%0 Journal Article %1 Schramm2005 %A Schramm, Alexander %A Schulte, Johannes H. %A Klein-Hitpass, Ludger %A Havers, Werner %A Sieverts, Hauke %A Berwanger, Bernd %A Christiansen, Holger %A Warnat, Patrick %A Brors, Benedikt %A Eils, Jürgen %A Eils, Roland %A Eggert, Angelika %D 2005 %J Oncogene %K Algorithms; Amplification; Analysis Analysis; Array Assessment; Cohort Decision Expression Gene Humans; Infant, Infant; Messenger, Neoplasm Neuroblastoma, Newborn; Nuclear Oligonucleotide Oncogene Profiling; Prognosis; Proteins, RNA, Receptor, Risk Sequence Staging; Studies; Survival Trees; analysis; genetics/pathology; genetics; trkA, %N 53 %P 7902--7912 %R 10.1038/sj.onc.1208936 %T Prediction of clinical outcome and biological characterization of neuroblastoma by expression profiling. %U http://dx.doi.org/10.1038/sj.onc.1208936 %V 24 %X Neuroblastoma is a common childhood tumor comprising cases with rapid disease progression as well as spontaneous regression. Although numerous prognostic factors have been identified, risk evaluation in individual patients remains difficult. To define a reliable prognostic predictor and gene signatures characteristic of biological subgroups, we performed mRNA expression profiling of 68 neuroblastomas of all stages. Expression data were analysed using support vector machines (SVM-rbf), prediction analysis of microarrays (PAM), k-nearest neighbors (k-NN) algorithms and multiple decision trees. SVM-rbf performed best of all methods, and predicted recurrence of neuroblastoma with an accuracy of 85\% (sensitivity 77\%, specificity 94\%). PAM identified a classifier of 39 genes reliably predicting outcome with an accuracy of 80\%. In comparison, conventional risk stratification based on stage, age and MYCN-status only reached a predictive accuracy of 64\%. Kaplan-Meier analysis using the PAM classifier indicated a 5-year survival of 20 versus 78\% for patients with unfavorably versus favorably predicted neuroblastomas, respectively (P = 0.0001). Significance analysis of microarrays (SAM) identified additional genes differentially expressed among subgroups. MYCN-amplification and high expression of NTRK1/TrkA demonstrated a strong association with specific gene expression patterns. Our data suggest that microarray-derived data in addition to traditional clinical factors will be useful for risk assessment and defining biological properties of neuroblastoma.

@article{Schramm2005, __markedentry = {[bbrors:6]}, abstract = {Neuroblastoma is a common childhood tumor comprising cases with rapid disease progression as well as spontaneous regression. Although numerous prognostic factors have been identified, risk evaluation in individual patients remains difficult. To define a reliable prognostic predictor and gene signatures characteristic of biological subgroups, we performed mRNA expression profiling of 68 neuroblastomas of all stages. Expression data were analysed using support vector machines (SVM-rbf), prediction analysis of microarrays (PAM), k-nearest neighbors (k-NN) algorithms and multiple decision trees. SVM-rbf performed best of all methods, and predicted recurrence of neuroblastoma with an accuracy of 85\% (sensitivity 77\%, specificity 94\%). PAM identified a classifier of 39 genes reliably predicting outcome with an accuracy of 80\%. In comparison, conventional risk stratification based on stage, age and MYCN-status only reached a predictive accuracy of 64\%. Kaplan-Meier analysis using the PAM classifier indicated a 5-year survival of 20 versus 78\% for patients with unfavorably versus favorably predicted neuroblastomas, respectively (P = 0.0001). Significance analysis of microarrays (SAM) identified additional genes differentially expressed among subgroups. MYCN-amplification and high expression of NTRK1/TrkA demonstrated a strong association with specific gene expression patterns. Our data suggest that microarray-derived data in addition to traditional clinical factors will be useful for risk assessment and defining biological properties of neuroblastoma.}, added-at = {2015-04-09T12:36:21.000+0200}, author = {Schramm, Alexander and Schulte, Johannes H. and Klein-Hitpass, Ludger and Havers, Werner and Sieverts, Hauke and Berwanger, Bernd and Christiansen, Holger and Warnat, Patrick and Brors, Benedikt and Eils, J{\"{u}}rgen and Eils, Roland and Eggert, Angelika}, biburl = {https://www.bibsonomy.org/bibtex/205495869b5a0a14e6e212a27939d066c/bbrors}, doi = {10.1038/sj.onc.1208936}, institution = {Department of Pediatric Oncology and Hematology, University Hospital of Essen, Hufelandstr 55, Essen 45122, Germany.}, interhash = {acbee7ae9e612e064ffe883896b1f645}, intrahash = {05495869b5a0a14e6e212a27939d066c}, journal = {Oncogene}, keywords = {Algorithms; Amplification; Analysis Analysis; Array Assessment; Cohort Decision Expression Gene Humans; Infant, Infant; Messenger, Neoplasm Neuroblastoma, Newborn; Nuclear Oligonucleotide Oncogene Profiling; Prognosis; Proteins, RNA, Receptor, Risk Sequence Staging; Studies; Survival Trees; analysis; genetics/pathology; genetics; trkA,}, language = {eng}, medline-pst = {ppublish}, month = Nov, number = 53, owner = {bbrors}, pages = {7902--7912}, pii = {1208936}, pmid = {16103881}, timestamp = {2015-04-09T12:36:21.000+0200}, title = {Prediction of clinical outcome and biological characterization of neuroblastoma by expression profiling.}, url = {http://dx.doi.org/10.1038/sj.onc.1208936}, volume = 24, year = 2005 }