Gene expression profiling of non-polyadenylated RNA-seq across species

Abstract The Multiple Myeloma Research Consortium (MMRC) has characterized over 300 hundred patient samples using a variety of platforms as part of the Multiple Myeloma Genomics Initiative (MMGI). Part of this large study includes a subset of 84 patients that were screened for somatic mutations using whole genome sequencing (WGS) or whole exome sequencing (WES) in combination with mRNA sequencing. This represents one of the first cohorts of myeloma patients with matched genome and transcriptome sequencing results. Given the historic value of microarray based gene expression profiling (GEP), this cohort provides the unique opportunity to compare gene expression measurements from the two platforms as Affymetrix U133Plus2.0 based GEP was performed on 42 of these samples. As part of the MMGI study, the Broad Institute has completed the genome sequencing, using WGS and WES, for 213 patients. A frequently mutated list of 9 genes including NRAS, KRAS, TP53, PNRC1, MAGED1, FAM46C, DIS3, CCND1 and ALOX12B were identified initially. Given the potential for RNAseq data to be used to define gene expression levels and to identify mutations in expressed genes we tested the feasibility of mutation calling on RNAseq alone. We independently called mutations on the entire transcriptome of the 84 patients and used a filtering method to eliminate likely germline variants in the absence of a matched normal control. We looked for point mutation concordance between, the calls identified by RNA-Seq alone and the previously reported variants through exome sequencing in the 9 frequently mutated genes. Out of the 66 SNV’s identified by these criteria using WGS or WES sequencing, 55(84%) were detected using RNA-Seq. Of the remaining 11 loci, 7(10%) were not detectably expressed and in 4(6%) cases the mutation was not detectable even though there was ample coverage. It is unclear if the last 6% represent false positives in the genome calls or the preferential expression of the wild-type allele. To interrogate the utility of RNAseq based GEP in myeloma we independently recapitulated many of common GEP measurements. First we independently used the 84 samples to define cutoffs for the implementation of the TC classification method. We compared our independent assignment of the 42 samples with matched gene expression array data, to their existing microarray assignments. This resulted in 40/42 (95%) samples being classified 40(95%) into identical TC classes. The two discordant samples MMRC0312 and MMRC0387 classified as TC class “none” by expression arrays were classified as other classes by RNAseq. MMRC0312 exhibited high CCND3 expression using RNA-Seq and was assigned to ‘6p21’ class. MMRC0387 exhibited elevated CCND1 expression and was classified as ‘D1’ using RNAseq. For the indexes we showed a strong correlation for the proliferation index (R2=0.971) and the NFKB index (R2=0.961) but only a moderate correlation for the 70-gene index (R2=0.761). The decreased correlation in the 70-gene index is clearly due to the large number of probesets used, which are associated with genes that are clearly not expressed by RNA-seq. One additional advantage of RNAseq over microarray based gene expression measurements is the potential to detect fusion transcripts. We have applied fusion transcript detection to this cohort of patients and 69 human myeloma cell lines, which were also screened by RNAseq and WES as part of the MMGI study. The most common fusion transcript detected is the @IGH-MMSET fusion characteristic of t(4;14). The next most common fusion we identified appears to be a promoter replacement event were the highly expressed gene, FCHSD2, is fused to multiple partners including known myeloma related genes, MMSET and MYC, and previously unreported genes in myeloma, CARNS1 and NCF2. Additional structural rearrangements involving FCHSD2 are also predicted based on the high frequency of copy number abnormalities encompassing the 5′ region of this gene as detected by comparative genomic hybridization in the MMGI study. This study should provide the basis for the migration of myeloma based gene expression profiling from microarrays to RNA sequencing based approaches. In the future RNA sequencing has the potential to provide novel classification schemes that leverage the multitude of measurements that can be made from this single assay. Disclosures: Levy: MMRC: Employment.

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RNA-Seq for Gene Expression Profiling of Human Necrotizing Enterocolitis: a Pilot Study

Journal of Korean Medical Science ◽

10.3346/jkms.2017.32.5.817 ◽

2017 ◽

Vol 32 (5) ◽

pp. 817 ◽

Cited By ~ 3

Author(s):

Kyuwhan Jung ◽

InSong Koh ◽

Jeong-Hyun Kim ◽

Hyun Sub Cheong ◽

Taejin Park ◽

...

