Tumor Biol. DOI 10.1007/s13277-015-3243-3
RESEARCH ARTICLE
Identification of potential erythrocyte phospholipid fatty acid biomarkers of advanced lung adenocarcinoma, squamous cell lung carcinoma, and small cell lung cancer Patricia Sánchez-Rodríguez & Marina C. Rodríguez & Jesús Sánchez-Yagüe
Received: 9 December 2014 / Accepted: 9 February 2015 # International Society of Oncology and BioMarkers (ISOBM) 2015
Abstract New biomarkers for lung cancer would be valuable. Our aim was to analyze the fatty acid profiles of the main phospholipid species in erythrocytes from patients with advanced squamous cell lung carcinoma (SCC), lung adenocarcinoma (ADC), and small cell lung cancer (SCLC) and benign lung diseases (chronic obstructive pulmonary disease (COPD) and asthma) to determine the fatty acids that could be use as lung cancer markers. Twenty-eight, 18, 14, 16, and 15 patients with, respectively, SCC, ADC, SCLC, asthma, and COPD and 50 healthy subjects were enrolled in the study. Fatty acid profiles were investigated using gas chromatography/mass spectrometry followed by receiver operating characteristic (ROC) curve analysis. The fatty acid profiles changed significantly in the different pathologies analyzed. Based on the diagnostic yields and operating characteristics, the most significant fatty acids that might be used as biomarkers were as follows: ADC—arachidonic acid (20:4n6) in phosphatidylcholine and oleic acid (18:1n9) in phosphatidylethanolamine (PE); SCC—eicosapentaenoic acid (20:5n3) in PE and palmitic acid (16:0) in phosphatidylserine + phosphatidylinositol (PS+PI); SCLC—eicosadienoic acid (20:2n6) in PS+PI and lignoceric acid (24:0) in sphingomyelin. In conclusion, fatty acids from erythrocyte phospholipid species might serve as biomarkers in the diagnosis, and probably in other aspects related to clinical disease management, of ADC, SCC, and SCLC. Electronic supplementary material The online version of this article (doi:10.1007/s13277-015-3243-3) contains supplementary material, which is available to authorized users. P. Sánchez-Rodríguez : J. Sánchez-Yagüe (*) Department of Biochemistry and Molecular Biology, University of Salamanca, Salamanca, Spain e-mail: [email protected] M. C. Rodríguez Lung Diseases Service, Santísima Trinidad Foundation Hospital, Salamanca, Spain
Keywords Tumor markers . Squamous cell lung carcinoma . Lung adenocarcinoma . Small cell lung cancer . Fatty acids . Erythrocyte
Introduction In most parts of the world, lung cancer ranks top in both incidence and mortality. It is the leading cause of death among men in Spain [1] as well as in the USA in comparison with other types of malignancies, and the 5-year overall survival rate does not exceed 15–16 % [2]. Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the two most common histological subtypes, and they belong to a heterogeneous group of cancers called non-small cell lung carcinoma (NSCLC), which alone accounts for almost 75–80 % of lung cancers. The remaining 20–25 % of cases involves so-called small cell lung cancer (SCLC). It is now imperative not only to pursue novel therapies for the treatment of lung cancer but at the same time to seek other strategies such as biomarker development with a view to accurately monitoring clinical responses in patients. Currently, there are no validated biomarkers or blood tests for lung cancer. The collection of accessible specimens such as blood products would also avoid the necessity of tumor biopsies in order to classify lung cancer. The latest genomic, proteomic, metabolomic, and bioinformatic techniques and analyses are the most promising tools for the discovery of new substances or parameters that could be used as biomarkers in the diagnosis, treatment, and prognosis of neoplastic diseases in general and of lung cancer and its different types in particular. Metabolic profiling can adapt non-conventional technology to tumor marker research and has been used in pharmacological analysis and disease diagnosis [3]. Metabolomic studies of samples from lung cancer patients have generally
Tumor Biol.
