研究緣起：肺癌是國人癌症致死率的首位，也是全世界最普遍的惡性腫瘤之一，全世界每年有超過百萬人死於肺癌，據統計罹患肺癌後五年的存活機率約10~15%，依據患者手術當時的癌症分期而定，愈晚期治療，存活率愈低。傳統上針對長期抽煙的人採用胸部X光照影以及痰液細胞學檢驗來進行篩選的策略，已經證實無法有效早期偵測肺癌來降低肺癌的致死率。因此利用靈敏性較高的分子指標進行早期偵測是目前刻不容緩，也是提高肺癌患者存活率的重要工作。
研究目的：癌細胞的轉變是一連串分子變異累積形成的，這些變異發生在抑癌基因 (Tumor suppressor gene, TSG)、致癌基因 (Oncogene)、DNA修補基因，以及內在或外在環境因子造成的基因不穩定現象。已有少數研究顯示在肺癌病人痰液、氣管灌流液以及血清樣本中，可以偵測出癌症相關基因啟動子高度甲基化、基因座缺失及微衛星序列重複次數改變所得之基因體不穩定現象。本研究主要目的在於更進一步確認可在同一肺癌患者的痰液 (sputum) 樣本或血漿 (plasma) 樣本中偵測到與肺癌細胞一致的分子變異指標，進一步挑選出多個靈敏性與專一性較高的分子指標作為未來早期肺癌相關分子指標群 (Early detection biomarkers)。
研究方法：Part I，在79位肺癌病人的肺癌細胞及痰液 (sputum) 樣本中，利用methylation-specific PCR (MSP) 共檢查了三個基因 (FHIT, p16INK4a, and RARβ) 的啟動子高度甲基化 (promoter hypermethylation)，並且偵測了八個微衛星序列 (D3S1234, D3S1285, D5S1456, D9S286, D9S942, GATA49D12, D13S170, and D17S786) 的異質性缺失 (loss of heterozygosity, LOH) 及微衛星不穩定現象(microsatellite instability, MSI)。Part II，63位肺癌病人的肺癌組織及血漿 (plasma) 樣本中，利用MSP共檢查了六個基因 (BLU, CDH13, FHIT, p16INK4a, RARβ, and RASSF1A) 的啟動子高度甲基化。另外還有22位無肺癌個體的痰液及血漿樣本的偵測，分別作為肺癌病人的痰液及血漿分析的對照組。
研究結果：Part I痰液分析，本研究於肺癌細胞及痰液的分子指標偵測結果分別進行靈敏性 (sensitivity)、專一性 (specificity)、一致性 (concordance) 及危險比 (odds ratio, OR) 的分析，挑選出的合適指標包括D9S286、D9S942、GATA49D12、D13S170的 LOH，D9S942 MSI，以及p16INK4a、RARβ的甲基化分析共七個變異；其中，痰液樣本中發生D9S942 LOH的危險比為4.9 (95% confidence interval, CI: 1.23~21.73, P=0.024)，p16INK4a甲基化的危險比為3.29 (95% CI: 1.00~14.93, P=0.049)。七個合適指標偵測結果採取聯集判定，其預測的靈敏度為81%，專一性有72%，一致性為77%，未來將以此方式應用於早期肺癌的臨床檢驗。另外，利用training set (53位肺癌病人，13位無肺癌個體) 的偵測結果進行迴歸分析，再以test set (26位肺癌病人，9位無肺癌個體) 的偵測結果帶入迴歸方程式進行驗證，得到80 %的符合率，最後再以所有樣本 (79位肺癌病人，22位無肺癌個體) 的偵測結果得到樣本數較多的新迴歸方程式Y = -0.87＋0.79 (D9S286 LOH)＋1.96 (D9S942 LOH)＋2.24 (GATA49D12 LOH)＋12.19 (D13S170 LOH)＋11.02 (D9S942 MSI)＋0.70 (p16INK4a methyl)＋1.25 (RARβ methyl)。
Part II血漿分析，甲基化頻率較高的指標有p16INK4a、RARβ及RASSF1A，且血漿中p16INK4a甲基化的危險比為5.56 (95%CI: 1.41~37.22, P=0.012)，RASSF1A甲基化的危險比為5.48 (95%CI: 1.37~37.00, P=0.014)。以p16INK4a、RARβ以及RASSF1A三基因任一個甲基化的聯集判定，預測效果的靈敏度為74%，專一性有78%，一致性為75%。利用training set (43位肺癌病人，13位無肺癌個體) 的偵測結果進行迴歸分析，再以test set (20位肺癌病人，9位無肺癌個體) 進行驗證，得到83%的符合率，最後以所有樣本 (63位肺癌病人，22位無肺癌個體) 的偵測結果計算得到的新迴歸方程式如下Y = 0.19＋0.52 (BLU methyl)＋1.92 (p16INK4a methyl)＋1.52 (RASSF1A methyl)，預測的靈敏性有77%，專一性為90%，一致性有79%，未來將以此方式應用於早期肺癌的臨床檢驗。
結論：分析結果所挑選出來的肺癌相關分子指標群，未來將應用於臨床大量篩檢，檢查結果為異常的個案將持續追蹤，期待能夠早期發現，早期治療，降低國人的肺癌致死機率。

Purpose: Lung cancer is the leading cause of cancer deaths in Taiwan. Traditional radiography and sputum cytology have not been successfully reducing lung cancer mortality. It’s urgent to develop more sensitive molecular marker panel for large early lung cancer screening.