Keyword(s):

Gene Expression ◽

Pilot Study ◽

Gene Expression Profiling ◽

Necrotizing Enterocolitis ◽

Expression Profiling ◽

Rna Seq

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Subtyping Diffuse Large B-Cell Lymphoma with Cell-of-Origin Using 32-Gene Expression Assay in Formalin-Fixed Paraffin-Embedded Tissue

Blood ◽

10.1182/blood-2019-126629 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 5337-5337

Author(s):

Xiangnan Jiang ◽

Wanhui Yan ◽

Yifeng Sun ◽

Qinghua Xu ◽

Xiaoyan Zhou ◽

...

Keyword(s):

Gene Expression ◽

B Cell ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Gold Standard ◽

B Cell Lymphoma ◽

Global Gene Expression ◽

Rna Seq ◽

Gene Assay ◽

Global Gene Expression Profiling

Introduction Diffuse large B-cell lymphoma (DLBCL) is a group of heterogeneous disease with distinct molecular subtypes. The most established subtyping algorithm, the Cell-of-Origin (COO) model, categorizes DLBCL into activated B-cell (ABC) and germinal center B-cell (GCB)-like subgroups through gene expression profiling. COO subtyping is mandatory for every newly diagnosed DLBCL patients, as it is critical for determining the therapeutic and surveillance strategies. We evaluated a newly developed assay using 32-gene expression profiling to determine the COO of DLBCL with formalin-fixed paraffin-embedded (FFPE) tissue. Methods The DLBCL-COO Test is a qPCR-based 32-gene expression assay for COO determination in FFPE samples. Biopsy of DLBCL patients with paired FFPE and fresh tissue were identified to assign COO, based on the immunohistochemistry (IHC) algorithm (Han's algorithm), DLBCL-COO qPCR assay and global gene expression profiling with RNA-seq, respectively. The global gene expression profiling with RNA-seq was taken as the "gold standard" for reference. Clinical information including the survival data were collected. Results 160 cases of DLBCL with evaluable COO assignments with IHC, DLBCL-COO 32-gene assay and global gene expression profiling with RNA-seq were identified. Comparing with the 77.5% concordance between IHC algorithm and gold standard, there is 91.9% concordance between DLBCL-COO 32-gene assay and gold standard (P =0.005). 72 patients assigned as ABC subtype and 14 patients assigned as Type-3 subtype demonstrated a significantly inferior overall survival than 42 patients assigned as GCB subtype using DLBCL-COO assay (P =0.023). However, COO based the IHC algorithm failed to provide the predictive value regarding overall survival (P =0.09). Conclusions DLBCL-COO assay provides flexibility and accuracy in DLBCL subtype characterization. These subtype distinctions should help guide disease prognosis and treatment options within DLBCL clinical practice. Disclosures Sun: Canhelp Genomics: Employment. Xu:Canhelp Genomics: Employment.

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Cardiac Pressure Overload Decreases ETV1 Expression in the Left Atrium, Contributing to Atrial Electrical and Structural Remodeling

Circulation ◽

10.1161/circulationaha.120.048121 ◽

2020 ◽

Author(s):

Naoko Yamaguchi ◽

Junhua Xiao ◽

Deven Narke ◽

Devin Shaheen ◽

Xianming Lin ◽

...

Keyword(s):

