Proteomic characterization of lung cancer and chronic obstructive pulmonary disease: a bronchoalveolar lavage fluid analysis

! ! Ana Barbosa de Sousa Nogal Proteomic characterization of lung cancer and chronic obstructive pulmonary disease: a bronchoalveolar lavage fluid analysis Tese de Candidatura ao grau de Doutor em Ciências Biomédicas submetida ao Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto. Orientador – Doutor Luis Paz-Ares Categoria – Director do Serviço de Oncologia Médica e Professor Titular de Medicina. Afiliação – Serviço de Oncologia Médica (Hospital Universitário Virgen del Rocío - HUVR, Sevilha). Instituto de Biomedicina de Sevilha (HUVR, Conselho Superior de Investigação Científica - CSIC, Universidade de Sevilha) Co-orientador – Doutora Maria Dolores Pastor Categoria – Investigadora pós-doutoral Afiliação – Instituto de Biomedicina de Sevilha (HUVR, Conselho Superior de Investigação Científica - CSIC, Universidade de Sevilha) Co-orientador – Doutor Rui Manuel Medeiros Silva Categoria – Professor associado com agregação Afiliação – Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto. Instituto Português de Oncologia do Porto FG, EPE. ! ! ! ! Depois da tempestade… ! ! Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! Ana!Barbosa!de!Sousa!Nogal! V! TABLE OF CONTENTS ACKNOWLEDGMENTS!.......................................................................................................................!IX! LIST!OF!ABBREVIATIONS!...................................................................................................................!XI! LIST!OF!PUBLICATIONS!...................................................................................................................!XVII! ABSTRACT!........................................................................................................................................!XIX! RESUMO!........................................................................................................................................!XXIII! INTRODUCTION!..................................................................................................................................!1! 1.1. Lung cancer ........................................................................................................... 3 1.1.1. Epidemiology and causes ................................................................................ 3 1.1.2. Classification and staging ................................................................................. 5 1.1.3. Diagnosis and treatment .................................................................................. 7 1.2. Chronic obstructive pulmonary disease ................................................................. 9 1.2.1. Epidemiology and causes ................................................................................ 9 1.2.2. Diagnosis, classification, and treatment ......................................................... 10 1.3. Links between LC and COPD .............................................................................. 12 1.4. Proteomics ........................................................................................................... 15 1.4.1. Introduction to proteomics .............................................................................. 15 1.4.2. Proteomics of COPD ...................................................................................... 19 1.4.3. Proteomics of LC ............................................................................................ 19 HYPOTHESIS!AND!OBJECTIVES!.........................................................................................................!23! 3.1. Main objective ...................................................................................................... 25 3.2. Specific objectives ................................................................................................ 25 MATERIALS!AND!METHODS!.............................................................................................................!27! 4.1. Patient selection and sample collection ............................................................... 29 4.2. Sample processing ............................................................................................... 29 4.2.1. Sample concentration ..................................................................................... 29 4.2.2. Protein quantitation ........................................................................................ 30 Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! Ana!Barbosa!de!Sousa!Nogal! VI! 4.2.3. Depletion of high abundance proteins ............................................................ 30 4.2.4. Sample cleaning ............................................................................................. 30 4.3. Two-dimensional gel electrophoresis ................................................................... 31 4.3.1. Isoelectric focusing ......................................................................................... 31 4.3.2. Strip equilibration ............................................................................................ 32 4.3.3. 2DPAGE ....................................................................................................... 32 4.3.4. Gel staining and imaging ................................................................................ 33 4.3.5. Gel image analysis ......................................................................................... 34 4.4. Mass spectrometry ............................................................................................... 35 4.4.1. MS sample preparation and analysis ............................................................. 35 4.4.2. Protein identification ....................................................................................... 36 4.5. Antibody arrays .................................................................................................... 37 4.6. Western blot ......................................................................................................... 40 4.7. Enzyme-Linked Immunosorbent Assay ................................................................ 41 4.8. Bioinformatics analysis and statistical analysis .................................................... 42 4.8.1. 2D-PAGE bioinformatics analysis .................................................................. 42 4.8.2. Antibody arrays statistical analysis ................................................................. 43 4.8.3. Antibody arrays expression data analysis ...................................................... 43 4.8.4. Diagnostic test validity analysis ...................................................................... 43 RESULTS!...........................................................................................................................................!45! 5.1. First specific objective: Identification of differently expressed proteins in BALF from patients with LC, COPD, COPD with LC, and without LC or COPD by comparative proteomic analysis. ........................................................................................................ 47 5.1.1. Selection of patients ....................................................................................... 47 5.1.2. Proteomic profiling .......................................................................................... 48 5.1.3. Selection of proteins and western blot validation ........................................... 53 5.1.4. LC and COPD pathway analysis .................................................................... 54 5.1.5. NF-kB functional analysis ............................................................................... 56 Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! Ana!Barbosa!de!Sousa!Nogal! VII! 5.2. Second specific objective: Analysis of the differential expression of inflammation related proteins (cytokines and growth factors) in LC and COPD patients. ...................................................................................................................... 58 5.2.1. Patient selection. ............................................................................................ 58 5.2.2. Expression of cytokines and grow factors in BALF from LC and COPD patients. ...................................................................................................................... 61 5.2.3. Analysis of Il-11 and CCL1 as adenocarcinoma biomarkers .......................... 64 GENERAL!DISCUSSION!AND!MAIN!CONCLUSIONS!...........................................................................!73! FUTURE!PERSPECTIVES!....................................................................................................................!87! REFERENCES!.....................................................................................................................................!91! ARTICLES!........................................................................................................................................!111! ! ! Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! IX! ACKNOWLEDGMENTS Ao Dr. Luis Paz-Ares, meu orientador, por me ter permitido cumprir um sonho de juventude, pela oportunidade de fazer parte de uma nova realidade, de um novo grupo de investigação, pelos braços abertos com que me recebeu, pela sua capacidade de liderança que inspira todos a alcançar sempre algo melhor. Obrigada por toda a sua ajuda, simpatia e disponibilidade. À Dra. Maria Dolores Pastor, minha co-orientadora, não existem linhas suficientes para agradecer tudo o que fez por mim: muito mais do que imagina ter feito. Muito obrigada pela compreensão, pela paciência com o meu mau espanhol (de início), pela amizade e simpatia extremas, pelo carinho nos momentos mais complicados e pela rapidez com que os resolvia, pela sua determinação e diligência e sobretudo obrigada pelo exemplo, pela inspiração e pela vontade de ser algum dia algo parecido com ela. Ao Dr. Rui Medeiros, meu co-orientador, que me permitiu entrar no campo da investigação quando era apenas uma “jovem” em 2005, muito obrigada pelo apoio e amizade demonstrados ao longo destes anos, pelas oportunidades e portas que me abriu, pela disponibilidade que demonstra sempre que lhe entro no gabinete por qualquer motivo. À Fundação para a Ciência e Tecnologia, que me concedeu a bolsa de doutoramento e me permitiu fazer, durante 4 anos, aquilo que mais gosto. Ao fabuloso grupo de “Oncología molecular y nuevas terapias”, o meu gigante agradecimento. Obrigada Sonia, Rocío, Ana, Ricardo e demais colegas do HUVR e IBIS pelo companheirismo, pela força que me deram, pela compreensão e paciência quando me explicavam as coisas muito lentamente, pela momentos divertidos, pelas “sevillanas maneras” que me foram ensinando, pelo trabalho que desenvolvem diariamente e do qual sinto muito orgulho e saudade. Obrigada sobretudo por me fazerem sentir em casa e por me fazerem esquecer durante a semana que estava longe... À Ana Coelho, Dr. Araújo e Raquel Catarino, pela orientação que me dão nestas andanças, mesmo sem achar que o estão a fazer. Obrigada pelo apoio, amizade, por fazerem crescer como pessoa e como investigadora. ! ! Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! XVII! LIST OF PUBLICATIONS According to article 31 of the Decree-Law nº230/2009, the present Thesis has already produced the following publications in peer-reviewed journals in addition to a book chapter: Nogal A, Pastor MD, Molina-Pinelo S, Carnero A, Paz-Ares L. Proteomic biomarkers in lung cancer. Clinical & translational oncology: official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico. 2013;15(9):671-82 (M. D. Pastor and A. Nogal contributed equally to the present work). Nogal A, Pastor MD, Molina-Pinelo S, Melendez R, Romero-Romero B, Mediano MD, et al. Identification of oxidative stress related proteins as biomarkers for lung cancer and chronic obstructive pulmonary disease in bronchoalveolar lavage. International journal of molecular sciences. 2013;14(2):3440-55 (The first two authors contributed equally to this work). Nogal A, Pastor MD, Molina-Pinelo S, Melendez R, Salinas A, Gonzalez De la Pena M, et al. Identification of proteomic signatures associated with lung cancer and COPD. Journal of proteomics. 2013;89:227-37 (The first two authors contributed equally to this work). Ma Dolores Pastor, Ana Nogal, Sonia Molina-Pinelo, Luis Paz-Ares and Amancio Carnero (2013). Oncoproteomic Approaches in Lung Cancer Research, Oncogenomics and Cancer Proteomics - Novel Approaches in Biomarkers Discovery and Therapeutic Targets in Cancer, Dr. Cesar Lopez (Ed.), ISBN: 978-953-51-1041-5, InTech, DOI: 10.5772/53873. Available from: http://www.intechopen.com/books/oncogenomics-andcancer-proteomics-novel-approaches-in-biomarkers-discovery-and-therapeutic-targets-incancer/oncoproteomic-approaches-in-lung-cancer-research I hereby declare that I have actively participated in the gathering and study of the material included in each of the publications presented and have written the manuscripts in collaboration with the other authors. ! ! ! ! ! ! ABSTRACT ! ! ! Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! XXI! ABSTRACT Cancer is a major worldwide public health issue and continues to be a leading cause of death in developed countries. On the top of incidence and mortality rates lies lung cancer (LC). This disease, caused mainly by tobacco smoking, has one of the lowest 5-year survival rates among all types of cancers: only 15%. Despite the recent advances in systemic therapies, such as chemotherapy and targeted therapies, prognosis remains strongly linked to early diagnosis. Unfortunately, most patients are diagnosed in advanced stages, when curative options are limited. Consequently, there is a pressing need for detection methods that may allow the diagnosis of LC early in its natural history, especially among high-risk individuals such as smokers, particularly if chronic obstructive pulmonary disease (COPD) is also present. The primary risk factor for COPD is, as for LC, tobacco smoking. However, this inflammatory disease is an independent risk factor for the development of LC and some pathogenic processes are thought to be important in the development of both diseases. A better understanding of the molecular and cellular mechanisms by which chronic inflammatory diseases, such as COPD, lead to the initiation of LC, may identify early diagnostic biomarkers and screening tests, valuable targets, and more effective therapeutic strategies. The search for early detection markers has gained an ally in the last several years. Proteomic technologies have begun to uncover the molecular complexity of lung tumours by allowing a rapid and complete analysis of the proteins that are expressed in the context of this disease. The analysis of lung samples from LC patients and high-risk patients by proteomic methodologies might allow us to gain more knowledge on the mechanisms of both diseases and possibly identify LC diagnostic biomarkers that could help in the clinical practice. The major aim of the present work was to study the protein profile of bronchoalveolar lavage fluid (BALF) in patients with LC and/or COPD using proteomic methodologies in an attempt to identify new LC biomarkers among high-risk patients. Patients who had required flexible bronchoscopy for diagnostic purposes at the Hospital Universitario Virgen del Rocío (Seville, Spain) were recruited for this study. BALF samples were treated and submitted to different proteomic technologies such as twodimensional polyacrylamide gel electrophoresis (2D-PAGE), mass spectrometry (MS), western blot, antibody arrays, and enzyme-linked immunosorbent assay (ELISA). The use of a 2D-PAGE MALDI-TOF/TOF MS methodology allowed the detection of 40 differentially expressed proteins between the control group (without LC or COPD) and the LC, COPD, and LC&COPD groups. Ingenuity pathway analysis (IPA) of these proteins revealed three top molecular mechanisms occurring in both LC and COPD: glycolysis and gluconeogenesis, free radical scavenging and oxidative stress response, Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! XXII! and inflammation. The major connector of all three pathways was the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), a key transcription factor in the inflammatory process and carcinogenesis. The importance of inflammation in LC and COPD was further evaluated by application of BALF samples to cytokine and growth factor antibody arrays. This analysis revealed IL-11 and CCL1 as markers for adenocarcinoma of the lung with areas under the curve (AUC) of 93 and 83%, respectively. Sensitivity and specificity values were 90 and 88% when evaluating IL-11 and 80 and 74% when considering CCL1. In conclusion, the present work has revealed the proteomic differences and similarities between LC and COPD. In addition, the proteomic methodology used has allowed the identification of two possible adenocarcinoma biomarkers that could be used to detect this type of LC. The application of a simple ELISA assay could be used to improve the diagnosis of lung adenocarcinoma in smokers, despite the presence or absence of COPD. The use of this assay in routine clinical practice, if validated in further studies, could in the future improve the early detection of lung adenocarcinoma, currently the most common form of NSCLC, and hopefully contribute to an increase of the 5-year survival rate associated with this disease. ! ! ! RESUMO ! ! Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! XXV! RESUMO O cancro é um problema de saúde pública em todo o mundo e continua a ser uma das principais causas de morte nos países desenvolvidos. No topo das taxas de incidência e mortalidade encontra-se o cancro do pulmão. Esta neoplasia, causada principalmente pelo fumo do tabaco, tem uma das menores taxas de sobrevivência a 5 anos entre todos os tipos de cancro: apenas 15%. Apesar dos recentes avanços nos tratamentos sistémicos como a quimioterapia e medicamentos direcionados, o prognóstico permanece fortemente ligado ao diagnóstico precoce. Infelizmente, a maioria dos pacientes é diagnosticada em estadios avançados da doença, onde as opções terapêuticas são limitadas. Consequentemente, há uma necessidade premente de encontrar métodos de detecção que permitiam o diagnóstico do cancro do pulmão no início de sua história natural, especialmente entre os indivíduos de alto risco, como fumadores e pessoas com doença pulmonar obstrutiva crónica. O principal fator de risco para a doença pulmonar obstrutiva crónica é, tal como para o cancro do pulmão, o fumo do tabaco. No entanto, esta doença inflamatória é um factor de risco independente para o desenvolvimento de cancro do pulmão e pensa-se que alguns processos patogénicos possam ser importantes para o desenvolvimento de ambas as doenças. Uma melhor compreensão dos mecanismos moleculares e celulares pelos quais doenças inflamatórias crónicas, como a doença pulmonar obstrutiva crónica, levam ao desenvolvimento de cancro do pulmão, pode identificar biomarcadores de diagnóstico precoce, alvos terapêuticos, e estratégias de combate à doença mais eficazes. A procura de marcadores de detecção precoce ganhou um forte aliado nos últimos anos. O uso de tecnologias proteómicas permitiu começar a descobrir a complexidade molecular dos tumores pulmonares, já que permitem uma análise rápida e completa das proteínas que são expressas no contexto desta doença. A análise de amostras de pacientes com cancro de pulmão e pacientes de alto risco através de técnicas proteómicas pode permitir um maior conhecimento sobre os mecanismos de ambas as doenças e possivelmente identificar biomarcadores de diagnóstico de cancro do pulmão que possam auxiliar na prática clínica. O principal objetivo do presente trabalho foi estudar o perfil proteómico de lavado bronco-alveolar em pacientes com cancro do pulmão e/ou doença pulmonar obstrutiva crónica através de métodos proteómicos, na tentativa de identificar novos biomarcadores diagnósticos de cancro do pulmão em pacientes de alto risco. Para o estudo foram recrutados pacientes aos quais tinham sido efectuadas broncoscopias para fins de diagnóstico no Hospital Universitário Virgen del Rocío (Sevilha, Espanha). Amostras de lavado bronco-alveolar foram tratadas e submetidas a Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! ! 6! ! cases. Adenocarcinomas are currently more often diagnosed in women, young males (<50 years old), non-smokers and Asians (20). This histological type is defined as a malignant epithelial tumour with glandular differentiation or mucin production and tends to arise in the periphery of the lungs and the alveoli (Figure 3B). There are several subtypes of adenocarcinoma, such as acinar, papillary, bronchioloalveolar, solid with mucin production, and mixed. However, most adenocarcinomas are histologically heterogeneous and consequently classified as mixed (20-22). Malignant epithelial tumours that show keratinisation and/or intercellular bridges are defined as SCC (23). Also known as epidermoid carcinomas, this group of lung tumours was the most frequent but now represents approximately 30% of all diagnosis (16). They are more common in men and smokers and the majority occur as slow growing tumours in the bronchial epithelium of the central airways (Figure 3C) (21). All NSCLCs that lack the cytological and architectural characteristics of the adenocarcinomas or SCCs are classified as LCCs (24). They represent approximately 10% of all lung cancer cases and their incidence is decreasing most likely due to improved diagnostic techniques (Figure 3D) (25). Although the accurate classification of a lung tumour is extremely important step in the evaluation of a patient, the correct determination of extent of the patients’ disease is also necessary to determine the course of treatment and prognosis. Figure 3: Histological patterns of LC. (A) Small cell lung cancer, from Jackman et al. (2005); (B) adenocarcinoma, from Colby et al. (2004); (C) squamous cell carcinoma, from Hammar et al. (2004); (D) large cell carcinoma, from Hammar et al. (2004). Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! ! 7! ! Lung cancer staging is based on the TNM classification for malignant tumours (TNM). This classification was originally proposed by Mountain and adopted by the American Joint Committee on Cancer (AJCC) in 1973 and one year later by the Union for International Cancer Control (UICC) (26). The TNM system is used for the majority of malignant tumours and is based on the extent of the disease. Its goal is to assist treatment planning, evaluation of treatment outcomes, and prognosis determination (27). The letter T stands for primary tumour and determines the size and degree of locoregional invasion. The N indicates the extent of regional lymph node involvement, and the M reveals the presence or absence of intrathoracic or distant metastasis (27, 28). The latest edition of the TNM classification of lung cancer can be found on Figure 4. Figure 4: Chart of the latest TNM classification of lung cancer, from Uybico et al. (2010). 1.1.3. Diagnosis and treatment Lung cancer diagnosis includes evaluation of clinical history, several routine blood exams such as anaemia and liver enzymes, bronchoscopy to allow the evaluation of the extent of the disease in the tracheobronchial tree, and the tumour identification after biopsy (29). Information on tumour size and local invasion are obtained by chest radiography (30). Computerized tomographies (CT) have been of great help in LC staging, given that they offer 3D information of the disease extent such as mediastinal invasion, chest wall invasion, or the presence of other pulmonary nodules (29). Mediastinum Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! ! 8! ! staging is further amended by endobronchial ultrasound, endoscopic ultrasound, mediastinoscopy, and positron emission tomography. The latter is also a sensitive method to detect metastatic spread of the tumour. Following the classification and staging of a patients’ tumour, course of treatment is decided, selecting between the available therapeutic options: surgery, radio and chemotherapy, used separately or in different combinations. Lung cancer is usually asymptomatic during its development and this causes the majority of patients to be diagnosed at advanced stages of the disease. The accurate diagnosis of LC, including classification and staging, is essential to the correct treatment and prognosis of a given patient. Tumour resection by surgery is the most successful curative option available for patients with early stage disease: stage I, II, and selected patients with stage IIIA. Nonetheless, the survival rate of these patients varies greatly depending on their disease stage (28). Stages IIIB and IV, the grand majority of diagnoses, are considered inoperable and other treatments, such as radiotherapy and chemotherapy, are applied. Radiotherapy is a valuable therapeutic option for patients who, despite the presence of early disease, are medically unfit or refuse to undergo surgical resection. It is also of use as adjuvant therapy for patients with incomplete resection or node-positive disease and as palliative therapy, providing symptomatic improvement (31). Chemotherapy was initially used in patients with advanced metastatic disease as a palliative measure. Since then, the application of drugs (cisplatin, taxanes, antimetabolites, topoisomerase inhibitors, among others) has been used with curative intent combined with surgery, neoadjuvant or adjuvant, and/or radiotherapy, in an attempt to improve the survival of NSCLC patients (32). In addition to these traditional drugs, targeted therapy has been improving in the last years, gathering information from molecular studies that identified specific alterations in groups of LC patients. The greatest achievements of this targeted therapy have been the development of monoclonal antibodies and tyrosine kinase inhibitors against the epidermal growth factor receptor (EGFR), such as cetuximab and erlotinib, and the vascular endothelial growth factor (VEGF), such as bevacizumab and sorafenib, among others (Figure 5) (33). In addition, novel agents that target the echinoderm microtubule-associated protein-like 4 (EML4) and the anaplastic lymphoma kinase (ALK) gene fusion, for example crizotinib, the insulin-like growth factor-1 receptor (IGF-1R), such as cixutumumab or linsitimib, the mammalian target of rapamycin (mTOR), for example deforolimus, and the hepatocyte growth factor receptor (MET), such as tivantinib, are currently being investigated in NSCLC clinical trials (Figure 5) (33). Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! ! 9! ! Although a lot of progress has been made, the 5-year survival rate for lung cancer is still approximately 15%, reinforcing the need for diagnostic markers that can aid in the early detection of this tumour. Figure 5: Most relevant pathways in NSCLC and developed targeted therapies, adapted from Pal et al. (2010). 1.2. Chronic obstructive pulmonary disease 1.2.1. Epidemiology and causes Chronic obstructive pulmonary disease (COPD) is, according to the Global Initiative for Chronic Obstructive Pulmonary Disease (GOLD), a “preventable and treatable disease with significant extrapulmonary effects. The pulmonary component of COPD is characterized by a progressive airflow limitation associated with an atypical inflammatory response of the lungs to noxious particles and gases” (34). The airflow limitation present in COPD patients is the result of damages to the small and large airways (chronic bronchiolitis and bronchitis, Figure 6A) and to the pulmonary Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! ! 10! ! parenchyma (emphysema, Figure 6B) (35). The majority of patients present signs of both bronchitis and emphysema instead of one component exclusively (36). Figure 6: Histological patterns of bronchitis with increased number of glands and thicker smooth muscle bundles (A) and emphysema with enlarged airway spaces (B), from Macnee et al. (2007). COPD is a leading cause of worldwide morbidity and mortality, apart from being a major economic and social burden. The WHO estimated that COPD was the fifth leading cause of death in 2006 and is expected to rise to fourth place by 2020 (3). It is clearly more frequent in men and the elderly, but incidence is increasing in women reflecting changes in their exposure to COPD risk factors since the middle of the 21st century The primary risk factor for COPD is, as for LC, tobacco smoking. The WHO assessed that 40 to 70% of all COPD mortality is related to smoking (37). Age of smoking initiation, pack-years smoked, and current smoking status, influence COPD development, prognosis, and mortality (38). Even so, reports state that 15-50% of smokers will develop some form of COPD over time (36, 37). There is a natural decline in lung function with age, but active smokers have an accelerated rate of deterioration that can be reversed to normal levels of decline with sustained smoking cessation. 1.2.2. Diagnosis, classification, and treatment COPD is a multicomponent and heterogeneous disease that varies within a group of patients with respect to lung pathology, disease progression, and comorbidities. Given that the clinical presentation is far from uniform, healthcare practitioners should consider the diagnosis of COPD if any patient is over 40 years of age, has been exposed to the Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! ! 