Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Gene-expression profiles of archived section of formalin-fixed paraffin-embedded (AS-FFPE) liver tissues from hepatocellular carcinoma patients

ABSTRACT: Background and Aims Formalin-fixed, paraffin-embedded (FFPE) tissue is the most commonly available form of archived clinical specimens, which are often stored as thin sections on glass slides. RNA isolated from such archived section (AS) of FFPE tissue is more degraded compared to freshly cut (FC) FFPE section because of prolonged air exposure. In this study, we evaluated performance of transcriptome profiling-based disease classification in AS-FFPE tissue. Methods Genome-wide gene-expression profiles of 5-year-old AS-FFPE tissues of 83 hepatocellular carcinoma (HCC) and 47 liver cirrhosis samples were generated by using whole-genome DASL assay (Illumina), and compared with the profiles previously produced by using FC tissue sections from the same FFPE blocks. Previously reported 186-gene liver signature of poor prognosis was also analyzed by digital transcript counting technology (nCounter assay, NanoString). Quality of the profiles and performance of gene signature-based class prediction were systematically evaluated. Results RNA quality and assay reproducibility of AS-FFPE RNA were comparable to intermediate ~ poor quality FC-FFPE samples (R2 as high as 0.93). Gene-expression signal was detected in lower number of probes in AS FFPE samples compared to FC-FFPE samples (proportion of probes with present signal (%P-call): 10-60% and 70-90% in AS- and FC-FFPE profiles, respectively). Based on %P-call quality threshold of 20%, 64/88 (77%) HCC and 37/48 (77%) liver profiles were judged as having relatively good quality data with comparable inter-sample correlation. Inter-sample correlation coefficient, as a measure to detect outlier profiles due to poor RNA quality, was also lower in AS-FFPE (0.4-0.9) compared to FC-FFPE (0.6-1.0). In the genome-wide profiling analysis, previously identified molecular subclasses of HCC tumors were reproduced in 67/83 (81%) samples, which was improved to 43/48 (90%) samples when we focused on statistically confident predictions (p<0.05). A 186-gene prognostic signature in liver cirrhosis was reproduced in 32/47 (68%) samples, which was slightly improved to 11/16 (69%) when focused on statistically significant predictions. Switch of prediction to another subclass was observed in 6% or less of the patients. nCounter assay yielded highly confident prediction: p<0.05 in 20/24 samples (83%). Switch of the prediction was observed in 2/24 samples (8%). Conclusions We observed decay of genome-wide transcriptional profiles in AS-FFPE tissues in a quantitative manner. However, disease classification was still possible, which suggests potential of AS-FFPE material for clinical diagnosis and prognosis. Digital transcript counting is a promising option to measure gene-expression signatures in AS-FFPE tissue. FFPE tissue sections (10 micron-thick) sliced from 5~16-year-old FFPE blocks and archived for 6~7 years on glass slide

