The 31 pairs of FF and FFPE data for the development of 72GC for FFPE in 2014
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PROVIDER: PRJNA992088 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The study aimed to develop the 72GC (the 72-gene classifier) for recurrence-risk prediction for patients with estrogen receptor positive and node-negative breast cancer. 72-GC could differentiate the high-risk from the low-risk patients with a high statistical significance, and is considered to be applicable to formalin-fixed, paraffin embedded (FFPE) tumor tissues because the results of 72-GC on FF (fresh-frozen) tissues and FFPE tissues showed a high concordance. J06 31 pairs of FF (fresh‑frozen) and FFPE data are included in the concordance analysis of 72GC high/low results between FF and FFPE (Table 3). A long time passed, and now it is unclear how these 31 cases were distributed among the analysis. *Note: This old data has been updated multiple times by the other members. Then, there are some differences from the original paper and unclear points still remain. Therefore, do not use it for formal analysis aimed at public insurance coverage etc. This is for research purposes only. Please cite this paper when writing a new paper. PMID: 24461457 DOI: 10.1016/j.clbc.2013.11.006
2023-07-11 | GSE236725 | GEO
Project description:We profiled human DLBCL tumor samples (FF and FFPE matched pairs) to identify the transcripts which are less prone to degradation in FFPE Keywords: DLBCL FF FFPE
2011-03-16 | GSE18377 | GEO
Project description:The study aimed to apply 95GC, originally developed using fresh‑frozen (FF) tissues, to formalin‑fixed paraffin‑embedded (FFPE) tissues, because FFPE tissues are routinely prepared and are readily available. Although we previously reported the applicability of 72GC to FFPE tissues (DOI: 10.1016/j.clbc.2013.11.006), the present study aimed to improve the accuracy of 95GC for FFPE tissues using the reference robust multiarray average (refRMA) method, optimized for FFPE tissues. Therefore, a 95GC RS (Recurrence Score) was first developed and then the accuracy of the newly developed 95GC algorithm for FFPE tissues was evaluated using the 95GC RS. These 31 pairs of FF and FFPE data are included in the concordance analysis of 95GC high/low results between FF and FFPE (Figure 3B). A long time passed, and now it is unclear how these 31 cases were distributed among the analysis. *Note: This old data has been updated multiple times by the other members. Then, there are some differences from the original paper and unclear points still remain. Therefore, do not use it for formal analysis aimed at public insurance coverage etc. This is for research purposes only. Please cite this paper when writing a new paper. PMID: 31638234 DOI: 10.3892/or.2019.7358
2023-06-16 | GSE234686 | GEO
Project description:We profiled human DLBCL tumor samples (FF and FFPE matched pairs) to identify the transcripts which are less prone to degradation in FFPE Keywords: DLBCL FF FFPE RNA profiles of human FF and FFPE samples (DLBCL)
2011-03-16 | E-GEOD-18377 | biostudies-arrayexpress
Project description:The study aimed to apply 95GC, originally developed using fresh‑frozen (FF) tissues, to formalin‑fixed paraffin‑embedded (FFPE) tissues, because FFPE tissues are routinely prepared and are readily available. Although we previously reported the applicability of 72GC to FFPE tissues (DOI: 10.1016/j.clbc.2013.11.006), the present study aimed to improve the accuracy of 95GC for FFPE tissues using the reference robust multiarray average (refRMA) method, optimized for FFPE tissues. Therefore, a 95GC RS (Recurrence Score) was first developed and then the accuracy of the newly developed 95GC algorithm for FFPE tissues was evaluated using the 95GC RS. These 14 pairs of FF and FFPE data are included in the concordance analysis of 95GC high/low results between FF and FFPE (Figure 3B). J05 was created in a form more suited to the actual clinical setting, such as leaving the postoperative samples for 4 hours before immersing them in formalin. A long time passed, and now it is unclear how these 14 cases were distributed among the analysis. *Note: This old data has been updated multiple times by the other members. Then, there are some differences from the original paper and unclear points still remain. Therefore, do not use it for formal analysis aimed at public insurance coverage etc. This is for research purposes only. Please cite this paper when writing a new paper. PMID: 31638234 DOI: 10.3892/or.2019.7358
2023-07-11 | GSE236723 | GEO
Project description:The 14 pairs of FF and FFPE data for the application of 95GC to FFPE in 2019
| PRJNA992086 | ENA
Project description:Aim of the project was to evaluate several MS-comaptible detergents for processing fresh frozen (FF) and formalin fixed paraffin embedded (FFPE) microdissected human kidney tissue. Here we have evaluated sensitivity of the methods and their applicability on FF and FFPE tissues, as well as investigated for the appropriateness of the use of FFPE tissues.
