Evans S, Lemon S J, Deters C, Fusaro R M, Durham C, Snyder C, Lynch H T
Hereditary Cancer Institute, Creighton University School of Medicine, Omaha, Nebraska 68178, USA.
Proc AMIA Annu Fall Symp. 1997:253-7.
In most hereditary cancer syndromes, finding a correspondence between various genetic mutations within a gene (genotype) and a patient's clinical cancer history (phenotype) is challenging; to date there are few clinically meaningful correlations between specific DNA intragenic mutations and corresponding cancer types. To define possible genotype and phenotype correlations, we evaluated the application of data mining methodology whereby the clinical cancer histories of gene-mutation-positive patients were used to define valid or "true" patterns for a specific DNA intragenic mutation. The clinical histories of patients with their corresponding detailed attributes without the same oncologic intragenic mutation were labeled incorrect or "false" patterns. The results of data mining technology yielded characterizing rules for the true cases that constituted clinical features which predicted the intragenic mutation. Some of the initial results derived correlations already independently known in the literature, adding to the confidence of using this methodological approach.
在大多数遗传性癌症综合征中，要找到基因内各种基因突变（基因型）与患者临床癌症病史（表型）之间的对应关系具有挑战性；迄今为止，特定DNA基因内突变与相应癌症类型之间几乎没有具有临床意义的相关性。为了确定可能的基因型和表型相关性，我们评估了数据挖掘方法的应用，即利用基因突变阳性患者的临床癌症病史来定义特定DNA基因内突变的有效或“真实”模式。具有相应详细属性但无相同肿瘤基因内突变的患者的临床病史被标记为不正确或“错误”模式。数据挖掘技术的结果产生了构成预测基因内突变的临床特征的真实病例的特征规则。一些初步结果得出了文献中已经独立知晓的相关性，增加了使用这种方法的信心。
