Researchers at the National Cancer Institute (NCI), have used artificial neural networks (ANNs) and DNA microarrays to successfully predict the clinical outcome of patients diagnosed with neuroblastoma (NB).
The ANNs also identified a minimal set of 19 genes whose expression levels were closely associated with this clinical outcome. Currently, the Children's Oncology Group (COG), sponsored by NCI, stratifies patients with neuroblastoma into high-, intermediate- and low-risk groups based on several factors. However, while stratification can guide patient treatment, it is not a predictor of survival. Now, the predictive power of microarray gene expression analysis coupled with ANNs could assist physicians in the treatment of individual patients.
Neural networks are specialized pattern recognition algorithms modeled after the human brain; they learn by experience. ANNs are often used in identification programs, such as fingerprint or voice recognition software. Javed Khan, M.D., and his team at NCI's Pediatric Oncology Branch, adapted an ANN algorithm to identify patterns in NB tumor gene expression. The study, which appears in the October 1, 2004, Cancer Research*, was performed in collaboration with colleagues from the NCI, Germany and Australia.
First, the researchers performed gene expression analysis using cDNA microarrays containing over 25,000 genes to create global gene expression profiles of primary tumors from 49 patients diagnosed with NB whose clinical outcome was known. The patients were divided into either good (event-free survival for greater than 3 years) or poor (death due to disease) outcome groups. "Setting aside independent test samples, neural networks were trained to recognize or predict 'alive' or 'dead' expression profiles from the remaining samples," said Khan. "Then we determined if we could predict the outcome for the test samples using these trained ANNs." They found that the ANNs could predict the clinical outcome from any individual gene profile with an accuracy of about 88 percent.
As these gene profiles consisted of over 25,000 genes, the researchers tried to optimize the profiles and find the minimum number of genes that could act as a predictor set. The ANNs identified 19 genes whose expression levels could accurately predict clinical outcome. When only looking at these 19 genes, ANN prediction accuracy increased to 95 percent, and performed much better than the current Children's Oncology Group (COG) risk stratification. Two of the genes in this group, MYCN and CD44, have previously been connected to NB prognosis -MYCN amplification is one of the strongest independent factors of poor prognosis -- and several of the other genes are known to be involved in neuronal development.