Transcriptomic classification of diffuse large B-cell lymphoma identifies a high-risk activated B-cell-like subpopulation with targetable MYC dysregulation.

Immunochemotherapy has been the mainstay of treatment for newly diagnosed diffuse large B-cell lymphoma (ndDLBCL) yet is inadequate for many patients. In this work, we perform unsupervised clustering on transcriptomic features from a large cohort of ndDLBCL patients and identify seven clusters, one called A7 with poor prognosis, and develop a classifier to identify these clusters in independent ndDLBCL cohorts. This high-risk cluster is enriched for activated B-cell cell-of-origin, low immune infiltration, high MYC expression, and copy number aberrations. We compare and contrast our methodology with recent DLBCL classifiers to contextualize our clusters and show improved prognostic utility. Finally, using pre-clinical models, we demonstrate a mechanistic rationale for IKZF1/3 degraders such as lenalidomide to overcome the low immune infiltration phenotype of A7 by inducing T-cell trafficking into tumors and upregulating MHC I and II on tumor cells, and demonstrate that TCF4 is an important regulator of MYC-related biology in A7.

Multiomic analysis identifies a high-risk signature that predicts early clinical failure in DLBCL.

Recent genetic and molecular classification of DLBCL has advanced our knowledge of disease biology, yet were not designed to predict early events and guide anticipatory selection of novel therapies. To address this unmet need, we used an integrative multiomic approach to identify a signature at diagnosis that will identify DLBCL at high risk of early clinical failure. Tumor biopsies from 444 newly diagnosed DLBCL were analyzed by WES and RNAseq. A combination of weighted gene correlation network analysis and differential gene expression analysis was used to identify a signature associated with high risk of early clinical failure independent of IPI and COO. Further analysis revealed the signature was associated with metabolic reprogramming and identified cases with a depleted immune microenvironment. Finally, WES data was integrated into the signature and we found that inclusion of ARID1A mutations resulted in identification of 45% of cases with an early clinical failure which was validated in external DLBCL cohorts. This novel and integrative approach is the first to identify a signature at diagnosis, in a real-world cohort of DLBCL, that identifies patients at high risk for early clinical failure and may have significant implications for design of therapeutic options.

Multiomic Analysis Identifies a High-Risk Metabolic and TME Depleted Signature that Predicts Early Clinical Failure in DLBCL.

PURPOSE: 60-70% of newly diagnosed diffuse large B-cell lymphoma (DLBCL) patients avoid events within 24 months of diagnosis (EFS24) and the remainder have poor outcomes. Recent genetic and molecular classification of DLBCL has advanced our knowledge of disease biology, yet were not designed to predict early events and guide anticipatory selection of novel therapies. To address this unmet need, we used an integrative multiomic approach to identify a signature at diagnosis that will identify DLBCL at high risk of early clinical failure. PATIENTS AND METHODS: Tumor biopsies from 444 newly diagnosed DLBCL were analyzed by WES and RNAseq. A combination of weighted gene correlation network analysis and differential gene expression analysis followed by integration with clinical and genomic data was used to identify a multiomic signature associated with high risk of early clinical failure. RESULTS: Current DLBCL classifiers are unable to discriminate cases who fail EFS24. We identified a high risk RNA signature that had a hazard ratio (HR, 18.46 [95% CI 6.51-52.31] P < .001) in a univariate model, which did not attenuate after adjustment for age, IPI and COO (HR, 20.8 [95% CI, 7.14-61.09] P < .001). Further analysis revealed the signature was associated with metabolic reprogramming and a depleted immune microenvironment. Finally, WES data was integrated into the signature and we found that inclusion of ARID1A mutations resulted in identification of 45% of cases with an early clinical failure which was validated in external DLBCL cohorts. CONCLUSION: This novel and integrative approach is the first to identify a signature at diagnosis that will identify DLBCL at high risk for early clinical failure and may have significant implications for design of therapeutic options.

Comprehensive analysis of treatment response phenotypes in rheumatoid arthritis for pharmacogenetic studies.