Gene Expression ◽

Heart Failure ◽

Gene Expression Profiling ◽

Left Atrium ◽

Expression Profiling ◽

Left Atrial ◽

Pressure Overload ◽

Neonatal Rat ◽

Rna Seq ◽

Structural Remodeling

Background: Elevated intracardiac pressure due to heart failure induces electrical and structural remodeling in the left atrium (LA) that begets atrial myopathy and arrhythmias. The underlying molecular pathways that drive atrial remodeling during cardiac pressure overload are poorly defined. The purpose of this study is to characterize the response of the ETV1 signaling axis in the LA during cardiac pressure overload in humans and mouse models and explore the role of ETV1 in atrial electrical and structural remodeling. Methods: We performed gene expression profiling in 265 left atrial samples from patients who underwent cardiac surgery. Comparative gene expression profiling was performed between two murine models of cardiac pressure overload, transverse aortic constriction (TAC) banding and Angiotensin II (AngII) infusion, and a genetic model of Etv1 cardiomyocyte-selective knockout ( Etv1 f/f Mlc2a Cre/+ ). Results: Using the Cleveland Clinic biobank of human LA specimens, we found that ETV1 expression is decreased in patients with reduced ejection fraction. Consistent with its role as an important mediator of the Neuregulin-1 (NRG1) signaling pathway and activator of rapid conduction gene programming, we identified a direct correlation between ETV1 expression level and NRG1, ERBB4, SCN5A , and GJA5 levels in human LA samples. In a similar fashion to heart failure patients, we showed that left atrial ETV1 expression is downregulated at the RNA and protein levels in murine pressure overload models. Comparative analysis of LA RNA-seq datasets from TAC and AngII treated mice showed a high Pearson correlation, reflecting a highly ordered process by which the LA undergoes electrical and structural remodeling. Cardiac pressure overload produced a consistent downregulation of ErbB4, Etv1, Scn5a, and Gja5 and upregulation of profibrotic gene programming, which includes Tgfbr1/2, Igf1, and numerous collagen genes. Etv1 f/f Mlc2a Cre/+ mice displayed atrial conduction disease and arrhythmias. Correspondingly, the LA from Etv1 f/f Mlc2a Cre/+ mice showed downregulation of rapid conduction genes and upregulation of profibrotic gene programming, whereas analysis of a gain-of-function ETV1 RNA-seq dataset from neonatal rat ventricular myocytes transduced with Etv1 showed reciprocal changes. Conclusions: ETV1 is downregulated in the LA during cardiac pressure overload, contributing to both electrical and structural remodeling.

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Variation-preserving normalization unveils blind spots in gene expression profiling

Scientific Reports ◽

10.1038/srep42460 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 10

Author(s):

Carlos P. Roca ◽

Susana I. L. Gomes ◽

Mónica J. B. Amorim ◽

Janeck J. Scott-Fordsmand

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Full Potential ◽

Implicit Assumption ◽

Rna Seq ◽

Cellular Processes ◽

Gene Expression Microarrays ◽

Expression Microarrays ◽

Gene Expression Levels

Abstract RNA-Seq and gene expression microarrays provide comprehensive profiles of gene activity, but lack of reproducibility has hindered their application. A key challenge in the data analysis is the normalization of gene expression levels, which is currently performed following the implicit assumption that most genes are not differentially expressed. Here, we present a mathematical approach to normalization that makes no assumption of this sort. We have found that variation in gene expression is much larger than currently believed, and that it can be measured with available assays. Our results also explain, at least partially, the reproducibility problems encountered in transcriptomics studies. We expect that this improvement in detection will help efforts to realize the full potential of gene expression profiling, especially in analyses of cellular processes involving complex modulations of gene expression.

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RNA-Seq gene expression profiling of HepG2 cells: the influence of experimental factors and comparison with liver tissue

BMC Genomics ◽

10.1186/1471-2164-15-1108 ◽

2014 ◽

Vol 15 (1) ◽

pp. 1108 ◽

Cited By ~ 22

Author(s):

Alexander V Tyakht ◽

Elena N Ilina ◽

Dmitry G Alexeev ◽

Dmitry S Ischenko ◽

Alexey Y Gorbachev ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Liver Tissue ◽

Hepg2 Cells ◽

Rna Seq

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Gene-expression profiling of single cells from archival tissue with laser-capture microdissection and Smart-3SEQ

10.1101/207340 ◽

2017 ◽

Cited By ~ 1

Author(s):

Joseph W. Foley ◽

Chunfang Zhu ◽

Philippe Jolivet ◽

Shirley X. Zhu ◽

Peipei Lu ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Laser Capture Microdissection ◽

Expression Profiling ◽

Single Cells ◽

Ffpe Tissue ◽

Small Samples ◽

Clinical Samples ◽

Rna Seq ◽

Laser Capture

AbstractRNA sequencing (RNA-seq) is a sensitive and accurate method for quantifying gene expression. Small samples or those whose RNA is degraded, such as formalin-fixed, paraffin-embedded (FFPE) tissue, remain challenging to study with nonspecialized RNA-seq protocols. Here we present a new method, Smart-3SEQ, that accurately quantifies transcript abundance even with small amounts of total RNA and effectively characterizes small samples extracted by laser-capture microdissection (LCM) from FFPE tissue. We also obtain distinct biological profiles from FFPE single cells, which have been impossible to study with previous RNA-seq protocols, and we use these data to identify possible new macrophage phenotypes associated with the tumor microenvironment. We propose Smart-3SEQ as a highly cost-effective method to enable large gene-expression profiling experiments unconstrained by sample size and tissue availability. In particular, Smart-3SEQ’s compatibility with FFPE tissue unlocks an enormous number of archived clinical samples, and combined with LCM it allows unprecedented studies of small cell populations and single cells isolated by their in situ context.

