Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, West Virginia, United States of America.
PLoS One. 2010 Aug 17;5(8):e12222. doi: 10.1371/journal.pone.0012222.
Lung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35-50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment.
METHODOLOGY/PRINCIPAL FINDINGS: From genome-wide mRNA expression profiles generated on 256 lung adenocarcinoma patients, a 12-gene signature was identified using combinatorial gene selection methods, and a risk score algorithm was developed with Naïve Bayes. The 12-gene model generates significant patient stratification in the training cohort HLM & UM (n = 256; log-rank P = 6.96e-7) and two independent validation sets, MSK (n = 104; log-rank P = 9.88e-4) and DFCI (n = 82; log-rank P = 2.57e-4), using Kaplan-Meier analyses. This gene signature also stratifies stage I and IB lung adenocarcinoma patients into two distinct survival groups (log-rank P<0.04). The 12-gene risk score is more significant (hazard ratio = 4.19, 95% CI: [2.08, 8.46]) than other commonly used clinical factors except tumor stage (III vs. I) in multivariate Cox analyses. The 12-gene model is more accurate than previously published lung cancer gene signatures on the same datasets. Furthermore, this signature accurately predicts chemoresistance/chemosensitivity to Cisplatin, Carboplatin, Paclitaxel, Etoposide, Erlotinib, and Gefitinib in NCI-60 cancer cell lines (P<0.017). The identified 12 genes exhibit curated interactions with major lung cancer signaling hallmarks in functional pathway analysis. The expression patterns of the signature genes have been confirmed in RT-PCR analyses of independent tumor samples.
CONCLUSIONS/SIGNIFICANCE: The results demonstrate the clinical utility of the identified gene signature in prognostic categorization. With this 12-gene risk score algorithm, early stage patients at high risk for tumor recurrence could be identified for adjuvant chemotherapy; whereas stage I and II patients at low risk could be spared the toxic side effects of chemotherapeutic drugs.
肺癌仍然是全球癌症相关死亡的主要原因。早期非小细胞肺癌患者的复发率范围为 35-50%。迄今为止，尚无经过充分验证且临床应用的用于个性化治疗的预后基因特征。
方法/主要发现：从 256 例肺腺癌患者的全基因组 mRNA 表达谱中，使用组合基因选择方法鉴定了 12 个基因特征，并使用朴素贝叶斯开发了风险评分算法。在训练队列 HLM 和 UM（n=256；对数秩 P=6.96e-7）以及两个独立验证集 MSK（n=104；对数秩 P=9.88e-4）和 DFCI（n=82；对数秩 P=2.57e-4）中，Kaplan-Meier 分析显示 12 个基因模型在患者分层方面具有显著差异。该基因特征还将 I 期和 IB 期肺腺癌患者分层为两个不同的生存组（对数秩 P<0.04）。在多变量 Cox 分析中，12 个基因风险评分比其他常用的临床因素（除肿瘤分期（III 期与 I 期）外）更为显著（危险比=4.19，95%CI：[2.08，8.46]）。在相同数据集上，与先前发表的肺癌基因特征相比，该 12 个基因模型更为准确。此外，该特征能够准确预测 NCI-60 癌细胞系中顺铂、卡铂、紫杉醇、依托泊苷、厄洛替尼和吉非替尼的化疗耐药性/化疗敏感性（P<0.017）。鉴定的 12 个基因在功能途径分析中与主要肺癌信号标志物存在经过策展的相互作用。在独立肿瘤样本的 RT-PCR 分析中验证了该特征基因的表达模式。
结论/意义：结果表明，所鉴定的基因特征在预后分类方面具有临床应用价值。通过使用该 12 个基因风险评分算法，可以识别出具有肿瘤复发高风险的早期患者，以进行辅助化疗；而具有低风险的 I 期和 II 期患者可以避免化疗药物的毒性副作用。
