Using mRNA-Seq and clinical data for 469 clear cell Renal Cell Carcinoma (ccRCC) samples from The Cancer Genome Atlas (TCGA), we develop a protocol to identify patients likely to have early recurrence of their disease. We first split the data into two sets, with 380 samples in the training set and 89 samples in the test set. Using the training set, we identify genes whose outlier status (high or low mRNA expression) is predictive of recurrence, based on Kaplan-Meier recurrence free survival log-rank p-value. We find a significant overlap among genes identified as predictive biomarkers in Reads per Kilobase Million (RPKM) normalized data and Raw Reads mRNA-Seq data. Using 80 consensus genes predictive in both RPKM and Raw Reads data, we define an outlier-based risk score R to stratify patients into two groups, a high-risk (early recurrence) group (R2). The KM recurrence curve using this stratification shows excellent separation in training and test sets. Restricting the analysis to patients who had recurrence within two years (109 cases) and those who had no recurrence in five years (107 cases) we find that the risk predictor achieves ca. 80 percent sensitivity and specificity. The 80 genes identified by the outlier analysis were used to develop a more intuitive classifier based on Generalized Matrix Learning Vector Quantization (GMLVQ). This method stratifies samples into risk classes based on defining prototypes in feature space and an appropriate distance metric. GMLVQ identified a subset of 12 genes that have high accuracy in predicting recurrence, which suggests that an assay with a small number of genes might be able to predict recurrence in ccRCC.

MOTIVATION: Animal models are widely used in biomedical research for reasons ranging from practical to ethical. An important issue is whether rodent models are predictive of human biology. This has been addressed recently in the framework of a series of challenges designed by the systems biology verification for Industrial Methodology for Process Verification in Research (sbv IMPROVER) initiative. In particular, one of the sub-challenges was devoted to the prediction of protein phosphorylation responses in human bronchial epithelial cells, exposed to a number of different chemical stimuli, given the responses in rat bronchial epithelial cells. Participating teams were asked to make inter-species predictions on the basis of available training examples, comprising transcriptomics and phosphoproteomics data. RESULTS: Here, the two best performing teams present their data-driven approaches and computational methods. In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers. The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible. However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality. The post hoc analysis of time-specific measurements sheds light on the signaling pathways in both species. AVAILABILITY AND IMPLEMENTATION: A detailed description of the dataset, challenge design and outcome is available at www.sbvimprover.com. The code used by team IGB is provided under http://github.com/uci-igb/improver2013. Implementations of the algorithms applied by team AMG are available at http://bhanot.biomaps.rutgers.edu/wiki/AMG-sc2-code.zip. CONTACT: [email protected].