2022-02-22 | PXD020793 | Pride
Project description:The 31 pairs of FF (fresh frozen) andFFPE (Formalin-fixed paraffin-embedded) data for the application of 95GC to FFPE in 2019.
| PRJNA982755 | ENA
Project description:Background: The KRAS gene is mutated in about 40% of colorectal cancer (CRC) cases, which has been clinically validated as a predictive mutational marker of intrinsic resistatnce to anti-EGFR inhibitor (EGFRi) therapy. Since nearly 60% of patients with a wild type KRAS fail to respond to EGFRi treatment, there is a need to develop more reliable molecular signatures to better predict response. Here we address the challenge of adapting a gene expression signature predictive of RAS pathway activation, created using fresh frozen (FF) tissues, for use with more widely available formalin fixed paraffin-embedded (FFPE) tissues. Methods: In this study, we evaluated the translation of an 18-gene RAS pathway signature score from FF to FFPE in 54 CRC cases, using a head-to-head comparison of five technology platforms. FFPE-based technologies included the Affymetrix GeneChip (Affy), NanoString nCounter(NanoS), Illumina whole genome RNASeq (RNA-Acc), Illumina targeted RNASeq(t-RNA), and Illumina stranded Total RNA-rRNA-depletion (rRNA). Results: Using Affy_FF as the gold standard, initial analysis of the 18-gene RAS scores on all 54 samples shows varying pairwise Spearman correlations, with (1) Affy_FFPE(r=0.233, p=0.090); (2) NanoS_FFPE(r=0.608, p<0.0001); (3) RNA-Acc_FFPE(r=0.175, p=0.21); (4) t-RNA_FFPE (r=-0.237, p=0.085); and (5) t-RNA (r=-0.012, p=0.93). These results suggest that only NanoString has successful FF to FFPE translation. The subsequent removal of identified problematic samples (n=15) and gene (n=2) further improves the correlations of Affy_FF with three of the five technologies: Affy_FFPE (r=0.672, p<0.0001); NanoS_FFPE (r=0.738, p<0.0001); and RNA-Acc_FFPE (r=0.483, p=0.002). Conclusions: Of the five technology platforms tested, NanoString technology provides a more faithful translation of the RAS pathway gene expression signature from FF to FFPE than the Affymetrix GeneChip and multiple RNASeq technologies. Moreover, NanoString was the most forgiving technology in the analysis of samples with presumably poor RNA quality. Using this approach, the RAS signature score may now be reasonably applied to FFPE clinical samples.
2016-09-20 | GSE86557 | GEO
Project description:Background: The KRAS gene is mutated in about 40% of colorectal cancer (CRC) cases, which has been clinically validated as a predictive mutational marker of intrinsic resistatnce to anti-EGFR inhibitor (EGFRi) therapy. Since nearly 60% of patients with a wild type KRAS fail to respond to EGFRi treatment, there is a need to develop more reliable molecular signatures to better predict response. Here we address the challenge of adapting a gene expression signature predictive of RAS pathway activation, created using fresh frozen (FF) tissues, for use with more widely available formalin fixed paraffin-embedded (FFPE) tissues. Methods: In this study, we evaluated the translation of an 18-gene RAS pathway signature score from FF to FFPE in 54 CRC cases, using a head-to-head comparison of five technology platforms. FFPE-based technologies included the Affymetrix GeneChip (Affy), NanoString nCounter(NanoS), Illumina whole genome RNASeq (RNA-Acc), Illumina targeted RNASeq(t-RNA), and Illumina stranded Total RNA-rRNA-depletion (rRNA). Results: Using Affy_FF as the gold standard, initial analysis of the 18-gene RAS scores on all 54 samples shows varying pairwise Spearman correlations, with (1) Affy_FFPE(r=0.233, p=0.090); (2) NanoS_FFPE(r=0.608, p<0.0001); (3) RNA-Acc_FFPE(r=0.175, p=0.21); (4) t-RNA_FFPE (r=-0.237, p=0.085); and (5) t-RNA (r=-0.012, p=0.93). These results suggest that only NanoString has successful FF to FFPE translation. The subsequent removal of identified problematic samples (n=15) and gene (n=2) further improves the correlations of Affy_FF with three of the five technologies: Affy_FFPE (r=0.672, p<0.0001); NanoS_FFPE (r=0.738, p<0.0001); and RNA-Acc_FFPE (r=0.483, p=0.002). Conclusions: Of the five technology platforms tested, NanoString technology provides a more faithful translation of the RAS pathway gene expression signature from FF to FFPE than the Affymetrix GeneChip and multiple RNASeq technologies. Moreover, NanoString was the most forgiving technology in the analysis of samples with presumably poor RNA quality. Using this approach, the RAS signature score may now be reasonably applied to FFPE clinical samples.
2016-09-20 | GSE86561 | GEO