Cytomics

Apparent molecular cell phenotypes in the naturally existing cellular heterogeneity of disease affected body cytomes represent an individualized correlate of the disease process as sum of the respective genotypic and exposure influences. The cell phenotypes contain information about the present disease status (diagnosis) as well as on its therapy dependent future development (outcome prediction), since diseases are caused by molecular changes in cell systems or organs. The analysis of the full heterogeneity of cellular data opens the way for individualised disease course predictions in stratified patient groups (e.g. according to Kaplan-Meier). Predictions provide a therapeutic lead time to prevent irreversible tissue damage. It may also be possible to retard or prevent disease outbreak like for asthma by early recognition of a sensibilisation phase. Immediate sanitation of patients environment may then postpone or prevent disease declaration as a significant advantage for patients.

Data classifications are presently considered predictive for individual patients at predictive values >95% for each classified disease category of the learning set while they are prognostic at values <95%. The effort will be to elevate this level to >99% through the search for more efficiently discriminating molecular data patterns.

a.) multiparametric cytometric determination of cell constituents or cell functions in disease associated cytomes
b.) analysis (1, 2) of all measured numeric parameters for all cell populations that is in practice for >95% of the collected cells typically at the time of diagnosis establishment
c.) data pattern (heat map) classification of this entire information against patient's future disease course during the learning phase by exhaustive knowledge extraction
d.) classification of the embedded test set of patient data, measured under the same conditions as the learning set but remaining unknown to the learning process. Typically, every 5th or 10th patient is assigned to the test set prior to the learning phase to exclude classification biases.
e.) prospective classification of data collected from subsequent new patients during the clinical evaluation phase