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Real-Time Molecular Classification of Diffuse Large B-Cell Lymphoma (DLBCL) By Gene Expression Profiling (GEP): Successful Delivery of a Routine Service for Randomization of Patients Onto the Multicenter Remodl-B Trial (ISRCTN 51837425)

Blood ◽

10.1182/blood.v126.23.331.331 ◽

2015 ◽

Vol 126 (23) ◽

pp. 331-331

Author(s):

Barrans Sharon ◽

Andrew J Davies ◽

Ming Wang ◽

Ming-Qing Du ◽

Christoph Mamot ◽

...

Keyword(s):

Gene Expression ◽

B Cell ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Research Funding ◽

Mutational Analysis ◽

Molecular Classification ◽

Rna Seq ◽

Cross Platform

Abstract Germinal Center (GCB) and activated (ABC) B-cell subtypes of DLBCL can be identified by Gene expression profiling (GEP). These subgroups are biologically distinct, harboring mutations in different pathways. Patients classified as ABC more often have mutations of the NF-kB pathway and an inferior response to standard R-CHOP therapy. The REMoDL-B trial utilised GEP to stratify patients for the addition of bortezomib to R-CHOP, based on the hypothesis that this agent may selectively improve the outcome of the ABC subtype. GEP was performed on RNA extracted from diagnostic formalin-fixed paraffin-embedded (FFPE) biopsies using Illumina WG-DASLTM during the 1st cycle of R-CHOP. Patients were classified as GCB, ABC or Unclassified before cycle 2 using the cross-platform DAC classifier (Care et al, PLOS ONE 2013) and randomised to continue R-CHOP+/-bortezomib. Work is underway to compare data generated on Affymetrix arrays and targeted RNA-seq (Illumina TRex), as well as validation by targeted mutational analysis of 18 genes associated with DLBCL (TNFAIP3, CARD11, CD79A, CD79B, MYD88, TRAF3, TNFRSF11A, PRDM1, TP53, FAS, B2M, CD58, EZH2, MLL2, MEF2B, EP300, CREBBP, KDM2B) using Fluidigm multiplex PCR and Illumina MiSeq on DNA also from the FFPE blocks. The trial closed to recruitment in May 2015 and 1147 samples have been analysed. One hundred and fifty three (13%) biopsies were unsuitable for GEP (for insufficient tumor tissue, inappropriate block sent). The remaining samples were classified as ABC (n=261, 23%), GCB (n=471, 44%) and Unclassified (n=214, 19%), with only 11 samples (1%) failing to yield a GEP result. GEP was successful in a range of sample types, including needle and endoscopic biopsies, bone marrow trephines and formal biopsies, with results obtained from as little as 40ng of total RNA, all from FFPE samples. Mutational data were available in 199 samples, with 73% of these having a mutation detectable in 1 or more genes (range 0-5) at a AAF (alternative allele frequency) cutoff at 10%. MYD88 was most commonly mutated (in 30% of ABC and 7% of GCB). EZH2 mutations were restricted to the GCB category (26%) and MYD88, CD79a/b and PRDM1 were more commonly associated with the ABC group. MYD88/PRDM1 were the most frequently associated events, with MYD88/CD79a/b and MYD88/NF-kB being mutually exclusive. Where MYD88 was seen in GCB cases, coexisting mutations imply an origin from transformed follicular lymphoma. B2M mutations were commonly identified across all subtypes (n=26), but specifically enriched in Type III (unclassified) cases (25%), which supports the hypothesis that mutational immune escape may be a feature of DLBCL, in common with other tumor types. Cross platform validation is highly concordant using Affymetrix arrays from a pilot series (27/27 gave the same classifier output, with correlating confidences also seen between platforms). RNA-seq analysis is ongoing, however initial analysis shows 86% concordance with the DASL output. Comprehensive cross platform comparison data will be available for presentation at the meeting. This study demonstrates the feasibility of GEP classification of DLBCL at diagnosis in a large international trial. The molecular classification can also be replicated using different technologies. Mutational analysis confirmed the association between DLBCL subtype and specific mutational hotspots. Disclosures Sharon: Johnson & Johnson: Other: Funded the laboratory work for the REMoDL-B trial (ISRCTN 51837425). Davies:Takeda: Honoraria; Seattle Genetics: Research Funding. Jack:Jannsen: Research Funding. Johnson:Johnson & Johnson: Other: Funded the laboratory work for the REMoDL-B trial (ISRCTN 51837425).

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