Multiclass cancer diagnosis using tumor gene expression signatures
Multiclass cancer diagnosis using tumor gene expression signatures
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DOI：
10.1073/pnas.211566398
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发表时间：
2001-12-18
影响因子：
11.1
通讯作者：
Golub, TR
中科院分区：
文献类型：
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作者：
Ramaswamy, S;Tamayo, P;Golub, TR
癌症患者的最佳治疗依赖于通过使用临床和组织病理学数据的复杂组合来建立准确的诊断。在某些情况下，这项任务是困难的或不可能的，因为不典型的临床表现或组织病理学。为了确定单纯通过分子分类能否实现多种常见成人恶性肿瘤的诊断，我们对14种常见肿瘤类型的218例肿瘤标本和90例正常组织标本进行了寡核苷酸微阵列基因表达分析。使用16,063个基因的表达水平和表达序列标签来评估基于支持向量机算法的多类分类器的准确性。总体分类准确率为78%，远远超过随机分类的准确率(91%)。低分化的癌症导致预测的置信度较低，无法根据起源的组织进行准确的分类，这表明它们是分子上截然不同的实体，与分化良好的癌症相比，基因表达模式截然不同。综上所述，这些结果证明了准确的、多分类的分子癌症分类的可行性，并为未来分子癌症诊断的临床实施提供了一种策略。
The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (91%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.