BACKGROUND: An individual patient's response to a particular drug is influenced by multiple factors, which may include genetic predisposition. Pharmacogenetic studies attempt to discover and estimate the contributions of genetic variants to the variability in response to a drug treatment. The task of identifying the genetic contribution is often complicated by response phenotypes that are based on imprecise or subjective clinical observations. Because the success of a pharmacogenetic study depends on the analysis of a heritable phenotype, it is important to identify phenotypes with a significant heritable component to ensure reliable and reproducible results in subsequent genetic association studies. METHODS: We retrospectively analyzed data collected from 436 rheumatoid arthritis patients treated with golimumab during the phase III GO-FURTHER study. We investigated the reliability of several potential response outcomes after golimumab treatment. Using whole-genome sequencing of the clinical trial cohort, we estimated the heritability of each potential outcome measure. We further performed a longitudinal analysis of the clinical data to estimate variability of outcome measures over time and the degree to which each response metric could be confounded by placebo response. RESULTS: We determined that the high degree of within-patient variation over time makes a single follow-up visit insufficient to assess an individual patient's response to golimumab treatment. We found that different potential response outcomes had varying degrees of heritability and that averaging across multiple follow-up visits yielded higher heritability estimates than single follow-up estimates. Importantly, we found that the change in swollen and tender joint counts were the most heritable outcome metrics we tested; however, we showed that they are also more likely to be confounded by a placebo response than objective phenotypes like the change in C-reactive protein levels. CONCLUSIONS: Our rigorous approach to finding robust and heritable response phenotypes could be beneficial to all pharmacogenetic studies and may lead to more reliable and reproducible results. TRIAL REGISTRATION: Clinicaltrials.gov NCT00973479 . Registered 4 September 2009.

HLA-C*06:02 Allele and Response to IL-12/23 Inhibition: Results from the Ustekinumab Phase 3 Psoriasis Program.

Several small studies suggest that the presence of the human leukocyte antigen (HLA)-Cw6 (C*06:02) allele may be a predictor of improved response to ustekinumab. This study was designed to assess the association of the HLA-C*06:02 allele with response to ustekinumab in large cohorts of patients from the phase 3 studies of ustekinumab in moderate-to-severe psoriasis. In this retrospective study, both HLA-C*06:02-positive and -negative patients demonstrated good responses to ustekinumab (86% vs. 76%, respectively, achieved at least a 75% improvement from baseline in Psoriasis Area and Severity Index [PASI 75] at week 24). A modestly higher proportion of HLA-C*06:02-positive than HLA-C*06:02-negative patients achieved PASI 75/90 responses at weeks 12 and 24. The largest response difference between the positive and negative patients (17.9%) was observed for PASI 75 (week 12), with smaller differences noted at later time points for PASI 90 (11.8% at week 24) and PASI 100 (10.2% at week 28). A differential response to ustekinumab has been confirmed in HLA-C*06:02-positive versus HLA-C*06:02-negative patients; however, this difference is modest, particularly at the higher response rate thresholds (PASI 90/100) and later time points (weeks 24/28).

Integrative genomic deconvolution of rheumatoid arthritis GWAS loci into gene and cell type associations.

BACKGROUND: Although genome-wide association studies (GWAS) have identified over 100 genetic loci associated with rheumatoid arthritis (RA), our ability to translate these results into disease understanding and novel therapeutics is limited. Most RA GWAS loci reside outside of protein-coding regions and likely affect distal transcriptional enhancers. Furthermore, GWAS do not identify the cell types where the associated causal gene functions. Thus, mapping the transcriptional regulatory roles of GWAS hits and the relevant cell types will lead to better understanding of RA pathogenesis. RESULTS: We combine the whole-genome sequences and blood transcription profiles of 377 RA patients and identify over 6000 unique genes with expression quantitative trait loci (eQTLs). We demonstrate the quality of the identified eQTLs through comparison to non-RA individuals. We integrate the eQTLs with immune cell epigenome maps, RA GWAS risk loci, and adjustment for linkage disequilibrium to propose target genes of immune cell enhancers that overlap RA risk loci. We examine 20 immune cell epigenomes and perform a focused analysis on primary monocytes, B cells, and T cells. CONCLUSIONS: We highlight cell-specific gene associations with relevance to RA pathogenesis including the identification of FCGR2B in B cells as possessing both intragenic and enhancer regulatory GWAS hits. We show that our RA patient cohort derived eQTL network is more informative for studying RA than that from a healthy cohort. While not experimentally validated here, the reported eQTLs and cell type-specific RA risk associations can prioritize future experiments with the goal of elucidating the regulatory mechanisms behind genetic risk associations.

Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing studies.

MOTIVATION: Next-generation sequencing (NGS) technologies have become much more efficient, allowing whole human genomes to be sequenced faster and cheaper than ever before. However, processing the raw sequence reads associated with NGS technologies requires care and sophistication in order to draw compelling inferences about phenotypic consequences of variation in human genomes. It has been shown that different approaches to variant calling from NGS data can lead to different conclusions. Ensuring appropriate accuracy and quality in variant calling can come at a computational cost. RESULTS: We describe our experience implementing and evaluating a group-based approach to calling variants on large numbers of whole human genomes. We explore the influence of many factors that may impact the accuracy and efficiency of group-based variant calling, including group size, the biogeographical backgrounds of the individuals who have been sequenced, and the computing environment used. We make efficient use of the Gordon supercomputer cluster at the San Diego Supercomputer Center by incorporating job-packing and parallelization considerations into our workflow while calling variants on 437 whole human genomes generated as part of large association study. CONCLUSIONS: We ultimately find that our workflow resulted in high-quality variant calls in a computationally efficient manner. We argue that studies like ours should motivate further investigations combining hardware-oriented advances in computing systems with algorithmic developments to tackle emerging 'big data' problems in biomedical research brought on by the expansion of NGS technologies.

A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma.

Recent advances in genetics have spurred rapid progress towards the systematic identification of genes involved in complex diseases. Still, the detailed understanding of the molecular and physiological mechanisms through which these genes affect disease phenotypes remains a major challenge. Here, we identify the asthma disease module, i.e. the local neighborhood of the interactome whose perturbation is associated with asthma, and validate it for functional and pathophysiological relevance, using both computational and experimental approaches. We find that the asthma disease module is enriched with modest GWAS P-values against the background of random variation, and with differentially expressed genes from normal and asthmatic fibroblast cells treated with an asthma-specific drug. The asthma module also contains immune response mechanisms that are shared with other immune-related disease modules. Further, using diverse omics (genomics, gene-expression, drug response) data, we identify the GAB1 signaling pathway as an important novel modulator in asthma. The wiring diagram of the uncovered asthma module suggests a relatively close link between GAB1 and glucocorticoids (GCs), which we experimentally validate, observing an increase in the level of GAB1 after GC treatment in BEAS-2B bronchial epithelial cells. The siRNA knockdown of GAB1 in the BEAS-2B cell line resulted in a decrease in the NFkB level, suggesting a novel regulatory path of the pro-inflammatory factor NFkB by GAB1 in asthma.

Loss of toll-like receptor 3 function improves glucose tolerance and reduces liver steatosis in obese mice.

OBJECTIVE: Emerging evidence suggests a link between innate immunity and development of type 2 diabetes mellitus (T2D); however, the molecular mechanisms linking them are not fully understood. Toll-like Receptor 3 (TLR3) is a pathogen pattern recognition receptor that recognizes the double-stranded RNA of microbial or mammalian origin and contributes to immune responses in the context of infections and chronic inflammation. The objective of this study was to determine whether TLR3 activity impacts insulin sensitivity and lipid metabolism. MATERIALS AND METHODS: Wild type (WT) and TLR3 knock-out (TLR3(-/-)) mice were fed a high fat diet (HFD) and submitted to glucose tolerance tests (GTTs) over a period of 33 weeks. In another study, the same group of mice was treated with a neutralizing monoclonal antibody (mAb) against mouse TLR3. RESULTS: TLR3(-/-) mice fed an HFD developed obesity, although they exhibited improved glucose tolerance and lipid profiles compared with WT obese mice. In addition, the increase in liver weight and lipid content normally observed in WT mice on an HFD was significantly ameliorated in TLR3(-/-) mice. These changes were accompanied by up-regulation of genes involved in cholesterol efflux such as PPARδ, LXRα, and LXRα-targeting genes and down-regulation of pro-inflammatory cytokine and chemokine genes in obese TLR3(-/-) mice. Furthermore, global gene expression profiling in liver demonstrated TLR3-specific changes in both lipid biosynthesis and innate immune response pathways. CONCLUSIONS: TLR3 affects glucose and lipid metabolism as well as inflammatory mediators, and findings in this study reveal a new role for TLR3 in metabolic homeostasis. This suggests antagonizing TLR3 may be a beneficial therapeutic approach for the treatment of metabolic diseases.

