Cell Biochemistry Martinsried
The future development of diseases is addressed
in most instances as prognosis. Prognosis reflects the
average statistical experience with disease development in
large patient groups under therapy. Prognostic indicators are
frequently utilized for patient stratification in therapy optimizing
trials in the effort to treat as many patients as possible
with the most efficient therapy.
- A frequently encountered problem concerns larger or smaller subgroups of patients who will not benefit from standard therapy and potentially even suffer from therapeutic side effects.
- It would be a significant help for the clinician if a-priori non responsive patients could be identified pretherapeutically (L1,L2). This would allow individualized therapy modifications as well as the use of alternative or preventive therapies.
- One may think that the description of more and more independant prognostic factors may automatically lead to prediction. This is not necessarily true since prognosis addresses patient groups and not individual patients. Smoking as an example is a good prognostic indicator for later lung cancer but no good predictor since not all smokers will develop lung cancer and lung cancer is also observed in non smokers for other reasons.
With this in mind it seems important to specifically
orient multiparameter data analysis in cytometry, chip
or bead arrays, clinical chemistry and clinical parameters
towards individualized predictions with > 95% or > 99%
Individualized predictions in multiparameter data analysis can be addressed by data sieving as a fast operating algorithmic method which requires no mathematical assumptions on the value distributions of analysed parameters and no substitution of missing data values or removal of patients with incomplete data sets.
Individualized risk assessment or prediction of
therapeutic success by data sieving analysis
was amongst other possible in:
acute myeloid leukemia (AML),
diffuse large B-cell lymphoma (DLBCL),
intensive care medicine,
cardiac surgery and
systemic lupus erythematosus (SLE)
Cell Biochemistry, Martinsried).
- The comparison of predictive and prognostic data patterns seems of particular importance because it may explain the inhomogeneuus therapeutic response in prognostically well characterized patient groups.
- Predictive and prognostic data patterns show only a limited coincidence of selected parameters in AML ( pred, prog) and DLBCL ( pred, prog).
The multiparametric information which is typically
collected in the clinical environment permits
to address individualized predictions or risk assessments
of high accuracy in daily medical practice.
- Besides the obvious advantage for the individual patient, the differences in predictive and prognostic data patterns allow significant new insights into the hetereogeneity and variability of disease processes.
- Considering the development of new drugs, the availability of disease specific predictive and prognostic data patterns provides substantially increased possibilities for the identification of suitable candidate target genes in pharmaceutical developments.
L1 Valet G. Predictive Medicine by Cytomics: Potential and Challenges. JBRHA 16:164-167, (2002)
L2 Valet G., A.Tarnok Cytomics in Predictive Medicine Cytometry (2003) in press
|© 2003 G.Valet|