An analytical method for multiclass molecular cancer classification
An analytical method for multiclass molecular cancer classification
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DOI：
10.1137/s0036144502411986
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发表时间：
2003-12-01
期刊：
影响因子：
10.2
通讯作者：
Mesirov, JP
中科院分区：
文献类型：
--
作者：
Rifkin, R;Mukherjee, S;Mesirov, JP
现代癌症治疗依赖于显微组织检查，根据起源的解剖部位对肿瘤进行分类。这种方法是有效的，但即使在经验丰富的临床医生和病理学家中也是主观和可变的。最近，DNA微阵列生成的基因表达数据已被用于构建分子癌症分类器。我们小组和其他人以前的工作证明了使用这种全局基因表达模式解决成对分类问题的方法。然而，跨多个原发性肿瘤类别的分类提出了新的方法和计算挑战。在本文中，我们描述了一种计算方法，结合类特定的（一个与所有）二进制支持向量机的多类预测。我们将这种方法应用于多种常见成人恶性肿瘤的诊断，使用来自198个肿瘤样本的DNA微阵列数据，跨越14种最常见的肿瘤类型。总体分类准确率为78%，远远超过随机分类的预期准确率。在一个大的样本子集（80%），该算法达到90%的准确性。本文中描述的方法既证明了准确的基于基因表达的多类癌症诊断是可能的，也强调了将这些策略应用于生物医学研究所固有的一些分析挑战。
Modern cancer treatment relies upon microscopic tissue examination to classify tumors according to anatomical site of origin. This approach is effective but subjective and variable even among experienced clinicians and pathologists. Recently, DNA microarray-generated gene expression data has been used to build molecular cancer classifiers. Previous work from our group and others demonstrated methods for solving pairwise classification problems using such global gene expression patterns. However, classification across multiple primary tumor classes poses new methodological and computational challenges. In this paper we describe a computational methodology for multiclass prediction that combines class-specific (one vs. all) binary support vector machines. We apply this methodology to the diagnosis of multiple common adult malignancies using DNA microarray data from a collection of 198 tumor samples, spanning 14 of the most common tumor types. Overall classification accuracy is 78%, far exceeding the expected accuracy for random classification. In a large subset of the samples (80%), the algorithm attains 90% accuracy. The methodology described in this paper both demonstrates that accurate gene expression-based multiclass cancer diagnosis is possible and highlights some of the analytic challenges inherent in applying such strategies to biomedical research.