Gene expression profiling and response signatures associated with differential responses to infliximab treatment in ulcerative colitis.

OBJECTIVES: Infliximab has been shown to induce clinical response and remission in ulcerative colitis (UC). To characterize the biological response of patients to infliximab, we analyzed the mRNA expression patterns of mucosal colonic biopsies taken from UC patients enrolled in the Active Ulcerative Colitis Trial 1 (ACT1) study. METHODS: Biopsies were obtained from 48 UC patients before treatment with 5 or 10 mg/kg infliximab, and at 8 and 30 weeks after treatment (n = 113 biopsies). Global gene expression profiling was performed using Affimetrix GeneChip Human Genome U133 Plus 2.0 arrays. Expression profiling results for selected genes were confirmed using qPCR. RESULTS: Infliximab had a significant effect on mRNA expression in treatment responders, with both infliximab dose and duration of treatment having an effect. Genes affected are primarily involved with inflammatory response, cell-mediated immune responses, and cell-to-cell signaling. Unlike responders, non-responders do not effectively modulate T(H1), T(H2), and T(H17) pathways. Gene expression can differentiate placebo and infliximab responders. CONCLUSIONS: Analysis of mRNA expression in mucosal biopsies following infliximab treatment provided insight into the response to therapy and molecular mechanisms of non-response.

Polymorphisms in the multiple drug resistance protein 1 and in P-glycoprotein 1 are associated with time to event outcomes in patients with advanced multiple myeloma treated with bortezomib and pegylated liposomal doxorubicin.

Single nucleotide polymorphisms (SNPs) in the multiple drug resistance protein 1 (MRP1) and P-glycoprotein 1 (MDR1) genes modulate their ability to mediate drug resistance. We therefore sought to retrospectively evaluate their influence on outcomes in relapsed and/or refractory myeloma patients treated with bortezomib or bortezomib with pegylated liposomal doxorubicin (PLD). The MRP1/R723Q polymorphism was found in five subjects among the 279 patient study population, all of whom received PLD + bortezomib. Its presence was associated with a longer time to progression (TTP; median 330 vs. 129 days; p = 0.0008), progression-free survival (PFS; median 338 vs. 129 days; p = 0.0006), and overall survival (p = 0.0045). MDR1/3435(C > T), which was in Hardy-Weinberg equilibrium, showed a trend of association with PFS (p = 0.0578), response rate (p = 0.0782) and TTP (p = 0.0923) in PLD + bortezomib patients, though no correlation was found in the bortezomib arm. In a recessive genetic model, MDR1/3435 T was significantly associated with a better TTP (p = 0.0405) and PFS (p = 0.0186) in PLD + bortezomib patients. These findings suggest a potential role for MRP1 and MDR1 SNPs in modulating the long-term outcome of relapsed and/or refractory myeloma patients treated with PLD + bortezomib. Moreover, they support prospective studies to determine if such data could be used to tailor therapy to the genetic makeup of individual patients.

Robust multi-tissue gene panel for cancer detection.

BACKGROUND: We have identified a set of genes whose relative mRNA expression levels in various solid tumors can be used to robustly distinguish cancer from matching normal tissue. Our current feature set consists of 113 gene probes for 104 unique genes, originally identified as differentially expressed in solid primary tumors in microarray data on Affymetrix HG-U133A platform in five tissue types: breast, colon, lung, prostate and ovary. For each dataset, we first identified a set of genes significantly differentially expressed in tumor vs. normal tissue at p-value = 0.05 using an experimentally derived error model. Our common cancer gene panel is the intersection of these sets of significantly dysregulated genes and can distinguish tumors from normal tissue on all these five tissue types. METHODS: Frozen tumor specimens were obtained from two commercial vendors Clinomics (Pittsfield, MA) and Asterand (Detroit, MI). Biotinylated targets were prepared using published methods (Affymetrix, CA) and hybridized to Affymetrix U133A GeneChips (Affymetrix, CA). Expression values for each gene were calculated using Affymetrix GeneChip analysis software MAS 5.0. We then used a software package called Genes@Work for differential expression discovery, and SVM light linear kernel for building classification models. RESULTS: We validate the predictability of this gene list on several publicly available data sets generated on the same platform. Of note, when analysing the lung cancer data set of Spira et al, using an SVM linear kernel classifier, our gene panel had 94.7% leave-one-out accuracy compared to 87.8% using the gene panel in the original paper. In addition, we performed high-throughput validation on the Dana Farber Cancer Institute GCOD database and several GEO datasets. CONCLUSIONS: Our result showed the potential for this panel as a robust classification tool for multiple tumor types on the Affymetrix platform, as well as other whole genome arrays. Apart from possible use in diagnosis of early tumorigenesis, some other potential uses of our methodology and gene panel would be in assisting pathologists in diagnosis of pre-cancerous lesions, determining tumor boundaries, assessing levels of contamination in cell populations in vitro and identifying transformations in cell cultures after multiple passages. Moreover, based on the robustness of this gene panel in identifying normal vs. tumor, mislabelled or misinterpreted samples can be pinpointed with high confidence.

ConsensusCluster: a software tool for unsupervised cluster discovery in numerical data.

We have created a stand-alone software tool, ConsensusCluster, for the analysis of high-dimensional single nucleotide polymorphism (SNP) and gene expression microarray data. Our software implements the consensus clustering algorithm and principal component analysis to stratify the data into a given number of robust clusters. The robustness is achieved by combining clustering results from data and sample resampling as well as by averaging over various algorithms and parameter settings to achieve accurate, stable clustering results. We have implemented several different clustering algorithms in the software, including K-Means, Partition Around Medoids, Self-Organizing Map, and Hierarchical clustering methods. After clustering the data, ConsensusCluster generates a consensus matrix heatmap to give a useful visual representation of cluster membership, and automatically generates a log of selected features that distinguish each pair of clusters. ConsensusCluster gives more robust and more reliable clusters than common software packages and, therefore, is a powerful unsupervised learning tool that finds hidden patterns in data that might shed light on its biological interpretation. This software is free and available from http://code.google.com/p/consensus-cluster .

Robust gene network analysis reveals alteration of the STAT5a network as a hallmark of prostate cancer.

We develop a general method to identify gene networks from pair-wise correlations between genes in a microarray data set and apply it to a public prostate cancer gene expression data from 69 primary prostate tumors. We define the degree of a node as the number of genes significantly associated with the node and identify hub genes as those with the highest degree. The correlation network was pruned using transcription factor binding information in VisANT (http://visant.bu.edu/) as a biological filter. The reliability of hub genes was determined using a strict permutation test. Separate networks for normal prostate samples, and prostate cancer samples from African Americans (AA) and European Americans (EA) were generated and compared. We found that the same hubs control disease progression in AA and EA networks. Combining AA and EA samples, we generated networks for low low (<7) and high (≥7) Gleason grade tumors. A comparison of their major hubs with those of the network for normal samples identified two types of changes associated with disease: (i) Some hub genes increased their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with gain of regulatory control in cancer (e.g. possible turning on of oncogenes). (ii) Some hubs reduced their degree in the tumor network compared to their degree in the normal network, suggesting that these genes are associated with loss of regulatory control in cancer (e.g. possible loss of tumor suppressor genes). A striking result was that for both AA and EA tumor samples, STAT5a, CEBPB and EGR1 are major hubs that gain neighbors compared to the normal prostate network. Conversely, HIF-lα is a major hub that loses connections in the prostate cancer network compared to the normal prostate network. We also find that the degree of these hubs changes progressively from normal to low grade to high grade disease, suggesting that these hubs are master regulators of prostate cancer and marks disease progression. STAT5a was identified as a central hub, with ~120 neighbors in the prostate cancer network and only 81 neighbors in the normal prostate network. Of the 120 neighbors of STAT5a, 57 are known cancer related genes, known to be involved in functional pathways associated with tumorigenesis. Our method is general and can easily be extended to identify and study networks associated with any two phenotypes.