MOTIVATION: Using gene expression to infer changes in protein phosphorylation levels induced in cells by various stimuli is an outstanding problem. The intra-species protein phosphorylation challenge organized by the IMPROVER consortium provided the framework to identify the best approaches to address this issue. RESULTS: Rat lung epithelial cells were treated with 52 stimuli, and gene expression and phosphorylation levels were measured. Competing teams used gene expression data from 26 stimuli to develop protein phosphorylation prediction models and were ranked based on prediction performance for the remaining 26 stimuli. Three teams were tied in first place in this challenge achieving a balanced accuracy of about 70%, indicating that gene expression is only moderately predictive of protein phosphorylation. In spite of the similar performance, the approaches used by these three teams, described in detail in this article, were different, with the average number of predictor genes per phosphoprotein used by the teams ranging from 3 to 124. However, a significant overlap of gene signatures between teams was observed for the majority of the proteins considered, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched in the union of the predictor genes of the three teams for multiple proteins. AVAILABILITY AND IMPLEMENTATION: Gene expression and protein phosphorylation data are available from ArrayExpress (E-MTAB-2091). Software implementation of the approach of Teams 49 and 75 are available at http://bioinformaticsprb.med.wayne.edu and http://people.cs.clemson.edu/∼luofeng/sbv.rar, respectively. CONTACT: [email protected] or [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Translating findings in rodent models to human models has been a cornerstone of modern biology and drug development. However, in many cases, a naive 'extrapolation' between the two species has not succeeded. As a result, clinical trials of new drugs sometimes fail even after considerable success in the mouse or rat stage of development. In addition to in vitro studies, inter-species translation requires analytical tools that can predict the enriched gene sets in human cells under various stimuli from corresponding measurements in animals. Such tools can improve our understanding of the underlying biology and optimize the allocation of resources for drug development. RESULTS: We developed an algorithm to predict differential gene set enrichment as part of the sbv IMPROVER (systems biology verification in Industrial Methodology for Process Verification in Research) Species Translation Challenge, which focused on phosphoproteomic and transcriptomic measurements of normal human bronchial epithelial (NHBE) primary cells under various stimuli and corresponding measurements in rat (NRBE) primary cells. We find that gene sets exhibit a higher inter-species correlation compared with individual genes, and are potentially more suited for direct prediction. Furthermore, in contrast to a similar cross-species response in protein phosphorylation states 5 and 25 min after exposure to stimuli, gene set enrichment 6 h after exposure is significantly different in NHBE cells compared with NRBE cells. In spite of this difference, we were able to develop a robust algorithm to predict gene set activation in NHBE with high accuracy using simple analytical methods. AVAILABILITY AND IMPLEMENTATION: Implementation of all algorithms is available as source code (in Matlab) at http://bhanot.biomaps.rutgers.edu/wiki/codes_SC3_Predicting_GeneSets.zip, along with the relevant data used in the analysis. Gene sets, gene expression and protein phosphorylation data are available on request. CONTACT: [email protected].

The presence of lymphocytic infiltration (LI) has been correlated with nodal metastasis and tumor recurrence in HER2+breast cancer (BC), making it important to study LI. The ability to detect and quantify extent of LI could serve as an image based prognostic tool for HER2+ BC patients. Lymphocyte segmentation in H & E-stained BC histopathology images is, however, complicated due to the similarity in appearance between lymphocyte nuclei and cancer nuclei. Additional challenges include biological variability, histological artifacts, and high prevalence of overlapping objects. Although active contours are widely employed in segmentation, they are limited in their ability to segment overlapping objects. In this paper, we propose a segmentation scheme (EMaGACOR) that integrates Expectation Maximization (EM) based segmentation with a geodesic active contour (GAC). Additionally, a novel heuristic edge-path algorithm exploits the size of lymphocytes to split contours that enclose overlapping objects. For a total of 62 HER2+ breast biopsy images, EMaGACOR was found to have a detection sensitivity of over 90% and a positive predictive value of over 78%. By comparison, EMaGAC (model without overlap resolution) and GAC (Randomly initialized geodesic active contour) model yielded corresponding sensitivities of 57.4% and 26.7%, respectively. Furthermore, EMaGACOR was able to resolve over 92% of overlaps. Our scheme was found to be robust, reproducible, accurate, and could potentially be applied to other biomedical image segmentation applications.

BACKGROUND: Clustering analysis of microarray data is often criticized for giving ambiguous results because of sensitivity to data perturbation or clustering techniques used. In this paper, we describe a new method based on principal component analysis and ensemble consensus clustering that avoids these problems. RESULTS: We illustrate the method on a public microarray dataset from 36 breast cancer patients of whom 31 were diagnosed with at least two of three pathological stages of disease (atypical ductal hyperplasia (ADH), ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC). Our method identifies an optimum set of genes and divides the samples into stable clusters which correlate with clinical classification into Luminal, Basal-like and Her2+ subtypes. Our analysis reveals a hierarchical portrait of breast cancer progression and identifies genes and pathways for each stage, grade and subtype. An intriguing observation is that the disease phenotype is distinguishable in ADH and progresses along distinct pathways for each subtype. The genetic signature for disease heterogeneity across subtypes is greater than the heterogeneity of progression from DCIS to IDC within a subtype, suggesting that the disease subtypes have distinct progression pathways. Our method identifies six disease subtype and one normal clusters. The first split separates the normal samples from the cancer samples. Next, the cancer cluster splits into low grade (pathological grades 1 and 2) and high grade (pathological grades 2 and 3) while the normal cluster is unchanged. Further, the low grade cluster splits into two subclusters and the high grade cluster into four. The final six disease clusters are mapped into one Luminal A, three Luminal B, one Basal-like and one Her2+. CONCLUSION: We confirm that the cancer phenotype can be identified in early stage because the genes altered in this stage progressively alter further as the disease progresses through DCIS into IDC. We identify six subtypes of disease which have distinct genetic signatures and remain separated in the clustering hierarchy. Our findings suggest that the heterogeneity of disease across subtypes is higher than the heterogeneity of the disease progression within a subtype, indicating that the subtypes are in fact distinct diseases.