employed techniques such as nuclear magnetic resonance [4], high-performance liquid chromatography/mass spectrometry [5], and gas chromatography/mass spectrometry (GC/MS) [5, 6]. Recently, plasma fatty acid metabolic profiling assessed by GC/MS has been used to detect biomarkers of hepatocellular [7] and pancreatic carcinomas [8]. Lipids, blood, and blood cells are linked to cancer development through at least three issues: the existence of alterations in lipid and fatty acid metabolism in cancer patients [9–11]; the fact that polyunsaturated fatty acids (PUFA) are critical targets in the complex metabolic stages that lead to, or are associated with, cancer, and finally the existence of changes in blood cells in malignant diseases [12–14], including lung cancer [15–18]. It has been reported that erythrocytes play a role in inflammatory disease and, by extension, in malignancy [19]. Moreover, hemostatic abnormalities have been reported in cancer [20]. Based on the above, we have previously reported that the analysis of erythrocyte and platelet fatty acid profiles might reveal different biomarker species for the diagnosis and/or management of lung cancer [21–23]. To build on these latter observations, and with the goal of uncovering fatty acids that might eventually be used as biomarkers of a specific type of lung cancer, in the present work, we analyzed the fatty acid profiles of specific phospholipid species in erythrocytes from patients with advanced lung ADC, SCC, and SCLC. As controls, healthy subjects and patients with benign lung diseases such as chronic obstructive pulmonary disease (COPD) and asthma were also enrolled in the study. Table 1
Subjects and methods Study design and eligibility The survey was approved by the institutional review boards, and participants were fully informed of the purpose of the present study and agreed to participate in it. This prospective study included 141 individuals: (1) 60 male and female patients (current smokers) with histologically confirmed primary NSCLC (28 squamous cell carcinomas, and 18 adenocarcinomas) or SCLC (14) according to the World Health Organization (WHO) classification; (2) 16 male and female patients with asthma (no history of smoking) recruited during a moderate exacerbation (Global Initiative for Asthma (GINA) guidelines), who underwent a complete clinical examination and chest X-ray to exclude the presence of concomitant acute illness such as pneumonia; (3) 15 current male and female COPD patients (smokers) recruited during a moderateto-severe exacerbation, whose medication (β2 agonists, anticholinergics, and inhaled corticosteroids) was suspended 12 h before the procedure; and (4) 50 healthy male and female volunteers as controls, whose age, body weight, blood lipids, blood pressure, and body mass index (BMI) were equivalent to those of the patient groups (Table 1) and whose smoking habits were similar to those of cancer or COPD patients (Table 2). No further control group of non-smokers was included in the study because in healthy subjects no differences depending on smoking are detected in erythrocyte lipids [22]. Regarding this, it has also been described that although smoking causes an increase in oxidative stress, the systemic
Characteristics of control subjects and patients
Variable
Controls (n=50)
COPD (n=15)
Asthma (n=16)
Adenocarcinoma (n=18)
Squamous cell carcinoma (n=28)
Small cell lung cancer (n=14)
