Laura Succony

Targeting EGFR signalling in chronic lung disease: therapeutic challenges and opportunities

Chronic respiratory diseases, including pulmonary fibrosis, chronic obstructive pulmonary disease (COPD) and lung cancer, are the second leading cause of death among Europeans. Despite this, there have been only a few therapeutic advances in these conditions over the past 20 years. In this review we provide evidence that targeting the epidermal growth factor receptor (EGFR) signalling pathway may represent a novel therapeutic panacea for treating chronic lung disease. Using evidence from human patient samples, transgenic animal models, and cell and molecular biology studies we highlight the roles of this signalling pathway in lung development, homeostasis, repair, and disease ontogeny. We identify mechanisms underlying lung EGFR pathway regulation and suggest how targeting these mechanisms using new and existing therapies has the potential to improve future lung cancer, COPD and pulmonary fibrosis patient outcomes. Deregulated EGFR pathway signalling is a common event and an important therapeutic target for many chronic lung diseases http://ow.ly/rH74p

S9 The role of LRIG1-dependent EGFR signalling in airway homoeostasis and squamous cell lung cancer development

Background Aberrations of EGFR signalling drive cancer development. In squamous cell lung cancer (SqCLC), EGFR is overexpressed. LRIG1 is a negative regulator of EGFR and patient pre-invasive SqCLC samples show LRIG1 loss, suggesting involvement in early disease pathogenesis. In skin and gut homeostasis, LRIG1 regulates stem cells. In the upper airway, basal cells act as stem cells and are the putative origin of SqCLC. We hypothesise LRIG1 has a key role in airway homeostasis and its loss promotes pre-invasive SqCLC development. Methods Lrig1 EGFP-ires-CreERT2 mice were used to delineate airway LRIG1 expression. Flow sorted LRIG1-positive and -negative murine basal cells were used in 2D and 3D colony-forming, spheroid and proliferation assays. A murine SqCLC model was set up through application of N-Nitrosotris-(2-chloroethyl)urea (NTCU). Pre-invasive lesions and tumour development were compared between wild-type (WT), heterozygous and LRIG1-knockout (KO) animals. Human basal cells obtained from bronchoscopy were sorted according to LRIG1 expression and used directly in colony-forming assays or maintained in primary culture to assess the effect of shRNA knockdown of LRIG1. LRIG1-knockdown cells were assessed in colony-forming and proliferation assays, and differentiation and invasion were assessed using organotypic models. Results LRIG1 is expressed by 40% of airway basal cells. LRIG1-expressing murine basal cells exhibit increased colony-forming capacity (p = 0.0286), spheroid formation (p = 0.0043) and proliferation (p = 0.0043) compared with LRIG1-negative cells. Similarly, LRIG1-expressing human airway basal cells isolated from endobronchial brush biopsy samples exhibit increased colony-forming capacity (p = 0.0469). Topical application of NTCU to mice recapitulates the development of human pre-invasive and SCLC lesions after 23 weeks. Results show lesions in LRIG1-KO mice to be larger than those of WT animals. Knock down of LRIG1 in cultured human airway basal cells alters cell phenotype, leading to an increased colony-forming efficiency and greater proliferation at cell confluence. Conclusions LRIG1 has an important role in stem cell homeostasis of the human and murine airway epithelium. Loss of LRIG1 promotes pre-cancerous lesion development in a murine SqCLC mouse model and behaviour of human epithelial cells in culture, indicating a potential target for chemoprevention of SqCLC in humans.

