A combination of artificial neural networks and DNA microarray technology allow physicians to successfully predict clinical outcome of patients with neuroblastoma, according to an article in the October 1st issue of Cancer Research. The networks also identified a minimal set of 19 genes whose expression levels were closely associated with clinical outcome.

Currently, the Children's Oncology Group, sponsored by the National Cancer Institute (USA), 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 artificial neural networks (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. Such networks are often used in identification programs, such as fingerprint or voice recognition software. Javed Khan, MD, senior author, his team at the Institute’s Pediatric Oncology Branch, and international colleagues adapted a network algorithm to identify patterns in neuroblastoma tumor gene expression.

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 neuroblastoma whose clinical outcome was known. The patients were divided into either good (event-free survival for greater than three 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 networks 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 networks identified 19 genes whose expression levels could accurately predict clinical outcome. When only looking at these 19 genes, network 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 neuroblastoma 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.

Using the 19 predictor genes, the networks were also able to partition the subset of patients classified as high-risk into good and poor outcome groups. "What was most exciting," said Khan, "was that we were able to predict which of the high- risk patients would fail conventional therapy. This has major clinical implication since we are now able to distinguish a group of ultra-high-risk patients who will not respond to conventional therapy and therefore require alternative treatment strategies. We may also be able to reduce the intensity and thereby reduce the toxicity of treatment regime to those predicted to survive based on their gene expression profile."

"And since we are using 19 genes instead of 25,000," Khan added, "we can translate our findings to the clinic because simple prognostic assays can be developed based on this small number of genes. In fact, three of the genes found to be over-expressed in poor outcome tumors encode proteins secreted into the blood, meaning they could be used as serum prognosis markers in a simple blood test."

In collaboration with industry, Khan's lab is now developing clinical assays based on these 19 genes and planning to test for the presence of these serum markers in other patients with neuroblastoma for the prognostic prediction.

Khan cautions that more validation studies are required. His lab now has begun a
larger validation study using 300 neuroblastoma tumor samples from national trials based in the United States (COG) and the United Kingdom (UKCCSG: United Kingdom Childhood Cancer Study Group).