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J Thorac Cardiovasc Surg 2002;123:484-491
© 2002 The American Association for Thoracic Surgery

General Thoraic Surgery (GTS)

Molecular staging of lung cancer: Real-time polymerase chain reaction estimation of lymph node micrometastatic tumor cell burden in stage I non-small cell lung cancer—preliminary results of cancer and leukemia group B trial 9761

From the Division of Cardiovascular and Thoracic Surgery, University of Minnesota Medical School, Minneapolis, Minn,^a Cancer and Leukemia Group B Statistical Center, Durham, NC,^b University Hospitals of Cleveland, Case Western Reserve University, Cleveland, Ohio,^c Thoracic Division, State University of New York Upstate Medical University, Syracuse, NY,^d and Section of General Thoracic Surgery, Division of Cardiothoracic Surgery, Washington University School of Medicine, St Louis, Mo.^e

Received for publication May 14, 2001; revisions requested July 9, 2001; revisions received July 30,2001; accepted for publication Aug 25, 2001. Address for reprints: Michael Maddaus, MD, Department of Surgery, University of Minnesota, 420 Delaware St SE, Box 207, Minneapolis, MN 55455 (E-mail:madda001@ tc.umn.edu

Objective: The 5-year survival for patients with surgically resected stage I non-small cell lung cancer is only 60% to 70%, probably because of undetected systemic occult micrometastases. Detection of occult micrometastases in lymph nodes by reverse-transcriptase polymerase chain reaction for carcinoembryonic antigen messenger RNA in non-small cell lung cancer has not been reported. Detection of occult micrometastases by standard reverse-transcriptase polymerase chain reaction provides only yes or no answers about their presence, whereas quantitative real-time reverse-transcriptase polymerase chain reaction permits reproducible quantitation of target molecules. This study evaluated the ability of quantitative reverse-transcriptase polymerase chain reaction to quantitate lymph node occult metastases with carcinoembryonic antigen messenger RNA as a tumor marker.
Methods: Standard reverse-transcriptase polymerase chain reaction and quantitative reverse-transcriptase polymerase chain reaction for carcinoembryonic antigen messenger RNA were performed on 232 lymph nodes from 53 patients with stage I disease (node negative according to histologic examination). Quantitative reverse-transcriptase polymerase chain reaction determined carcinoembryonic antigen messenger RNA quantity by detecting fluorescence increase at a threshold polymerase chain reaction cycle. Threshold polymerase chain reaction cycle values were correlated with standard curves created from serially diluted carcinoembryonic antigen-positive HTB-174 tumor cells to estimate the number of micrometastatic tumor cells in a lymph node.
Results: Detection rates of occult metastases were similar for standard reverse-transcriptase polymerase chain reaction and quantitative reverse-transcriptase polymerase chain reaction at 38 of 232 (16.4 %) and 59 of 232 (25.4 %), respectively. Upstaging rates among 53 cases of stage I non-small cell lung cancer were also similar for standard reverse-transcriptase polymerase chain reaction and quantitative reverse-transcriptase polymerase chain reaction at 23 of 53 (43.4 %) and 30 of 53 (56.6%), respectively. Comparison of positive lymph node stations according to quantitative reverse-transcriptase polymerase chain reaction (threshold polymerase chain reaction cycle <45) with HTB-174 tumor cell standard curves yielded estimates of metastatic tumor cell burden of 1.07 x 10³to 3.24 x 10⁵cells per lymph node station (median 7190 tumor cells per lymph node station).
Conclusions: Standard and quantitative real-time reverse-transcriptase polymerase chain reaction for carcinoembryonic antigen detected occult metastases in patients with stage I non-small cell lung cancer at similar rates; both upstaged about 50% of cases. Quantitative reverse-transcriptase polymerase chain reaction allows estimation of the number of metastatic cells per lymph node, however, which potentially allows greater precision in predicting recurrence risk.

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