Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

Nat Med. 2001 Jun;7(6):673-9. doi: 10.1038/89044.


The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Burkitt Lymphoma / classification
  • Burkitt Lymphoma / diagnosis
  • Burkitt Lymphoma / genetics
  • Data Interpretation, Statistical
  • Gene Expression Profiling*
  • Humans
  • Models, Biological
  • Neoplasms / classification*
  • Neoplasms / diagnosis*
  • Neoplasms / genetics
  • Neural Networks, Computer*
  • Neuroblastoma / classification
  • Neuroblastoma / diagnosis
  • Neuroblastoma / genetics
  • Oligonucleotide Array Sequence Analysis*
  • Rhabdomyosarcoma / classification
  • Rhabdomyosarcoma / diagnosis
  • Rhabdomyosarcoma / genetics
  • Sarcoma, Ewing / classification
  • Sarcoma, Ewing / diagnosis
  • Sarcoma, Ewing / genetics
  • Tumor Cells, Cultured