions by integrating live-cell imaging approaches with single-cell sequencing technologies. We used this approach to measure both the dynamics of lipopolysaccharide-induced NF-κB activation and the global transcriptional response in the same individual cell. Our results identify multiple, distinct cytokine expression patterns that are correlated with NF-κB activation dynamics, establishing a functional role for NF-κB dynamics in determining cellular phenotypes. Applications of this approach to other model systems and single-cell sequencing technologies have significant potential for discovery, as it is now possible to trace cellular behavior from the initial stimulus, through the signaling pathways, down to genome-wide changes in gene expression, all inside of a single cell

Mechanically resolved imaging of bacteria using expansion microscopy.

Imaging dense and diverse microbial communities has broad applications in basic microbiology and medicine, but remains a grand challenge due to the fact that many species adopt similar morphologies. While prior studies have relied on techniques involving spectral labeling, we have developed an expansion microscopy method (μExM) in which bacterial cells are physically expanded prior to imaging. We find that expansion patterns depend on the structural and mechanical properties of the cell wall, which vary across species and conditions. We use this phenomenon as a quantitative and sensitive phenotypic imaging contrast orthogonal to spectral separation to resolve bacterial cells of different species or in distinct physiological states. Focusing on host-microbe interactions that are difficult to quantify through fluorescence alone, we demonstrate the ability of μExM to distinguish species through an in vitro defined community of human gut commensals and in vivo imaging of a model gut microbiota, and to sensitively detect cell-envelope damage caused by antibiotics or previously unrecognized cell-to-cell phenotypic heterogeneity among pathogenic bacteria as they infect macrophages

Impaired SHP2-Mediated Extracellular Signal-Regulated Kinase Activation Contributes to Gefitinib Sensitivity of Lung Cancer Cells with Epidermal Factor Receptor-Activating Mutations

November 1st, 2010Most non–small cell lung cancers (NSCLC) display elevated expression of epidermal growth factor receptor (EGFR), but response to EGFR kinase inhibitors is predominantly limited to NSCLC harboring EGFR-activating mutations. These mutations are associated with increased activity of survival pathways, including phosphatidylinositol 3-kinase/AKT and signal transducer and activator of transcription 3/5. We report that EGFR-activating mutations also surprisingly lead to decreased ability to activate extracellular signal-regulated kinase (ERK) compared with wild-type EGFR. In NSCLC cells and mouse embryonic fibroblasts expressing mutant EGFR, this effect on ERK correlates with decreased EGFR internalization and reduced phosphorylation of SHP2, a tyrosine phosphatase required for the full activation of ERK. We further show that ERK activation levels affect cellular response to gefitinib. NSCLC cells with EGFR mutation display reduced gefitinib sensitivity when ERK activation is augmented by expression of constitutively active mutants of mitogen-activated protein kinase/ERK kinase (MEK). Conversely, in a NSCLC cell line expressing wild-type EGFR, gefitinib treatment along with or following MEK inhibition increases death response compared with treatment with gefitinib alone. Our results show that EGFR-activating mutations may promote some survival pathways but simultaneously impair others. This multivariate alteration of the network governing cellular response to gefitinib, which we term “oncogene imbalance,” portends a potentially broader ability to treat gefitinib-resistant NSCLC.National Cancer Institute (U.S.). Integrative Cancer Biology Program (Grant U54-CA112967)National Cancer Institute (U.S.) Cancer Center Support (Grant P30-CA14051)National Institutes of Health (U.S.). National Research Service Award Postdoctoral Fellowship

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

<div><p>Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.</p></div

Performing image segmentation with deep convolutional neural networks.

<p>(a) Image segmentation can be recast as an image classification task that is amenable to a supervised machine learning approach. A manually annotated image is converted into a training dataset by sampling regions around boundary, interior, and background pixels. These sample images are then used to train an image classifier that can then be applied to new images. (b) The mathematical structure of a conv-net. A conv-net can be broken down into two components. The first component is dimensionality reduction through the iterative application of three operations—convolutions, a transfer function, and down sampling. The second component is a classifier that uses the representation and outputs scores for each class.</p

Sample images from live-cell experiments that were segmented using conv-nets.

<p>Images of bacterial and mammalian cells were segmented using trained conv-nets and additional downstream processing. Thresholding for bacterial cells and an active contour based approach for mammalian cells were used to convert the conv-net prediction into a segmentation mask.</p

Extracting dynamic measurements of live-cell imaging experiments using conv-nets.

<p>(a) Single-cell growth curves for <i>E</i>. <i>coli</i>. Because conv-nets allow for the robust segmentation of bacterial cells, we can construct single-cell growth curves from movies of growing bacterial micro-colonies. A linear assignment problem based method was used for lineage construction. (b) By computing the change in area from frame to frame for each cell, we can construct a histogram of the instantaneous growth rate. (c) Using the instantaneous growth rate and the segmentation masks, we can construct a spatial map of growth rates with single-cell resolution. Such a map allows rapid identification of slowly dividing cells (such as metabolically inactive cells).</p

Comparison of segmentation performance among different segmentation algorithms and datasets.

<p>The Jaccard (JI) and Dice (DI) indices were computed on a validation data set as previously described [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005177#pcbi.1005177.ref053" target="_blank">53</a>]. The Jaccard index for <i>E</i>. <i>coli</i> was estimated from the segmentation error rate (0.5% in this work and 2.6% in [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005177#pcbi.1005177.ref017" target="_blank">17</a>]) assuming each segmentation error was due to the incorrect joining of 2 bacteria. NA—Not available.</p