11! ! aforementioned risk factor, and presents symptoms such as breathlessness during exercise, increased effort to breath, cough, or excessive mucus production (39, 40). If these symptoms are present, physical examination and spirometric tests are required to make the accurate diagnosis and disease staging. Spirometry is the gold standard for the detection of COPD, given its high reproducibility and availability (38). It is a simple and non-invasive test that measures lung function: the amount (volume) and/or speed (flow) of air that can be inhaled and exhaled. The evaluated parameters are: post-bronchodilator forced expiratory volume in 1 second (FEV1) and the ratio between FEV1 and forced vital capacity (FEV1/FVC). These two measurements are used to detect COPD and assess its severity by comparison with reference values that vary with age, gender, race and height (34). The classification of COPD ranges from mild to very severe. A normal spirometry implies a FEV1/FVC ratio superior to 70% and a FEV1 superior to 80%. COPD is diagnosed when the FEV1/FVC ratio is below 70% and its severity varies from mild to very severe depending on the reduction of post-bronchodilator FEV1 compared to the predicted in a healthy population (Table 1) (41). Radiological imaging can be used in the assessment of COPD severity since it can provide an accurate characterization of changes in the lung parenchyma and airway wall thickness, as well as exclude alternative diagnoses (42). Arterial blood gas measurement, α1-antitrypsin deficiency screening, and the six minute walk test are also useful in the diagnosis of this disease (34). Table 1: COPD classification for patients with a FEV1/FVC ratio below 70% Gold stage Description FEV1 values GOLD 1 Mild FEV1 ≥ 80% predicted GOLD 2 Moderate 50% ≤ FEV1 < 80% predicted GOLD 3 Severe 30% ≤ FEV1 < 50% predicted GOLD 4 Very severe FEV1 < 30% predicted Given that no cure is currently available and there are no therapies than can halt the lung function decline, the treatment of COPD varies enormously across patients. The main goals of the treatment are to reduce symptoms and improve quality of life, reduce the frequency and severity of the exacerbations, and reduce mortality (43). One of the first steps in the treatment and management of these patients is smoking cessation. Nonpharmacological treatments include long term oxygen therapy (that can modify long term lung function decline), non-invasive ventilation, lung volume reduction surgery (for patients with emphysema), and pulmonary rehabilitation that can provide relief from dyspnoea, Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! ! 12! ! increases exercise tolerance, and improves quality of life (34). Pharmacological treatments are patient-specific and can include the use of antibiotics, bronchodilators, corticosteroids, and mucolytic agents, alone or in combinations (34, 42). 1.3. Links between LC and COPD Lung cancer and COPD are two of the major causes of mortality in the world. Apart from being caused by the same major etiological factor, tobacco smoking, epidemiological data demonstrates a clear connection between both diseases. The odds ratio (OR) value for LC increases 2.8 fold in patients with moderate to severe COPD (44). Independently of smoking history, reduced FEV1 is associated with an increased risk for developing LC: even small changes in this parameter, such as 10% differences, raise LC risk by 30-60% (45). Indeed, 50 to 70% of all LC patients have spirometric evidences of COPD (46). In addition, the presence of mild emphysema, also independent of cigarette smoking and history of other lung diseases, confers a greater risk to develop LC (47). Since only a fraction of long-term smokers develop these diseases, complex mechanisms and interactions are thought to determine an individual’s risk of developing COPD, LC, or both. At a first glance, it is difficult to envision any common mechanism between both diseases: LC is characterized by unlimited cell proliferation, sustained angiogenesis and self-sufficiency in growth factors, tissue invasion, evasion of apoptosis, and insensitivity to anti-growth signals, whereas COPD has increased levels of apoptosis, extracellular matrix degradation, limited angiogenesis, and ineffective tissue repair (48, 49). Understanding the shared mechanisms between these two dissimilar diseases is critical for developing new methods of early diagnosis and treatment of LC. Throughout the years specific processes have been suggested to be of importance in the pathogenesis of both COPD and LC (Figure 7). Reactive oxygen species (ROS) caused by tobacco smoke are produced in excess in the setting of COPD and can ultimately produce DNA alterations that lead to LC development. The balance between cellular proliferation and cell death is also important in the lung given its contribution to the development of both diseases. Hypoxia and angiogenesis can also affect the development of COPD and LC: airflow limitation can lead hypoxia that may cause destruction of alveolar capillaries and airway enlargement, in addition to being capable of promoting, through angiogenesis, the progression and metastasis of lung tumours (46, 50). However, these processes are all influenced by and interact with a major mechanism activated by tobacco smoking that is inflammation. Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! ! 13! ! Figure 7: Mechanisms and pathways potentially involved in the development of COPD and LC, adapted from Yang et al. (2011). Inflammation is one of the body’s natural protective mechanisms against harmful stimuli. It is the result of complex interactions between immune cells and soluble factors that arises in tissues as a response to infections, trauma, or toxic substances such as the ones present in tobacco smoke (51). Its major role is to remove the source of aggression and to initiate the healing process. Nevertheless, a persistent inflammatory response in the lung can result in the development of lung diseases, such as COPD and lung tumours. COPD was early recognized as an inflammatory disease, with profound abnormalities in the inflammatory pathways, and that airway inflammation starts at an early stage, many years prior to the onset of clinical symptoms. Tobacco smoke induces inflammation in all smokers, but those who develop COPD have a greater degree of inflammation that persists even after smoking cessation (50). Tobacco smoke is a complex mixture of thousands of chemicals generated by tobacco combustion such as carcinogenic toxins, addictive substances, and more than 1014 oxidants and free radicals (46). These compounds first interface with the lungs at the mucosal surfaces lining the airways. There they can damage epithelial cells and resident inflammatory cells by inducing peroxidation of lipids and other cell membrane constituents, activate oxidativesensitive cellular pathways, induce DNA damage, and activate epithelial cell intracellular signalling cascades that lead to inflammatory gene activation (52). The release of these inflammatory mediators initiates the recruitment and activation of more pro-inflammatory cells such as neutrophils, macrophages, and lymphocytes that propagate the inflammatory response by releasing more cytokines, for example interleukin 8 (IL-8) and tumour necrosis factor α (TNF-α), and ROS, generating a vicious cycle of persistent Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! ! 14! ! inflammation. Interestingly, the immune cells and mediators that drive chronic inflammation and the progression of COPD are also known to promote a tumour friendly microenvironment (53). The observation of a relationship between cancer and inflammation is not a recent one. Almost 2000 years ago, Galenus described the similarities between inflamed tissue and cancerous tissue. Rudolph Virchow, in the year of 1863, reported the infiltration of leukocytes in malignant tissues and suggested that cancers arise in sites of chronic inflammation (54). More than one hundred years later Dvorak observed that cancer and inflammation share some developmental mechanisms and types of infiltrating immune cells, apart from classifying tumours as “wounds that do not heal” (55). The ultimate recognition of inflammation as a major player in cancer development came with the 2011 update article on cancer hallmarks by Hanahan and Weinberg where it was classified as an enabling characteristic of tumours. The evidence gathered over the last years showed that inflammation contributes to the appearance of multiple cancer hallmark capabilities by supplying important molecules to the tumour microenvironment. Those molecules include growth factors that sustain the proliferative signalling, survival factors that limit apoptosis, pro-angiogenic factors, extracellular matrix-modifying enzymes that favour angiogenesis, invasion, and metastasis. Furthermore, inflammation manifests itself at the earliest stages of tumour progression and is capable of nurturing insipient neoplasias into developed cancers. In addition, inflammatory cells can release a number of chemicals, such as ROS, that are actively mutagenic and promote malignancy even further (14). Studies on LC and chronic inflammation have revealed that the likelihood of developing LC was increased in patients with higher levels of C-reactive protein (CRP), an acute-phase protein that is a marker for inflammation (56). Also, the risk for developing LC was decreased among COPD patients who had been treated with inhaled corticosteroids (57). Studies on animal models have showed that, after infection by Haemophilus influenza, mice develop a bronchial inflammation similar to COPD and a subsequent predisposition to lung carcinogenesis (58). The constant lung injury and repair cycle triggered by chronic inflammation, such as the one that occurs in COPD, can enhance cell proliferation and genetic damage accumulation, epithelial to mesenchymal transition (EMT), and ultimately lung carcinogenesis (46). Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! ! 15! ! 1.4. Proteomics In the last several years genomic and proteomic technologies have begun to uncover the molecular complexity of lung tumours by allowing a rapid and complete analysis of the genes and proteins that are expressed in the context of this disease. The use of genomic technologies such as microarrays and quantitative reverse transcription polymerase chain reaction (RT-PCR) on LC studies has allowed the separation of adenocarcinoma patients into new molecular sub-classes with different outcomes, the early identification of high risk patients, and the detection of molecular signatures associated with metastasis (59). While genomic technologies have revealed important data regarding the biology of LC, the direct evaluation of proteins offers information that is not obtainable through the study of DNA and RNA. Proteins are crucial operators in the majority of biological systems and signalling pathways. Although they can determine the cells’ phenotype, RNA levels often poorly correlate with protein expression (60). 1.4.1. Introduction to proteomics The proteome is defined as the entire collection of proteins that is produced by a cell or tissue in a given time, making it a very dynamic entity: the type of expressed proteins, their relative abundance, and their subcellular location depend on the physiological state of a cell or tissue (61). Proteomics, the analysis of the proteome, is performed through a variety of technologies that are continuously evolving. Traditional protein analysis technologies include approaches such as western blot, immunohistochemistry (IHC), and enzymelinked immunosorbent assay (ELISA). Protein microarrays, which consist of large number of antibodies robotically immobilized on a slide, have a higher throughput and allow the identification of several proteins at a time in the same sample (62). Protein microarrays are rapid, automated, economical, and highly sensitive, consuming small quantities of samples and reagents. However, the previous techniques are target driven, meaning they require a previous knowledge of the proteins that are expected to appear in a given sample. In contrast to these techniques, two methods do not require a previous assumption of the identity or number of proteins in a sample and are considered classical proteomic technologies: two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and mass spectrometry (MS). The use of electrospray ionization (ESI) and liquid chromatography, second generation proteomics or shotgun proteomics, has more recently allowed the identification of complex and so far unknown proteomes. Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! ! 22! ! The analysis of the proteomic profiles of LC and COPD patients might allow us to gain more knowledge on the mechanisms of both diseases, to identify possible LC proteomic diagnostic biomarkers, as well as elucidate why some COPD patients end up developing LC. ! ! HYPOTHESIS AND OBJECTIVES ! ! Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 25! ! 2. HYPOTHESIS There are differences in the protein expression (inflammation and/or other pathways) of BALF among patients with LC and/or COPD as compared to healthy individuals. These differentially expressed proteins could help to understand the molecular pathogens of LC and COPD, and may provide useful biomarker for diagnosis. 3. OBJECTIVES 3.1. Main objective The present work focused on identifying new biomarkers as well as biological pathways involved in the development of LC and/or COPD among high-risk patients by using proteomic methodologies, focusing particularly on the inflammatory pathway. 3.2. Specific objectives 3.2.1. Identification of differently expressed proteins in BALF from patients with LC, COPD, COPD with LC, and without LC or COPD by comparative proteomic analysis. 3.2.2. Analysis of the differential expression of inflammation related proteins (cytokines and growth factors) in LC and COPD patients. ! ! ! ! MATERIALS AND METHODS ! ! Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 29! ! 4. MATERIALS AND METHODS 4.1. Patient selection and sample collection From 2009 to 2011, a total of 925 patients who had required flexible bronchoscopy for diagnostic purposes at the Hospital Universitario Virgen del Rocío (Seville, Spain), were collected for this prospective study. From the original first group only 419 patients respected the selection criteria: 1) patients were under pneumologist consultation due to haemoptysis and/or a pulmonary nodule; 2) patients were requested by their physician to perform a flexible bronchoscopy for diagnostic purposes; 3) patients were smokers or exsmokers of more than 20 pack-years; 4) patients had to be older than 40 years of age. Exclusion criteria included: 1) diagnosis of a neoplastic disease other than lung cancer; 2) active pulmonary tuberculosis; 3) previous lung resection; 4) history of drug abuse; 5) presence of other acute or chronic inflammatory diseases. The study protocol was conducted in agreement with the Helsinki declaration and approved by the Hospital's Ethical Committee, and a written informed consent was obtained from all patients prior to their addition to the study. Subjects were prepared with a combination of topical anaesthesia (20% benzocaine spray to the pharynx plus 2% topical lidocaine as needed) and conscious sedation using midazolam and meperidine according to institutional guidelines. All BALF samples were obtained by instillation and aspiration of 40 to 60 ml aliquots of 0.9% sterile saline in the appropriate bronchopulmonary segment. Recovered fluid was immediately passed through a 100 µm sterile nylon filter (Becton Dickinson, San Jose, CA) to remove mucus and transported on ice to the laboratory. The total volume was then centrifuged for 10 min at 1800xg and 4ºC. The supernatant was aliquoted into 2 ml tubes and frozen at – 80ºC until further use. 4.2. Sample processing 4.2.1. Sample concentration The amount of proteins in BALF samples is very small, diluted in the sterile saline used for its recovery. In order to use these samples for proteomics, they required concentration and this was accomplished by the use of a Concentrator Plus vacuum concentrator (Eppendorf, Hamburg, Germany). BALF samples were thawed on ice with a protease inhibitor cocktail kit (Pierce, Thermo Scientific, Rockford, IL, USA). Samples were then aliquoted into new tubes and placed on the vacuum concentrator. The initial volume of the samples, usually 4-8 ml, was reduced to 250-450 µl in 2-6 hours. Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 30! ! 4.2.2. Protein quantitation A number of colorimetric methods were tested, such as bicinchoninic acid assay (BCA from Pierce, Thermo Scientific, Rockford, IL, USA, Rockford, IL, USA), Bradford (Bio-Rad, Hercules, CA, USA) and RC-DC (Bio-Rad, Hercules, CA, USA). The RC-DC method was selected given that it was reducing agents and detergent compatible. It is based on the principle of Lowry estimation where proteins react with alkaline copper and subsequently reduce the folin reagent, leading to colour development. Protein quantitation was assessed according to the manufacturer’s instructions. Absorbance was measured at 750 nm and the linear equation created by different bovine serum albumin (BSA) dilutions allowed the determination of the protein concentration in our samples. Protein quantitation was assessed before and after sample concentration, depletion of high abundance proteins, and sample cleaning procedures, which will be described in the following corresponding sections. 4.2.3. Depletion of high abundance proteins The presence of high abundance plasma proteins obscures the presence of low abundant ones of possible interest. Revealing these important proteins required that a depletion process be applied for the analysis of BALF proteomes. Several commercial kits of depletion columns were tested: Proteominer (Bio-Rad, Hercules, CA, USA), ProteoPrep (Sigma-Aldrich, St. Louis, MO, USA), and SpinTrap (GE Healthcare, Waukesha, WI, USA). The latter one showed the best results in albumin and immunoglobulin G removal in our samples. Depletion protocol followed the manufacturer’s instructions. The SpinTrap columns were inverted repeatedly to resuspend the storage medium, and then centrifuged to remove it. Binding buffer was added and centrifuged out to wash the column before the addition of the concentrated BALF sample. The sample was allowed to incubate for 5 minutes to let the existent albumin and immunoglobulin G bind to the column. The column was then centrifuged and the eluate collected. Binding buffer was added twice to the column and the eluate was again collected. The depleted BALF samples were cleaned immediately or frozen at -80ºC until further use. 4.2.4. Sample cleaning After the depletion step BALF samples required the removal of salts, thiols, denaturants, and other contaminants that could be present and interfere with the 2DPAGE protocol. Of all available methods to clean protein samples (trichloroacetic acid Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 31! ! precipitation, two-step precipitation, ultra filtration, and dialysis) and taking into account their advantages and disadvantages, depleted samples were cleaned by the 2-D clean-up kit (GE Healthcare, Waukesha, WI, USA) following the manufacturer’s directions. At the end, the protein pellet was rehydrated with a solution containing urea at 7M, thiourea at 2M, and 2% CHAPS, suitable for 2D-PAGE (all three reagents from GE Healthcare, Waukesha, WI, USA). 4.3. Two-dimensional gel electrophoresis Two-dimensional gel electrophoresis is a protein analysis method that allows the separation of proteins according to two innate characteristics: isoelectric point (IP) and molecular weight (MW). In the first dimension, also described as isoelectric focusing (IEF), proteins are separated in a gel strip that contains a predetermined pH range, allowing the proteins to separate according to their isoelectric point. In the second dimension, the strip is placed on a SDS-PAGE gel in which the focused proteins migrate according to their molecular weight. The final result is a SDS-PAGE gel where, after staining, protein spots are observed. 4.3.1. Isoelectric focusing Treated BALF samples (n=60), equally distributed among four groups of patients (controls, LC, COPD, and LC&COPD), were independently submitted to IEF in 7 cm IPG DryStrips with a 3-11 NL pH range. A mixture of 75 µg of protein from each sample, DeStreak rehydration solution, and 0.5% of 3-11 NL pH IPG Buffer, in a final volume of 125 µl, was prepared. This mixture was then applied to the strip holder. The strip’s cover foil was carefully removed from the positive end and the strip placed, gel-side down and positive end first, onto the strip holder, distributing the solution evenly under the strip. The strip was then overlaid with cover fluid and the strip holders placed on the Ettan IPGphor II for the IEF protocol (Table 2). All reagents and instruments were from GE Healthcare (Waukesha, WI, USA). Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 38! ! GenePix 4100 A (Molecular Devices, Sunnyvale, CA, USA), which was also used to perform data extraction. Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 39! ! Table 3: Cytokines and growth factors evaluated by the antibody array Cytokine symbol Cytokine name Growth factor symbol Growth factor name BLC B-cell lymphoma AR Androgen receptor Eotaxin Eotaxin BNDF Brain-derived neurotrophic factor Eotaxin2 (CCL24) Eotaxin 2 bFGF Basic fibroblast growth factor G-CSF Granulocyte colony-stimulating factor BMP4 Bone morphogenetic protein 4 GM-CSF Granulocyte-macrophage colony-stimulating factor BMP5 Bone morphogenetic protein 5 I309 (CCL1) Chemokine (C-C motif) ligand 1 BMP7 Bone morphogenetic protein 7 ICAM-1 Inter-Cellular Adhesion Molecule 1 B-NGF Beta nerve growth factor IFN γ Interferon-gamma EGF Epidermal growth factor IL-1α Interleukine-1 alpha EGFR Epidermal growth factor receptor IL-1β Interleukine-1 beta EG-VEGF Endocrine gland-derived vascular endothelial growth factor IL-1Ra Interleukin-1 receptor antagonist FGF4 Fibroblast growth factor 4 IL-2 Interleukine-2 FGF7 Fibroblast growth factor 7 IL-4 Interleukine-4 GDF15 Growth differentiation factor 15 IL-5 Interleukine-5 GDNF Glial cell-derived neurotrophic factor IL-6 Interleukine-6 GH Growth hormone IL-6sR Interleukine-6 soluble receptor HB-EGF Heparin-binding EGF-like growth factor IL-7 Interleukine-7 HGF Hepatocyte growth factor IL-8 Interleukine-8 IGFBP1 Insulin-like growth factor-binding protein 1 IL-10 Interleukine-10 IGFBP2 Insulin-like growth factor-binding protein 2 IL-11 Interleukine-11 IGFBP3 Insulin-like growth factor-binding protein 3 IL-12p40 Interleukine-12 p40 IGFBP4 Insulin-like growth factor-binding protein 4 IL-12p70 Interleukine-12 p70 IGFBP6 Insulin-like growth factor-binding protein 6 IL-13 Interleukine-13 IGF-1 Insulin-like growth factor 1 IL-15 Interleukine-15 Insulin Insulin IL-16 Interleukine-16 MCFR Macrophage chemotactic factor receptor IL-17 Interleukine-17 NGFR Nerve growth factor receptor MCP-1 (CCL2) Monocyte chemotactic protein-1/ Chemokine (C-C motif) ligand 2 NT-3 Neurotrophin 3 MCSF Mouse stem cell factor NT-4 Neurotrophin 4 MIG (CXCL9) Monokine induced by IFN-Gamma/ Chemokine (C-X-C motif) ligand 9 OPG Osteoprotegerin MIP-1α (CCL3) Macrophage inflammatory protein 1 alpha/ Chemokine (C-C motif) ligand 3 PDGFAA Platelet-derived growth factor AA MIP-1β (CCL4) Macrophage inflammatory protein 1 beta/ Chemokine (C-C motif) ligand 4 PIGF Phosphatidylinositol-glycan biosynthesis class F MIP-1δ Macrophage inflammatory protein 1 delta SCF Stem cell factor PDGFBB Platelet-derived growth factor BB SCFR Stem cell factor receptor Rantes (CCL5) Regulated upon Activation, Normal T-cell Expressed, and Secreted/ Chemokine (C-C motif) ligand 5 TGF-α Transforming growth factor alpha TIMP-1 Tissue inhibitor of metalloproteinases 1 TGF-β1 Transforming growth factor beta 1 TIMP-2 Tissue inhibitor of metalloproteinases 2 TGF-β3 Transforming growth factor beta 3 TNF-α Tumour necrosis factor alpha VEGF Vascular endothelial growth factor TNF-β Tumour necrosis factor beta VEGFR2 Vascular endothelial growth factor receptor 2 TNFRI Tumour necrosis factor receptor 1 VEGFR3 Vascular endothelial growth factor receptor 3 TNFRII Tumour necrosis factor receptor 2 VEGFD Vascular endothelial growth factor D Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 40! ! 4.6. Western blot Western blot analyses were performed to allow the validation of results from the previous experiments, The BALF samples were prepared with Laemmli buffer 4X that contained a TrisHCl 250 mM solution with a pH of 6.8, SDS at 4% (w/v), glycerol at 60% (v/v), and 2mercaptoethanol at 10% (v/v). They were submitted to SDS-PAGE separation after boiling for 5 min at 100ºC to denature proteins. Depending on the molecular weight of the protein, SDS-PAGE separating gels (1.5 mm) were made with 7.5, 10, and 12.5% acrylamide as previously described. Gels were covered with water and left to polymerize. As soon as the gels were set, a stacker gel with 4% acrylamide was made and placed on top of the separating gels with a comb to create wells. The electrophoresis cell was filled with 1x Tris/Glycine/SDS Buffer running buffer (Tris-Base 25mM, GIycine 192mM, SDS 0.1% (w/v)) (Bio-Rad, Hercules, CA, USA) and the electrophoresis began. The run started at a low voltage (30-50 V) until the samples had left the stacker gel. Then, the voltage was raised to 100-120 V and was kept constant until the end of the run. Gels were then transferred to polyvinylidene fluoride (PVDF) membranes (BioRad, Hercules, CA, USA) in a transfer cell with 1x Tris/Glycine transfer buffer (25 mM Tris, 192 mM glycine, 0.1% SDS, and 20% methanol) (Bio-Rad, Hercules, CA, USA), at 4ºC for 3-4 hours. The membranes were then removed from the transfer cassettes and blocked for 1 hour in Tris-buffered saline (TBS) with 0.05% Tween-20 (Sigma-Aldrich, St. Louis, MO, USA) (0.05% TBST) and with 5% BSA (Sigma-Aldrich, St. Louis, MO, USA). Afterwards, the blots were incubated overnight at 4ºC with their corresponding primary antibody (in TBST with 1% BSA), in the appropriate proportion. The used antibodies and their respective ratios can be seen on Table 4. Membranes were then washed to remove the excess primary antibodies and the secondary antibody, in the suitable ratio, was applied to the membranes for 1 hour in TBST with 1% BSA, at room temperature. Membranes were then washed to remove the excess secondary antibodies and placed on water. Membranes were revealed using enhanced chemiluminescence ECL according to the instructions provided by the manufacturer (GE Healthcare, Waukesha, WI, USA) and the membranes were exposed in the image analyser Mini LAS-3000 (Fujifilm, Tokyo, Japan). The relative protein levels were calculated by comparison to the amount of β-actin protein (1:1000 Abcam, Cambridge, MA, USA). The experiments were repeated three times independently. The analysis of the different expression values of the 8 proteins Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 41! ! obtained by western blot was performed by densitometry. The densitometry analysis of the scanned blots was done using Image J software (118). Results were normalized with the expression of the control protein (β-actin). Table 4: Western blot validation antibodies and used ratios Purpose Name Ratios 2D-PAGE validation Anti-HSP70, Anti-AKR1B10, Anti-PRDX1 (Epitomics, Burlingame, CA, USA), AntiPKM2, Anti-NF-κBp65, Anti-pNF-κBp65 (Cell Signaling, Beverly, MA, USA) 1/1000 Antibody array validation Anti-IL-11, Anti-CCL1 (I309) (Abcam, Cambridge, MA, USA) 1/200 4.7. Enzyme-Linked Immunosorbent Assay Proteins previously validated by western blot were evaluated by ELISA in a first validation cohort of 139 patients and later in a second validation cohort of 160 patients. BALF samples were applied as instructed by the manufacturer, using sandwich ELISA kits (RayBiotech, Norcross, GA, USA). All reagents were placed at room temperature (18 - 25°C) before use. Standards were prepared at different concentrations to create a standard curve. One hundred ml of each standard and samples were added into their respective wells. All samples and standards were assayed in duplicate and the same plate was used. The ELISA plate was covered and left to incubate for 2.5 hours at room temperature with gentle shaking. The wells were washed 4 times with 1x Wash Solution by filling each well with Wash Buffer (300 µl). After the last wash, the plate was inverted and blotted against clean paper towels to remove all excess Wash Solution. The 1x prepared biotinylated antibody was added to each well (100 µl) and left to incubate for 1 hour at room temperature with gentle shaking. The wells were then washed 4 times, as previously described. Prepared Streptavidin solution (100 µl) was added to each well and incubated for 45 minutes at room temperature with gentle shaking. The wells were then washed 4 times, as previously described. Finally, 100 µl of TMB One-Step Substrate Reagent were placed on each well. Incubation was performed for 30 minutes at room temperature in the dark with gentle shaking. The Stop Solution was then added to each well and plates were read at 450 nm Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 42! ! immediately on an EMax Microplate Reader (Molecular Devices, Minneapolis, Minn., USA). The mean absorbance for each set of duplicate standards and samples was subtracted by the average zero standard optical density. The standard curve was created on an Excel spread sheet, with the standard concentration on the x-axis and absorbance on the y-axis. Sample concentration was calculated by using the equation provided by linear trend graph. A graphic representation of the proteomic analysis of BALF from LC and COPD patients by antibody arrays and ELISA can be found below on Figure 13. Figure 13: Workflow of the employed antibody arrays and ELISA methodology. 4.8. Bioinformatics analysis and statistical analysis 4.8.1. 2D-PAGE bioinformatics analysis The proteins selected from the 2D-PAGE experiments and subsequently identified by MS were analysed by the Ingenuity Pathway Analysis (IPA) web-based application (www.ingenuity.com, Ingenuity® Systems, Redwood City, CA, USA). It uses a database that contains up-to-date information on thousands of genes and proteins, over one million biological interactions, and one hundred canonical pathways. This allows the identification of relationships, biological mechanisms, functions, and relevant pathways associated with the studied proteins. The IPA Core analysis allows the assessment of the pathways and biological processes most significantly altered in our dataset, the associated diseases and Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 43! ! disorders, the most important molecular and cellular functions, and the identification of the signalling and metabolic pathways most relevant in our data set. Only proteins identified by the IPA software were submitted to core analysis, that revealed the most important biological functions and pathways associated with our proteins, and also created interaction networks between them. 4.8.2. Antibody arrays statistical analysis All continuous variables of patients’ characteristics were expressed as a median for each variable (interquartile [IQR] range) and categorical variables as number of cases and percentage. The mean and SD of selected proteins were calculated for every patient in all study groups. The differences among groups were evaluated by the Mann–Whitney U test. Differences were considered statistically significant if their p value was ≤0.05 (*p≤0.05, **p<0.01, ***p<0.001). Statistical analysis was performed using the Statistical Package for the Social Sciences software (SPSS 17, Chicago, Illinois, USA). 4.8.3. Antibody arrays expression data analysis Hierarchical supervised clustering analysis was performed using the function UPGMA (Unweight Pair Group Method with Arithmetic Mean). Before statistical analysis, protein expression levels were standardized, protein by protein, across all conditions using the medians and standard deviation (SD) values. The sample conditions were clustered using euclidean distance metric tests. The results were visualized and analysed with Babelomics 4.2 (babelomics.bioinfo.cipf.es) (119). The expression level of each protein, relative to its median expression level across all conditions, was represented by a colour, with red representing expression greater than the median, blue representing expression lesser than the median, and various intermediate colour intensities representing the magnitude of variance from the median. 4.8.4. Diagnostic test validity analysis Receiver operating characteristics curves were constructed to assess sensitivity, specificity, and respective areas under the curve (AUCs) with 95% confidence interval (CI) of possible LC biomarkers. We investigated the optimum cut-off value, which was chosen Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 44! ! in order to reflect the best cooperation between sensitivity, specificity, positive predictive value, negative predictive value, and likelihood ratio to predict the diagnosis of lung adenocarcinoma. To test the diagnostic accuracy of several biomarkers measured together, the chi-squared test was used. ! ! RESULTS ! ! Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 47! ! 5. RESULTS 5.1. First specific objective: Identification of differently expressed proteins in BALF from patients with LC, COPD, COPD with LC, and without LC or COPD by comparative proteomic analysis. 5.1.1. Selection of patients Sixty samples from the patients in the test cohort were divided into four groups: control (without LC or COPD), COPD, LC, and LC&COPD. The principal characteristic from test cohort, were all male, with a median age of 61 (41-80) years. The majority of patients were current smokers, with similar pack-years 30 (28.637.2). In the COPD group, the majority of patients had moderate and severe cases of COPD, and in the LC&COPD group, over half of patients had mild COPD. The adenocarcinoma histology was the predominant histology of LC in the LC and LC&COPD groups (Table 5). Overall patient characteristics were well matched among the four study groups. Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 54! ! The western blot analysis of the selected proteins, together with the levels of the reference protein β-actin, are shown in Figure 17. The samples used for western blot validation were randomly selected. Our results showed that the AKR1B10 protein had increased expression only in the LC group. On the other hand, HSP70 was over-expressed by all disease groups when compared to the control group. The PRDX1 protein showed an increased expression in the COPD and LC&COPD groups compared to the LC and control groups. Finally, PKM2 was clearly over-expressed in the BALF of the patients in the LC and LC&COPD groups compared to those in the COPD and control groups. The results of the western blot validation process confirmed those obtained by the 2D-PAGE experiments. Figure 17: Western blots for AKR1B10, HSP70, PKM2, PRDX1, and β-actin in the control, COPD, LC, and LC&COPD groups. Differences in expression, normalized with the β-actin protein, are illustrated by a bar chart on the right. 5.1.4. LC and COPD pathway analysis The connections and interactions between our 40 differentially expressed proteins and also the biological mechanisms, functions, and pathways in which they were possibly involved in were evaluated by the IPA software. In addition, the association between certain diseases and disorders and our data set were also analysed by IPA. Core analysis was performed to include direct and indirect connections, and endogenous chemicals, and to consider only molecules and/or associations, creating an interaction network that can be seen on Figure 18. Cellular compartments as designated by IPA (gene ontology based), indicated that the majority of identified proteins were of cytoplasmatic origin. Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 55! ! Figure 18: IPA interaction network considering the differentially expressed proteins between the control, LC, COPD, and LC&COPD groups. The mapping of our differentially expressed proteins onto known molecular pathways and biological functions revealed three top processes occurring in the BALF samples from our four groups of patients: inflammation, free radical scavenging and oxidative stress response, and glycolysis and gluconeogenesis. Together with these findings, the recognition of the NF-kB complex as the major connection node between these three mechanisms was also accomplished. The majority of identified proteins, a total of 22 proteins, were associated to inflammatory mechanisms (AKR1B10, AKR1C3, ALDOA, ANXA1, ANXA2, ANXA5, ARHGDIB, CA1, CAT, CFL1, CRP, ENO1, EZR, GSR, HSP70, LCN2, PEBP4, PPIA, PRDX1, PRDX2, SELENBP1, and TKT), 16 proteins were related to free radical scavenging and oxidative stress (ARHGDIB, CAT, CRP, CTSD, EZR, GSR, GSTA1, GSTA2, GSTP, IDH1, PPIA, PRDX1, PRDX2, PRDX5, SERPINB1, and TXN), and 8 proteins were found to be connected to the glycolysis and gluconeogenesis processes (ALDH3A1, ALDOA, ENO1, FBP1, IDH1, PEBP4, PKM2, and PYGM), as seen on Figure 19. Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 56! ! Figure 19: Top biological functions and pathways identified by IPA; inflammation related proteins (p value: 1,35*10-08–1,42*10-02), free radical scavenging and oxidative stress response (p value: 4,93*10-11–1,27*10-02), and glycolysis and gluconeogenesis related proteins (p value: 7,39*10-09– 1,58*10-02). 5.1.5. NF-kB functional analysis NF-κB was the main connector between the differentially expressed proteins, even though this protein was not identified as differentially expressed among the four groups of patients analysed by 2D-PAGE. Consequently, we analysed NF-kB in BALF samples from the control, COPD, LC, and LC&COPD groups of patients, by measuring the expression levels of the NF-κBp65 protein and its phosphorylated (activated) form, pNF-κBp65, by western blot (Figure 20). Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 57! ! Figure 20: Western blots for NF-κBp65, p NF-κBp65, and β-actin in the control, COPD, LC, and LC&COPD groups. Differences in expression, normalized with the β-actin protein, are illustrated by a bar chart on the right. High expression levels of NF-κBp65 were detected in all groups, with a slightly higher intensity in the COPD group. The phosphorylated form of NF-κBp65 showed higher expression levels only in the disease groups (COPD, LC, and LC&COPD), with the LC group presenting the highest intensity. These western blot analyses are compatible with the fact that activation of NF-κBp65 might be involved in the pathogenesis of both diseases. Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 58! ! 5.2. Second specific objective: Analysis of the differential expression of inflammation related proteins (cytokines and growth factors) in LC and COPD patients. 5.2.1. Patient selection. The antibody array methodology was applied to samples from 60 patients, whose characteristics can be found on Table 8. All selected patients were male, with median ages around 61 (41-80) years. The majority of patients were current smokers, with similar pack-years smoking exposure. Moderate and severe COPD were the most common stages in the COPD group of patients, and mild COPD was predominant in the LC&COPD group of patients. The adenocarcinoma histology was the most common among the patients in the LC group, contrary to the patients in the LC&COPD group, who had been predominantly diagnosed with the SCC histology. All other characteristics were well matched among the study groups. Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 59! ! Table 8: Characteristics of the discovery cohort patients for the antibody arrays methodology Controls n=16 COPD n=15 LC n=17 LC&COPD n=12 Gender Male 100.0% (16) 100.0% (15) 100.0% (17) 100.0% (12) Female 0.0% (0) 0.0% (0) 0.0% (0) 0.0% (0) Median age [range] 61.3 [41.0-80.0] 61.5 [45.0-78.0] 60.7 [46.0-69.0] 60.7 [49.0-68.0] Smoking status Smokers 68.8% (11) 53.3% (8) 52.9% (9) 83.3% (10) Ex-smokers 31.2% (5) 46.7% (7) 47.1% (8) 16.7% (2) Pack-years [range] 38.8 [31.0-53.2] 50.0 [42.0-65.0] 58.2 [41.0-65.7] 52.3 [41.0-63.2] COPD Mild 0.0% (0) 20.0% (3) 0.0% (0) 58.3% (7) Moderate 0.0% (0) 33.3% (5) 0.0% (0) 25.0% (3) Severe 0.0% (0) 26.7% (4) 0.0% (0) 0.0% (0) Very severe 0.0% (0) 20.0% (3) 0.0% (0) 16.7% (2) LC Histology Adenocarcinoma Stages I-II 0.0% (0) 0.0% (0) 11.8% (2) 0.0% (0) Stages III-IV 0.0% (0) 0.0% (0) 58.8% (10) 33.3% (4) SCC Stages I-II 0.0% (0) 0.0% (0) 5.9% (1) 8.3% (1) Stages III-IV 0.0% (0) 0.0% (0) 23.5% (4) 58.4% (7) SCC: squamous cell carcinoma To validate the results obtained by the training cohort, a group of 299 patients was selected and two distinct validation cohorts were created. The first validation cohort was made up of 139 patients that were separated into four distinct groups. The patients in this cohort had similar clinical and pathological characteristics to those in the training cohorts (Table 9). Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 60! ! Table 9: Characteristics of the patients in the first validation cohort Controls n=20 COPD n=29 LC n=40 LC&COPD n=50 Gender Male 60.0% (12) 86.2% (25) 82.5% (33) 96.0% (48) Female 40.0% (8) 13.8% (4) 17.5% (7) 4.0% (2) Median age [range] 52.3 [42.0-58.0] 65.2 [45.0-78.0] 61.2 [48.0-75.0] 64.3 [48.0-75.0] Smoking status Smokers 100.0% (20) 51.7% (15) 50.0% (20) 52.0% (26) Ex-smokers 0.0% (0) 48.3% (14) 50.0% (20) 48.0% (24) Pack-years [range] 41.8 [33.0-57.2] 52.8 [41.0-69.2] 55.0 [39.0-73.2] 57.0 [43.0-75.2] COPD Mild 0.0% (0) 27.6% (8) 0.0% (0) 26.0% (13) Moderate 0.0% (0) 51.7% (15) 0.0% (0) 50.0% (25) Severe 0.0% (0) 20.7% (6) 0.0% (0) 24.0% (12) Very severe 0.0% (0) 0.0% (0) 0.0% (0) 0.0% (0) LC Histology Adenocarcinoma Stages I-II 0.0% (0) 0.0% (0) 10.0% (4) 10.0% (5) Stages III-IV 0.0% (0) 0.0% (0) 27.5% (11) 36.0% (18) SCC Stages I-II 0.0% (0) 0.0% (0) 25.0% (10) 30.0% (15) Stages III-IV 0.0% (0) 0.0% (0) 37.5% (15) 24.0% (12) SCC: squamous cell carcinoma The characteristics of the 160 patients in the second validation cohort can be seen on Table 10. This cohort was significantly different from the first one given that it did not include a group of patients with only COPD and that the LC groups had patients diagnosed not only with adenocarcinoma and SCC, but also LCC and SCLC. Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 61! ! Table 10: Characteristics of the patients in the second validation cohort Controls n=20 LC n=66 LC&COPD n=74 Gender Male 60.0% (12) 83.3% (55) 94.6% (70) Female 40.0% (8) 16.7% (11) 5.4% (4) Median age [range] 52.3 [42.0-58.0] 61.2 [48.0-75.0] 64.3 [48.0-75.0] Smoking status Smokers 100.0% (20) 50.0% (33) 52.7% (39) Ex-smokers 0.0% (0) 50.0% (33) 47.3% (35) Pack-years [range] 33.6 [30.0-51.2] 58.8 [41.0-77.2] 62.0 [43.0-79.2] LC Histology Adenocarcinoma Stages I-II 0.0% (0) 4.5% (3) 2.7% (2) Stages III-IV 0.0% (0) 18.2% (12) 13.6% (10) SCC Stages I-II 0.0% (0) 6.1% (4) 8.1% (6) Stages III-IV 0.0% (0) 12.1% (8) 5.4% (4) LCC Stages I-II 0.0% (0) 4.5% (3) 8.1% (6) Stages III-IV 0.0% (0) 7.6% (5) 10.8% (8) SCLC Limited stage 0.0% (0) 19.7% (13) 21.6% (16) Extensive stage 0.0% (0) 27.3% (18) 29.7% (22) SCC: squamous cell carcinoma; LCC: large cell carcinoma; SCLC: small cell lung cancer 5.2.2. Expression of cytokines and grow factors in BALF from LC and COPD patients. The initial protective response, elicited by the damaging complex mixture of chemicals present in tobacco smoke, can generate the release and recruitment of more pro-inflammatory immune cells and cytokines and create a chronic inflammation scenario and a tumour friendly microenvironment. So, we aimed at analysing which inflammatory proteins had altered expression in our four groups of patients. For this purpose we used Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 62! ! antibody arrays that measured the levels of 80 cytokines and growth factors implicated in the inflammatory process. A total of 15 cytokines and growth factor arrays were carried out and the data analysis was performed by measuring the different expression levels between the 80 inflammatory proteins and the positive controls present in the arrays. Protein expression levels were then standardized, protein by protein, across all conditions by using the medians and SD values. Results were visualized and analysed with Babelomics 4.2 (babeIomics.bioinfo.cipf.es). We created a hierarchical supervised clustering analysis of the protein arrays using the UPGMA function. The generated dendograms of the cytokines and growth factors expression levels and the groups of patients, and heatmap are represented below in Figure 21. The original four groups of patients (control, COPD, LC, and LC&COPD) were subdivided into six groups taking into consideration the histological sub-types of NSCLC; control, COPD, adenocarcinoma (ADC), SCC, ADC&COPD, and SCC&COPD. Analysis revealed that 20% of the evaluated proteins in the cytokine and growth factor arrays were over-expressed in the disease groups compared to the control group. These 16 proteins were MIP-1β, MIG, IGFBP1, EGF, VEGF, TNFRI and TNFRII, IL-6Sr, GDF15, IL-1Ra, MCP-1, Eotaxin2, PDGFAA, IGFBP2, IL-11, and CCL1. A first group of proteins (MIP-1β, MIG, IGFBP2, and EGF) was highly expressed across all disease groups, compared to the control group. A second group of proteins (VEGF, TNFRI and TNFRII, IL-6Sr, GDF15, IL-1Ra, MCP-1, Eotaxin2, and PDGFAA) also had a higher expression in the disease groups, but not as high as the previous group of proteins. Interestingly, there were clear differences in protein expression between the adenocarcinoma histological subtype and the other groups. In fact, the expression of IL-11 and CCL1 was increased differentially in adenocarcinoma. Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 63! ! Figure 21: Heatmap of the differences in protein expression across the disease groups, compared to the control group, obtained from the inflammatory protein arrays; protein expression levels relative to its median expression level across all conditions, are represented by a colour, with red representing expression greater than the median, blue representing expression lesser than the median, and various intermediate colour intensities representing the magnitude of variance from the median. Statistical analysis of the cytokine and growth factor arrays, seen on Figure 22, revealed significant statistical differences (p≤0.05) among groups for the majority of proteins, despite the existing dispersion between them due to patient variability. Among the 16 inflammatory proteins that had statistically significant different expression levels when compared between the patients of the disease groups and the control group patients, IL-11 and CCL1 were observed to have a similar behaviour. These two proteins were only over-expressed in the BALF samples from patients with ADC and Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 70! ! Figure 28: IL-11 and CCL1 expression levels measured by ELISA in the second validation cohort. Statistically significant expression differences (p<0.0001), obtained by the Mann-Whitney U test, were found between the ADC groups and all remaining groups. The ROC curve analysis of the second validation cohort can be seen on Figure 29. This analysis revealed that the optimum diagnostic cut-off value for IL-11 was 29.5 pg/ml (AUC – 0.95; 95%CI: 0.92 - 0.98). The AUC associated with this cut-off indicated that a patient with lung adenocarcinoma had higher levels of IL-11 than 95% of controls. The estimated cut-off value for CCL1 was 24.25 pg/ml (AUC – 0.91; 95%CI: 0.87 - 0.96), which showed that a lung adenocarcinoma patient would have higher levels of CCL1 than 91% of patients in the control group. Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 71! ! Figure 29: Diagnostic outcomes for IL-11 and CCL1 levels in BALF in the diagnosis of adenocarcinoma (versus all groups) in the second validation cohort. In order to further validate the results from cohort validation first, the cut-off values from the first cohort were applied to the data from the second validation cohort. Predictive values and likelihood ratios for IL-11 and CCL1 in the second validation cohort can be found on Table 12. Positive levels of IL-11, CCL1, IL-11 and CCL1, and IL-11 and/or CCL1 were detected on approximately 91%, 92%, 71%, and 92% of patients with lung adenocarcinoma (Figure 30). These percentages were similar to those obtained in the first validation cohort. Likewise, specificity and PPV increased when both markers were analysed together compared to measuring each one in separate (96.3% and 84.1% respectively). Increased values of sensitivity (92.3%) and NPV (98.1%) were observed when the evaluation of only one marker was required to positively predict the adenocarcinoma outcome, similar to what occurred with the first validation cohort. Specificity (84%) and PPV (62.5%) also increased in this case, contrary to what occurred in the first validation cohort. 1- Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal!! ! 72! ! Table 12: Analysis of IL-11 and CCL1 levels as diagnostic markers of adenocarcinoma in BALF of patients from the second validation cohort, with cut-off values from the first validation cohort Adenocarcinoma vs all patients IL-11 CCL1 IL-11 and CCL1 IL-11 and/or CCL1 AUC (95%CI) 0.95 (0.92-0.98) 0.91 (0.87-0.96) - - Sensitivity (95%CI) 90.6% (79.7-95.9%) 91.7% (80.4-96.7%) 71.2% (57.7-81.7%) 92.3% (82.6-98.1%) Specificity (95%CI) 83% (80.8-87.7%) 77.5% (71.0-82.9%) 96.3% (92.5-98.2%) 84% (78-88.5%) PPV (95%CI) 60.8% (49.7-70.8%) 51.2% (40.8-61.4%) 84.1% (70.6-92.4%) 62.5% (51.5-72.3%) NPV (95%CI) 96.8% (92.7-98.6%) 97.3% (93.3-99%) 92.3% (87.7-95.3%) 98.1% (94.6-99.4%) Positive LR 5.32 (3.81-7.41) 4.08 (3.09-5.04) 19.1 (9.0-41.13) 5.88 (4.21-8.22) Negative LR 0.11 (0.05-0.26) 0.11 (0.04-0.28) 0.3 (0.19-0.46) 0.07 (0.02-0.20) AUC= area under curve. PPV= positive predictive value. NPV= negative predictive value. LR= likelihood ratio. The diagnostic cut-off values of IL-11 and CCL1 were 42 pg/ml and 39.5 pg/ml respectively. Figure 30: Proportion of adenocarcinoma patients from the second validation cohort with positive levels of IL-11, CCL1, IL-11 and CCL1, and IL-11 or CCL1. ! ! GENERAL DISCUSSION AND MAIN CONCLUSIONS ! ! ! Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! ! Ana!Barbosa!de!Sousa!Nogal! ! 75! 6. GENERAL DISCUSSION AND MAIN CONCLUSIONS Lung cancer is one of the most commonly diagnosed malignancies and kills more patients than any type of cancer in the world. Approximately 90% of all these LC types are attributable to one major factor: tobacco smoking. The diagnosis of LC is a crucial step given its influence in the prognosis of a patient. The use of chest radiographies, bronchoscopies, among other tests, is common when searching for LC. Afterwards, available therapeutic options include surgery, radio and chemotherapy, used separately or in different combinations. However, LC is usually asymptomatic during its development and this causes the majority of patients to be diagnosed at advanced stages of the disease, limiting therapeutic options and prognosis. When a patient is diagnosed at an early stage, the 5-year survival rate lies between 4373%. With a stage II diagnosis, 25-45% of people will live for at least 5 years. When considering more advanced stages, the outcomes change dramatically: only 7-24% of patients diagnosed with stage III will live more than 5 years after their initial diagnosis; stage IV, characterized by the presence of metastasis, has a 5-year survival rate between 2 and 13% (120). Radiological screenings have been used for many years for the early detection of LC. Still, data from several screening trials has revealed that performing annual chest radiographies in smokers, former smokers, and non-smokers is not effective in reducing LC mortality and cannot be recommended for clinical practice (121). More recently, low dose CT scanning was associated with a significant reduction in LC mortality (20%), in one large study of high-risk individuals. However, more data are needed on the cost effectiveness of screening that takes into account the frequency of screening and both the benefits and harms (such as false positives and over diagnosis) before large-scale screening programmes are created (122). In addition to radiological methods, the cytological analysis of certain samples is also helpful to the diagnosis of LC. However, pulmonary cytology has variable sensitivity and specificity values, and false positive diagnoses are somewhat common, giving that the cytological evaluation of the lungs has some limitations (123). The use of bronchoscopy to localize lesions and early LC presents some challenges: SCC is mostly detected in the central areas of the lungs, whereas adenocarcinomas, the most common type of LC in recent years, are mostly found in the peripheral airways of the lungs, unreachable to the majority of bronchoscopes. In conclusion, the diagnosis of LC involves a combination of the radiological and histological evaluation of a symptomatic patient or a suspicious lesion, requiring the cytological examination sputum or in bronchoscopy Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! ! Ana!Barbosa!de!Sousa!Nogal!! ! 76! ! obtained samples, such as biopsies and BALF (124). These tests are sometimes inconclusive, making it necessary to perform invasive surgical procedures in patients that may or may not have the disease. The abovementioned classical methods of diagnosing LC are currently evolving to less invasive, molecular oriented approaches to the detection of this disease. The use of autofluorescence bronchoscopy, lung tissue molecular markers, blood based LC markers, airway based biomarkers, among others, are currently being evaluated for the early detection of LC. Although some recent studies have shown that these molecular biomarkers can have a positive impact in the improvement of LC diagnosis, extensive validation of the most promising molecules that arise from these studies is crucial before they can be used to screen individuals at high risk of developing LC (125-127). Apart from being the major cause of LC development, cigarette smoke is also responsible for another common illness that affects the respiratory system, which is COPD. For a long time it was though that the link between both diseases was their aetiological factor. Nonetheless, the observation that COPD patients had high LC mortality rates challenged that concept. Despite the increased attention given to the study of the commonalities between both diseases, either by clinical studies and research, the true nature of the link that connects both diseases, remains elusive. However, as discussed below, there are key shared mechanisms that may represent links between the two diseases, and potentially diagnostic and therapeutic targets. Some common genetic alterations play a role in the development of COPD and LC, for example, the α1 antitrypsin protein inhibits the neutrophil elastase that degrades elastin and has anti apoptotic capabilities. The α1 antitrypsin deficiency causes the development of emphysema and increases the risk of developing LC by 70% (128). Other genes that have been associated with LC and COPD include those that code proteinases, detoxifying enzymes, nicotine receptors, and inflammatory proteins (129). The enzymes that contribute to the metabolism of the compounds found in tobacco smoke can occasionally transform these molecules into more harmful substances, such as carcinogens and ROS. The action of these metabolites in the lung epithelial cells can promote the development of both LC and COPD. Another seemingly important mechanism connecting both diseases is inflammation. Macrophage and neutrophil infiltration is a common occurrence in all smokers, but those who have COPD develop a more marked inflammatory response that correlates with disease severity (130). In addition, the cells that contribute to the development of, for example, emphysema exist both in the airways and alveolar airspaces and can therefore contribute to the development of proximal and distal LC (129). Although both diseases have chronic inflammation as a feature, the nature of the microenvironment Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! ! Ana!Barbosa!de!Sousa!Nogal!! ! 77! ! in COPD and LC is different and contributes to the distinct behaviours that characterize both diseases. The COPD microenvironment is more cytotoxic, genotoxic, and has more matrix degrading capabilities than the LC microenvironment, which is more angiogenic and growth promoting (129). There are still many unanswered questions concerning LC and COPD and the links between both diseases. In addition, a pressing need exists to discover new molecular biomarkers that can aid in the early detection of LC. This latter area of research has been granted a major ally with the development and the improvement of certain analytical technologies, such as genomics and proteomics. Both technologies were designed to perform the rapid and complete analysis of the genes and proteins that are expressed in a given cell or tissue in a given time. Despite the better understanding of the genetic alterations present in LC given by genomic approaches, such as mutations and polymorphism in key genes, the importance of the evaluation of proteins cannot be overlooked. Proteomics complements the genomic-based approaches by providing additional information that cannot be obtained by the study of genes alone. One of the most important advantages of the proteomic approach in cancer research is based on the fact that there is generally a poor correlation between the transcriptional levels of many genes and the relative abundance of the corresponding protein. Furthermore, due to differential splicing, one gene can encode several protein variants with distinct properties (131). In addition to relative abundance, the activity of a given protein is not a direct consequence of its genetic expression, but the result of post-translational modifications such as phosphorylation, glycosylation, methylation, ubiquitination, cleavage, and acetylation (132). Despite the many advantages when compared to other approaches, the use of proteomic technologies has to be applied carefully in order to provide relevant biological results and to be successfully translated into the clinical practice. Challenges associated with proteomics include the heterogeneity of cells that exist in the lungs, which can be malignant or normal, the broad dynamic range of protein abundances in proximal body fluids and serum that difficult the identification of biomarkers in those types of samples, and finally the need for extensive validation of the molecular changes in independent cohorts of patients using different techniques (131). Proteomic methodologies can be applied to the discovery of new LC biomarkers as well as aid with the prognosis, prediction and monitoring of a patient response to a given therapeutic intervention. Such markers of disease can be released directly from cancer cells or can be attributable to the host’s response against the malignancy itself. The detection of these candidate biomarkers can be performed in a variety of body fluids collected by a non-invasive method. Biomarkers obtained by these methods are usually Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! ! Ana!Barbosa!de!Sousa!Nogal!! ! 