Nanoparticle technology: addressing the fundamental roadblocks to protein biomarker discovery.

Clinically relevant biomarkers exist in blood and body fluids in extremely low concentrations, are masked by high abundance high molecular weight proteins, and often undergo degradation during collection and transport due to endogenous and exogenous proteinases. Nanoparticles composed of a N-isopropylacrylamide hydrogel core shell functionalized with internal affinity baits are a new technology that can address all of these critical analytical challenges for disease biomarker discovery and measurement. Core-shell, bait containing, nanoparticles can perform four functions in one step, in solution, in complex biologic fluids (e.g. blood or urine): a) molecular size sieving, b) complete exclusion of high abundance unwanted proteins, c) target analyte affinity sequestration, and d) complete protection of captured analytes from degradation. Targeted classes of protein analytes sequestered by the particles can be concentrated in small volumes to effectively amplify (up to 100 fold or greater depending on the starting sample volume) the sensitivity of mass spectrometry, western blotting, and immunoassays. The materials utilized for the manufacture of the particles are economical, stable overtime, and remain fully soluble in body fluids to achieve virtually 100 percent capture of all solution phase target proteins within a few minutes.

Marker identification and classification of cancer types using gene expression data and SIMCA.

OBJECTIVES: High-throughput technologies are radically boosting the understanding of living systems, thus creating enormous opportunities to elucidate the biological processes of cells in different physiological states. In particular, the application of DNA micro-arrays to monitor expression profiles from tumor cells is improving cancer analysis to levels that classical methods have been unable to reach. However, molecular diagnostics based on expression profiling requires addressing computational issues as the overwhelming number of variables and the complex, multi-class nature of tumor samples. Thus, the objective of the present research has been the development of a computational procedure for feature extraction and classification of gene expression data. METHODS: The Soft Independent Modeling of Class Analogy (SIMCA) approach has been implemented in a data mining scheme, which allows the identification of those genes that are most likely to confer robust and accurate classification of samples from multiple tumor types. RESULTS: The proposed method has been tested on two different microarray data sets, namely Golub's analysis of acute human leukemia and the small round blue cell tumors study presented by Khan et al.. The identified features represent a rational and dimensionally reduced base for understanding the biology of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for classification of pathological states. CONCLUSIONS: The analysis of the SIMCA model residuals allows the identification of specific phenotype markers. At the same time, the class analogy approach provides the assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances.

PCA disjoint models for multiclass cancer analysis using gene expression data.

MOTIVATION: Microarray expression profiling appears particularly promising for a deeper understanding of cancer biology and to identify molecular signatures supporting the histological classification schemes of neoplastic specimens. However, molecular diagnostics based on microarray data presents major challenges due to the overwhelming number of variables and the complex, multiclass nature of tumor samples. Thus, the development of marker selection methods, that allow the identification of those genes that are most likely to confer high classification accuracy of multiple tumor types, and of multiclass classification schemes is of paramount importance. RESULTS: A computational procedure for marker identification and for classification of multiclass gene expression data through the application of disjoint principal component models is described. The identified features represent a rational and dimensionally reduced base for understanding the basic biology of diseases, defining targets for therapeutic intervention, and developing diagnostic tools for the identification and classification of multiple pathological states. The method has been tested on different microarray data sets obtained from various human tumor samples. The results demonstrate that this procedure allows the identification of specific phenotype markers and can classify previously unseen instances in the presence of multiple classes.