Data pattern analysis for the individualised pretherapeutic identification of high-risk diffuse large B-cell lymphoma (DLBCL) patients by cytomics.

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.

Cytomics in predictive medicine.

Patient-specific, disease-course predictions with >95% or >99% accuracy during therapy would be highly valuable for everyday medicine. If these predictors were available, disease aggravation or progression, frequently accompanied by irreversible tissue damage or therapeutic side effects, could then potentially be avoided by early preventive therapy. The molecular analysis of heterogeneous cellular systems (cytomics) by cytometry in conjunction with pattern-oriented bioinformatic analysis of the multiparametric cytometric and other data provides a promising approach to individualized or personalized medical treatment or disease management. As a consequence, better patient care and new forms of inductive scientific hypothesis development based on the interpretation of predictive data patterns are at reach.