Using genome-wide approaches, we have elucidated the regulatory circuitry governed by the XBP1 transcription factor, a key effector of the mammalian unfolded protein response (UPR), in skeletal muscle and secretory cells. We identified a core group of genes involved in constitutive maintenance of ER function in all cell types and tissue- and condition-specific targets. In addition, we identified a cadre of unexpected targets that link XBP1 to neurodegenerative and myodegenerative diseases, as well as to DNA damage and repair pathways. Remarkably, we found that XBP1 regulates functionally distinct targets through different sequence motifs. Further, we identified Mist1, a critical regulator of differentiation, as an important target of XBP1, providing an explanation for developmental defects associated with XBP1 loss of function. Our results provide a detailed picture of the regulatory roadmap governed by XBP1 in distinct cell types as well as insight into unexplored functions of XBP1.

We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the United States Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed, for these and qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Batch effects are the systematic non-biological differences between batches (groups) of samples in microarray experiments due to various causes such as differences in sample preparation and hybridization protocols. Previous work focused mainly on the development of methods for effective batch effects removal. However, their impact on cross-batch prediction performance, which is one of the most important goals in microarray-based applications, has not been addressed. This paper uses a broad selection of data sets from the Microarray Quality Control Phase II (MAQC-II) effort, generated on three microarray platforms with different causes of batch effects to assess the efficacy of their removal. Two data sets from cross-tissue and cross-platform experiments are also included. Of the 120 cases studied using Support vector machines (SVM) and K nearest neighbors (KNN) as classifiers and Matthews correlation coefficient (MCC) as performance metric, we find that Ratio-G, Ratio-A, EJLR, mean-centering and standardization methods perform better or equivalent to no batch effect removal in 89, 85, 83, 79 and 75% of the cases, respectively, suggesting that the application of these methods is generally advisable and ratio-based methods are preferred.

BackgroundProliferation of malignant plasma cells is a strong adverse prognostic factor in multiple myeloma and simultaneously targetable by available (e.g. tubulin polymerase inhibitors) and upcoming (e.g. aurora kinase inhibitors) compounds. Design and MethodsWe assessed proliferation using gene expression-based indices in 757 samples including independent cohorts of 298 and 345 samples of CD138-purified myeloma cells from previously untreated patients undergoing high-dose chemotherapy, together with clinical prognostic factors, chromosomal aberrations, and gene expression-based high-risk scores. ResultsIn the two cohorts, 43.3% and 39.4% of the myeloma cell samples showed a proliferation index above the median plus three standard deviations of normal bone marrow plasma cells. Malignant plasma cells of patients in advanced stages or those harboring disease progressionassociated gain of 1q21 or deletion of 13q14.3 showed significantly higher proliferation indices; patients with gain of chromosome 9, 15 or 19 (hyperdiploid samples) had significantly lower proliferation indices. Proliferation correlated with the presence of chromosomal aberrations in metaphase cytogenetics. It was significantly predictive for event-free and overall survival in both cohorts, allowed highly predictive risk stratification (e.g. event-free survival 12.7 versus 26.2 versus 40.6 months, P<0.001) of patients, and was largely independent of clinical prognostic factors, e.g. serum β2-microglobulin, International Staging System stage, associated high-risk chromosomal aberrations, e.g. translocation t(4;14), and gene expression-based high-risk scores. ConclusionsProliferation assessed by gene expression profiling, being independent of serum-β2-microglobulin, International Staging System stage, t(4;14), and gene expression-based risk scores, is a central prognostic factor in multiple myeloma. Surrogating a biological targetable variable, gene expression-based assessment of proliferation allows selection of patients for riskadapted anti-proliferative treatment on the background of conventional and gene expressionbased risk factors.

