Validation of the SHOX2/PTGER4 DNA Methylation Marker Panel for Plasma-Based Discrimination between Patients with Malignant and Nonmalignant Lung Disease

Gunter Weiss, Anne Schlegel, Denise Kottwitz, Thomas König, Reimo Tetzner, Gunter Weiss, Anne Schlegel, Denise Kottwitz, Thomas König, Reimo Tetzner

Abstract

Introduction: Low-dose computed tomography (LDCT) is used for screening for lung cancer (LC) in high-risk patients in the United States. The definition of high risk and the impact of frequent false-positive results of low-dose computed tomography remains a challenge. DNA methylation biomarkers are valuable noninvasive diagnostic tools for cancer detection. This study reports on the evaluation of methylation markers in plasma DNA for LC detection and discrimination of malignant from nonmalignant lung disease.

Methods: Circulating DNA was extracted from 3.5-mL plasma samples, treated with bisulfite using a commercially available kit, purified, and assayed by real-time polymerase chain reaction for assessment of DNA methylation of short stature homeobox 2 gene (SHOX2), prostaglandin E receptor 4 gene (PTGER4), and forkhead box L2 gene (FOXL2). In three independent case-control studies these assays were evaluated and optimized. The resultant assay, a triplex polymerase chain reaction combining SHOX2, PTGER4, and the reference gene actin, beta gene (ACTB), was validated using plasma from patients with and without malignant disease.

Results: A panel of SHOX2 and PTGER4 provided promising results in three independent case-control studies examining a total of 330 plasma specimens (area under the receiver operating characteristic curve = 91%-98%). A validation study with 172 patient samples demonstrated significant discriminatory performance in distinguishing patients with LC from subjects without malignancy (area under the curve = 0.88). At a fixed specificity of 90%, sensitivity for LC was 67%; at a fixed sensitivity of 90%, specificity was 73%.

Conclusions: Measurement of SHOX2 and PTGER4 methylation in plasma DNA allowed detection of LC and differentiation of nonmalignant diseases. Development of a diagnostic test based on this panel may provide clinical utility in combination with current imaging techniques to improve LC risk stratification.

Keywords: Circulating tumor DNA; DNA methylation; Liquid biopsy; Lung cancer early detection; PTGER4; SHOX2.

Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1
Figure 1
Sample disposition, study setup, and polymerase chain reaction (PCR) assay formats. Boxes in the bottom line indicate number of valid results, number of PCR replicates, and bisDNA input volume (in parenthesis) per PCR assay. For more details, see Materials and Methods section. ACTB, actin, beta gene; FOXL2, forkhead box L2 gene; PTGER4, prostaglandin E receptor 4 gene; SHOX2, short stature homeobox 2 gene.
Figure 2
Figure 2
Receiver operating characteristic and area under the curve (AUC) analysis of pilot study 1 (AUC = 0.98) (A), study 2 (AUC = 0.91) (B), and study 3 (AUC = 0.95) (C).
Figure 3
Figure 3
Receiver operating characteristic and area under the curve (AUC) analysis of validation study: (A) Lung cancer (LC) versus all controls for training (AUC = 0.93) and validation study (AUC = 0.88), (B) LC versus nonmalignant disease (AUC = 0.86), (C) LC versus healthy controls (AUC = 0.91), (D) nonmalignant disease versus healthy controls (AUC = 0.58).
Figure 4
Figure 4
Comparison of protein (area under the curve [AUC] = 0.79) and methylation (AUC = 0.91) marker panel. The difference in the AUCs was statistically significant (p value = 0.004).

