Han-Yu Chuang

The Subsystems Approach to Genome Annotation and its Use in the Project to Annotate 1000 Genomes

The release of the 1000th complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to Annotate 1000 Genomes. The project is built around the principle that the key to improved accuracy in high-throughput annotation technology is to have experts annotate single subsystems over the complete collection of genomes, rather than having an annotation expert attempt to annotate all of the genes in a single genome. Using the subsystems approach, all of the genes implementing the subsystem are analyzed by an expert in that subsystem. An annotation environment was created where populated subsystems are curated and projected to new genomes. A portable notion of a populated subsystem was defined, and tools developed for exchanging and curating these objects. Tools were also developed to resolve conflicts between populated subsystems. The SEED is the first annotation environment that supports this model of annotation. Here, we describe the subsystem approach, and offer the first release of our growing library of populated subsystems. The initial release of data includes 180 177 distinct proteins with 2133 distinct functional roles. This data comes from 173 subsystems and 383 different organisms.

Network-based classification of breast cancer metastasis

Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large‐scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein‐network‐based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non‐metastatic tumors.

Inferring pathway activity toward precise disease classification.

The advent of microarray technology has made it possible to classify disease states based on gene expression profiles of patients. Typically, marker genes are selected by measuring the power of their expression profiles to discriminate among patients of different disease states. However, expression-based classification can be challenging in complex diseases due to factors such as cellular heterogeneity within a tissue sample and genetic heterogeneity across patients. A promising technique for coping with these challenges is to incorporate pathway information into the disease classification procedure in order to classify disease based on the activity of entire signaling pathways or protein complexes rather than on the expression levels of individual genes or proteins. We propose a new classification method based on pathway activities inferred for each patient. For each pathway, an activity level is summarized from the gene expression levels of its condition-responsive genes (CORGs), defined as the subset of genes in the pathway whose combined expression delivers optimal discriminative power for the disease phenotype. We show that classifiers using pathway activity achieve better performance than classifiers based on individual gene expression, for both simple and complex case-control studies including differentiation of perturbed from non-perturbed cells and subtyping of several different kinds of cancer. Moreover, the new method outperforms several previous approaches that use a static (i.e., non-conditional) definition of pathways. Within a pathway, the identified CORGs may facilitate the development of better diagnostic markers and the discovery of core alterations in human disease.

A decade of systems biology.

Systems biology provides a framework for assembling models of biological systems from systematic measurements. Since the field was first introduced a decade ago, considerable progress has been made in technologies for global cell measurement and in computational analyses of these data to map and model cell function. It has also greatly expanded into the translational sciences, with approaches pioneered in yeast now being applied to elucidate human development and disease. Here, we review the state of the field with a focus on four emerging applications of systems biology that are likely to be of particular importance during the decade to follow: (a) pathway-based biomarkers, (b) global genetic interaction maps, (c) systems approaches to identify disease genes, and (d) stem cell systems biology. We also cover recent advances in software tools that allow biologists to explore system-wide models and to formulate new hypotheses. The applications and methods covered in this review provide a set of prime exemplars useful to cell and developmental biologists wishing to apply systems approaches to areas of interest.

ROR1 Is Expressed in Human Breast Cancer and Associated with Enhanced Tumor-Cell Growth

Receptor-tyrosine-kinase-like orphan receptor 1 (ROR1) is expressed during embryogenesis and by certain leukemias, but not by normal adult tissues. Here we show that the neoplastic cells of many human breast cancers express the ROR1 protein and high-level expression of ROR1 in breast adenocarcinoma was associated with aggressive disease. Silencing expression of ROR1 in human breast cancer cell lines found to express this protein impaired their growth in vitro and also in immune-deficient mice. We found that ROR1 could interact with casein kinase 1 epsilon (CK1ε) to activate phosphoinositide 3-kinase-mediated AKT phosphorylation and cAMP-response-element-binding protein (CREB), which was associated with enhanced tumor-cell growth. Wnt5a, a ligand of ROR1, could induce ROR1-dependent signaling and enhance cell growth. This study demonstrates that ROR1 is expressed in human breast cancers and has biological and clinical significance, indicating that it may be a potential target for breast cancer therapy.

Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species

The function of a protein is intimately tied to its subcellular localization. Although localizations have been measured for many yeast proteins through systematic GFP fusions, similar studies in other branches of life are still forthcoming. In the interim, various machine-learning methods have been proposed to predict localization using physical characteristics of a protein, such as amino acid content, hydrophobicity, side-chain mass and domain composition. However, there has been comparatively little work on predicting localization using protein networks. Here, we predict protein localizations by integrating an extensive set of protein physical characteristics over a proteins extended protein–protein interaction neighborhood, using a classification framework called ‘Divide and Conquer k-Nearest Neighbors’ (DC-kNN). These predictions achieve significantly higher accuracy than two well-known methods for predicting protein localization in yeast. Using new GFP imaging experiments, we show that the network-based approach can extend and revise previous annotations made from high-throughput studies. Finally, we show that our approach remains highly predictive in higher eukaryotes such as fly and human, in which most localizations are unknown and the protein network coverage is less substantial.

B-cell activating factor and v-Myc myelocytomatosis viral oncogene homolog (c-Myc) influence progression of chronic lymphocytic leukemia

Mice bearing a v-Myc myelocytomatosis viral oncogene homolog (c-Myc) transgene controlled by an Ig-alpha heavy-chain enhancer (iMycCα mice) rarely develop lymphomas but instead have increased rates of memory B-cell turnover and impaired antibody responses to antigen. We found that male progeny of iMycCα mice mated with mice transgenic (Tg) for CD257 (B-cell activating factor, BAFF) developed CD5+ B-cell leukemia resembling human chronic lymphocytic leukemia (CLL), which also displays a male gender bias. Surprisingly, leukemic cells of Myc/Baff Tg mice expressed higher levels of c-Myc than did B cells of iMycCα mice. We found that CLL cells of many patients with progressive disease also expressed high amounts of c-MYC, particularly CLL cells whose survival depends on nurse-like cells (NLC), which express high-levels of BAFF. We find that BAFF could enhance CLL-cell expression of c-MYC via activation the canonical IκB kinase (IKK)/NF-κB pathway. Inhibition of the IKK/NF-κB pathway in mouse or human leukemia cells blocked the capacity of BAFF to induce c-MYC or promote leukemia-cell survival and significantly impaired disease progression in Myc/Baff Tg mice. This study reveals an important relationship between BAFF and c-MYC in CLL which may affect disease development and progression, and suggests that inhibitors of the canonical NF-κB pathway may be effective in treatment of patients with this disease.

Chronic lymphocytic leukemia of Emu-TCL1 transgenic mice undergoes rapid cell turnover that can be offset by extrinsic CD257 to accelerate disease progression.

Results of heavy-water labeling studies have challenged the notion that chronic lymphocytic leukemia (CLL) represents an accumulation of noncycling B cells. We examined leukemia cell turnover in Emu-TCL1 transgenic (TCL1-Tg) mice, which develop a CLL-like disease at 8 to 12 months of age. We found that leukemia cells in these mice not only had higher proportions of proliferating cells but also apoptotic cells than did nonleukemic lymphocytes. We crossed TCL1-Tg with BAFF-Tg mice, which express high levels of CD257. TCL1 x BAFF-Tg mice developed CLL-like disease at a significantly younger age and had more rapid disease progression and shorter survival than TCL1-Tg mice. Leukemia cells of TCL1 x BAFF-Tg mice had similar proportions of proliferating cells, but fewer proportions of dying cells, than did the CLL cells of TCL1-Tg mice. Moreover, leukemia cells from either TCL1 x BAFF-Tg or TCL1-Tg mice produced more aggressive disease when transferred into BAFF-Tg mice than into wild-type (WT) mice. Neutralization of CD257 resulted in rapid reduction in circulating leukemia cells. These results indicate that the leukemia cells of TCL1-Tg mice undergo high levels of spontaneous apoptosis that is offset by relatively high rates of leukemia cell proliferation, which might allow for acquisition of mutations that contribute to disease evolution.