MicroRNAs (miRNAs) are noncoding RNAs that regulate global gene expression. miRNAs often act synergistically to repress target genes, and their dysregulation can contribute to the initiation and progression of a variety of cancers. The clinical relationship between global expression of miRNA and mRNA in cancer has not been studied in detail. We used whole-genome microarray analyses of CD138-enriched plasma cells from 52 newly diagnosed cases of multiple myeloma to correlate miRNA expression profiles with a validated mRNA-based risk stratification score, proliferation index, and predefined gene sets. In stark contrast to mRNAs, we discovered that all tested miRNAs were significantly up-regulated in high-risk disease as defined by a validated 70-gene risk score (P < 0.01) and proliferation index (P < 0.05). Increased expression of EIF2C2/AGO2, a master regulator of the maturation and function of miRNAs and a component of the 70-gene mRNA risk model, is driven by DNA copy number gains in MM. Silencing of AGO2 dramatically decreased viability in MM cell lines. Genome-wide elevated expression of miRNAs in high-risk MM may be secondary to deregulation of AGO2 and the enzyme complexes that regulate miRNA maturation and function. DICER1 | expression profile | multiple myeloma | risk stratification | system biology M icroRNAs (miRNAs) belong to a class of noncoding small RNAs with mature sequences that contain ≈22 nucleotides (1). As repressors of gene expression, miRNAs can bind to the 3′ untranslated region (3′UTR) of an mRNA and inhibit its translation or induce its degradation (1).Dysregulation of miRNA is involved in cancer initiation and progression (2, 3), and miRNA expression profiles have prognostic implications (4-6). Inhibiting miRNA has proved effective in vivo (7) and therefore could be a novel therapeutic strategy for cancer (8, 9). To date, few studies have investigated the roles of miRNA in multiple myeloma (MM), a plasma cell dyscrasia that homes to and expands in the bone marrow and produces disease manifestations that include osteolytic bone destruction with hypercalcemia, anemia, immunosuppression, and end-organ damage (10). Several miRNAs have been implicated in survival and growth of myeloma cells and myeloma tumor growth. For instance, miR-21 is a target of Stat3 and thus a critical component in IL-6/Stat3-dependent survival and growth pathways of myeloma cells (11). In addition, IL-6 inhibitor SOCS1 and p53 pathway component p300-CBPassociated factor are targets of multiple miRNAs, including miR-106b-25 cluster, miR-32, miR-181a/b, and miR-19a/b (12); suppression of these miRNAs inhibited myeloma tumor growth in nude mice (12).Applying miRNA expression profiles, Pichiorri et al. (12) identified miRNAs that were differentially expressed in plasma cells of healthy donors, subjects with a benign precursor to MM (monoclonal gammopathy of undetermined significance), and patients with MM. In other miRNA expression profiling studies, Roccaro et al. (13) To further investigate the potential involvement o...

Integrins are a family of transmembrane glycoprotein signaling receptors that can transmit bioinformation bidirectionally across the plasma membrane. Integrin αIIbβ3 is expressed at a high level in platelets and their progenitors, where it plays a central role in platelet functions, hemostasis, and arterial thrombosis. Integrin αIIbβ3 also participates in cancer progression, such as tumor cell proliferation and metastasis. In resting platelets, integrin αIIbβ3 adopts an inactive conformation. Upon agonist stimulation, the transduction of inside-out signals leads integrin αIIbβ3 to switch from a low-to high-affinity state for fibrinogen and other ligands. Ligand binding causes integrin clustering and subsequently promotes outside-in signaling, which initiates and amplifies a range of cellular events to drive essential platelet functions such as spreading, aggregation, clot retraction, and thrombus consolidation. Regulation of the bidirectional signaling of integrin αIIbβ3 requires the involvement of numerous interacting proteins, which associate with the cytoplasmic tails of αIIbβ3 in particular. Integrin αIIbβ3 and its signaling pathways are considered promising targets for antithrombotic therapy. This review describes the bidirectional signal transduction of integrin αIIbβ3 in platelets, as well as the proteins responsible for its regulation and therapeutic agents that target integrin αIIbβ3 and its signaling pathways.

Gene expression profiling (GEP) of purified plasma cells 48 hours after thalidomide and dexamethasone test doses showed these agents' mechanisms of action and provided prognostic information for untreated myeloma patients on Total Therapy 2 (TT2). Bortezomib was added in Total Therapy 3 (TT3), and 48 hours after bortezomib GEP analysis identified 80 highly survival-discriminatory genes in a training set of 142 TT3A patients that were validated in 128 patients receiving TT3B. The 80-gene GEP model (GEP80) also distinguished outcomes when applied at baseline in both TT3 and TT2 protocols. In context of our validated 70-gene model (GEP70), the GEP80 model identified 9% of patients with a grave prognosis among those with GEP70-defined low-risk disease and 41% of patients with favorable prognosis among those with GEP70-defined high-risk disease. PMSD4 was 1 of 3 genes common to both models. Residing on chromosome 1q21, PSMD4 expression is highly sensitive to copy number. Both higher PSMD4 expression levels and higher 1q21 copy numbers affected clinical outcome adversely. GEP80 baseline-defined high risk, high lactate dehydrogenase, and low albumin were the only independent adverse variables surviving multivariate survival model. We are investigating whether second-generation proteasome inhibitors (eg, carfilzomib) can overcome resistance associated with high PSMD4 levels.