Multiclass cancer diagnosis using tumor gene expression signatures

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 (9%). 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.

癌症患者的最优治疗方案，有赖于结合临床与组织病理学的多维度数据以建立精准诊断。在部分临床场景中，由于临床表现非典型或组织病理学特征异常，该诊断任务难以完成甚至无法开展。为验证多种常见成人恶性肿瘤能否仅通过分子分类实现诊断，我们对覆盖14种常见肿瘤类型的218份肿瘤样本以及90份正常组织样本，开展了寡核苷酸微阵列（oligonucleotide microarray）基因表达分析。我们以16063个基因及表达序列标签（expressed sequence tags, EST）的表达水平为评估指标，对基于支持向量机（support vector machine, SVM）算法的多分类分类器的诊断准确率进行了验证。整体分类准确率达78%，远高于随机分类的准确率（9%）。低分化癌的预测置信度较低，无法根据其起源组织实现精准分类，这表明低分化癌在分子层面属于独立实体，其基因表达模式与高分化对应肿瘤存在显著差异。综上，本研究结果证实了精准多分类分子癌症诊断的可行性，并为未来分子癌症诊断的临床应用提供了可行策略。