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Predictive Medicine by Cytomics former:
Cell Biochemistry Group |
= external links
a. Predictive Medicine by cytomics (molecular cell systems analysis) represents a new concept for therapy related predictions of future disease course in individual patients with > 95% or higher accuracy. The concept is based on differential data pattern classification of molecular cell phenotypes from cytometric or other measurements. Cells constitute the elementary function units of cell systems (cytomes), organs and organisms. Diseases are caused by molecular changes in cells. This leads directly or indirectly to altered molecular cell phenotypes. Molecular cell phenotypes are the result of genotype and exposure influences (fig.1) during cell life. They are in a substantial number of instances more closely linked to the actual disease process in individual patients and to its future development than either genomic status or environmental influences alone.
b. Differential data patterns of molecular cell phenotypes constitute a direct or indirect molecular equivalent of the disease process, depending on whether diseased or disease associated cells are investigated. Individual data patterns may vary to a certain degree from patient to patient due to differences in the combination of genotype and exposure influences. This does, however, not necessarily influence the accuracy of the classification process (fig.9c/d). The individually optimal therapy in stratified patient groups can be selected by data pattern classification from several principally possible therapies following development of suitable classifiers (individualized medicine, personalized medicine for patient groups stratified for example according to Kaplan-Meier). The presented concept of personalized medicine addresses the care of diseased patients or of persons during disease development. It does not aim at the prediction of future disease occurrence from the person's individual genotype (transparent patient, vitreous man). The concept has a significantly wider application range than the pharmacogenomics or predictive medicine by genomics concepts of personalized medicine when confined to genotype analysis.
c. Patients with a prediction for "disease aggravation" may convert under therapy within some time to "non-complication" patients such as e.g. in intensive care medicine. The early detection of disease aggravation or amelioration provides a lead time for preventive therapy onset or for therapy reduction (preventive medicine).
d. The therapeutic lead time may increase overall therapeutic efficiency by the prevention or reduction of disease induced irreversible tissue damage or of unwanted therapeutic side effects (adverse drug reactions (ADRs)). It may also permit to identify risk patients prior to disease declaration like in asthma, rheumatic diseases or diabetes. This may help to delay disease outbreak and reduce complication rates as an important practical consequence.
e. The accuracy level for individualized disease course predictions can be increased in principle from presently 95% to 99% or higher upon merging the most informative parameters from different studies into the disease classification masks. The knowledge extraction by data pattern classification is independent of mathematical assumptions concerning the value distribution of parameters, the optimal classification is obtained in unsupervis that is automated way without danger of erroneously selecting suboptimal data patterns. The classification is also comparatively robust against the misclassification of random statistical aberrations as true aberrations.
f. The genomic information serves as inventory for the biomolecular capacity of organisms. It is used for the hypothesis-driven parameter selection to perform differential molecular cell phenotype screens of diseased and healthy organisms, in combination with the hypothesis-free multiparameter data pattern classification (analysis) of all investigated cells in their entire heterogeneity to identify disease associated molecular hotspots. This data-driven top-down approach is largely independent of prior knowledge about the ultimate molecular causes of disease. With the disease process as nature induced systematic perturbation, it is in particular not required to first analyze the molecular effects of hypothesis driven systematic perturbations of cellular model systems as a prerequisite for the subsequent study of specific molecular disease processes as in the bottom-up concept of systems biology (system biology). Cytome research bypasses initially the investigation of the frequently highly redundant molecular pathways of genome realisation and should thus simplify research work significantly.
g. Disease inducing molecular pathways are explored by a retrograde molecular analysis strategy (molecular reverse engineering) of molecular cell phenotype differentials at the cell systems level. The pathways can be mathematically modeled (biomedical cell systems biology) to further increase the predictive capacity. It is likely that new target molecules and lead structures for drug discovery will be detected by hypothesis-free data pattern classification due to its capacity to address unknown molecular knowledge spaces remaining hidden to the usual hypothesis development. In this sense cytomics represents an entry to biomedical cell systems biology.
h. The described classification concept reaches from the expressed
molecular cell phenotype as disease equivalent
down to the molecular coding information at the genome level.
The potential of the single patient, single cell oriented analysis
concept consists in its general applicability to various areas of clinical
or ambulant medicine. This is illustrated below by a number of
collaborative projects
with individual hospitals and
institutions as well as within the framework of the
European Working Group on Clinical Cell Analysis
(
EWGCCA)
in the context of clinical cytomics.
The apparent challenge is to advance this effort
to the patient level in a multistep effort
of scientists, clinicians and industry for example in the context of
recent efforts to conceptualize a
human cytome project
(PPT,
ref181,
1,
2,
3,
ref175,
ref170,
concepts,
definitions,
cytomics references)
or to materialize the concept for the establishment of a
periodic system of cells.
predictions in children cardiac surgery) Download the ZIP file containing all Cell Biochemistry pages for example into directory: d:\classimed\, unzip into the same directory, enter the address: file:///d:/classimed/cellbio.html into the URL field of the Internet browser to directly access text & figures on your harddisk free of network delays (further information).
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