Usefulness of Blood Biomarkers for Overall Survival in NSCLC

NCT ID: NCT01936571

Last Updated: 2014-04-11

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 250 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2013-09-30
• Study Completion Date: 2014-03-31

Brief Summary

Lung cancer is the most common cancer type worldwide, with more than 1.1 million annual deaths. There are two types of the disease, namely non-small cell lung cancer (NSCLC) and small-cell lung cancer (SCLC), with the first accounting for 85% of the total number of cases. The 5-year survival across stages remains disappointingly low, around 10% in most countries, due to a high incidence of both loco-regional and distant failure [3]. However, during the last decade improved radiotherapy techniques allowed an increase of the radiation dose, while at the same time more effective chemo radiation schemes are being applied. These developments have lead to improved outcome in terms of survival. As the TNM staging system is highly inaccurate for the prediction of survival outcome for non-surgical patients, attempts have been made to develop a more accurate risk stratification for these patients [1,2]. A model based on clinical variables yielded an AUC of 0.74, which was encouraging, but also left room for improvement [2]. An extended model, which included clinical as well as biomarker variables, reached a higher AUC, but the limited number of patients included in this study made it impossible to draw definitive conclusions [1].

New prognostic parameters can be retrieved from several sources, which include anatomic, molecular and functional imaging, genomics, proteomics and clinical analysis of patients. The unlimited amount of information is expected to lead to more accurate predictions of individual treatment outcome [4].

The analysis of biomarkers, including proteins, is a fast developing, promising and challenging area of research. Biomarkers can measure or evaluate normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention. Oncoproteins are produced by, or in response to tumor cells, and may be secreted in the circulation of patients. As tissue sampling is often not possible in lung cancer patients, blood sample collection by venepuncture offers an attractive alternative, which is safe and easy to implement. A number of studies described the prognostic and predictive value of blood biomarkers for NSCLC [5-7]. In this study we will investigate the prognostic value of blood biomarkers related to 1) hypoxia: Osteopontin (OPN), carbonic anhydrase IX (CA-9), and lactate dehydrogenase (LDH); 2) inflammation - interleukin 6 (IL-6), IL-8, and C-reactive protein (CRP), and α-2-macroglobulin (α-2M); and 3) tumor load: Carcinoembryonic antigen (CEA) and cytokeratin fragment (CYFRA 21-1).

1. Dehing-Oberije C, Aerts H, Yu S, De Ruysscher D, Menheere P, Hilvo M, et al. Development and validation of a prognostic model using blood biomarker information for prediction of survival of non-small-cell lung cancer patients treated with combined chemotherapy and radiation or radiotherapy alone (NCT00181519, NCT00573040, and NCT00572325). Int J Radiat Oncol Biol Phys. 2011 Oct 1;81(2):360-368.

2. Dehing-Oberije C, Yu S, De Ruysscher D, Meersschout S, Van Beek K, Lievens Y, et al. Development and external validation of prognostic model for 2-year survival of non-small-cell lung cancer patients treated with chemoradiotherapy. Int J Radiat Oncol Biol Phys. 2009 Jun 1;74(2):355-362.

3. Travis WD, Brambilla E, Müller-Hermelink HK, Harris CC. World Health Organization Classification of Tumours: Pathology and Genetics of Tumours of the Lung, Pleura, Thymus and Heart. Paul Kleihues MD, Leslie H. Sobin MD, editors. Lyon, France: IARC Press, International Agency for Research on Cancer; 2004.

4. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012 Mar;48(4):441-446.

5. Donati V, Boldrini L, Dell'Omodarme M, Prati MC, Faviana P, Camacci T, et al. Osteopontin expression and prognostic significance in non-small cell lung cancer. Clin Cancer Res. 2005 Sep 15;11(18):6459-6465.

6. Muley T, Fetz TH, Dienemann H, Hoffmann H, Herth FJ, Meister M, et al. Tumor volume and tumor marker index based on CYFRA 21-1 and CEA are strong prognostic factors in operated early stage NSCLC. Lung Cancer. 2008 Jun;60(3):408-415.

7. Pine SR, Mechanic LE, Enewold L, Chaturvedi AK, Katki HA, Zheng YL, et al. Increased levels of circulating interleukin 6, interleukin 8, C-reactive protein, and risk of lung cancer. J Natl Cancer Inst. 2011 Jul 20;103(14):1112-1122.

Detailed Description

Lung cancer is the most common cancer type worldwide, with more than 1.1 million annual deaths. There are two types of the disease, namely non-small cell lung cancer (NSCLC) and small-cell lung cancer (SCLC), with the first accounting for 85% of the total number of cases. The 5-year survival across stages remains disappointingly low, around 10% in most countries, due to a high incidence of both loco-regional and distant failure [3]. However, during the last decade improved radiotherapy techniques allowed an increase of the radiation dose, while at the same time more effective chemo radiation schemes are being applied. These developments have lead to improved outcome in terms of survival. As the TNM staging system is highly inaccurate for the prediction of survival outcome for non-surgical patients, attempts have been made to develop a more accurate risk stratification for these patients [1,2]. A model based on clinical variables yielded an AUC of 0.74, which was encouraging, but also left room for improvement [2]. An extended model, which included clinical as well as biomarker variables, reached a higher AUC, but the limited number of patients included in this study made it impossible to draw definitive conclusions [1].

New prognostic parameters can be retrieved from several sources, which include anatomic, molecular and functional imaging, genomics, proteomics and clinical analysis of patients. The unlimited amount of information is expected to lead to more accurate predictions of individual treatment outcome [4].