Role of LRIG1-dependent EGFR signalling on pathway inhibition in airway homoeostasis and lung cancer development

Abstract Background LRIG1 is a negative regulator of epidermal growth factor receptor (EGFR) signalling, and regulates stem-cell compartments in skin and gut epithelial cells. Interestingly, LRIG1 is lost in samples from patients with preinvasive squamous cell lung cancer. Basal cells are the upper airway stem cells and the putative origin of this cancer. We aimed to delineate LRIG1 expression within the airway, its effect on the stem-cell compartment, and whether its loss causes preinvasive disease. Methods Lrig1 (EGFP-IRES-CreERT2) mice were used to determine LRIG1 expression in the airways with immunofluorescence and flow cytometry. Basal stem-cell expression of LRIG1 and correlation with cell proliferation were examined. LRIG1+ cells were isolated with flow cytometry, and colony-formation and spheroid-formation assays were used to compare LRIG1+ and LRIG1– basal cells. To lineage trace LRIG+ cells, Lrig1 mice were crossed with reporters and were transgene activated. In-vivo and ex-vivo studies were performed. Data are given as mean (SE). Findings LRIG1 expression was detected throughout the airway epithelium and within 66·3% (3·01) of basal cells. This LRIG1+ subpopulation of basal cells showed increased proliferation compared with LRIG1– cells (12·52% [1·82] vs 6·27 [2·13], p=0·0156). When plated in colony-formation assays, flow-sorted LRIG1+ expressing basal cells showed increased colony-forming efficacy and also had a greater spheroid-forming capacity (spheroid-forming capacity 3·3% [0·7] vs 0·56 [0·26], p=0·031). Ex-vivo lineage tracing assessed with spheroid assays indicated the development of clonal patches derived from LRIG+ basal cells. Formation of in-vivo clonal patches also occurred. Interpretation Our initial data indicate that LRIG1 has a role in airway stem-cell progenitor regulation and that it marks a more proliferative basal cell population. These data highlight the need to further investigate how the loss of LRIG1 leads to epithelial dysregulation and development of malignancy in patients. Funding The Wellcome Trust, Cancer Research UK.

S36 DIFFERENTIATION OF TUBERCULOSIS AND SARCOIDOSIS BY TRANSCRIPTIONAL PROFILING OF IMMUNE RESPONSES IN MEDIASTINAL LYMPH NODE SAMPLES

Introduction and Objectives Differentiating tuberculosis and sarcoidosis can be difficult, particularly in the context of mediastinal lymphadenopathy, because both diseases are characterised by overlapping clinical phenotypes and histologically similar granulomatous inflammation. Currently, diagnosis relies heavily on microbiological confirmation of tuberculosis which is only available in <50% of cases. Therefore, novel diagnostic strategies are needed to prevent morbidity associated with delayed or inappropriate treatment. We tested the hypothesis that genome wide transcriptional profiling of mediastinal lymph node samples obtained by minimally invasive endobronchial ultrasound guidance could identify gene signatures that differentiate tuberculosis and sarcoidosis. Methods In vivo immune responses were compared in mediastinal lymph node biopsies obtained via endobronchial ultrasound guidance from patients with tuberculosis, sarcoidosis or non-granulomatous disease using genome-wide transcriptional profiling. Machine learning algorithms were used to test the discriminatory power of identified gene signatures which distinguished granulomatous from non-granulomatous disease or tuberculosis from sarcoidosis. Results Comparison of lymph node genome‑wide transcriptional profiles by principal component analysis revealed clear differences between granulomatous and non-granulomatous disease. Granulomatous profiles showed significant enrichment for genes involved in antigen presentation, inflammatory responses, innate immune responses and T cell activation, in keeping with the processes involved in granuloma generation. As expected, sarcoidosis and tuberculosis sample profiles were very similar, however, significant gene expression differences were still evident between these two groups. In particular, several genes related to development of granuloma architecture were more highly expressed in sarcoidosis samples. Next we used machine learning tools in order to test the discriminatory power of differentially expressed gene signatures and found that the support vector machines algorithm correctly classified up to 97% of granulomatous and non-granulomatous disease cases. Importantly, this technique successfully distinguished sarcoidosis from tuberculosis in up to 100% cases. Conclusions Transcriptomic analysis of lymph node samples from the site of disease identifies gene signatures that can reliably distinguish tuberculosis from sarcoidosis using computational classification tools. Our data highlight the superior discriminatory power of multiple gene expression differences over a single marker in complex disease and generate a pathway for biomarker discovery in the management of tuberculosis and sarcoidosis.