Age (years) Weight (kg) Mean arterial pressure (mm Hg) Platelets (×103 mL) Erythrocytes (×106 mL) Total cholesterol (mg/dL) HDL-cholesterol (mg/dL) Triacylglycerols (mg/dL) Smoking history (pack-years) BMI (kg/m2) FEV1, % predicted FEV1/FVC ratio % Illness duration (years)
62±7 65±9 92±9
67±5 66±10 94±4
59±11 67±10 95±4
68±6 67±10 95±4
68±5 66±10 94±5
63±6 65±10 96±5
200±64 5.1±0.8 188±37 50±9 82±30 >19
240±68 4.7±0.9 195±30 52±11 88±24 >20
225±10 4.5±0.2 200±12 58±12 73±10 0
258±60 4.6±0.8 177±20 50±12 89±25 ≥20
260±60 4.7±0.9 190±29 53±10 83±18 ≥20
249±75 4.6±0.5 182±28 50±9 86±32 ≥20
25.3±4.1 87±6 84±6 NA
24.9±2.7 44±19 56±5 19±6
25.3±5.0 70±13 73±10 15±7
24.3±5.0 NA NA NA
25.1±4.9 NA NA NA
24.1±3.5 NA NA NA
Values are given as mean±SD BMI body mass index, FEV1 forced expiratory volume in 1 s, FVC forced vital capacity, NA not applicable, pack-years number of cigarettes smoked per day×number of years smoked/20, HDL high-density lipoprotein
Tumor Biol. Table 2 Changes in the fatty acid composition of phosphatidylcholine from erythrocytes in control subjects and benign diseases (COPD and asthma), lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung cancer patients Fatty acid
Controls
Benign
Adenocarcinoma
Squamous cell carcinoma
Small cell lung cancer
14:0 15:0 16:0 16:1n7
0.74±0.43 0.26±0.17 37.84±6.52 0.97±0.57
0.89±0.44 0.41±0.17c 39.97±6.31 0.92±0.37
0.72±0.16 0.40±0.08c 38.17±5.45 1.27±0.75
0.77±0.35 0.38±0.12c 37.42±7.49 1.22±0.67
0.70±0.41 0.41±0.11 34.95±4.80 1.23±0.40
17:0 17:1 18:0 18:1n9 18:2n6 20:0 20:1n9 20:2n6 20:4n6 20:5n3 21:0 22:0 22:1n9 22:2 22:4n6 22:5n3+24:0 22:6n3+24:1n9 SFA
0.55±0.21 0.21±0.15 14.50±2.19 20.99±3.48 20.46±3.18 0.28±0.26 0.68±0.93 0.19±0.31 2.88±1.02 0.29±0.24 1.12±0.66 0.18±0.23 0.45±0.39 0.07±0.27 0.15±0.20 0.52±0.36 1.15±0.61 55.60±7.45
0.80±0.27b 0.27±0.21 16.90±4.49e 24.57±5.58b 9.21±3.71a 0.33±0.34 1.66±0.94a 0.21±0.37 1.24±0.96a 0.33±0.34 0.75±0.56f 0.42±0.43 0.44±0.42 0.58±0.51a ND 1.09±0.82e 1.34±0.60 57.78±7.08f
0.78±0.16b 0.33±0.29 15.37±3.44 23.42±3.37f 13.08±4.56a, l 0.29.±0.20 0.95±0.41c, k 0.37±0.26f 1.15±0.78a 0.11±0.10d 0.52±0.32e 0.19±0.16 0.22±0.26 0.36±0.39c 0.11±0.24k 0.34±0.40k 0.94±0.67 56.68±5.76
0.72±0.19c 0.25±0.15 15.24±2.90 22.76±3.42f 16.81±6.08c, g, n 0.40±0.40 0.63±0.63g, m 0.35±0.58 1.52±1.13a 0.09±0.12b, k 0.83±0.43 0.26±0.36 0.39±0.55 0.16±0.24e, j 0.06±0.18c 0.45±0.68k 0.59±0.50a, g 52.05±6.56
0.79±0.17f 0.23±0.16 15.55±1.03 22.30±2.10 17.37±3.74i 0.25±0.31 0.70±0.39k 1.00±0.82d, k, o 1.07±0.49c 0.12±0.16 0.87±0.74 0.19±0.22 0.04±0.07e, l 0.25±0.17b ND 0.51±0.42 0.25±0.21c, i, m 53.88±4.53
MUFA PUFA Total UFA SFA/UFA UI
22.29±5.80 21.97±9.34 44.26±7.61 1.34±0.49 76.84±17.98
28.29±5.79a 13.13±3.97a 41.42±7.23f 1.45±0.37f 63.56±11.03
26.48±4.05e 16.78±5.66e, l 43.26±5.77 1.35±0.31 66.84±12.62
24.34±6.47l 19.47±6.60e, h 43.80±6.56 1.33±0.37 69.63±13.31
24.57±2.16l 21.55±3.55j 46.12±4.53 1.19±0.22 73.09±9.37
MUFA monounsaturated fatty acids, PUFA polyunsaturated fatty acids, SFA saturated fatty acids, UFA unsaturated fatty acids, UI unsaturation index, calculated as the sum of the percentage by weight of each fatty acid times the number of olefinic bonds, ND not detected a
P
RESEARCH ARTICLE
Identification of potential erythrocyte phospholipid fatty acid biomarkers of advanced lung adenocarcinoma, squamous cell lung carcinoma, and small cell lung cancer Patricia Sánchez-Rodríguez & Marina C. Rodríguez & Jesús Sánchez-Yagüe
Received: 9 December 2014 / Accepted: 9 February 2015 # International Society of Oncology and BioMarkers (ISOBM) 2015
