Predictive Medicine by Cytomics
Cell Biochemistry Group
1.1 Pharmaceuticals are typically developped according to best group (cohort) efficiency. Once approved they are applied to similar groups of patients. Some patients may, however, not benefit from a presently optimal therapy and are potentially harmed by unwanted therapeutic side effects (adverse drug reactions (ADRs)) despite the improved prognosis (=group future) of the entire patient group. This is suboptimal. Accurate predictions for the reactivity of the individual patient in such groups prior to therapy onset constitute therefore a primordial goal of predictive medicine by cytomics. Individualized disease course predictions will improve overall therapeutic efficiency, better comply with the " primum nil nocere" principle in medicine and meet the central patient interest to be cured of disease by an individually optimized therapy.
1.2 Predictive medicine by cytomics (molecular cell system analysis) (fig.1) aims at > 95% or higher accuracies for therapy related disease course or outcome predictions in individual patients by differential data pattern classification (predictive differentials, predictive differential classification) of molecular cell phenotypes or other molecular measurements in patients. Cells constitute the elementary function units of cell systems (cytomes), organs and organisms. Diseases are caused by molecular changes in cells. This means for the detection of early disease processes, cells know it always first, emphasizing the cytometric potential of determining altered molecular cell phenotypes emerging from genotype and exposure influences. In case disease inducing cells are not available, reactivity signs of immune indicator cells like cellular or humoral responses of lympho-/monocytes or granulocyte activation in blood or other body fluids can be probed.
Similar diseases may result either from high genotypic susceptibility and low exposure or alternatively from low genotypic susceptibility at high exposure. The high genotypic diversity in man at a comparatively low number of possible diseases emphasizes the potential of molecular cell phenotypes as diagnostic, therapy guiding and outcome prediction indicators in individual patients. Instead of searching to cure patients along their individual genotype it may be more promising to therapeutically alter molecuar cell phenotypes to reduce the number of potential therapies.
fig.1 System cytometry and cell systems biology
1.3 Differential classification masks are obtained by the iterative selection of the most discriminatory parameters from the initial triple matrix patterns constituted by all available patient parameters. The optimization process provides disease and patient classification masks (rightmost table columns) (hotspot heat masks). They represent direct or indirect molecular equivalents of disease processes. Such classification masks can be established for diseased or for disease associated cells such as for example inflammatory cells for the standardized classification of differential immune reactions. Either patterns may vary to a certain degree from patient to patient due to different combinations of genotype and exposure influences. This does, however, not influence the accuracy of the robust classification process. The individually optimal therapy (individualized medicine, personalized medicine) can be selected by data pattern classification of patient groups stratified for example according to Kaplan-Meier. The presented concept of personalized medicine concerns 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 wider application range than the pharmacogenomics or predictive medicine by genomics concepts of personalized medicine. It is based on algorithmically determined data patterns without requirement for statistical or correlation (dendrogram) analysis.
1.4 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).
1.5 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. 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.
1.6 Accuracy levels for individualized disease course predictions can be increased in principle from presently around 95% to 99% or higher upon merging the most informative parameters from different studies into the disease classification masks ("disease signatures"). The knowledge extraction by data pattern classification is independent of mathematical assumptions concerning the value distribution of parameters and the optimal classification is obtained unsupervised that is in an automated way with high certainty for the selection of the correct data pattern. The classification is also comparatively robust against the misclassification of random statistical aberrations as true aberrations.
1.7 The two-step research strategy consists of
i) hypothesis-driven parameter selection to establish
differential molecular cell phenotype patterns of diseased versus
healthy individuals, followed by ii) hypothesis-free
multiparameter data pattern classification (analysis, mining) for all
investigated cells in their full heterogeneity.
(periodic system of cells)
are obtained by the use of patient reference groups.
Discriminatory parameters from studies with different
hypotheses (deductive approach) are combined and classified
to enrich parameters (inductive approach) pointing
to disease causing molecular processes that remain, due
to a lack of knowledge, presently inaccessible to
The concept is to identify disease associated molecular hotspots in this way. This data-driven molecular top-down approach is in the early phase comparatively independent of prior knowledge about the ultimate molecular causes of disease. In particular there is no need to first analyze the molecular effects of hypothesis driven systematic perturbations of cellular model systems as they are frequently used to acquire knowledge about disease affected molecular pathways. Subsequently these pathways are investigated in detail by the bottom-up concept of systems biology (system biology). Concept-driven research such as molecular cytome exploration, in contrast, analyzes differential molecular disease patterns in patient cells, thus avoiding the detour of investigating molecular pathways in unsuitable cellular model systems. The cytome approach provides information on therapy dependent future disease development in individual patients and has the potential to simplify investigations on disease mechanisms significantly.
1.8 Once a certain molecular knowledge has been assembled, disease inducing molecular pathways can be 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. In this sense cytomics represents an entry to biomedical cell systems biology.
1.9 The described classification concept concentrates the differentially most informative molecular cell parameters within specific disease classification masks containing typically between 5 and 30 parameters and does not advocate for the determination of ever increasing parameter sets generating frequently interpretation difficulties at the individual patient level. The concept reaches from the expressed molecular cell phenotype as disease indicator 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 as illustrated below by 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 as proposed in the context of the human cytome project (PPT, ref181, 1, 2, 3, ref175, ref170, concepts, definitions, cytomics references) or in the establishment of a periodic system of cells with stem cells or other cell compartments as reference. Despite ressemblance in name, this concept differs significantly from the earlier concept for a plant periodic cell system.
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