ORGANISM(S): Homo sapiens

SUBMITTER: Yujin Hoshida

PROVIDER: E-GEOD-47594 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Background and Aims Formalin-fixed, paraffin-embedded (FFPE) tissue is the most commonly available form of archived clinical specimens, which are often stored as thin sections on glass slides. RNA isolated from such archived section (AS) of FFPE tissue is more degraded compared to freshly cut (FC) FFPE section because of prolonged air exposure. In this study, we evaluated performance of transcriptome profiling-based disease classification in AS-FFPE tissue. Methods Genome-wide gene-expression profiles of 5-year-old AS-FFPE tissues of 83 hepatocellular carcinoma (HCC) and 47 liver cirrhosis samples were generated by using whole-genome DASL assay (Illumina), and compared with the profiles previously produced by using FC tissue sections from the same FFPE blocks. Previously reported 186-gene liver signature of poor prognosis was also analyzed by digital transcript counting technology (nCounter assay, NanoString). Quality of the profiles and performance of gene signature-based class prediction were systematically evaluated. Results RNA quality and assay reproducibility of AS-FFPE RNA were comparable to intermediate ~ poor quality FC-FFPE samples (R2 as high as 0.93). Gene-expression signal was detected in lower number of probes in AS FFPE samples compared to FC-FFPE samples (proportion of probes with present signal (%P-call): 10-60% and 70-90% in AS- and FC-FFPE profiles, respectively). Based on %P-call quality threshold of 20%, 64/88 (77%) HCC and 37/48 (77%) liver profiles were judged as having relatively good quality data with comparable inter-sample correlation. Inter-sample correlation coefficient, as a measure to detect outlier profiles due to poor RNA quality, was also lower in AS-FFPE (0.4-0.9) compared to FC-FFPE (0.6-1.0). In the genome-wide profiling analysis, previously identified molecular subclasses of HCC tumors were reproduced in 67/83 (81%) samples, which was improved to 43/48 (90%) samples when we focused on statistically confident predictions (p<0.05). A 186-gene prognostic signature in liver cirrhosis was reproduced in 32/47 (68%) samples, which was slightly improved to 11/16 (69%) when focused on statistically significant predictions. Switch of prediction to another subclass was observed in 6% or less of the patients. nCounter assay yielded highly confident prediction: p<0.05 in 20/24 samples (83%). Switch of the prediction was observed in 2/24 samples (8%). Conclusions We observed decay of genome-wide transcriptional profiles in AS-FFPE tissues in a quantitative manner. However, disease classification was still possible, which suggests potential of AS-FFPE material for clinical diagnosis and prognosis. Digital transcript counting is a promising option to measure gene-expression signatures in AS-FFPE tissue.

Project description:Background and Aims Formalin-fixed, paraffin-embedded (FFPE) tissue is the most commonly available form of archived clinical specimens, which are often stored as thin sections on glass slides. RNA isolated from such archived section (AS) of FFPE tissue is more degraded compared to freshly cut (FC) FFPE section because of prolonged air exposure. In this study, we evaluated performance of transcriptome profiling-based disease classification in AS-FFPE tissue. Methods Genome-wide gene-expression profiles of AS-FFPE tissues of 83 hepatocellular carcinoma (HCC) and 47 liver cirrhosis samples were generated by using whole-genome DASL assay (Illumina), and compared with the profiles previously produced by using FC tissue sections from the same FFPE blocks. Quality of the profiles and performance of gene signature-based class prediction were systematically evaluated. Results RNA quality and assay reproducibility of AS-FFPE RNA were comparable to intermediate ~ poor quality FC-FFPE samples (R2 as high as 0.93). Gene-expression signal was detected in lower number of probes in AS FFPE samples compared to FC-FFPE samples (proportion of probes with present signal (%P-call): 10%~60% and 70%~90% in AS- and FC-FFPE profiles, respectively). Based on %P-call quality threshold of 20%, 64/88 (77%) HCC and 37/48 (77%) liver profiles were judged as having relatively good quality data with comparable inter-sample correlation. Inter-sample correlation coefficient, as a measure to detect outlier profiles due to poor RNA quality, was also lower in AS-FFPE (0.4~0.9) compared to FC-FFPE (0.6~1.0). In the genome-wide profiling analysis, previously identified molecular subclasses of HCC tumors were reproduced in 67/83 (81%) samples, which was improved to 43/48 (90%) samples when we focused on statistically confident predictions (p<0.05). A 186-gene prognostic signature in liver cirrhosis was reproduced in 32/47 (68%) samples, which was slightly improved to 11/16 (69%) when focused on statistically significant predictions. Conclusions We observed decay of genome-wide transcriptional profiles in AS-FFPE tissues in quantitative manner. However, disease classification was still possible, which suggests potential of AS-FFPE material for clinical diagnosis and prognosis. FFPE tissue sections (10 micron-thick) sliced from 5~16-year-old FFPE blocks and archived for 6~7 years on glass slide Gene-expression profiles of archived section of formalin-fixed paraffin-embedded (AS-FFPE) liver tissues from HCC patients: 47 samples Gene-expression profiles of archived section of formalin-fixed paraffin-embedded (AS-FFPE) tumor tissues from HCC patients: 83 samples

