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Data pattern analysis for the individualised pretherapeutic identification of high‐risk diffuse large B‐cell lymphoma (DLBCL) patients by cytomics
Author(s) -
Valet Günter K.,
Hoeffkes Heinz Gert
Publication year - 2004
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.20057
Subject(s) - diffuse large b cell lymphoma , classifier (uml) , medicine , prospective cohort study , survival analysis , oncology , lymphoma , microarray analysis techniques , training set , bioinformatics , gene , gene expression , computer science , biology , artificial intelligence , biochemistry
Abstract Background Clinical outcome predictions in phase III studies are mostly derived for patient groups, but not for individual patients, although individualised predictions are an ultimate goal to permit a personalised fine tuning of therapy. This may permit earlier application of target therapies, minimise general damage to the organism, and result in improved complete remission rates in malignant diseases. Methods In this study, Lymphochip cDNA microarray gene expression results of DLBCL patients, from a published prospective meta‐analysis study on the prediction of group prognosis, were analysed for individualised predictions using a nonstatistical data pattern classification approach. The training set was comprised of the same 160 DLBCL patients as in the prognosis study, with the validation set of 80 patients remaining unknown to the learning process. This permits the assessment of prospective classifier performance towards unknown patients. Results Pretherapeutic predictions for the training and validation set patients were correct in 98.1% and 78.3% of the cases for nonsurvival and in 67.3% and 45.3% for survival. The discriminatory data pattern consisted of 14 known and 10 unknown gene products. Conclusions The better than 95% correct pretherapeutic prediction for about one‐half of the ultimately nonsurviving high‐risk patients of the training set is promising for clinical considerations about individualised therapy in such cases. Reliable individualised survival predictions are not possible with the information content of the present dataset. It seems necessary to investigate additional gene products, since survival may significantly depend on non–lymphocyte‐associated genes that escape to the lymphocyte‐oriented Lymphochip gene activation analysis. © 2004 Wiley‐Liss, Inc.