Su A I, Welsh J B, Sapinoso L M, Kern S G, Dimitrov P, Lapp H, Schultz P G, Powell S M, Moskaluk C A, Frierson H F, Hampton G M
Department of Chemistry, The Scripps Research Institute, La Jolla, California 92037, USA.
Cancer Res. 2001 Oct 15;61(20):7388-93.
Classification of human tumors according to their primary anatomical site of origin is fundamental for the optimal treatment of patients with cancer. Here we describe the use of large-scale RNA profiling and supervised machine learning algorithms to construct a first-generation molecular classification scheme for carcinomas of the prostate, breast, lung, ovary, colorectum, kidney, liver, pancreas, bladder/ureter, and gastroesophagus, which collectively account for approximately 70% of all cancer-related deaths in the United States. The classification scheme was based on identifying gene subsets whose expression typifies each cancer class, and we quantified the extent to which these genes are characteristic of a specific tumor type by accurately and confidently predicting the anatomical site of tumor origin for 90% of 175 carcinomas, including 9 of 12 metastatic lesions. The predictor gene subsets include those whose expression is typical of specific types of normal epithelial differentiation, as well as other genes whose expression is elevated in cancer. This study demonstrates the feasibility of predicting the tissue origin of a carcinoma in the context of multiple cancer classes.
根据人类肿瘤的原发解剖学起源部位进行分类，对于癌症患者的最佳治疗至关重要。在此，我们描述了如何利用大规模RNA分析和监督机器学习算法，构建前列腺癌、乳腺癌、肺癌、卵巢癌、结直肠癌、肾癌、肝癌、胰腺癌、膀胱癌/输尿管癌和胃食管癌的第一代分子分类方案，这些癌症在美国约占所有癌症相关死亡人数的70%。该分类方案基于识别其表达代表每个癌症类别的基因子集，并且我们通过准确且可靠地预测175例癌（包括12例转移灶中的9例）中90%的肿瘤起源解剖部位，来量化这些基因作为特定肿瘤类型特征的程度。预测基因子集包括那些表达典型于特定类型正常上皮分化的基因，以及其他在癌症中表达升高的基因。这项研究证明了在多种癌症类别背景下预测癌组织起源的可行性。
