Artificial neural networks and DNA microarrays to successfully predict clinical outcomes of neuroblastoma patients

This is actually much more interesting than it sounds. Basically, these researchers used neural networks to train a model that could predict the patient outcome from the patient's genes:

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 (!! ed)

Using the 19 predictor genes, the ANNs 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."

Wow -- that's pretty cool.

Full story: http://www.news-medical.net/?id=5222