Project description:Background and Aims Formalin-fixed, paraffin-embedded (FFPE) tissue is the most commonly available form of archived clinical specimens, which are often stored as thin sections on glass slides. RNA isolated from such archived section (AS) of FFPE tissue is more degraded compared to freshly cut (FC) FFPE section because of prolonged air exposure. In this study, we evaluated performance of transcriptome profiling-based disease classification in AS-FFPE tissue. Methods Genome-wide gene-expression profiles of AS-FFPE tissues of 83 hepatocellular carcinoma (HCC) and 47 liver cirrhosis samples were generated by using whole-genome DASL assay (Illumina), and compared with the profiles previously produced by using FC tissue sections from the same FFPE blocks. Quality of the profiles and performance of gene signature-based class prediction were systematically evaluated. Results RNA quality and assay reproducibility of AS-FFPE RNA were comparable to intermediate ~ poor quality FC-FFPE samples (R2 as high as 0.93). Gene-expression signal was detected in lower number of probes in AS FFPE samples compared to FC-FFPE samples (proportion of probes with present signal (%P-call): 10%~60% and 70%~90% in AS- and FC-FFPE profiles, respectively). Based on %P-call quality threshold of 20%, 64/88 (77%) HCC and 37/48 (77%) liver profiles were judged as having relatively good quality data with comparable inter-sample correlation. Inter-sample correlation coefficient, as a measure to detect outlier profiles due to poor RNA quality, was also lower in AS-FFPE (0.4~0.9) compared to FC-FFPE (0.6~1.0). In the genome-wide profiling analysis, previously identified molecular subclasses of HCC tumors were reproduced in 67/83 (81%) samples, which was improved to 43/48 (90%) samples when we focused on statistically confident predictions (p<0.05). A 186-gene prognostic signature in liver cirrhosis was reproduced in 32/47 (68%) samples, which was slightly improved to 11/16 (69%) when focused on statistically significant predictions. Conclusions We observed decay of genome-wide transcriptional profiles in AS-FFPE tissues in quantitative manner. However, disease classification was still possible, which suggests potential of AS-FFPE material for clinical diagnosis and prognosis.

Project description:OBJECTIVE: The number of patients with HCV-related cirrhosis is increasing, leading to a rising risk of complications and death. Prognostic stratification in patients with early-stage cirrhosis is still challenging. We aimed to develop and validate a clinically useful prognostic index based on genomic and clinical variables to identify patients at high risk of disease progression. DESIGN: We developed a prognostic index, comprised of a 186-gene signature validated in our previous genome-wide profiling study, bilirubin (>1 mg/dL) and platelet count (<100 000/mm3), in an Italian HCV cirrhosis cohort (training cohort, n=216, median follow-up 10 years). The gene signature test was implemented using a digital transcript counting (nCounter) assay specifically developed for clinical use and the prognostic index was evaluated using archived specimens from an independent cohort of HCV-related cirrhosis in the USA (validation cohort, n=145, median follow-up 8 years). RESULTS: In the training cohort, the prognostic index was associated with hepatic decompensation (HR=2.71, p=0.003), overall death (HR=6.00, p<0.001), hepatocellular carcinoma (HR=3.31, p=0.001) and progression of Child-Turcotte-Pugh class (HR=6.70, p<0.001). The patients in the validation cohort were stratified into high-risk (16%), intermediate-risk (42%) or low-risk (42%) groups by the prognostic index. The high-risk group had a significantly increased risk of hepatic decompensation (HR=7.36, p<0.001), overall death (HR=3.57, p=0.002), liver-related death (HR=6.49, p<0.001) and all liver-related adverse events (HR=4.98, p<0.001). CONCLUSIONS: A genomic and clinical prognostic index readily available for clinical use was successfully validated, warranting further clinical evaluation for prognostic prediction and clinical trial stratification and enrichment for preventive interventions. 90 liver tissue needle biopsy specimens from patients with histologically proven cirrhosis lacking evidence and a history of hepatic decompensation or HCC.