Genome-wide analysis of mouse myeloma cell lines expressing therapeutic antibodies.

Manufacturing cell line development involves transfection of therapeutic antibody genes into host cell lines and isolation of primary transfectomas that upon subcloning yield high expressing cell lines secreting the desired antibody. In an attempt to increase productivity of these cell lines, we set out to identify cellular genes whose expression level may affect antibody productivity. For this purpose, three different sets of mouse myeloma production cell lines expressing variable levels of three different therapeutic antibodies were subjected to microarray analysis using Murine GeneChip MG_U74Av2 arrays. A total of 456 genes were identified showing significant differential expression between at least one high expresser versus the control or its corresponding low expresser. Among these, 161 genes were common among at least one set of cell lines, and 26 genes were common among two or more sets of cell lines. Functional classification revealed that a majority of these genes have biological process function related to cell metabolism and cell growth. A subset of the 26 genes that were identified as commonly regulated among any two or all three sets of cell lines were selected (by several criteria) for quantitative PCR confirmation of the microarray methodology. The expression level of two genes, Secretory Leukocyte Protease Inhibitor (SLPI) and Cell Division Cycle-6 (Cdc6), correlated with antibody productivity in at least two sets of cell lines, suggesting that they can potentially be utilized as targets for engineering a superior transfection host cell line. Additionally, these genes may be used for screening murine myeloma production cell lines for superior productivity.

Dermokine: an extensively differentially spliced gene expressed in epithelial cells.

Studies performed to discover genes overexpressed in inflammatory diseases identified dermokine as being upregulated in such disease conditions. Dermokine is a gene that was first observed as expressed in the differentiated layers of skin. Its two major isoforms, alpha and beta, are transcribed from different promoters of the same locus, with the alpha isoform representing the C terminus of the beta isoform. Recently, additional transcript variants have been identified. Extensive in silico analysis and reverse transcriptase (RT)-PCR cloning has confirmed the existence of these variants in human cells and tissues, identified a new human isoform as well as the gamma isoform in mouse. Recombinant expression and analysis of the C-terminal truncated isoform indicate that the molecule is O-linked glycosylated and forms multimers in solution. In situ hybridization and immunohistochemistry has shown that the gene is differentially expressed in various cells and tissues, other than the skin. These results show that the dermokine gene is expressed in epithelial tissues other than the skin and this expression is transcriptionally and posttranscriptionally complex.

A pathway analysis of poly(I:C)-induced global gene expression change in human peripheral blood mononuclear cells.

To gain global pathway perspective of ex vivo viral infection models using human peripheral blood mononuclear cells (PBMCs), we conducted expression analysis on PBMCs of healthy donors. RNA samples were collected at 3 and 24 h after PBMCs were challenged with the Toll-like receptor-3 (TLR3) agonist polyinosinic acid-polycytidylic acid [poly(I:C)] and analyzed by internally developed cDNA microarrays and TaqMan PCR. Our results demonstrate that poly(I:C) challenge can elicit certain gene expression changes, similar to acute viral infection. Hierarchical clustering revealed distinct immediate early, early-to-late, and late gene regulation patterns. The early responses were innate immune responses that involve TLR3, the NF-kappaB-dependent pathway, and the IFN-stimulated pathway, whereas the late responses were mostly cell-mediated immune response that involve activation of cell adhesion, cell mobility, and phagocytosis. Overall, our results expanded the utilities of this ex vivo model, which could be used to screen molecules that can modulate viral stress-induced inflammation, in particular those mediated via TLRs.