A gene expression signature predicts survival of patients with stage I non-small cell lung cancer.
A gene expression signature predicts survival of patients with stage I non-small cell lung cancer.
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DOI：
10.1371/journal.pmed.0030467
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发表时间：
2006-12
期刊：
影响因子：
15.8
通讯作者：
You, Ming
中科院分区：
文献类型：
--
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
肺癌是美国癌症相关死亡的主要原因。尽管手术切除，近50%的I期和II期非小细胞肺癌（NSCLC）患者仍会死于复发性疾病。目前还没有可靠的临床或分子预测指标来确定那些有复发性疾病高风险的人。因此，不可能选择那些高风险患者进行更积极的治疗，而将较不积极的治疗分配给复发风险较低的患者。在这项研究中，我们对来自7个不同的非小细胞肺癌微阵列研究的数据集进行了荟萃分析，以寻找与生存时间（2岁以下和5岁以上）相关的差异表达基因。从这些研究中选择了4905个一致的基因，并通过距离加权辨别（DWD）对数据集进行系统偏倚调整。我们发现了一个由64个基因组成的基因表达特征，它可以高度预测哪些I期肺癌患者可能从更积极的治疗中受益。Kaplan-Meier分析具有64个基因表达特征的I期NSCLC患者的总生存率表明，高危组和低危组的总生存率存在显著差异。在这64个基因中，11个与癌症转移有关（APC、CDH8、IL8RB、LY6D、PCDHGA12、DSP、NID、ENPP2、CCR2、CASP8、CASP10）， 8个与细胞凋亡有关（CASP8、CASP10、PIK3R1、BCL2、SON、INHA、PSEN1、BIK）。我们的研究结果表明，来自几个数据集的基因表达特征可以协调一致。由此产生的特征可用于预测I期NSCLC的生存，并可能有助于告知治疗决策。对几项肺癌基因表达研究的荟萃分析得出了64个基因的表达谱，这些基因的表达谱可用于预测早期肺癌患者的生存，并可能为治疗决策提供信息。肺癌是全球癌症相关死亡的最常见原因。大多数病例属于非小细胞肺癌（NSCLC），主要由吸烟引起。像其他癌症一样，如何治疗非小细胞肺癌取决于它被检测到的“阶段”。IA期非小细胞肺癌很小，局限于肺部，可以手术切除；稍大的IB期肿瘤患者通常在手术后接受化疗。在II期非小细胞肺癌中，癌细胞可能存在于肿瘤附近的淋巴结。手术加化疗是这个阶段和一些III期非小细胞肺癌的常用治疗方法。然而，在这个阶段，肿瘤可能出现在整个胸部，手术并不总是可行的。对于这类病例以及肿瘤已经扩散到全身的IV期NSCLC，患者只能接受化疗。发现非小细胞肺癌的阶段也决定了患者对治疗的反应。那些可以手术治疗的人比那些不能手术治疗的人做得好得多。因此，尽管只有2%的四期肺癌患者在诊断后存活了5年，但大约70%的一期或二期肺癌患者至少活了5年。即使是I期和II期肺癌也经常复发，而且没有准确的方法来确定患者是否会发生这种情况。如果有的话，这些患者可以接受积极的化疗，因此研究人员正在寻找一种“分子特征”，以帮助确定哪些非小细胞肺癌可能复发。与正常细胞不同，癌细胞分裂不受控制，可以在体内移动。这些行为差异是由它们的遗传物质的变化引起的，这种变化改变了它们的RNA转录和蛋白质表达模式。在这项研究中，研究人员调查了来自几项微阵列研究（一种用于对细胞中表达的基因进行分类的技术）的数据是否可以汇总，以构建一个预测I期NSCLC患者生存的基因表达标记。研究人员从七个独立的微阵列研究（包括他们自己的一项新研究）中获取数据，这些研究记录了与I期NSCLC生存时间（少于2年和大于5年）相关的基因表达谱。由于这些研究是在不同的地方进行的，使用的技术略有不同，研究人员使用了一种称为距离加权歧视的统计工具，在确定64个与生存相关的基因表达之前，消除了研究之间的系统差异。这些基因大多参与细胞粘附、细胞运动、细胞增殖和细胞死亡，所有这些过程在癌细胞中都发生了改变。研究人员随后开发了一个统计模型，使他们能够使用基因表达和生存数据来计算五个数据集中近200名患者的风险评分。当他们根据这些评分将患者分为高危组和低危组时，两组的生存时间有显著差异。事实上，基因表达特征在预测预后方面比常规分期更好。最后，研究人员通过在两个独立的数据集中显示其预测生存的准确率超过85%来验证基因表达特征。这里确定的64个基因表达特征可以帮助临床医生为I期NSCLC患者制定治疗计划。因为它能准确预测腺癌或鳞状细胞癌（非小细胞肺癌的两种主要亚型）患者的生存率，它有可能提示哪些患者应该接受积极的化疗，哪些患者可以避免这种不愉快的治疗。以前建立基因表达特征来预测结果的尝试使用了来自小群体患者的数据，并且在其他患者中进行测试时失败了。相比之下，这个新的特征似乎是一般化的。然而，在肿瘤学家将其作为常规治疗方案之前，其预测结果的能力必须在进一步的研究中得到证实。请通过本摘要的在线版本http://dx.doi.org/10.1371/journal.pmed.0030467访问这些网站。美国国家癌症研究所关于肺癌患者和卫生专业人员的信息。MedlinePlus百科全书关于小细胞和非小细胞肺癌的条目。英国癌症研究中心，关于肺癌患者各方面的信息。关于DNA微阵列和表达谱的维基百科页面（注意，维基百科是一个免费的在线百科全书，任何人都可以编辑）。
Lung cancer is the leading cause of cancer-related death in the United States. Nearly 50% of patients with stages I and II non-small cell lung cancer (NSCLC) will die from recurrent disease despite surgical resection. No reliable clinical or molecular predictors are currently available for identifying those at high risk for developing recurrent disease. As a consequence, it is not possible to select those high-risk patients for more aggressive therapies and assign less aggressive treatments to patients at low risk for recurrence. In this study, we applied a meta-analysis of datasets from seven different microarray studies on NSCLC for differentially expressed genes related to survival time (under 2 y and over 5 y). A consensus set of 4,905 genes from these studies was selected, and systematic bias adjustment in the datasets was performed by distance-weighted discrimination (DWD). We identified a gene expression signature consisting of 64 genes that is highly predictive of which stage I lung cancer patients may benefit from more aggressive therapy. Kaplan-Meier analysis of the overall survival of stage I NSCLC patients with the 64-gene expression signature demonstrated that the high- and low-risk groups are significantly different in their overall survival. Of the 64 genes, 11 are related to cancer metastasis (APC, CDH8, IL8RB, LY6D, PCDHGA12, DSP, NID, ENPP2, CCR2, CASP8, and CASP10) and eight are involved in apoptosis (CASP8, CASP10, PIK3R1, BCL2, SON, INHA, PSEN1, and BIK). Our results indicate that gene expression signatures from several datasets can be reconciled. The resulting signature is useful in predicting survival of stage I NSCLC and might be useful in informing treatment decisions. Meta-analysis of several lung cancer gene expression studies yields a set of 64 genes whose expression profile is useful in predicting survival of patients with early-stage lung cancer and possibly informing treatment decisions. Lung cancer is the commonest cause of cancer-related death worldwide. Most cases are of a type called non-small cell lung cancer (NSCLC) and are mainly caused by smoking. Like other cancers, how NSCLC is treated depends on the “stage” at