Abstract New biomarkers for lung cancer would be valuable. Our aim was to analyze the fatty acid profiles of the main phospholipid species in erythrocytes from patients with advanced squamous cell lung carcinoma (SCC), lung adenocarcinoma (ADC), and small cell lung cancer (SCLC) and benign lung diseases (chronic obstructive pulmonary disease (COPD) and asthma) to determine the fatty acids that could be use as lung cancer markers. Twenty-eight, 18, 14, 16, and 15 patients with, respectively, SCC, ADC, SCLC, asthma, and COPD and 50 healthy subjects were enrolled in the study. Fatty acid profiles were investigated using gas chromatography/mass spectrometry followed by receiver operating characteristic (ROC) curve analysis. The fatty acid profiles changed significantly in the different pathologies analyzed. Based on the diagnostic yields and operating characteristics, the most significant fatty acids that might be used as biomarkers were as follows: ADC—arachidonic acid (20:4n6) in phosphatidylcholine and oleic acid (18:1n9) in phosphatidylethanolamine (PE); SCC—eicosapentaenoic acid (20:5n3) in PE and palmitic acid (16:0) in phosphatidylserine + phosphatidylinositol (PS+PI); SCLC—eicosadienoic acid (20:2n6) in PS+PI and lignoceric acid (24:0) in sphingomyelin. In conclusion, fatty acids from erythrocyte phospholipid species might serve as biomarkers in the diagnosis, and probably in other aspects related to clinical disease management, of ADC, SCC, and SCLC. Electronic supplementary material The online version of this article (doi:10.1007/s13277-015-3243-3) contains supplementary material, which is available to authorized users. P. Sánchez-Rodríguez : J. Sánchez-Yagüe (*) Department of Biochemistry and Molecular Biology, University of Salamanca, Salamanca, Spain e-mail: [email protected] M. C. Rodríguez Lung Diseases Service, Santísima Trinidad Foundation Hospital, Salamanca, Spain
Keywords Tumor markers . Squamous cell lung carcinoma . Lung adenocarcinoma . Small cell lung cancer . Fatty acids . Erythrocyte
Introduction In most parts of the world, lung cancer ranks top in both incidence and mortality. It is the leading cause of death among men in Spain [1] as well as in the USA in comparison with other types of malignancies, and the 5-year overall survival rate does not exceed 15–16 % [2]. Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the two most common histological subtypes, and they belong to a heterogeneous group of cancers called non-small cell lung carcinoma (NSCLC), which alone accounts for almost 75–80 % of lung cancers. The remaining 20–25 % of cases involves so-called small cell lung cancer (SCLC). It is now imperative not only to pursue novel therapies for the treatment of lung cancer but at the same time to seek other strategies such as biomarker development with a view to accurately monitoring clinical responses in patients. Currently, there are no validated biomarkers or blood tests for lung cancer. The collection of accessible specimens such as blood products would also avoid the necessity of tumor biopsies in order to classify lung cancer. The latest genomic, proteomic, metabolomic, and bioinformatic techniques and analyses are the most promising tools for the discovery of new substances or parameters that could be used as biomarkers in the diagnosis, treatment, and prognosis of neoplastic diseases in general and of lung cancer and its different types in particular. Metabolic profiling can adapt non-conventional technology to tumor marker research and has been used in pharmacological analysis and disease diagnosis [3]. Metabolomic studies of samples from lung cancer patients have generally
Tumor Biol.
employed techniques such as nuclear magnetic resonance [4], high-performance liquid chromatography/mass spectrometry [5], and gas chromatography/mass spectrometry (GC/MS) [5, 6]. Recently, plasma fatty acid metabolic profiling assessed by GC/MS has been used to detect biomarkers of hepatocellular [7] and pancreatic carcinomas [8]. Lipids, blood, and blood cells are linked to cancer development through at least three issues: the existence of alterations in lipid and fatty acid metabolism in cancer patients [9–11]; the fact that polyunsaturated fatty acids (PUFA) are critical targets in the complex metabolic stages that lead to, or are associated with, cancer, and finally the existence of changes in blood cells in malignant diseases [12–14], including lung cancer [15–18]. It has been reported that erythrocytes play a role in inflammatory disease and, by extension, in malignancy [19]. Moreover, hemostatic abnormalities have been reported in cancer [20]. Based on the above, we have previously reported that the analysis of erythrocyte and platelet fatty acid profiles might reveal different biomarker species for the diagnosis and/or management of lung cancer [21–23]. To build on these latter observations, and with the goal of uncovering fatty acids that might eventually be used as biomarkers of a specific type of lung cancer, in the present work, we analyzed the fatty acid profiles of specific phospholipid species in erythrocytes from patients with advanced lung ADC, SCC, and SCLC. As controls, healthy subjects and patients with benign lung diseases such as chronic obstructive pulmonary disease (COPD) and asthma were also enrolled in the study. Table 1
Subjects and methods Study design and eligibility The survey was approved by the institutional review boards, and participants were fully informed of the purpose of the present study and agreed to participate in it. This prospective study included 141 individuals: (1) 60 male and female patients (current smokers) with histologically confirmed primary NSCLC (28 squamous cell carcinomas, and 18 adenocarcinomas) or SCLC (14) according to the World Health Organization (WHO) classification; (2) 16 male and female patients with asthma (no history of smoking) recruited during a moderate exacerbation (Global Initiative for Asthma (GINA) guidelines), who underwent a complete clinical examination and chest X-ray to exclude the presence of concomitant acute illness such as pneumonia; (3) 15 current male and female COPD patients (smokers) recruited during a moderateto-severe exacerbation, whose medication (β2 agonists, anticholinergics, and inhaled corticosteroids) was suspended 12 h before the procedure; and (4) 50 healthy male and female volunteers as controls, whose age, body weight, blood lipids, blood pressure, and body mass index (BMI) were equivalent to those of the patient groups (Table 1) and whose smoking habits were similar to those of cancer or COPD patients (Table 2). No further control group of non-smokers was included in the study because in healthy subjects no differences depending on smoking are detected in erythrocyte lipids [22]. Regarding this, it has also been described that although smoking causes an increase in oxidative stress, the systemic
Characteristics of control subjects and patients
Variable
Controls (n=50)