Proteome-wide discovery of mislocated proteins in cancer

Several studies have sought systematically to identify protein subcellular locations, but an even larger task is to map which of these proteins conditionally relocates in disease (the mislocalizome). Here, we report an integrative computational framework for mapping conditional location and mislocation of proteins on a proteome-wide scale, called a conditional location predictor (CoLP). Using CoLP, we mapped the locations of over 10,000 proteins in normal human brain and in glioma. The prediction showed 0.9 accuracy using 100 location tests of 20 randomly selected proteins. Of the 10,000 proteins, over 150 have a strong likelihood of mislocation under glioma, which is striking considering that few mislocation events have been identified in this disease previously. Using immunofluorescence and Western blotting in both primary cells and tissues, we successfully experimentally confirmed 15 mislocations. The most common type of mislocation occurs between the endoplasmic reticulum and the nucleus; for example, for RNF138, TLX3, and NFRKB. In particular, we found that the gene for the mislocating protein GFRA4 had a nonsynonymous point mutation in exon 2. Moreover, redirection of GFRA4 to its normal location, the plasma membrane, led to marked reductions in phospho-STAT3 and proliferation of glioma cells. This framework has the potential to track changes in protein location in many human diseases.

Abstract 3868: ROR1 is expressed in human breast cancer and associated with enhanced tumor-cell growth

Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL The receptor-tyrosine-kinase-like orphan receptor 1 (ROR1) was identified by a polymerase chain reaction (PCR)-based search for tyrosine kinases similar to the tropomyocin receptor kinase (Trk) neurotropic receptors. ROR1 and a related protein, ROR2, were identified as orphan receptors with an extracellular Frizzled-like, cysteine-rich domain, an extracellular, membrane-proximal kringle domain, and an intracellular tyrosine-kinase-like domain. Both ROR proteins are primarily expressed during embryogenesis. In prior studies, we and others found that ROR1 was expressed by leukemia cells and some cancer cell lines, and was involved in cell survival. However, it was not known whether breast tumor cells expressed ROR1 or whether its expression had functional and clinical significance. In the present study we used a high-affinity mAb specific for ROR1 (named 4A5) to examine human breast cancers and investigate its functional role for tumor growth. We examined fresh-frozen tumor biopsy specimens or tissue microarrays of neoplastic and normal adult tissues for ROR1. The neoplastic cells of high proportions of human breast cancers (70%) expressed ROR1, which was not detected on non-neoplastic adult breast tissues. We interrogated available DNA microarray datasets on primary human breast cancers and cancer cell lines, and fount that breast cancer cell lines or primary breast cancers that expressed high-levels of ROR1 were more likely to lack expression of estrogen or progesterone receptors or HER2/Neu. Conceivably, tumors that are poorly differentiated are more likely to express ROR1. Patients who had primary breast cancers that expressed higher levels of ROR1 had a significantly shorter median survival than did patients with primary breast cancers that had low-to-negligible expression of ROR1. We silenced ROR1 expression in breast cancer cell lines to evaluate its function on tumors. When silenced for ROR1 for MDA-MB-231 cells that expressed ROR1, MDA-MB-231 cells were more sensitive to spontaneous apoptosis and had an impaired cell growth in vitro and in vivo. On the other hand, MCF-7 cells that were transduced to express ROR1 had more aggressive growth characteristics than did control MCF-7 cells. We investigated the mechanism by ROR1 induced cell survival and found that ROR1 could interact with casein kinase 1 epsilon (CK1α) to activate phosphoinositide 3-kinase-mediated AKT phosphorylation and cAMP-response-element-binding protein (CREB), which in turn induce expression of genes (CCNB1, BCL2, and CCND1) that can enhance resistance to apoptosis and/or promote tumor growth. Moreover, Wnt5a, a ligand of ROR1, could induce ROR1-dependent signaling and enhance cell growth. This study demonstrates that ROR1 is expressed in human breast cancers and has biological and clinical significance, indicating that it may be a potential target for breast cancer therapy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3868. doi:1538-7445.AM2012-3868