which it is detected. Stage IA NSCLCs are small and confined to the lung and can be removed surgically; patients with slightly larger stage IB tumors often receive chemotherapy after surgery. In stage II NSCLC, cancer cells may be present in lymph nodes near the tumor. Surgery plus chemotherapy is the usual treatment for this stage and for some stage III NSCLCs. However, in this stage, the tumor can be present throughout the chest and surgery is not always possible. For such cases and in stage IV NSCLC, where the tumor has spread throughout the body, patients are treated with chemotherapy alone. The stage at which NSCLC is detected also determines how well patients respond to treatment. Those who can be treated surgically do much better than those who can't. So, whereas only 2% of patients with stage IV lung cancer survive for 5 years after diagnosis, about 70% of patients with stage I or II lung cancer live at least this long. Even stage I and II lung cancers often recur and there is no accurate way to identify the patients in which this will happen. If there was, these patients could be given aggressive chemotherapy, so the search is on for a “molecular signature” to help identify which NSCLCs are likely to recur. Unlike normal cells, cancer cells divide uncontrollably and can move around the body. These behavioral differences are caused by changes in their genetic material that alter their patterns of RNA transcription and protein expression. In this study, the researchers have investigated whether data from several microarray studies (a technique used to catalog the genes expressed in cells) can be pooled to construct a gene expression signature that predicts the survival of patients with stage I NSCLC. The researchers took the data from seven independent microarray studies (including a new study of their own) that recorded gene expression profiles related to survival time (less than 2 years and greater than 5 years) for stage I NSCLC. Because these studies had been done in different places with slightly different techniques, the researchers applied a statistical tool called distance-weighted discrimination to smooth out any systematic differences among the studies before identifying 64 genes whose expression was associated with survival. Most of these genes are involved in cell adhesion, cell motility, cell proliferation, and cell death, all processes that are altered in cancer cells. The researchers then developed a statistical model that allowed them to use the gene expression and survival data to calculate risk scores for nearly 200 patients in five of the datasets. When they separated the patients into high and low risk groups on the basis of these scores, the two groups were significantly different in terms of survival time. Indeed, the gene expression signature was better at predicting outcome than routine staging. Finally, the researchers validated the gene expression signature by showing that it predicted survival with more than 85% accuracy in two independent datasets. The 64 gene expression signature identified here could help clinicians prepare treatment plans for patients with stage I NSCLC. Because it accurately predicts survival in patients with adenocarcinoma or squamous cell cancer (the two major subtypes of NSCLC), it potentially indicates which of these patients should receive aggressive chemotherapy and which can be spared this unpleasant treatment. Previous attempts to establish gene expression signatures to predict outcome have used data from small groups of patients and have failed when tested in additional patients. In contrast, this new signature seems to be generalizable. Nevertheless, its ability to predict outcomes must be confirmed in further studies before it is routinely adopted by oncologists for treatment planning. Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0030467. US National Cancer Institute information on lung cancer for patients and health professionals. MedlinePlus encyclopedia entries on small-cell and non-small-cell lung cancer. Cancer Research UK, information on patients about all aspects of lung cancer. Wikipedia pages on DNA microarrays and expression profiling (note that Wikipedia is a free online encyclopedia that anyone can edit).
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影响因子：
11.5
作者：
Baldus, SE;Mönig, SP;Dienes, HP
通讯作者：
Dienes, HP
影响因子：
9.8
作者：
Chaparro, J;Reeds, DN;Li, E
通讯作者：
Li, E
影响因子：
5.8
作者：
Benito, M;Parker, J;Marron, JS
通讯作者：
Marron, JS
DOI：
10.1093/jnen/63.5.441
发表时间：
2004-05-01
影响因子：
3.2
作者：
Eberhart, CG;Kratz, J;Burger, PC
通讯作者：
Burger, PC
影响因子：
82.9
作者：
Beer, DG;Kardia, SLR;Hanash, S
通讯作者：
Hanash, S