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Cell Biochemistry Martinsried |
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1. Background:
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.
2. Goal:
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%
accuracy.
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.
3. Results:
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),
colorectal cancer,
intensive care medicine,
cardiac surgery and
systemic lupus erythematosus (SLE)
(further
examples,
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).
4. Conclusion:
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.
Literature References:
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 |