The analysis of biomarkers, including proteins, is a fast developing, promising and challenging area of research. Biomarkers can measure or evaluate normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention. Oncoproteins are produced by, or in response to tumor cells, and may be secreted in the circulation of patients. As tissue sampling is often not possible in lung cancer patients, blood sample collection by venepuncture offers an attractive alternative, which is safe and easy to implement. A number of studies described the prognostic and predictive value of blood biomarkers for NSCLC [5-7]. In this study we will investigate the prognostic value of blood biomarkers related to 1) hypoxia: Osteopontin (OPN), carbonic anhydrase IX (CA-9), and lactate dehydrogenase (LDH); 2) inflammation - interleukin 6 (IL-6), IL-8, and C-reactive protein (CRP), and α-2-macroglobulin (α-2M); and 3) tumor load: Carcinoembryonic antigen (CEA) and cytokeratin fragment (CYFRA 21-1).

1. Dehing-Oberije C, Aerts H, Yu S, De Ruysscher D, Menheere P, Hilvo M, et al. Development and validation of a prognostic model using blood biomarker information for prediction of survival of non-small-cell lung cancer patients treated with combined chemotherapy and radiation or radiotherapy alone (NCT00181519, NCT00573040, and NCT00572325). Int J Radiat Oncol Biol Phys. 2011 Oct 1;81(2):360-368.

2. Dehing-Oberije C, Yu S, De Ruysscher D, Meersschout S, Van Beek K, Lievens Y, et al. Development and external validation of prognostic model for 2-year survival of non-small-cell lung cancer patients treated with chemoradiotherapy. Int J Radiat Oncol Biol Phys. 2009 Jun 1;74(2):355-362.

3. Travis WD, Brambilla E, Müller-Hermelink HK, Harris CC. World Health Organization Classification of Tumours: Pathology and Genetics of Tumours of the Lung, Pleura, Thymus and Heart. Paul Kleihues MD, Leslie H. Sobin MD, editors. Lyon, France: IARC Press, International Agency for Research on Cancer; 2004.

4. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012 Mar;48(4):441-446.

5. Donati V, Boldrini L, Dell'Omodarme M, Prati MC, Faviana P, Camacci T, et al. Osteopontin expression and prognostic significance in non-small cell lung cancer. Clin Cancer Res. 2005 Sep 15;11(18):6459-6465.

6. Muley T, Fetz TH, Dienemann H, Hoffmann H, Herth FJ, Meister M, et al. Tumor volume and tumor marker index based on CYFRA 21-1 and CEA are strong prognostic factors in operated early stage NSCLC. Lung Cancer. 2008 Jun;60(3):408-415.

7. Pine SR, Mechanic LE, Enewold L, Chaturvedi AK, Katki HA, Zheng YL, et al. Increased levels of circulating interleukin 6, interleukin 8, C-reactive protein, and risk of lung cancer. J Natl Cancer Inst. 2011 Jul 20;103(14):1112-1122.

The investigators hypothesize that:

• Higher levels of blood biomarkers are associated with worse survival
• The biomarker information will improve the performance of prediction models, that were previously developed and validated [1, 2]
• Subgroups of patients can be identified that benefit most in terms of a more accurate prediction of survival when using biomarker information

Measurement procedure: Blood samples, that were collected, processed and stored in the Maastro biobank in a standardized way, will be used to measure CRP, LDH, Osteopontin, CA-9 IL-6, IL-8, CEA, CYFRA 21-1, and α-2M. Clinical data will be retrieved from the electronic medical files.

Conditions

Non-small Cell Lung Cancer

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

NSCLC

The cohort consists of approximately 250 patients. As a rule of thumb 5-10 events per variable are needed to avoid overfitting a model. To model 6 clinical variables + 9 biomarker variables 75-150 events are needed. Assuming a two-year survival of 40%, the calculated (constant) hazard rate is 0.46 per year. With an inclusion rate of 50 patients per year, and a follow-up time varying between 0.5 and 4 year, at the time of analysis (November/December 2013) it is expected that there will be 138 events available for analysis.

Blood samples

Intervention Type: OTHER

Blood samples, that were collected, processed and stored in the Maastro biobank in a standardized way, will be used to measure CRP, LDH, Osteopontin, CA-9 IL-6, IL-8, CEA, CYFRA 21-1, and α-2M. Clinical data will be retrieved from the electronic medical files.

Interventions

Blood samples

Blood samples, that were collected, processed and stored in the Maastro biobank in a standardized way, will be used to measure CRP, LDH, Osteopontin, CA-9 IL-6, IL-8, CEA, CYFRA 21-1, and α-2M. Clinical data will be retrieved from the electronic medical files.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

The cohort consists of approximately 250 patients. As a rule of thumb 5-10 events per variable are needed to avoid overfitting a model. To model 6 clinical variables + 9 biomarker variables 75-150 events are needed. Assuming a two-year survival of 40%, the calculated (constant) hazard rate is 0.46 per year. With an inclusion rate of 50 patients per year, and a follow-up time varying between 0.5 and 4 year, at the time of analysis (November/December 2013) it is expected that there will be 138 events available for analysis.

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Maastricht Radiation Oncology

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Cary Oberije, PhD

Role: PRINCIPAL_INVESTIGATOR

Maastro Clinic, The Netherlands

Locations

MAASTRO clinic

Maastricht, Limburg, Netherlands

Countries

Netherlands

Other Identifiers

Blood Biomarkers

Identifier Type: -

Identifier Source: org_study_id