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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. 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. 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. Analysis of mRNA expression in mucosal biopsies following infliximab treatment provided insight into the response to therapy and molecular mechanisms of non-response.

Up to 40% of diffuse large B-cell lymphoma (DLBCL) patients do not experience a durable response to frontline immunochemotherapy, and prospective identification of high-risk cases that may benefit from personalized therapeutic management remains an unmet need. Molecular phenotyping techniques have established a landscape of genomic variants in diagnostic DLBCL; however, these have not yet been applied in large-scale studies of relapsed/refractory DLBCL, resulting in incomplete characterization of mechanisms driving tumor progression and treatment resistance. Here, we performed an integrated multiomic analysis on 228 relapsed/refractory DLBCL samples, including 24 with serial biopsies. Refined cell-of-origin subtyping identified patients harboring GCB and DZsig+ relapsed/refractory tumors in cases with primary refractory disease with remarkably poor outcomes, and comparative analysis of genomic features between relapsed and diagnostic samples identified subtype-specific mechanisms of therapeutic resistance driven by frequent alteration to MYC , BCL2 , BCL6 , and TP53 among additional strong lymphoma driver genes. Tumor evolution dynamics suggest innate mechanisms of chemoresistance are present in many DLBCL tumors at diagnosis, and that relapsed/refractory tumors are primarily comprised of a homogenous clonal expansion with reduced tumor microenvironment activity. Adaptation of personalized therapeutic strategies targeting DLBCL subtype-specific resistance mechanisms should be considered to benefit these high-risk populations.

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.

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.

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. Researchers identified 7 biological group of diffuse large B-cell lymphoma, one with poor prognosis. They developed a gene expression classifier to detect these groups, potentially improving prognosis and future treatments for the high-risk patients.

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.

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, α and β, are transcribed from different promoters of the same locus, with the α isoform representing the C terminus of the β 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 γ 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 transciptionally and posttranscriptionally complex.

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.

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.