Hybrid models identified a 12-gene signature for lung cancer prognosis and chemoresponse prediction.
Hybrid models identified a 12-gene signature for lung cancer prognosis and chemoresponse prediction.
复制标题
DOI：
10.1371/journal.pone.0012222
复制
发表时间：
2010-08-17
期刊：
影响因子：
3.7
通讯作者：
Guo NL
中科院分区：
文献类型：
--
作者：
Wan YW;Sabbagh E;Raese R;Qian Y;Luo D;Denvir J;Vallyathan V;Castranova V;Guo NL
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为35-50%。到目前为止，还没有完全验证和临床应用的预后基因签名用于个性化治疗。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个基因签名，并使用朴素贝叶斯开发了风险评分算法。使用Kaplan-Meier分析，12基因模型在训练群组HLM & UM（n = 256;对数秩P = 6.96e-7）和两个独立验证集MSK（n = 104;对数秩P = 9.88e-4）和DFCI（n = 82;对数秩P =2.57e-4）中产生显著的患者分层。 该基因标记还将I期和IB期肺腺癌患者分成两个不同的存活组（对数秩P<0.04）。在多变量考克斯分析中，12基因风险评分比除肿瘤分期（III期与I期）外的其他常用临床因素更显著（风险比=4.19，95% CI：[2.08，8.46]）。 12基因模型比以前在相同数据集上发表的肺癌基因特征更准确。此外，该特征准确地预测了NCI-60癌细胞系中对顺铂、卡铂、紫杉醇、依托泊苷、厄洛替尼和吉非替尼的化学抗性/化学敏感性（P<0.017）。在功能途径分析中，所鉴定的12个基因表现出与主要肺癌信号传导标志的策划相互作用。标签基因的表达模式已在独立肿瘤样品的RT-PCR分析中得到证实。结果证明了所鉴定的基因标签在预后分类中的临床效用。利用这种12基因风险评分算法，可以确定肿瘤复发高风险的早期患者进行辅助化疗;而低风险的I期和II期患者可以避免化疗药物的毒副作用。
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. 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. 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.
登录
查看更多内容
影响因子：
5
作者：
Pepe, MS;Janes, H;Newcomb, P
通讯作者：
Newcomb, P
影响因子：
4
作者：
Baker, Stuart G;Kramer, Barnett S;Srivastava, Sudhir
通讯作者：
Srivastava, Sudhir
影响因子：
15.8
作者：
Lu, Yan;Lemon, William;Liu, Peng-Yuan;Yi, Yijun;Morrison, Carl;Yang, Ping;Sun, Zhifu;Szoke, Janos;Gerald, William L.;Watson, Mark;Govindan, Ramaswamy;You, Ming
通讯作者：
You, Ming
影响因子：
82.9
作者：
Beer, DG;Kardia, SLR;Hanash, S
通讯作者：
Hanash, S
影响因子：
64.8
作者：
Bild, AH;Yao, G;Nevins, JR
通讯作者：
Nevins, JR