Identification of prognostic and predictive genomic markers requires long-term clinical follow-up of patients. Extraction of high-quality DNA from archived formalin-fixed, paraffin-embedded material is essential for such studies. Of particular importance is a robust reproducible method of whole genome amplification for small tissue samples. This is especially true for high-resolution analytical approaches because different genomic regions and sequences may amplify differentially. We have tested a number of protocols for DNA amplification for array-based comparative genomic hybridization (CGH), in which relative copy number of the entire genome is measured at 1 to 2 mb resolution. Both random-primed amplification and degenerate oligonucleotide-primed amplification approaches were tested using varying amounts of fresh and paraffin-extracted normal and breast tumor input DNAs. We found that randomprimed amplification was clearly superior to degenerate oligonucleotide-primed amplification for arraybased CGH. The best quality and reproducibility strongly depended on accurate determination of the amount of input DNA using a quantitative polymerase chain reaction-based method. Reproducible and highquality results were attained using 50 ng of input DNA, and some samples yielded quality results with as little as 5 ng input DNA. We conclude that randomprimed amplification of DNA isolated from paraffin sections is a robust and reproducible approach for array-based CGH analysis of archival tumor samples. (J Mol Diagn 2005, 7:65-71)Genomic analysis of tumor DNA allows identification of alterations in sequence and copy number for individualized diagnostic, prognostic, and therapeutic decision making. This is especially relevant as personalized treatments for cancer patients based on specific genomic alterations become clinically available. Although DNA extracted from freshly acquired samples is optimum for these analyses, it is not always feasible to freeze away such samples given the constraints of clinical practice. Thus, having optimized protocols for extraction of DNA from formalin blocks is a necessary adjunct to recently developed clinical testing for tumor DNA alterations.One of the most useful approaches for analysis of DNA copy number alterations over the entire genome uses an array-based comparative genomic hybridization (CGH) analysis of DNA clones at 1-mb resolution.1-3 Current protocols for array CGH commonly require g quantities of high-quality DNA.4 Such material is generally not available from paraffin sections of formalin-fixed material, especially when tumors are small or require microdissection to remove contaminating normal or necrotic elements.A number of protocols have been reported for extraction and genomic amplification of archival material for application to standard (chromosome-based) or even array-based CGH, with varying quality of the resultant analyses. Degenerate oligonucleotide-primed (DOP) polymerase chain reaction (PCR) 5,6 and genomic representation amplification 7,8 have been tested using s...

Breast cancer subtype-specific molecular variations can dramatically affect patient responses to existing therapies. It is thought that differentially phosphorylated protein isoforms might be a useful prognostic biomarker of drug response in the clinic. However, the accurate detection and quantitative analysis of cancer-related protein isoforms and phospho-isoforms in tumors are limited by current technologies. Using a novel, fully automated nanocapillary electrophoresis immunoassay (NanoPro TM 1000) designed to separate protein molecules based on their isoelectric point, we developed a reliable and highly sensitive assay for the detection and quantitation of AKT isoforms and phosphoforms in breast cancer. This assay enabled the measurement of activated AKT1/2/3 in breast cancer cells using protein produced from as few as 56 cells. Importantly, we were able to assign an identity for the phosphorylated S473 phosphoform of AKT1, the major form of activated AKT involved in multiple cancers, including breast, and a current focus in clinical trials for targeted intervention. The ability of our AKT assay to detect and measure AKT phosphorylation from very low amounts of total protein will allow the accurate evaluation of patient response to drugs targeting activated PI3K-AKT using scarce clinical specimens. Moreover, the capacity of this assay to detect and measure all three AKT isoforms using one single pan-specific antibody enables the study of the multiple and variable roles that these isoforms play in AKT tumorigenesis. Molecular & Cellular Proteomics

Graphical models are widely used to make inferences concerning interplay in multivariate systems. In many applications, data are collected from multiple related but nonidentical units whose underlying networks may differ but are likely to share features. Here we present a hierarchical Bayesian formulation for joint estimation of multiple networks in this nonidentically distributed setting. The approach is general: given a suitable class of graphical models, it uses an exchangeability assumption on networks to provide a corresponding joint formulation. Motivated by emerging experimental designs in molecular biology, we focus on time-course data with interventions, using dynamic Bayesian networks as the graphical models. We introduce a computationally efficient, deterministic algorithm for exact joint inference in this setting. We provide an upper bound on the gains that joint estimation offers relative to separate estimation for each network and empirical results that support and extend the theory, including an extensive simulation study and an application to proteomic data from human cancer cell lines. Finally, we describe approximations that are still more computationally efficient than the exact algorithm and that also demonstrate good empirical performance.

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With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight across multiple dimensions. For this potential to be realized, we need a suitable representation to understand the data. Since a wide range of experiments and the unknown complexity of the underlying system contribute to the heterogeneity of biological data, we propose a method based on Robust Principal Component Analysis (RPCA), which is well suited for extracting principal components when there are corrupted observations. The proposed method provides us a new representation of these data sets in terms of a common and aberrant response. This representation might help users to acquire a new insight from data. Author SummaryOne of the most exciting trends and important themes in science and engineering involves the use of high-throughput measurement data. With different dimensions, for example, various perturbations, different doses of drug or cell lines characteristics, such multidimensional data sets enable us to understand commonalities and differences across multiple dimensions. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensional data sets is to examine and analyze the large-scale features and then to focus on the interesting details. With this notion, we propose an RPCA-based method which models common variations as approximately the low-rank component and anomalies as the sparse component. We show that the proposed method is able to find distinct subtypes and classify data sets in a robust way without any prior knowledge by separating these common responses and abnormal responses.