COPD (n=15)
Asthma (n=16)
Adenocarcinoma (n=18)
Squamous cell carcinoma (n=28)
Small cell lung cancer (n=14)
Age (years) Weight (kg) Mean arterial pressure (mm Hg) Platelets (×103 mL) Erythrocytes (×106 mL) Total cholesterol (mg/dL) HDL-cholesterol (mg/dL) Triacylglycerols (mg/dL) Smoking history (pack-years) BMI (kg/m2) FEV1, % predicted FEV1/FVC ratio % Illness duration (years)
62±7 65±9 92±9
67±5 66±10 94±4
59±11 67±10 95±4
68±6 67±10 95±4
68±5 66±10 94±5
63±6 65±10 96±5
200±64 5.1±0.8 188±37 50±9 82±30 >19
240±68 4.7±0.9 195±30 52±11 88±24 >20
225±10 4.5±0.2 200±12 58±12 73±10 0
258±60 4.6±0.8 177±20 50±12 89±25 ≥20
260±60 4.7±0.9 190±29 53±10 83±18 ≥20
249±75 4.6±0.5 182±28 50±9 86±32 ≥20
25.3±4.1 87±6 84±6 NA
24.9±2.7 44±19 56±5 19±6
25.3±5.0 70±13 73±10 15±7
24.3±5.0 NA NA NA
25.1±4.9 NA NA NA
24.1±3.5 NA NA NA
Values are given as mean±SD BMI body mass index, FEV1 forced expiratory volume in 1 s, FVC forced vital capacity, NA not applicable, pack-years number of cigarettes smoked per day×number of years smoked/20, HDL high-density lipoprotein
Tumor Biol. Table 2 Changes in the fatty acid composition of phosphatidylcholine from erythrocytes in control subjects and benign diseases (COPD and asthma), lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung cancer patients Fatty acid
Controls
Benign
Adenocarcinoma
Squamous cell carcinoma
Small cell lung cancer
14:0 15:0 16:0 16:1n7
0.74±0.43 0.26±0.17 37.84±6.52 0.97±0.57
0.89±0.44 0.41±0.17c 39.97±6.31 0.92±0.37
0.72±0.16 0.40±0.08c 38.17±5.45 1.27±0.75
0.77±0.35 0.38±0.12c 37.42±7.49 1.22±0.67
0.70±0.41 0.41±0.11 34.95±4.80 1.23±0.40
17:0 17:1 18:0 18:1n9 18:2n6 20:0 20:1n9 20:2n6 20:4n6 20:5n3 21:0 22:0 22:1n9 22:2 22:4n6 22:5n3+24:0 22:6n3+24:1n9 SFA
0.55±0.21 0.21±0.15 14.50±2.19 20.99±3.48 20.46±3.18 0.28±0.26 0.68±0.93 0.19±0.31 2.88±1.02 0.29±0.24 1.12±0.66 0.18±0.23 0.45±0.39 0.07±0.27 0.15±0.20 0.52±0.36 1.15±0.61 55.60±7.45
0.80±0.27b 0.27±0.21 16.90±4.49e 24.57±5.58b 9.21±3.71a 0.33±0.34 1.66±0.94a 0.21±0.37 1.24±0.96a 0.33±0.34 0.75±0.56f 0.42±0.43 0.44±0.42 0.58±0.51a ND 1.09±0.82e 1.34±0.60 57.78±7.08f
0.78±0.16b 0.33±0.29 15.37±3.44 23.42±3.37f 13.08±4.56a, l 0.29.±0.20 0.95±0.41c, k 0.37±0.26f 1.15±0.78a 0.11±0.10d 0.52±0.32e 0.19±0.16 0.22±0.26 0.36±0.39c 0.11±0.24k 0.34±0.40k 0.94±0.67 56.68±5.76
0.72±0.19c 0.25±0.15 15.24±2.90 22.76±3.42f 16.81±6.08c, g, n 0.40±0.40 0.63±0.63g, m 0.35±0.58 1.52±1.13a 0.09±0.12b, k 0.83±0.43 0.26±0.36 0.39±0.55 0.16±0.24e, j 0.06±0.18c 0.45±0.68k 0.59±0.50a, g 52.05±6.56
0.79±0.17f 0.23±0.16 15.55±1.03 22.30±2.10 17.37±3.74i 0.25±0.31 0.70±0.39k 1.00±0.82d, k, o 1.07±0.49c 0.12±0.16 0.87±0.74 0.19±0.22 0.04±0.07e, l 0.25±0.17b ND 0.51±0.42 0.25±0.21c, i, m 53.88±4.53
MUFA PUFA Total UFA SFA/UFA UI
22.29±5.80 21.97±9.34 44.26±7.61 1.34±0.49 76.84±17.98
28.29±5.79a 13.13±3.97a 41.42±7.23f 1.45±0.37f 63.56±11.03
26.48±4.05e 16.78±5.66e, l 43.26±5.77 1.35±0.31 66.84±12.62
24.34±6.47l 19.47±6.60e, h 43.80±6.56 1.33±0.37 69.63±13.31
24.57±2.16l 21.55±3.55j 46.12±4.53 1.19±0.22 73.09±9.37
MUFA monounsaturated fatty acids, PUFA polyunsaturated fatty acids, SFA saturated fatty acids, UFA unsaturated fatty acids, UI unsaturation index, calculated as the sum of the percentage by weight of each fatty acid times the number of olefinic bonds, ND not detected a
P