References

    1. Ferlay J., Soerjomataram I., Dikshit R. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136:E359–E386.
    1. Aberle D.R., Adams A.M., Berg C.D. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365:395–409.
    1. US Centers for Disease Control and Prevention. Lung cancer screening guidelines and recommendations. . Accessed May 5, 2016.
    1. Aberle D.R., DeMello S., Berg C.D. Results of the two incidence screenings in the National Lung Screening Trial. N Engl J Med. 2013;369:920–931.
    1. van Klaveren R.J., Oudkerk M., Prokop M. Management of lung nodules detected by volume CT scanning. N Engl J Med. 2009;361:2221–2229.
    1. Horeweg N., Scholten E.T., de Jong P.A. Detection of lung cancer through low-dose CT screening (NELSON): a prespecified analysis of screening test performance and interval cancers. Lancet Oncol. 2014;15:1342–1350.
    1. Tammemägi M.C., Katki H.A., Hocking W.G. Selection criteria for lung-cancer screening. N Engl J Med. 2013;368:728–736.
    1. Marcus M.W., Chen Y., Raji O.Y. LLPi: Liverpool Lung Project risk prediction model for lung cancer incidence. Cancer Prev Res (Phila) 2015;8:570–575.
    1. McKee B.J., Regis S.M., McKee A.B. Performance of ACR Lung-RADS in a clinical CT lung screening program. J Am Coll Radiol. 2016;(suppl 2):R25–R29.
    1. Manos D., Seely J.M., Taylor J. The Lung Reporting and Data System (LU-RADS): a proposal for computed tomography screening. Can Assoc Radiol J. 2014;65:121–134.
    1. McWilliams A., Tammemagi M.C., Mayo J.R. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med. 2013;369:910–919.
    1. Hasan N., Kumar R., Kavuru M.S. Lung cancer screening beyond low-dose computed tomography: the role of novel biomarkers. Lung. 2014;192:639–648.
    1. Sharma S., Kelly T.K., Jones P.A. Epigenetics in cancer. Carcinogenesis. 2010;31:27–36.
    1. Shivapurkar N., Gazdar A.F. DNA methylation based biomarkers in non-invasive cancer screening. Curr Mol Med. 2010;10:123–132.
    1. Food and Drug Administration. Department of Health and Human Services. Letter confirming approval of application for premarket approval of Colorguard. . Accessed May 24, 2016.
    1. Food and Drug Administration. Department of Health and Human Services. Letter confirming approval of application for premarket approval of Epi proColon test. . Accessed May 24, 2016.
    1. Ahlquist D.A., Zou H., Domanico M. Next-generation stool DNA test accurately detects colorectal cancer and large adenomas. Gastroenterology. 2012;142(2):248–256.
    1. Lidgard G.P., Domanico M.J., Bruinsma J.J. Clinical performance of an automated stool DNA assay for detection of colorectal neoplasia. Clin Gastroenterol Hepatol. 2013;11:1313–1318.
    1. Church T.R., Wandell M., Lofton-Day C. Prospective evaluation of methylated SEPT9 in plasma for detection of asymptomatic colorectal cancer. Gut. 2014;63:317–325.
    1. Imperiale T.F., Ransohoff D.F., Itzkowitz S.H. Multitarget stool DNA testing for colorectal-cancer screening. N Engl J Med. 2014;370:1287–1297.
    1. Potter N.T., Hurban P., White M.N. Validation of a real-time PCR-based qualitative assay for the detection of methylated SEPT9 DNA in human plasma. Clin Chem. 2014;60:1183–1191.
    1. Johnson D.A., Barclay R.L., Mergener K. Plasma Septin9 versus fecal immunochemical testing for colorectal cancer screening: a prospective multicenter study. PLoS One. 2014;9:e98238.
    1. Schmidt B., Liebenberg V., Dietrich D. SHOX2 DNA methylation is a biomarker for the diagnosis of lung cancer based on bronchial aspirates. BMC Cancer. 2010;10:600.
    1. Kneip C., Schmidt B., Seegebarth A. SHOX2 DNA methylation is a biomarker for the diagnosis of lung cancer in plasma. J Thorac Oncol. 2011;6:1632–1638.
    1. Ilse P., Biesterfeld S., Pomjanski N. SHOX2 DNA methylation is a tumour marker in pleural effusions. Cancer Genomics Proteomics. 2013;10:217–223.
    1. Darwiche K., Zarogoulidis P., Baehner K. Assessment of SHOX2 methylation in EBUS-TBNA specimen improves accuracy in lung cancer staging. Ann Oncol. 2013;24:2866–2870.
    1. Schmidt B., Beyer J., Dietrich D. Quantification of cell-free mSHOX2 plasma DNA for therapy monitoring in advanced stage non-small cell (NSCLC) and small-cell lung cancer (SCLC) patients. PLoS One. 2015;10:e0118195.
    1. Instructions for Use Epi proColon 2.0 CE. . Accessed May 24, 2016.
    1. Cottrell S.E., Distler J., Goodman N.S. A real-time PCR assay for DNA-methylation using methylation-specific blockers. Nucleic Acids Res. 2004;32:e10.
    1. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. . Accessed May 24, 2016.
    1. Molina R., Auge J.M., Escudero J.M. CA 125, CA 19.9, CA 15.3 and TAG-72.3 as tumor markers in patients with lung cancer: comparison with CYFRA 21-1, CEA, SCC and NSE. Tumor Biol. 2008;29:371–380.
    1. Hassanein M., Callison J.C., Callaway-Lane C., Aldrich M.C., Grogan E.L., Massion P.P. The state of molecular biomarkers for the early detection of lung cancer. Cancer Prev Res (Phila) 2012;5:992–1006.
    1. Youden W.J. Index for rating diagnostic tests. Cancer. 1950;3:32–35.
    1. Li X.J., Hayward C., Fong P.Y. A blood-based proteomic classifier for the molecular characterization of pulmonary nodules. Sci Transl Med. 2013;5 207ra142.
    1. Vachani A., Hammoud Z., Springmeyer S. Clinical utility of a plasma protein classifier for indeterminate lung nodules. Lung. 2015;193:1023–1027.
    1. Vachani A., Pass H.I., Rom W.N. Validation of a multiprotein plasma classifier to identify benign lung nodules. J Thorac Oncol. 2015;10:629–637.
    1. Chapman C.J., Healey G.F., Murray A. EarlyCDT-Lung test: improved clinical utility through additional autoantibody assays. Tumour Biol. 2012;33:1319–1326.
    1. Jett J.R., Peek L.J., Fredericks L. Audit of the autoantibody test, EarlyCDT-lung, in 1600 patients: an evaluation of its performance in routine clinical practice. Lung Cancer. 2014;83:51–55.
    1. Bianchi F., Nicassio F., Marzi M. A serum circulating miRNA diagnostic test to identify asymptomatic high-risk individuals with early stage lung cancer. EMBO Mol Med. 2011;3:495–503.
    1. Sozzi G., Boeri M., Rossi M. Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: a correlative MILD trial study. J Clin Oncol. 2014;32:768–773.

Source: PubMed

3
Iratkozz fel