- Email: [email protected]
Biomarkers for lung cancer
respiratory investigation 52 (2014) 277–278
Contents lists available at ScienceDirect
Respiratory Investigation journal homepage: www.elsevier.com/locate/resinv
Editorial
Biomarkers for lung cancer Low dose CT-scanning for lung cancer has identified many noncalcified nodules 44 mm considered to be false positives or malignant lesions. The prevalence of these nodules 44 mm ranges from 25% in the National Lung Cancer Screening Trial to 50% in the New York urban area studied by the NYU Lung Cancer Biomarker Center [1]. Biomarkers for the early detection of lung cancer have been a research priority for the National Cancer Institute since the formation of the Early Detection Research Network (EDRN) in 2000. This network has funded a series of biomarker discovery laboratory grants, and clinical validation centers for collecting human samples. The EDRN has been organized by organ site, e.g. lung, breast and women’s cancers, prostate, and gastrointestinal cancers. Workshops and executive committee meetings allow the Principal Investigators and researchers interested in biomarker discovery and testing to meet on a regular basis and develop collaborations. For lung cancer, there are a variety of tissues and samples that can be used with blood being one of the most accessible for serum, plasma, and RNA; urine; exhaled breath; sputum; brushings from buccal cavity, nasal epithelium, and bronchial epithelium [2–5]. Second, most assays take advantage of the ‘omics revolution and high-throughput technologies’. Third, biostatistical analyses and bioinformatics programs are necessary for data interpretation. Lastly, study design is critical beginning with pilot studies to small case: control studies, to larger sample sizes, to multi-site epidemiological studies to prospective cohorts and validation studies. Serum or plasma proteins can be assayed with proteomics platforms using various forms of mass spectrometry; more recently, multiple reaction monitoring (MRM) has identified the mass/charge peaks for specific proteins that can then be quantified with differences between cancer and control determined. Integrated Diagnostics has developed a panel of plasma proteins to differentiate malignant from benign nodules in the indeterminate range 8–30 mm in size. Out of 371 proteins identified from lung cancer tissue, cell lines, and the literature, they developed a profile of 13 proteins that could accurately separate lung cancer cases from controls [6]. Another biotechnology company, SomaLogic, developed nucleic acid aptamers that could bind specific proteins with high avidity [7]. They have developed more than 1200 aptamers for various serum proteins and have a high-throughput
platform to perform a million or more reactions in a week. In a case: control study with samples from four sites, they identified a panel of 12 proteins that could separate lung cancer from smoker controls with 88% accuracy and with an area under the curve of 0.91. Half of the proteins were increased and half were decreased. The presence of a benign nodule or emphysema did not affect the findings. Interestingly, they were related to cancer pathways in some regard, e. g. cell growth or metabolism, invasion, inflammation, oxidative stress, or anti-apoptosis. They were able to separate proteins involved in acute phase reactions to compensate for differences in blood draws and processing. Variant proteins may be a marker for non-small cell lung cancer detected by immunoblot [8]. A single protein has been utilized to follow lung nodules over time as they are followed to determine their growth rate. The rate of velocity of increase of osteopontin correlated with lung cancer versus benign nodule status [9]. We have found that screen detected lung cancers separate into three somewhat equal groups: prevalent cancers detected on initial screen, incident lung cancers appearing between periodic CT-screens, and indolent lung cancers that grow very slowly and after five or more years of followup, are still stage I adenocarcinomas upon pathologic evaluation of surgical resections [1]. The dilemma of small, slowgrowing indolent tumors could well use a blood biomarker to identify malignant nodules. Another approach is to utilize Affymetrix micro-arrays for gene expression (transcriptomics). Special tubes called PaxGene tubes can be used to preserve RNA at 80 1C and facilitate separation of peripheral blood mononuclear cells. Showe and colleagues were able to identify a 29-gene signature capable of separating lung cancer from smoker controls [10]. Furthermore, they identified microRNAs that influenced these genes, and categorized pathways into immune response, and cancer signaling pathways. Following surgical resection, the gene signature disappeared demonstrating the specificity of the findings [11]. Since lung cancer represents aberrant cellular functions and abnormal or changed proteins are secreted, the immune system reacts and auto-antibodies are common. Early studies evaluated autoantibodies against proteins related to cancer such as p53, but panels to cell cycle proteins, apoptosis-related proteins, oncogene products, invasion and signaling pathways have been productive. We previously reported that a panel of
http://dx.doi.org/10.1016/j.resinv.2014.06.002 2212-5345/$ - see front matter & 2014 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.