Strategy: Carcinogenesis is a multi-step process resulting from the accumulation of errors in vital regulatory pathways. The present study was designed to select multiple DNA markers, which have high sensitivity and specificity to serve as diagnostic biomarkers for lung cancer detection.
Methods: Part I, we examined the promoter hypermethylation of three tumor suppressor genes (FHIT, p16INK4a, and RARβ) by methylation-specific PCR (MSP), and the instability of eight microsatellite markers (D3S1234, D3S1285, D5S1456, D9S286, D9S942, GATA49D12, D13S170, and D17S786) by loss of heterozygosity (LOH) and microsatellite instability (MSI) analyses in lung tumor cells and matched sputum specimens from 79 lung cancer patients. Part II, we examined the promoter hypermethylation of six tumor suppressor genes (BLU, CDH13, FHIT, p16INK4a, RARβ, and RASSF1A) by MSP assay in lung tumor tissues and matched plasma specimens from 63 lung cancer patients. In addition, there were additional sputum and plasma specimens from 22 cancer-free individuals to be the negative control of part I and part II studies.
Results: Part I sputum study, based on the results of sensitivity, specificity, and concordance from each marker analyzed, we selected seven biomarkers, which are LOH of D9S286, D9S942, GATA49D12, and D13S170, MSI of D9S942, and methylation of p16INK4a and RARβ. In addition, the odds ratio of D9S942 LOH in sputum was 4.9 (95% confidence interval, CI: 1.23~21.73, P=0.024), and the odds ratio of p16INK4a methylation in sputum was 3.29 (95% CI: 1.00~14.93, P=0.049). Using a definition that patient with cancer risk had alteration in more than two among seven selected biomarkers, we achieved a sensitivity of 81%, a specificity of 72%, and a concordance of 77%. In addition, the regression model calculated from the training set (53 cancer patients, 13 cancer-free individuals) had a match score of 80% while applying to the test set (26 cancer patients, 9 cancer-free individuals). The new regression model Y = -0.87＋0.79 (D9S286 LOH)＋1.96 (D9S942 LOH)＋2.24 (GATA49D12 LOH)＋12.19 (D13S170 LOH)＋11.02 (D9S942 MSI)＋0.70 (p16INK4a methyl)＋1.25 (RARβ methyl) thus was generated by calculating all cases (79 cancer patients, 22 cancer-free individuals) and this led to a sensitivity of 86%, a specificity of 22%, and a concordance of 78%.
Part II plasma study, p16INK4a, RARβ, and RASSF1A genes had higher promoter hypermethylation frequencies. In addition, the odds ratio of p16INK4a methylation and RASSF1A methylation in plasma was 5.56 (95%CI: 1.41~37.22, P=0.012) and 5.48 (95% CI: 1.40~37.00, P=0.014), respectively. Using a definition of risk individual showing alteration in more than one of the three selected biomarkers, we achieved a sensitivity of 74%, a specificity of 78%, and a concordance of 75%. The regression model calculated from the training set (43 cancer patients, 13 cancer-free individuals) had a match score of 83% comparing to the test set (20 cancer patients, 9 cancer-free individuals). The new regression model Y = 0.19＋0.52 (BLU methyl)＋1.92 (p16INK4a methyl)＋1.52 (RASSF1A methyl), which calculated by overall cases (63 cancer patients, 22 cancer-free individuals), achieved a sensitivity of 77%, a specificity of 90%, and a concordance of 79%. Therefore, the new regression model will be used in the future clinical screening because its high sensitivity, specificity, and concordance.
Conclusion: These selected early-etiologically associated biomarkers can potentially be tested as supplement biomarkers for early lung cancer detection in the future.

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