78! ! more sensitive, more specific, and more easily detected than invasive biomarkers (obtained by procedures such as surgery). In addition, they are associated with less anxiety and discomfort to the patients. Samples such as blood, urine, serum, and saliva are major sources of non-invasive biomarkers and commonly used in cancer research. Although more invasive than the previous samples, BALF is a great source of possible biomarkers of lung diseases given that it is thought to reflect most faithfully the protein and cell composition of the pulmonary airways among the non-invasive techniques (133). The present work aimed at evaluating this particular type of sample by different proteomic techniques with the aim of identifying proteins that could be used as biomarkers for the diagnosis of LC, something that to the best of our knowledge has not been done before. The first proteomic technique used in this study was 2D-PAGE. This tool has been the driving force behind the development of proteomics and protein analysis throughout the years. This technique has been extensively tested and compared between different laboratories throughout the years makes it one of the most robust proteomic methods. Our results showed that the protein composition of BALF was different among the four groups of studied patients, especially when we compared the control group to the disease groups. A total of 40 were differentially expressed among the four groups of patients and the expression changes were superior to 2 fold (p<0.05). The distribution of these differentially expressed proteins allowed us to determine the specific proteomic profiles of the various groups of patients. Seventeen proteins were over-expressed in all the patients from the disease groups (LC, COPD, and LC&COPD) compared to those in the control group. These proteins were AMY1A, AMY2A, ANXA1, ANXA2, ANXA5, ARHGDIB, CA1, CRP, C3, ENO1, GSR, HSP70, IDH1, PEBP4, SERPINB1, TPPP3, and TXN. The expression of HSP70, which in the present study had an average over-expression of 3.6 fold across the three disease groups, was also found significantly elevated in serum samples from COPD patients, when compared to controls (134). It has also been linked to the number of cigarettes smoked daily in a study of lung adenocarcinoma in smokers (135). The use of proteomic approaches has revealed the association between the over-expression of HSP70 and the differentiation level and/or aggressiveness of several types of cancer, such as gastric adenocarcinomas, hepatocarcinomas, and oesophageal cancer (136138). Furthermore, various oncoproteomic studies have correlated the elevated levels of HSP70 with therapeutic resistance (139-142). Another important protein in this group is CA1. This protein is a marker of cellular hypoxia, which is a natural phenotype of solid tumours. Several studies have investigated the potential of carbonic anhydrases as cancer diagnostic, prognostic, and therapeutic markers (143). Recently, the over-expression of CA1 was reported in carcinoma cell lines Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! ! Ana!Barbosa!de!Sousa!Nogal!! ! 79! ! from multiple organs, such as lung, breast, cervix, bladder, oesophagus, colorectal, kidney, and head and neck (144). PEBP4 which plays a role in the inhibition of the mitogen-activated protein kinase (MAPK) signalling pathway but is also involved in the inhibition of the c-Jun N-terminal kinases (JNK) pathway that promotes the activation of protein kinase B (AKT), was recently associated with an increased invasion and metastasis in NSCLC and colorectal tumours (145-147). The IDH1 enzyme is essential for cell metabolism and energy production, given its role in the Krebs cycle. In the present study, the IDH1 protein had an average overexpression of 2.3 fold in the disease groups. The oncogenic potential of this enzyme has been studied in acute myeloid leukaemia (148). An also recent report has observed the over-expression of IDH1 in more than 70% of NSCLC tumours. It was also correlated with lower 5-year survival rates and considered an independent unfavourable prognostic marker for overall survival of NSCLC patients in a multivariate analysis (149). In addition, IDH1 plasma levels were considered a biomarker for lung adenocarcinoma with relatively high sensitivity and specificity (150). The next group of differentially expressed proteins appeared in the LC and LC&COPD groups of patients. In the LC group of patients the AKR1B10, ALDOA, CTSD, EZR, FBP1, and TKT proteins were over-expressed when compared to the control group and the SELENBP1 protein was under-expressed in this group of patients compared to those in the control group. A number of studies have associated the presence of CTSD, Ezrin, and SELENBP1 with increased cancer growth, invasion, and metastasis in various types of tumours (151-153). Furthermore, three different studies have highlighted the importance and potential role of these proteins as prognostic biomarkers of lung tumours (154-156). Another over-expressed protein in our results was the AKR1B10 protein. In other studies, this over-expression was associated in most cases with smoking, suggesting a possible involvement of this enzyme in tobacco-induced LC (157-159). In fact, AKR1B10 has a high catalytic efficiency for the reduction of retinoids, and retinoic acid deficiency has been linked to airway epithelial squamous metaplasia and epithelialto-mesenchymal transition (160). In addition, AKR1B10 has also been recently involved in the resistance to different chemotherapeutic drugs, such as cisplatin (161). Apart from these differentially expressed proteins, the LC group of patients together with the LC&COPD group of patients, had an over-expression of the following proteins when compared to the control group; ALDH3A1, AKR1C3, PYGM, PKM2, and PPIA. The ALDH3A1 protein is a phase II drug-metabolizing enzyme that is highly expressed in the lung, stomach, keratinocytes, and cornea. Cytosolic ALDH3A1, which is induced by polycyclic aromatic hydrocarbons or chlorinated compounds (present in tobacco smoke), is thought to play an important role in alveolar pneumocyte physiology, Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! ! Ana!Barbosa!de!Sousa!Nogal!! ! 86! ! encouraging. The present work, through the application of a 2D-PAGE MALDI-TOF/TOF methodology, identified distinct proteomic profiles characteristic of LC, COPD and LC&COPD, which were later validated. The bioinformatics analysis of the 40 differentially expressed proteins identified in BALF suggested that LC and COPD shared some pathogenic pathways such as inflammation, free radical scavenging and oxidative stress response, and glycolysis and gluconeogenesis. In addition, IPA analysis exposed a major connector of these pathogenic mechanisms: the NF-kB transcription factor. The further exploration of the inflammatory process in our patients revealed that the determination of IL-11 and CCL1 levels in BALF samples by a simple ELISA assay, could be used to improve the diagnosis of lung adenocarcinoma in smokers, regardless the presence or absence of COPD. The application of this assay to routine clinical practice could in the future improve the early detection of lung adenocarcinoma, the currently most common form of NSCLC, and possibly contribute to an increase in the 5-year survival rate associated with this disease. ! ! FUTURE PERSPECTIVES ! ! Proteomic!characterization!of!lung!cancer!and!chronic!obstructive!pulmonary!disease:!a!bronchoalveolar!lavage!fluid!analysis! ! Ana!Barbosa!de!Sousa!Nogal! ! 89! ! 7. FUTURE PERSPECTIVES The search for molecular biomarkers in easily accessible samples is of vital interest in LC research. The diagnosis of LC is currently commonly obtained in advanced stages of the disease and is unfortunately associated with poor survival and a lack of therapeutic options. Our results revealed the existence of different proteomic profiles between patients with LC and COPD. The proteins included in these signatures deserve further validation and investigation as potential biomarkers for early diagnosis and prognosis, and possibly as therapeutic targets. The exploration of the inflammatory process in the samples from our patients revealed the potential of two proteins as LC biomarkers. Higher levels of IL11 and CCL1 in BALF from smokers were associated with the presence of adenocarcinoma of the lung. Future studies aiming at validating the findings in the present work should include a larger sample size, both in LC cases and controls. Further validation of the diagnostic performance of IL-11 and CCL1 in more accessible fluids such as plasma would be desirable and more practical for eventual screening programs or routine diagnosis. In addition, the evaluation of IL-11 and CCL1 as prognostic and therapeutic markers should also be addressed in future investigations. Furthermore, the performance of the IL-11 and CCL1 proteins on the differential diagnosis of pleural effusions (between lung adenocarcinoma, mesothelioma, adenocarcinoma of other origins, and non tumoral) would also be useful. The association of these two proteins with the development of other types of inflammation-related cancers should also be of interest. Finally, the functional role of IL-11 and CCL1 in the carcinogenic process should also be assessed. 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Finally, by comparing serum from SCC patients and controls, five over-expressed (HP, APO-A4, complement component C3c, SAA, and Ras-related protein 7B—RAB7B) and five under-expressed (AHSG, hemopexin precursor—HPX, proapolipoprotein, antithrombin III—SERPINC1, and CLU) proteins were identified and were able to distinguish SCC patients [62]. A summary of the most recurrently identified diagnostic biomarkers on lung cancer tissue samples and in fluid samples can be found on Tables 1and 2, respectively. Prognostic When the expression levels of a protein correlate with the natural history of the disease they are considered to have prognostic value. The study of prognostic biomarkers in lung cancer has been made by correlating the expression of a molecule to the patient survival. An alternative approach is to compare groups of patients with different clinical stages of disease, based on the assumption that a more advanced tumour is more aggressive and may express proteins that drive the metastatic process. The published proteomic studies that have focused on finding prognostic biomarkers for lung cancer have only made use of samples from NSCLC patients as a whole or in separate as adenocarcinoma or SCC patients. The grand majority of studies using samples from NSCLC patients were performed using tissue. The proteins small ubiquitin-related modifier-2 protein (SUMO-2), TMSB4, and ubiquitin were identified in a 15 MS peak profile that distinguished between patients with resected NSCLC who had poor or good prognosis [63]. Using a similar approach, 25 proteins (not all identified, but including TMSB4, TMSB10, ribosomal protein L39 and S30, S100A6, and histone H2A.2) were associated with survival among NSCLC patients and appeared to distinguish those with poor prognosis from those with good prognosis [46]. In a different study, the levels of TMSB4, TMSB10, and calmodulin were also associated with patient survival. In addition, low levels of CFL1 were associated with better outcome for patients with negative lymph nodes and high levels of CFL1 with better outcome for those with positive lymph nodes [64]. TMSB4 is a regulator of actin polymerisation whose over-expression seems to stimulate lung tumour metastasis [64]. Improved survival of NSCLC was additionally associated with the levels of S100A6 [65]. The S100 proteins are involved in the regulation of a number of cellular processes such as cell cycle progression, differentiation, and also inflammation. Proteomic studies of adenocarcinoma tissue samples identified CFL1, an actin-modulating protein that is increased in some invasive cancers, and PKM2, a key glycolytic enzyme during tumorigenesis, as associated with poor prognosis in adenocarcinoma patients [54]. The comparison between adenocarcinoma and adjacent lung tissue revealed that specific isoforms of CK7, 8, 18, and 19 were associated with patient survival [66]. Cytoskeletal reorganization is a central process regulating cell migration and metastasis, and CKs, a family of cytoskeletal intermediate filaments, have been suggested to play a role in carcinogenesis, by promoting cellular architecture reorganisation during tumour development and progression. Another adenocarcinoma study associated the expression of phosphoglycerate kinase 1 (PGK1), HSP70, CK19, PGAM1, and G protein-coupled receptor 4 (GRK4) with poor survival [67]. Several annexins (ANXA1, ANXA2, and ANXA3) were associated with advanced clinical stage, by presenting higher expression levels in lymph node metastatic tissue [68,69]. The deregulation of annexins has been reported in numerous cancers and is thought to influence the patterns of cellular behaviour, such as cell proliferation, motility, invasiveness and signalling pathways. Finally, Maeda and colleagues revealed that the absence of expression of LC– MS/MS identified myosin IIA and vimentin, two proteins associated with the cytoskeleton and cell motility, correlated with good prognosis in adenocarcinoma patients who did not receive post-operative adjuvant chemotherapy, indicating that these patients do not require such treatment [70]. Considering SCC proteomic studies searching for prognostic biomarkers, the previously identified IDH1 protein was associated with poor overall survival [60]. The evaluation of micro-dissected primary SCC and matched lymph node metastatic tissues revealed the under-expression of SNF in lymph node metastatic tumour versus primary SCC [71]. Yao and co-workers compared tissue from SCC with and without lymph node metastasis. The ANXA2, HSP27, and CK17 proteins were over-expressed in metastatic SCC, and SNF was under-expressed in these tissues [72]. A summary of these potential prognostic biomarkers can be found on Table 3. 676 Clin Transl Oncol (2013) 15:671–682 123 Table 1 Candidate proteomic diagnostic biomarkers for lung cancer identified in tissue samples Diagnostic biomarker Lung cancer type Proteomic technique S100 A6 SCC MALDI–TOF–MS [38] NSCLC MALDI–MS [46] A8 SCC iTRAQ, LC-Q-MS/MS [58] A9 SCC iTRAQ, LC-Q-MS/MS [58] SCCA1 (squamous cell carcinoma antigen 1) SCC iTRAQ, LC-Q-MS/MS [58] Cystatin A Lung cancer MALDI–TOF–MS [24] Tubulin (a,b) SCLC 2D-PAGE; 2D-PAGE, MALDI–TOF–MS [35,36] UCH-L1 (ubiquitin carboxy-terminal hydrolase L1) Lung cancer 2D-PAGE, MALDI–TOF–MS [26] SCLC 2D-PAGE, MALDI–TOF–MS [36] Adenocarcinoma 2D-PAGE, MALDI–MS [51] Peroxiredoxin PRDX4 NSCLC, Adenocarcinoma 1D-LC–ESI–MS/MS [44]; 2D-PAGE, MALDI–MS [51] PRDX1 NSCLC 2D-PAGE [43] PRDX2 SCC 2D-DIGE, MALDI–TOF–TOF [60] PRDX3 NSCLC 2D-PAGE [43] PRDX6 SCC 2D-PAGE, MALDI–TOF–MS [61] TIM (triose-phosphate isomerase) Adenocarcinoma 2D-PAGE, MALDI–MS [51] SCC 2D-PAGE, MALDI–TOF–MS [57,61] MIF (macrophage migration inhibitory factor) Lung cancer MALDI–TOF–MS [22] NSCLC MALDI–TOF–MS; IMAC, LC–MS/MS [24,45] CyP-A (Cyclophilin A) Lung cancer MALDI–TOF–MS [22] Adenocarcinoma 2D-DIGE, LC–MS/MS [52] TAGLN (Transgelin) NSCLC 2D-PAGE, MALDI–TOF–MS [42] Adenocarcinoma 2D-DIGE, LC–MS/MS [52] CA (Carbonic anhydrase) SCLC 2D-PAGE, MALDI–TOF–MS [36] NSCLC 2D-PAGE, MALDI–TOF–MS [42] ENO1 (Alpha enolase) NSCLC 2D-PAGE, MALDI–TOF–MS [42] SCC 2D-PAGE, MALDI–TOF–MS [57,61] 14-3-3 gLung cancer 1D-PAGE, nanoESI–MS/MS [23] r(SNF) Adenocarcinoma 2D-PAGE, MALDI–TOF–MS, Q-TOF–MS/MS [53] SCC 2D-DIGE, MALDI–TOF–TOF [60] SOD2 (Superoxide dismutase 2) Adenocarcinoma 2D-PAGE, MALDI–TOF–MS, Q-TOF–MS/MS [53] SCC 2D-DIGE, MALDI–TOF–TOF; 2D-PAGE, MALDI–TOF–MS [60,61] Heat shock protein HSP73 SCLC 2D-PAGE [35] HSP90 SCLC 2D-PAGE [35] HSP20-like NSCLC 2D-PAGE, MALDI–TOF–MS [42] HSP70 SCC 2D-PAGE, MALDI–TOF–MS [61] HSP60 SCC 2D-PAGE, MALDI–TOF–MS [59,61] HSP27 SCC iTRAQ, LC-Q-MS/MS [58] Annexin ANXA1 Adenocarcinoma 2D-PAGE, MALDI–TOF–MS, Q-TOF–MS/MS [53] ANXA2 SCC 2D-PAGE, MALDI–TOF–MS [61] ANXA3 SCC 2D-PAGE, MALDI–TOF–MS [59] ANXA5 SCC 2D-PAGE, MALDI–TOF–MS [59] Clin Transl Oncol (2013) 15:671–682 677 123 Table 1 continued Diagnostic biomarker Lung cancer type Proteomic technique ANXA6 SCC 2D-PAGE, MALDI–TOF–MS [59] ACTG1 (Gamma actin) SCLC 2D-PAGE, MALDI–TOF–MS [36] SCC 2D-PAGE, MALDI–TOF–MS [59] Thymosin b4 Lung cancer MALDI–TOF–MS [24] NSCLC MALDI–MS [46] b10 NSCLC MALDI–MS [46] CFL1 (Cofilin-1) Adenocarcinoma 2D-PAGE, ESI-Q-TOF–MS/MS [54] NSCLC non-small cell lung cancer, SCLC small cell lung cancer, SCC squamous cell carcinoma Table 2 