278
respiratory investigation 52 (2014) 277 –278
cell cycle autoantibodies, survivin, and an oncogene, c-myc, could distinguish lung cancer sera from smokers, and nonsmoker controls with a sensitivity of 81% and specificity of 97% [12]. Auto-antibodies against glycan structures may also be detected by high-throughput assays with 400 glycan structures printed on glass slides for a high-throughput assay. Glycan node structures or abnormal amounts of glycans can be detected by mass spectrometry. Abnormal methylation of promoters of tumor suppressor genes and other key cancer genes may silence them causing loss of function [13]. Cancer is characterized by global hypomethylation so hypermethylation of promoters is an unusual cancer-related abnormality that can be detected by bisulfite treatment and methylation-specific polymerase chain reaction (PCR) with primers specific for the promoter region. Once again, grouping a panel of genes for methylation-specific PCR produces a biomarker with greater accuracy. Noncoding RNAs such as microRNAs that are short 19–22 circulating RNAs in plasma exosomes can be detected with PCR and are mostly down-regulated in cancer [14]. There are panels of microRNAs that correlate with biomarkers for lung cancer with high accuracy [15]. The future of blood biomarkers is validation. Large surveys of noncalcified nodules in the range of 8–30 mm are needed with low-dose CT scans for biomarker correlation to determine if the nodule is malignant or benign.
references
[1] Greenberg AK, Lu F, Goldberg JD, et al. CT scan screening for lung cancer: risk factors for nodules and malignancy in a high-risk urban cohort. PLoS ONE 2012;7(7):e39403 (Epub 2012 Jul 2. PMID: 22768300). [2] Kim CE, Tchou-Wong KM, Rom WN. Sputum-based molecular biomarkers for the early detection of lung cancer: limitations and promise. Cancers 2011;3:2975–89. http://dx.doi.org/10.33890/cancers3032975. [3] Roy HK, Subramanian H, Damania D, et al. Biophotonic detection of buccal epithelial nano-architectural alterations in patients harboring lung cancer: implications for screening. Cancer Res 2010;70(20):7748–54. http://dx.doi.org/10.1158/ 0008-5472.CAN-10-1686. [4] Tuck MK, Chan DW, Chia D, et al. Standard operating procedures for serum and plasma collection: early detection research network consensus statement standard operating procedure integration working group. J Proteome Res 2008;8:113–7. [5] Phillips M, Brand DA, Cataneo RN, et al. Detection of lung cancer with volatile markers in the breath. Chest 2003;123:2115–23.
