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Artificial Neural Networks Can Predict Clinical Outcomes Of Neuroblastoma Patients

Date:
October 4, 2004
Source:
NIH/National Cancer Institute
Summary:
Researchers at the National Cancer Institute (NCI), part of the National Institutes of Health (NIH), have used artificial neural networks (ANNs) and DNA microarrays to successfully predict the clinical outcome of patients diagnosed with neuroblastoma (NB).

Researchers at the National Cancer Institute (NCI), part of the National Institutes of Health (NIH), 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 Oct. 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 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 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.

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.” 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 NB for the prognostic prediction.

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

For more information about cancer, visit the NCI Web site at http://www.cancer.gov or call NCI's Cancer Information Service at 1-800-4-CANCER (1-800-422-6237).

###

* Wei JS, Greer BT, Westermann F, Steinberg SM, Son CG, Chen QR, Whiteford CC, Bilke S, Krasnoselsky AL, Cenacchi N, Catchpoole D, Berthold F, Schwab M, and Khan J. Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma. Cancer Research, Oct. 1, 2004; vol. 64(19).


Story Source:

The above story is based on materials provided by NIH/National Cancer Institute. Note: Materials may be edited for content and length.


Cite This Page:

NIH/National Cancer Institute. "Artificial Neural Networks Can Predict Clinical Outcomes Of Neuroblastoma Patients." ScienceDaily. ScienceDaily, 4 October 2004. <www.sciencedaily.com/releases/2004/10/041004083341.htm>.
NIH/National Cancer Institute. (2004, October 4). Artificial Neural Networks Can Predict Clinical Outcomes Of Neuroblastoma Patients. ScienceDaily. Retrieved July 31, 2014 from www.sciencedaily.com/releases/2004/10/041004083341.htm
NIH/National Cancer Institute. "Artificial Neural Networks Can Predict Clinical Outcomes Of Neuroblastoma Patients." ScienceDaily. www.sciencedaily.com/releases/2004/10/041004083341.htm (accessed July 31, 2014).

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