Dataset Information

3'-end sequencing for expression quantification (3SEQ) from archival tumor samples.

ABSTRACT: Gene expression microarrays are the most widely used technique for genome-wide expression profiling. However, microarrays do not perform well on formalin fixed paraffin embedded tissue (FFPET). Consequently, microarrays cannot be effectively utilized to perform gene expression profiling on the vast majority of archival tumor samples. To address this limitation of gene expression microarrays, we designed a novel procedure (3'-end sequencing for expression quantification (3SEQ)) for gene expression profiling from FFPET using next-generation sequencing. We performed gene expression profiling by 3SEQ and microarray on both frozen tissue and FFPET from two soft tissue tumors (desmoid type fibromatosis (DTF) and solitary fibrous tumor (SFT)) (total n = 23 samples, which were each profiled by at least one of the four platform-tissue preparation combinations). Analysis of 3SEQ data revealed many genes differentially expressed between the tumor types (FDR<0.01) on both the frozen tissue (approximately 9.6K genes) and FFPET (approximately 8.1K genes). Analysis of microarray data from frozen tissue revealed fewer differentially expressed genes (approximately 4.64K), and analysis of microarray data on FFPET revealed very few (69) differentially expressed genes. Functional gene set analysis of 3SEQ data from both frozen tissue and FFPET identified biological pathways known to be important in DTF and SFT pathogenesis and suggested several additional candidate oncogenic pathways in these tumors. These findings demonstrate that 3SEQ is an effective technique for gene expression profiling from archival tumor samples and may facilitate significant advances in translational cancer research.

SUBMITTER: Beck AH

PROVIDER: S-EPMC2808244 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

3'-end sequencing for expression quantification (3SEQ) from archival tumor samples.

Beck Andrew H AH Weng Ziming Z Witten Daniela M DM Zhu Shirley S Foley Joseph W JW Lacroute Phil P Smith Cheryl L CL Tibshirani Robert R van de Rijn Matt M Sidow Arend A West Robert B RB

PloS one 20100119 1

Gene expression microarrays are the most widely used technique for genome-wide expression profiling. However, microarrays do not perform well on formalin fixed paraffin embedded tissue (FFPET). Consequently, microarrays cannot be effectively utilized to perform gene expression profiling on the vast majority of archival tumor samples. To address this limitation of gene expression microarrays, we designed a novel procedure (3'-end sequencing for expression quantification (3SEQ)) for gene expressio ...[more]

PMID: 20098735

Similar Datasets

Project description:Archival tumor samples represent a rich resource of annotated specimens for translational genomics research. However, standard variant calling approaches require a matched normal sample from the same individual, which is often not available in the retrospective setting, making it difficult to distinguish between true somatic variants and individual-specific germline variants. Archival sections often contain adjacent normal tissue, but this tissue can include infiltrating tumor cells. As existing comparative somatic variant callers are designed to exclude variants present in the normal sample, a novel approach is required to leverage adjacent normal tissue with infiltrating tumor cells for somatic variant calling. Here we present lumosVar 2.0, a software package designed to jointly analyze multiple samples from the same patient, built upon our previous single sample tumor only variant caller lumosVar 1.0. The approach assumes that the allelic fraction of somatic variants and germline variants follow different patterns as tumor content and copy number state change. lumosVar 2.0 estimates allele specific copy number and tumor sample fractions from the data, and uses a to model to determine expected allelic fractions for somatic and germline variants and to classify variants accordingly. To evaluate the utility of lumosVar 2.0 to jointly call somatic variants with tumor and adjacent normal samples, we used a glioblastoma dataset with matched high and low tumor content and germline whole exome sequencing data (for true somatic variants) available for each patient. Both sensitivity and positive predictive value were improved when analyzing the high tumor and low tumor samples jointly compared to analyzing the samples individually or in-silico pooling of the two samples. Finally, we applied this approach to a set of breast and prostate archival tumor samples for which tumor blocks containing adjacent normal tissue were available for sequencing. Joint analysis using lumosVar 2.0 detected several variants, including known cancer hotspot mutations that were not detected by standard somatic variant calling tools using the adjacent tissue as presumed normal reference. Together, these results demonstrate the utility of leveraging paired tissue samples to improve somatic variant calling when a constitutional sample is not available.

Project description:BACKGROUND:In KEYNOTE-010, pembrolizumab versus docetaxel improved overall survival (OS) in patients with programmed death-1 protein (PD)-L1-positive advanced non-small-cell lung cancer (NSCLC). A prespecified exploratory analysis compared outcomes in patients based on PD-L1 expression in archival versus newly collected tumor samples using recently updated survival data. PATIENTS AND METHODS:PD-L1 was assessed centrally by immunohistochemistry (22C3 antibody) in archival or newly collected tumor samples. Patients received pembrolizumab 2 or 10?mg/kg Q3W or docetaxel 75?mg/m2 Q3W for 24?months or until progression/intolerable toxicity/other reason. Response was assessed by RECIST v1.1 every 9?weeks, survival every 2?months. Primary end points were OS and progression-free survival (PFS) in tumor proportion score (TPS) ?50% and ?1%; pembrolizumab doses were pooled in this analysis. RESULTS:At date cut-off of 24 March 2017, median follow-up was 31?months (range 23-41) representing 18 additional months of follow-up from the primary analysis. Pembrolizumab versus docetaxel continued to improve OS in patients with previously treated, PD-L1-expressing advanced NSCLC; hazard ratio (HR) was 0.66 [95% confidence interval (CI): 0.57, 0.77]. Of 1033 patients analyzed, 455(44%) were enrolled based on archival samples and 578 (56%) on newly collected tumor samples. Approximately 40% of archival samples and 45% of newly collected tumor samples were PD-L1 TPS ?50%. For TPS ?50%, the OS HRs were 0.64 (95% CI: 0.45, 0.91) and 0.40 (95% CI: 0.28, 0.56) for archival and newly collected samples, respectively. In patients with TPS ?1%, OS HRs were 0.74 (95% CI: 0.59, 0.93) and 0.59 (95% CI: 0.48, 0.73) for archival and newly collected samples, respectively. In TPS ?50%, PFS HRs were similar across archival [0.63 (95% CI: 0.45, 0.89)] and newly collected samples [0.53 (95% CI: 0.38, 0.72)]. In patients with TPS ?1%, PFS HRs were similar across archival [0.82 (95% CI: 0.66, 1.02)] and newly collected samples [0.83 (95% CI: 0.68, 1.02)]. CONCLUSION:Pembrolizumab continued to improve OS over docetaxel in intention to treat population and in subsets of patients with newly collected and archival samples. TRIAL REGISTRATION:ClinicalTrials.gov: NCT01905657.

Dataset Information

3'-end sequencing for expression quantification (3SEQ) from archival tumor samples.

Publications

3'-end sequencing for expression quantification (3SEQ) from archival tumor samples.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets