An integrated approach to prognosis using protein microarrays and nonparametric methods

Abstract

Over the past several years, multivariate approaches have been developed that address the problem of disease diagnosis. Here, we report an integrated approach to the problem of prognosis that uses protein microarrays to measure a focused set of molecular markers and non-parametric methods to reveal non-linear relationships among these markers, clinical variables, and patient outcome. As proof-of-concept, we applied our approach to the prediction of early mortality in patients initiating kidney dialysis. We found that molecular markers are not uniformly prognostic, but instead vary in their value depending on a combination of clinical variables. This may explain why reports in this area aiming to identify prognostic markers, without taking into account clinical variables, are either conflicting or show that markers have marginal prognostic value. Just as treatments are now being tailored to specific subsets of patients, our results show that prognosis can also benefit from a 'personalized' approach.

Human Systems Biology

“…To human samples is that the elevated amount of admonition gathered can facade to more robust models able to anticipate vulnerability to disease, answer to treatment and, conceivably even more challenging, help in the prognosis of disease aftereffect. It is the latter question of prognosis that is declamation in the study by MacBeath and co-workers [5], where assimilation of clinical parameters with protein microarray magnitude of blood samples allows augmented prediction of early mortality of patients initiating a kidney dialysis treatment. Progressive application of these technologies is likely to be instrumental in opening the door to the epoch of personalized medicine with perfectly strategies circumferential all dimensions of clinical practice, including prevention, diagnosis, treatment and prognosis.…”

Intestinal Ischemia/Reperfusion Injury: Defining the Role of the Gut Microbiome

“…Uniquely, it permits a 'top down' view of complex biological systems; for instance, by studying hard biological end points (metabolites), it is possible to extrapolate backwards in a more efficient manner to targeted biological pathways and gene sequences [120]. Recent systems approaches suggest that protein microarrays of patients undergoing kidney dialysis analyzed by nonparametric methods are able to reveal nonlinear relationships among specific biomarkers, the clinical variables and, perhaps most importantly, patient outcome [121]. Indeed, the authors were able to accurately predict early mortality in patients started on dialysis.…”

Fusing Tissue Engineering and Systems Biology Toward Fulfilling Their Promise

“…A wide variety of high-throughput and multiplex experimental techniques are utilized to collect data sets for systems models. These include mass spectrometry, 67 kinase activity assays, 28,59 immunoblotting, 14 'in-cell westerns', 41 bead-based arrays, 48 protein microarrays, 40,58 and multicolor flow 54,57 and image 11,46 cytometry.…”