Using data mining to characterize DNA mutations by patient clinical features.
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.
Evans
S
S
Hereditary Cancer Institute, Creighton University School of Medicine, Omaha, Nebraska 68178, USA.
Lemon
S J
SJ
Deters
C
C
Fusaro
R M
RM
Durham
C
C
Snyder
C
C
Lynch
H T
HT
D017668:Age of Onset; D000465:Algorithms; D024682:BRCA2 Protein; D001943:Breast Neoplasms / Q000453:epidemiology / Q000235:genetics*; D005190:Family; D005260:Female; D019398:Genes, BRCA1; D006801:Humans; D008432:Mathematical Computing; D009154:Mutation; D009363:Neoplasm Proteins / Q000235:genetics; D010051:Ovarian Neoplasms / Q000453:epidemiology / Q000235:genetics*; D012984:Software; D014157:Transcription Factors / Q000235:genetics
D024682:BRCA2 Protein; D009363:Neoplasm Proteins; D014157:Transcription Factors