We describe our methods for performing quantum chromodynamics (QCD) simulations that sustain up to 20% of the peak performance on BlueGene supercomputers. We present our methods, scaling properties, and first cutting edge results relevant to QCD. We show how this enables unprecedented computational scale that brings lattice QCD to the next generation of calculations. We present our QCD simulation that achieved 12.2 Teraflops sustained performance with perfect speedup to 32K CPU cores. Among other things, these calculations are critical for cosmology, for the heavy ion experiments at RHIC-BNL, and for the upcoming experiments at CERN-Geneva. Furthermore, we demonstrate how QCD dramatically exposes memory and network latencies inherent in any computer system and propose that QCD should be used as a new, powerful HPC benchmark. Our sustained performance demonstrates the excellent properties of the BlueGene/L system.

Blue Gene/L represents a new way to build supercomputers, using a large number of low power processors, together with multiple integrated interconnection networks. Whether real applications can scale to tens of thousands of processors (on a machine like Blue Gene/L) has been an open question. In this paper, we describe early experience with several physics and material science applications on a 32,768 node Blue Gene/L system, which was installed recently at the Lawrence Livermore National Laboratory. Our study shows some problems in the applications and in the current software implementation, but overall, excellent scaling of these applications to 32K nodes on the current Blue Gene/L system. While there is clearly room for improvement, these results represent the first proof point that MPI applications can effectively scale to over ten thousand processors. They also validate the scalability of the hardware and software architecture of Blue Gene/L.

Summary form only given. Large powerful networks coupled to state-of-the-art processors have traditionally dominated supercomputing. As technology advances, this approach is likely to be challenged by a more cost-effective System-On-A-Chip approach, with higher levels of system integration. The scalability of applications to architectures with tens to hundreds of thousands of processors is critical to the success of this approach. Significant progress has been made in mapping numerous compute-intensive applications, many of them grand challenges, to parallel architectures. Applications hoping to efficiently execute on future supercomputers of any architecture must be coded in a manner consistent with an enormous degree of parallelism. The BG/L program is developing a peak nominal 180 TFLOPS (360 TFLOPS for some applications) supercomputer to serve a broad range of science applications. BG/L generalizes QCDOC, the first System-On-A-Chip supercomputer that is expected in 2003. BG/L consists of 65,536 nodes, and contains five integrated networks: a 3D torus, a combining tree, a Gb Ethernet network, barrier/global interrupt network and JTAG.

This paper gives an overview of the BlueGene/L Supercomputer. This is a jointly funded research partnership between IBM and the Lawrence Livermore National Laboratory as part of the United States Department of Energy ASCI Advanced Architecture Research Program. Application performance and scaling studies have recently been initiated with partners at a number of academic and government institutions,including the San Diego Supercomputer Center and the California Institute of Technology. This massively parallel system of 65,536 nodes is based on a new architecture that exploits system-on-a-chip technology to deliver target peak processing power of 360 teraFLOPS (trillion floating-point operations per second). The machine is scheduled to be operational in the 2004-2005 time frame, at price/performance and power consumption/performance targets unobtainable with conventional architectures.

We study a neuron functional form (which we call the absolute value neuron) that arises naturally in analog devices in which the neuron synapses are realized using coherent oscillatory wave signals. We discuss algorithmic aspects of this neuron at both the single neuron and network level, including computational capabilities, generalization, and training. A numerical study of absolute value neural networks on two data sets is presented, which demonstrates performance competitive with standard neural networks.