Project description:OBJECTIVE: The number of patients with HCV-related cirrhosis is increasing, leading to a rising risk of complications and death. Prognostic stratification in patients with early-stage cirrhosis is still challenging. We aimed to develop and validate a clinically useful prognostic index based on genomic and clinical variables to identify patients at high risk of disease progression. DESIGN: We developed a prognostic index, comprised of a 186-gene signature validated in our previous genome-wide profiling study, bilirubin (>1?mg/dL) and platelet count (<100?000/mm3), in an Italian HCV cirrhosis cohort (training cohort, n=216, median follow-up 10?years). The gene signature test was implemented using a digital transcript counting (nCounter) assay specifically developed for clinical use and the prognostic index was evaluated using archived specimens from an independent cohort of HCV-related cirrhosis in the USA (validation cohort, n=145, median follow-up 8?years). RESULTS: In the training cohort, the prognostic index was associated with hepatic decompensation (HR=2.71, p=0.003), overall death (HR=6.00, p<0.001), hepatocellular carcinoma (HR=3.31, p=0.001) and progression of Child-Turcotte-Pugh class (HR=6.70, p<0.001). The patients in the validation cohort were stratified into high-risk (16%), intermediate-risk (42%) or low-risk (42%) groups by the prognostic index. The high-risk group had a significantly increased risk of hepatic decompensation (HR=7.36, p<0.001), overall death (HR=3.57, p=0.002), liver-related death (HR=6.49, p<0.001) and all liver-related adverse events (HR=4.98, p<0.001). CONCLUSIONS: A genomic and clinical prognostic index readily available for clinical use was successfully validated, warranting further clinical evaluation for prognostic prediction and clinical trial stratification and enrichment for preventive interventions. 145 liver tissue needle biopsy specimens from U.S. HCV cirrhosis cohort patients with histologically proven cirrhosis lacking evidence and a history of hepatic decompensation or HCC.

Project description:The most frequent mature aggressive B-cell lymphomas are diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL). Patients suffering from molecularly defined BL (mBL) but treated with a regimen developed for DLBCL show an unfavorable outcome compared to mBL treated with chemotherapy regimens for BL. Distinguishing BL from DLBCL by conventional histopathology is challenging in lymphomas that have features common to both diseases (aggressive B-cell lymphoma unclassifiable with features of DLBCL and BL [intermediates]). Moreover, DLBCL are a heterogeneous group of lymphomas comprising distinct molecular subtypes: the activated B-cell (ABC)-like, the germinal center B-cell-like (GCB) and the unclassifyable subtype as defined by gene expression profiling (GEP). Attempts to replace GEP with techniques applicable to formalin-fixed paraffin-embedded (FFPE) tissue led to algorithms for immunohistochemical stainings (IHS). Disappointingly, the algorithms yielded conflicting results with respect to their prognostic potential, raising concerns about their validity. Furthermore, IHS algorithms did not provide a fully resolved classification: They did not identify mBL; nor did they separate ABC from unclassified DLBCL. 29 diffuse large B-Cell lymphoma samples were hybridized to HGU133A Affymetrix GeneChips. In addition, this study contains 22 already published samples whereas 11 of them contribute to GSE22470, 6 contribute to GSE10172, 3 to GSE44164 and 2 to GSE4475. No re-normalisation of published samples was performed. We used digital multiplexed gene expression (DMGE) with FFPE derived RNA to classify agressive B-cell lymphomas. Our assay comprised only 30 genes (10 for the detection of mBL and 20 for the detection of ABC and GCB). We chose these genes by reanalysis of the microarray data reported in a previous study. 39 samples from mature aggressive B-cell lymphomas were analyzed using DMGE (nCounter, NanoString Technologies Inc., Seattle, WA, USA) of FFPE- and fresh-frozen derived RNA. All cases were previously characterized by the Molecular Mechanisms of Malignant Lymphoma (MMML) consortium using the Affymetrix GeneChip technology (gold standard of classification). Please note that there are total 40 FFPE-derived and 50 fresh-frozen derived samples, with 39 samples derived from both materials (allowing direct comparison).

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Gene-expression profiles of archived section of formalin-fixed paraffin-embedded (AS-FFPE) liver tissues from hepatocellular carcinoma patients

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