[6] Li X, Hayward C, Fong PY, et al. A systems biology-derived, blood-based proteomic classifier for the molecular characterization of pulmonary nodules. Sci Transl Med 2013;5:207ra142. [7] Ostroff RM, Bigbee WL, Franklin W, et al. Unlocking biomarker discovery: large scale application of aptamer proteomic technology for early detection of lung cancer. PLoS ONE 2010;5:e15003. http://dx.doi.org/10.137/journal/ pone.0015003. [8] Higgins G, Roper KM, Watson IJ, et al. Variant Ciz1 is a circulating biomarker for early-stage lung cancer. Proc Natl Acad Sci USA 2012;109(45):E3128–35. http://dx.doi.org/ 10.1073/pnas.1210107109. [9] Joseph S, Harrington R, Walter D, et al. Osteopontin velocity differentiates lung cancers from controls in a CT Screening population. Cancer Biomark 2013;12(4–5):177–84. http://dx. doi.org/10.3233/CBM-130306. [10] Showe MK, Vachani A, Kossenkov A, et al. Gene expression profiles in peripheral blood mononuclear cells can distinguish patients with non-small-cell lung cancer from patients with non-malignant lung disease. Cancer Res 2009;69:9202–10. http://dx.doi.org/10.1158/0008-5472.CAN09-1378. [11] Kossenkov A, Vachani A, Chang C, et al. Resection of nonsmall cell lung cancers reverses tumor-induced gene expression changes in the peripheral immune system. Clin Cancer Res 2011;17:5867–77. [12] Rom WN, Goldberg JD, Addrizzo-Harris D, et al. Identification of an autoantibody panel to separate lung cancer from smokers and nonsmokers. BMC Cancer 2010;10:234. [13] Ostrow K, Hoque M, Loyo M, et al. Molecular analysis of plasma DNA for the early detection of lung cancer by quantitative methylation specific PCR. Clin Cancer Res 2010;16(13):3463–72. [14] Cazzoli R, Buttita F, Nicola MD, et al. MicroRNAs derived from circulating exosomes as non-invasive biomarkers for screening and diagnosing lung cancer. J Thorac Oncol 2013;8 (9):1156–62. http://dx.doi.org/10.1097/JTO.0b013e318299ac32. [15] Boeri M, Verri C, Conte D, et al. MicroRNA signatures in tissues and plasma predict development and prognosis of computed tomography detected lung cancer. Proc Natl Acad Sci USA 2011;108(9):3713–8. http://dx.doi.org/10.1073/ pnas.1100048108 (Epub 2011 Feb7).
Sol and Judith Bergstein Professor of Medicine and Environmental Medicine William N. Rom, MD, MPH Department of Medicine, NYU School of Medicine, 550 1st Avenue, New York, NY 10016, United States Department of Environmental Medicine, NYU School of Medicine, 550 1st Avenue, New York, NY 10016, United States E-mail address: [email protected]
Contents lists available at ScienceDirect
Respiratory Investigation journal homepage: www.elsevier.com/locate/resinv
Editorial
Biomarkers for lung cancer Low dose CT-scanning for lung cancer has identified many noncalcified nodules 44 mm considered to be false positives or malignant lesions. The prevalence of these nodules 44 mm ranges from 25% in the National Lung Cancer Screening Trial to 50% in the New York urban area studied by the NYU Lung Cancer Biomarker Center [1]. Biomarkers for the early detection of lung cancer have been a research priority for the National Cancer Institute since the formation of the Early Detection Research Network (EDRN) in 2000. This network has funded a series of biomarker discovery laboratory grants, and clinical validation centers for collecting human samples. The EDRN has been organized by organ site, e.g. lung, breast and women’s cancers, prostate, and gastrointestinal cancers. Workshops and executive committee meetings allow the Principal Investigators and researchers interested in biomarker discovery and testing to meet on a regular basis and develop collaborations. For lung cancer, there are a variety of tissues and samples that can be used with blood being one of the most accessible for serum, plasma, and RNA; urine; exhaled breath; sputum; brushings from buccal cavity, nasal epithelium, and bronchial epithelium [2–5]. Second, most assays take advantage of the ‘omics revolution and high-throughput technologies’. Third, biostatistical analyses and bioinformatics programs are necessary for data interpretation. Lastly, study design is critical beginning with pilot studies to small case: control studies, to larger sample sizes, to multi-site epidemiological studies to prospective cohorts and validation studies. Serum or plasma proteins can be assayed with proteomics platforms using various forms of mass spectrometry; more recently, multiple reaction monitoring (MRM) has identified the mass/charge peaks for specific proteins that can then be quantified with differences between cancer and control determined. Integrated Diagnostics has developed a panel of plasma proteins to differentiate malignant from benign nodules in the indeterminate range 8–30 mm in size. Out of 371 proteins identified from lung cancer tissue, cell lines, and the literature, they developed a profile of 13 proteins that could accurately separate lung cancer cases from controls [6]. Another biotechnology company, SomaLogic, developed nucleic acid aptamers that could bind specific proteins with high avidity [7]. They have developed more than 1200 aptamers for various serum proteins and have a high-throughput