Candidate proteomic diagnostic biomarkers for lung cancer identified in fluid samples Diagnostic biomarker Sample type Lung cancer type Proteomic technique CLU (Clusterin) Urine Adenocarcinoma 1D-PAGE, HPLC–MS/MS [20] Serum SCC 2D-DIGE, MALDI–TOF–MS [62] Gelsolin Urine Adenocarcinoma 1D-PAGE, HPLC–MS/MS [20] Serum NSCLC 2D-DIGE, MALDI–TOF–MS [49] Pleural effusion, plasma Adenocarcinoma iTRAQ, LC–MS/MS [56] LRG1 (leucine-rich alpha-2glycoprotein) Urine Adenocarcinoma 1D-PAGE, HPLC–MS/MS [20] Serum Lung cancer 2D-DIGE, LC–MS/MS [27] Urine NSCLC 1D-PAGE, nanoHPLC-MS/MS [50] SAA (Serum amyloid A) Serum Lung cancer SELDI–TOF–MS; MALDI–MS [30,32] NSCLC MALDI–TOF–MS [48] Adenocarcinoma 2D-PAGE, MALDI–TOF–PMF [55] SCC 2D-DIGE, MALDI–TOF–MS [62] Pleural effusion, serum Lung cancer 1D-PAGE, LC–ESI–MS/MS [31] HP (haptoglobin) Serum Lung cancer 2D-DIGE, LC–MS/MS; 2D-DIGE, MALDI–TOF–MS [27,29] SCLC SDS–PAGE, MALDI–TOF–MS [39] NSCLC 2D-DIGE, MALDI–TOF/TOF [47] Adenocarcinoma 2D-PAGE, MALDI–TOF–PMF [55] SCC 2D-DIGE, MALDI–TOF–MS [62] Plasma, serum Lung cancer 1D-PAGE, LC–ESI–MS/MS [28] Saliva Lung cancer 2D-DIGE, MALDI–TOF–MS, LC–MS/MS [21] TTR (Transthyretin) Serum Lung cancer SELDI–TOF–MS [33] Adenocarcinoma 2D-PAGE, MALDI–TOF–PMF [55] Complement component C4 Serum Lung cancer 2D-DIGE, LC–MS/MS [27] C3, C3c Lung cancer, SCC 2D-DIGE, LC–MS/MS [27]; 2D-DIGE, MALDI–TOF– MS [62] A1AT (a-1-antitrypsin) Serum Lung cancer 2D-DIGE, MALDI–TOF–MS [29] Pleural effusion, plasma Adenocarcinoma iTRAQ, LC–MS/MS [56] 678 Clin Transl Oncol (2013) 15:671–682 123 Table 2 continued Diagnostic biomarker Sample type Lung cancer type Proteomic technique Apolipoprotein A1 Serum Adenocarcinoma 2D-PAGE, MALDI–TOF–PMF [55] A4 SCC 2D-DIGE, MALDI–TOF–MS [62] CK8 (Cytokeratin 8) Pleural effusion, plasma Adenocarcinoma iTRAQ, LC–MS/MS [56] AHSG (alpha-2-HSglycoprotein) Pleural effusion Lung cancer 1D-PAGE, LC–ESI–MS/MS [34] Serum SCC 2D-DIGE, MALDI–TOF–MS [62] S100A8 and S100A9 Pleural effusion, serum NSCLC 2D-DIGE, MALDI–TOF–MS [49] SCCA1 (Squamous cell carcinoma antigen 1) Serum Lung cancer 2D-DIGE, MALDI–TOF–MS [29] Cystatin C3 Pleural effusion Lung cancer 1D-PAGE, LC–ESI–MS/MS [34] Pleural effusion, plasma Adenocarcinoma iTRAQ, LC–MS/MS [56] NSCLC non-small cell lung cancer, SCLC small cell lung cancer, SCC squamous cell carcinoma Table 3 Candidate proteomic prognostic biomarkers for lung cancer Prognostic biomarker Sample type Lung cancer type Proteomic technique S100A6 Tissue NSCLC MALDI–MS [46] Tissue, plasma, pleural effusion NSCLC SELDI–TOF–MS [65] Thymosin b4 Tissue NSCLC MALDI–MS; MALDI–TOF–MS [63,64] b10 NSCLC MALDI–MS; MALDI–TOF–MS [46,64] CFL1 (Cofilin-1) Tissue NSCLC MALDI–TOF–MS [64] Adenocarcinoma 2D-PAGE, ESI-Q-TOF–MS/MS [54] IDH1 (isocitrate dehydrogenase 1) Tissue SCC 2D-DIGE, MALDI–TOF–TOF [60] Cytokeratin CK7, CK8, CK18 Tissue Adenocarcinoma 2D-PAGE, MALDI–TOF–MS [66] CK19 Adenocarcinoma 2D-PAGE, MALDI–TOF–MS; 2D-PAGE [66,67] SCC 2D-DIGE, MALDI–TOF–PMF [72] Heat shock protein HSP70 Tissue Adenocarcinoma 2D-PAGE, MALDI–MS [51] HSP27 SCC 2D-DIGE, MALDI–TOF–PMF [72] Annexin ANXA1 Tissue Adenocarcinoma 2D-DIGE, MALDI–TOF–PMF [69] ANXA2 Adenocarcinoma 2D-DIGE, MALDI–TOF–PMF [69] SCC 2D-DIGE, MALDI–TOF–PMF [72] ANXA3 Adenocarcinoma 2D-DIGE, MALDI–TOF–MS; 2D-DIGE, MALDI–TOF– PMF [68,69] Myosin IIA Tissue Adenocarcinoma LC–MS/MS [70] Vimentin SNF (14-3-3 r) Tissue SCC 2D-PAGE, MALDI–TOF–MS; 2D-DIGE, MALDI–TOF– PMF [71,72] NSCLC non-small cell lung cancer, SCC squamous cell carcinoma Clin Transl Oncol (2013) 15:671–682 679 123 Predictive A good predictive biomarker can anticipate the efficacy of a specific treatment. It aims at individualising therapies in lung cancer and studies have been based on studying clinical samples from responding and non-responding patients and then validating results on selected cohorts. Proteomic studies developed with this purpose have focused on the response to EGFR inhibitors. Okano and co-workers analysed lung adenocarcinoma tissue from patients who showed a different response to gefitinib treatment by 2D-DIGE–LC–MS/MS. High levels of fatty acid-binding protein heart (H-FABP) were associated with partial and complete responses. This protein participates in intracellular lipid transport, storage, and metabolism, in addition to having a possible role in cancer biology [73]. In addition to this study, a serum MALDI– MS study conducted by Taguchi and co-workers in NSCLC patients treated with gefitinib and erlotinib revealed an 8-peak profile predictive of outcome [74]. This 8-peak signature was commercially launched as a commercial product (Veristrat Ò , Biodesix, Broom field, CO, USA) and has been used to discriminate NSCLC patients treated with combinations of erlotinib, gefitinib, bevacizumab, or cetuximab with good and poor prognosis in a number of studies [75–82]. The SAA1 protein was recently identified as part of this proteomic signature [82]. A summary of the abovementioned predictive biomarkers can be found on Table 4. Conclusions The detection of new biomarkers is of foremost importance in lung cancer research. The vast majority of lung cancers are still diagnosed at advanced stages of the disease, reducing in some cases the available therapeutic options to only palliative care, resulting in one of the lowest 5-year survival rates among cancers. The proteomic field is expanding and the development of high-throughput technologies has allowed the identification of several protein profiles with diagnostic, prognostic, and predictive value for lung cancer. Nonetheless, it is necessary to take into account several key points order to consider this proteins as biomarkers. First, an extensive clinical validation with strict statistical criteria is required to evaluate these profiles, to reduce the occurrence of false positive results. Second, large cohorts of carefully selected patients to determine the actual usefulness of the biomarkers are necessary. Third, it would be interesting to study the reproducibility of the obtained protein profiles and this requires a validation in different biological samples and different laboratories to prove their sensitivity. Finally, it is necessary to address the problems of heterogeneity to the lung cancer patients. 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SAA1 is overexpressed in plasma of non small cell lung cancer patients with poor outcome after treatment with epidermal growth factor receptor tyrosine-kinase inhibitors. J Proteomics. 2012. 682 Clin Transl Oncol (2013) 15:671–682 123 Int. J. Mol. Sci. 2013, 14, 3440-3455; doi:10.3390/ijms14023440 International Journal of Molecular Sciences ISSN 1422-0067 www.mdpi.com/journal/ijms Article Identification of Oxidative Stress Related Proteins as Biomarkers for Lung Cancer and Chronic Obstructive Pulmonary Disease in Bronchoalveolar Lavage Maria Dolores Pastor 1,†, Ana Nogal 1,†, Sonia Molina-Pinelo 1, Ricardo Meléndez 1, Beatriz Romero-Romero 2, Maria Dolores Mediano 1, Jose L. López-Campos 1,2, Rocío García-Carbonero 1,3, Amparo Sanchez-Gastaldo 1,3, Amancio Carnero 1,4 and Luis Paz-Ares 1,3,* 1 Biomedicine Institute of Seville, Seville 41013, Spain; E-Mails: [email protected] (M.D.P.); [email protected] (A.N.); [email protected] (S.M.-P.); ricardomelendezcaden[email protected] (R.M.); [email protected] (M.D.M.); josel.lopezcam[email protected] (J.L.L.-C.); [email protected] (R.G.-C.); [email protected] (A.S.-G.); acar[email protected] (A.C.) 2 Respiratory Diseases Medical and Surgical Unit of the Virgen del Rocío University Hospital, Seville 41013, Spain; E-Mail: [email protected] 3 Oncology Unit of the Virgen del Rocío University Hospital, Seville 41013, Spain 4 Superior Counsil of Scientific Research, Seville 41013, Spain † The authors contributed equally to this work. * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +34-955-013-414; Fax: +34-955-013-292. Received: 24 December 2012; in revised form: 23 January 2013 / Accepted: 31 January 2013 / Published: 6 February 2013 Abstract: Lung cancer (LC) and chronic obstructive pulmonary disease (COPD) commonly coexist in smokers, and the presence of COPD increases the risk of developing LC. Cigarette smoke causes oxidative stress and an inflammatory response in lung cells, which in turn may be involved in COPD and lung cancer development. The aim of this study was to identify differential proteomic profiles related to oxidative stress response that were potentially involved in these two pathological entities. Protein content was assessed in the bronchoalveolar lavage (BAL) of 60 patients classified in four groups: COPD, COPD and LC, LC, and control (neither COPD nor LC). Proteins were separated into spots by two dimensional polyacrylamide gel electrophoresis (2D-PAGE) and examined by OPEN ACCESS Int. J. Mol. Sci. 2013, 14 3441 matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF/TOF). A total of 16 oxidative stress regulatory proteins were differentially expressed in BAL samples from LC and/or COPD patients as compared with the control group. A distinct proteomic reactive oxygen species (ROS) protein signature emerged that characterized lung cancer and COPD. In conclusion, our findings highlight the role of the oxidative stress response proteins in the pathogenic pathways of both diseases, and provide new candidate biomarkers and predictive tools for LC and COPD diagnosis. Keywords: bronchoalveolar lavage; lung cancer; screening; biomarker; inflammation; proteomics; ROS; oxidative stress 1. Introduction Cigarette smoking has been recognized as the most important causative factor of COPD and it is associated with more than 90% of lung cancer cases [1]. Lung cancer accounts for 12% of all cancer diagnoses worldwide, making it the largest cause of cancer-associated death worldwide, accounting for more than one million casualties per year worldwide. COPD is also a major independent risk factor for lung carcinoma, among long-term smokers. In fact, the presence of COPD increases the risk of lung cancer up to 4.5-fold. Indeed, 50%–70% of patients diagnosed with lung cancer have spirometric evidence of COPD [2]. Cigarette smoke (CS) contains over 1014 free radicals per puff that include reactive oxygen species (ROS) [3]. Inhaled oxidants from smoke generate cellular damage by directly targeting proteins, lipids, and nucleic acids, and deplete the level of antioxidants in the lung, thereby overwhelming the oxidant/antioxidant balance of the lung, leading to increased oxidative stress [4]. ROS can lead to the activation of various cell signaling components. Examples include the extracellular signal regulated kinases (ERKs), c-jun N-terminal kinases (JNKs), p38 MAPKs, PKC, PI3K/Akt, and growth factor tyrosine kinases receptors pathways, all of which lead to increased inflammatory gene transcription. Indeed, oxidative stress in the lungs has been implicated in COPD severity and lung carcinogenesis [5]. This process is one of the mechanisms proposed in the common pathogenesis of lung cancer and COPD, along with inflammation, epithelial-mesenchymal transition (EMT), altered DNA repair, and cellular proliferation [6]. Proteins are important molecular signposts of oxidative damage. Different proteomic approaches have been developed and used for the detection and identification of ROS related proteins [7]. In the last few years, combined proteomics, mass spectrometry (MS), and affinity chemistry-based methodologies have contributed in a significant way to provide a better understanding of protein oxidative modifications occurring in various biological specimens under different physiological and pathological conditions. Bronchoalveolar lavage (BAL) is the clinical biofluid sampling of the soluble proteins contents of the airway lumen. A comparison between serum and BAL proteomes reveals that a certain number of proteins are present at a higher level in BAL than in plasma, suggesting that they are specifically produced in the airways. These proteins are, therefore, potential candidates for becoming lung-specific biomarkers [8]. 2D-PAGE is considered one of the best techniques for separation of complex mixtures of soluble proteins [9]. Several studies of BAL protein profiles obtained by 2D-PAGE analysis aimed at revealing the Int. J. Mol. Sci. 2013, 14 3442 differences between smokers and never smokers [10,11] as well as studies directed to determine the risk of developing COPD [12–14]. However, to the best of our knowledge, there are no 2D-PAGE studies in lung cancer using BAL. The 2D-PAGE studies of LC have been performed mainly in plasma and tissue. These analyses have focused on a better understanding of the molecular basis of cancer pathogenesis [15–17], as well as on the identification of new diagnostic, prognostic, and predictive markers for lung cancer [18,19]. In this regard, analyzing the protein composition of BAL mediated by a high-throughput technology, given its vicinity to tumor cells and enrichment in tumor-derived proteins, would be insightful. In this study, we have investigated the changes occurring in the proteome of BAL samples from lung cancer and/or COPD patients, and found a set of redox regulative proteins differentially expressed in each disease. 2. Results 2.1. Proteome Profiles of Comparison of LC and/or COPD The experiments were performed in BAL samples extracted from a cohort of 60 patients divided into four groups (control group and LC and/or COPD groups) whose characteristics are described in Table 1. The spots that showed significant increment or reduction of their expression compared to control group (neither COPD nor LC) were identified by MALDI-TOF/TOF-MS. The MS/MS data were acquired and compared to the Swiss-Prot database using MASCOT software. Candidate proteins were selected from each spot, taking into consideration several variables such as isoelectric point, molecular mass, matched peptides, and sequence coverage (Figure 1). A total of 123 protein spots were successfully identified. Of these, 40 proteins spots had consistent significant differences (>2-fold, p < 0.05) between lung cancer and/or COPD groups and the control group. Among them, a major group of 16 proteins were oxidative stress regulatory proteins (Table 2). The spots corresponding to this group of ROS regulatory proteins are marked on the representative gel 2D image in Figure 1. The rest of identified proteins were distributed in other variable groups such as inflammation, glycolysis and gluconeogenesis (Data not show). Table 1. Patients characteristics. Controls n = 15 COPD n = 15 LC n = 15 LC&COPD n = 15 Gender Male 100.0% (15) 100.0% (15) 100.0% (15) 100.0% (15) Female 0.0% (0) 0.0% (0) 0.0% (0) 0.0% (0) Average age (range) 61.3 (41–80) 61.5 (45–78) 60.7 (46–69) 60.7 (49–68) Smoking status Smokers 73.3% (11) 53.3% (8) 53.3% (8) 80.0% (12) Ex-smokers 26.7% (4) 46.7% (7) 46.7% (7) 20.0% (3) Packs-year 21.82 32.20 35.21 30.78 COPD Mild - 20.0% (3) - 53.3% (8) Moderate - 33.3% (5) - 26.7% (4) Severe - 26.7% (4) - - Very severe - 20.0% (3) - 20.0% (3) Histology Adenocarcinoma - - 73.3% (11) 66.7% (10) Squamous cell carcinoma - - 26.7% (4) 33.3% (5) Abbreviations: COPD: chronic obstructive pulmonary disease; LC: lung cancer.