Dataset Information

Proteomic profiles of FFPE breast normal and tumor tissue

ABSTRACT: Accessing the proteome of formalin fixed, paraffin-embedded (FFPE) tissue could lead to discovery of new biomarkers and development of clinically useful assays. A critical step to realizing this potential is developing a simple and reproducible method to obtain proteomic profiles from FFPE tissue. An objective of this work is to develop and optimize a method to obtain proteomic profiles from FFPE breast tissue using a protocol commonly applied in pathology laboratories. The outcome is a method that incorporates steps used for immunohistochemical analyses of FFPE tissue that results in highly reproducible proteomic profiles. Implementing this assay with normal breast tissue and breast tumor tissue produced proteome profiles that reproducibly demonstrate substantial differences between normal vs. tumor tissue.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Cell, Breast

DISEASE(S): Breast Cancer

SUBMITTER: Ten-Yang Yen

LAB HEAD: Robert Yen

PROVIDER: PXD007088 | Pride | 2020-07-17

REPOSITORIES: Pride

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Publications

Over-Expression of RNA Processing, Heat Shock, and DNA Repair Proteins in Breast Tumor Compared to Normal Tissue.

Yen Ten-Yang TY Wong Richard R Pizzo Donald D Thein Moe M Macher Bruce A BA Timpe Leslie C LC

Proteomics 20200714

This study identifies the main changes in protein expression in human breast tumors compared to normal breast tissue. Malignant tumors (32) and normal breast tissue samples (23), from formaldehyde-fixed, paraffin-embedded specimens are subjected to discovery proteomics using liquid chromatography/tandem mass spectrometry, with spectral counts for quantitation. The dataset contains 1406 proteins. Differential expression is measured using a method that takes advantage of estimates of the percentag ...[more]

PMID: 32663359

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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. FFPE tissue sections (10 micron-thick) sliced from 5~16-year-old FFPE blocks and archived for 6~7 years on glass slide

Dataset Information

Proteomic profiles of FFPE breast normal and tumor tissue

Publications

Over-Expression of RNA Processing, Heat Shock, and DNA Repair Proteins in Breast Tumor Compared to Normal Tissue.

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