platform to perform a million or more reactions in a week. In a case: control study with samples from four sites, they identified a panel of 12 proteins that could separate lung cancer from smoker controls with 88% accuracy and with an area under the curve of 0.91. Half of the proteins were increased and half were decreased. The presence of a benign nodule or emphysema did not affect the findings. Interestingly, they were related to cancer pathways in some regard, e. g. cell growth or metabolism, invasion, inflammation, oxidative stress, or anti-apoptosis. They were able to separate proteins involved in acute phase reactions to compensate for differences in blood draws and processing. Variant proteins may be a marker for non-small cell lung cancer detected by immunoblot [8]. A single protein has been utilized to follow lung nodules over time as they are followed to determine their growth rate. The rate of velocity of increase of osteopontin correlated with lung cancer versus benign nodule status [9]. We have found that screen detected lung cancers separate into three somewhat equal groups: prevalent cancers detected on initial screen, incident lung cancers appearing between periodic CT-screens, and indolent lung cancers that grow very slowly and after five or more years of followup, are still stage I adenocarcinomas upon pathologic evaluation of surgical resections [1]. The dilemma of small, slowgrowing indolent tumors could well use a blood biomarker to identify malignant nodules. Another approach is to utilize Affymetrix micro-arrays for gene expression (transcriptomics). Special tubes called PaxGene tubes can be used to preserve RNA at 80 1C and facilitate separation of peripheral blood mononuclear cells. Showe and colleagues were able to identify a 29-gene signature capable of separating lung cancer from smoker controls [10]. Furthermore, they identified microRNAs that influenced these genes, and categorized pathways into immune response, and cancer signaling pathways. Following surgical resection, the gene signature disappeared demonstrating the specificity of the findings [11]. Since lung cancer represents aberrant cellular functions and abnormal or changed proteins are secreted, the immune system reacts and auto-antibodies are common. Early studies evaluated autoantibodies against proteins related to cancer such as p53, but panels to cell cycle proteins, apoptosis-related proteins, oncogene products, invasion and signaling pathways have been productive. We previously reported that a panel of
http://dx.doi.org/10.1016/j.resinv.2014.06.002 2212-5345/$ - see front matter & 2014 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.
278
respiratory investigation 52 (2014) 277 –278
cell cycle autoantibodies, survivin, and an oncogene, c-myc, could distinguish lung cancer sera from smokers, and nonsmoker controls with a sensitivity of 81% and specificity of 97% [12]. Auto-antibodies against glycan structures may also be detected by high-throughput assays with 400 glycan structures printed on glass slides for a high-throughput assay. Glycan node structures or abnormal amounts of glycans can be detected by mass spectrometry. Abnormal methylation of promoters of tumor suppressor genes and other key cancer genes may silence them causing loss of function [13]. Cancer is characterized by global hypomethylation so hypermethylation of promoters is an unusual cancer-related abnormality that can be detected by bisulfite treatment and methylation-specific polymerase chain reaction (PCR) with primers specific for the promoter region. Once again, grouping a panel of genes for methylation-specific PCR produces a biomarker with greater accuracy. Noncoding RNAs such as microRNAs that are short 19–22 circulating RNAs in plasma exosomes can be detected with PCR and are mostly down-regulated in cancer [14]. There are panels of microRNAs that correlate with biomarkers for lung cancer with high accuracy [15]. The future of blood biomarkers is validation. Large surveys of noncalcified nodules in the range of 8–30 mm are needed with low-dose CT scans for biomarker correlation to determine if the nodule is malignant or benign.
references
[1] Greenberg AK, Lu F, Goldberg JD, et al. CT scan screening for lung cancer: risk factors for nodules and malignancy in a high-risk urban cohort. PLoS ONE 2012;7(7):e39403 (Epub 2012 Jul 2. PMID: 22768300). [2] Kim CE, Tchou-Wong KM, Rom WN. Sputum-based molecular biomarkers for the early detection of lung cancer: limitations and promise. Cancers 2011;3:2975–89. http://dx.doi.org/10.33890/cancers3032975. [3] Roy HK, Subramanian H, Damania D, et al. Biophotonic detection of buccal epithelial nano-architectural alterations in patients harboring lung cancer: implications for screening. Cancer Res 2010;70(20):7748–54. http://dx.doi.org/10.1158/ 0008-5472.CAN-10-1686. [4] Tuck MK, Chan DW, Chia D, et al. Standard operating procedures for serum and plasma collection: early detection research network consensus statement standard operating procedure integration working group. J Proteome Res 2008;8:113–7. [5] Phillips M, Brand DA, Cataneo RN, et al. Detection of lung cancer with volatile markers in the breath. Chest 2003;123:2115–23.
[6] Li X, Hayward C, Fong PY, et al. A systems biology-derived, blood-based proteomic classifier for the molecular characterization of pulmonary nodules. Sci Transl Med 2013;5:207ra142. [7] Ostroff RM, Bigbee WL, Franklin W, et al. Unlocking biomarker discovery: large scale application of aptamer proteomic technology for early detection of lung cancer. PLoS ONE 2010;5:e15003. http://dx.doi.org/10.137/journal/ pone.0015003. [8] Higgins G, Roper KM, Watson IJ, et al. Variant Ciz1 is a circulating biomarker for early-stage lung cancer. Proc Natl Acad Sci USA 2012;109(45):E3128–35. http://dx.doi.org/ 10.1073/pnas.1210107109. [9] Joseph S, Harrington R, Walter D, et al. Osteopontin velocity differentiates lung cancers from controls in a CT Screening population. Cancer Biomark 2013;12(4–5):177–84. http://dx. doi.org/10.3233/CBM-130306. [10] Showe MK, Vachani A, Kossenkov A, et al. Gene expression profiles in peripheral blood mononuclear cells can distinguish patients with non-small-cell lung cancer from patients with non-malignant lung disease. Cancer Res 2009;69:9202–10. http://dx.doi.org/10.1158/0008-5472.CAN09-1378. [11] Kossenkov A, Vachani A, Chang C, et al. Resection of nonsmall cell lung cancers reverses tumor-induced gene expression changes in the peripheral immune system. Clin Cancer Res 2011;17:5867–77. [12] Rom WN, Goldberg JD, Addrizzo-Harris D, et al. Identification of an autoantibody panel to separate lung cancer from smokers and nonsmokers. BMC Cancer 2010;10:234. [13] Ostrow K, Hoque M, Loyo M, et al. Molecular analysis of plasma DNA for the early detection of lung cancer by quantitative methylation specific PCR. Clin Cancer Res 2010;16(13):3463–72. [14] Cazzoli R, Buttita F, Nicola MD, et al. MicroRNAs derived from circulating exosomes as non-invasive biomarkers for screening and diagnosing lung cancer. J Thorac Oncol 2013;8 (9):1156–62. http://dx.doi.org/10.1097/JTO.0b013e318299ac32. [15] Boeri M, Verri C, Conte D, et al. MicroRNA signatures in tissues and plasma predict development and prognosis of computed tomography detected lung cancer. Proc Natl Acad Sci USA 2011;108(9):3713–8. http://dx.doi.org/10.1073/ pnas.1100048108 (Epub 2011 Feb7).
Sol and Judith Bergstein Professor of Medicine and Environmental Medicine William N. Rom, MD, MPH Department of Medicine, NYU School of Medicine, 550 1st Avenue, New York, NY 10016, United States Department of Environmental Medicine, NYU School of Medicine, 550 1st Avenue, New York, NY 10016, United States E-mail address: [email protected]