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: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 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.

			Action	DRS
	1-HCC_1.wiff	Wiff
	1-HCC_1.wiff.scan	Wiff
	1-HCC_1.wiff2	Wiff
	11-HCC_3.wiff	Wiff
	11-HCC_3.wiff.scan	Wiff

Dataset Information

FFPE proteomics for hepatocellular carcinoma

Dataset's files

Publications

Time-Enduring Proteomic Fidelity in Over 30-Year-Old FFPE Tissues: Distinct Proteomic Signatures of Hepatocellular Carcinoma and Adjacent Non-Tumor Liver Tissue.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets