Project description:Advantages of RNA-Seq over array based platforms are quantitative gene expression and discovery of expressed single nucleotide variants (eSNVs) and fusion transcripts from a single platform, but the sensitivity for each of these characteristics is unknown. We measured gene expression in a set of manually degraded RNAs, nine pairs of matched fresh-frozen, and FFPE RNA isolated from breast tumor with the hybridization based, NanoString nCounter, (226 gene panel) and with whole transcriptome RNA-Seq using RiboZeroGold ScriptSeq V2 library preparation kits. We performed correlation analyses of gene expression between samples and across platforms. We then specifically assessed whole transcriptome expression of lincRNA and discovery of eSNVs and fusion transcripts in the FFPE RNA-Seq data. For gene expression in the manually degraded samples, we observed Pearson correlation of >0.94 and >0.80 with NanoString and ScriptSeq protocols respectively. Gene expression data for matched fresh-frozen and FFPE samples yielded mean Pearson correlations of 0.874 and 0.783 for NanoString (226 genes) and ScriptSeq whole transcriptome protocols respectively. Specifically for lincRNAs, we observed superb Pearson correlation (0.988) between matched fresh-frozen and FFPE pairs. FFPE samples across NanoString and RNA-Seq platforms gave a mean Pearson correlation of 0.838. In FFPE libraries, we detected 53.4% of high confidence SNVs and 24% of high confidence fusion transcripts. Sensitivity of fusion transcript detection was not overcome by an increase in depth of sequencing up to 3-fold (increase from ~56 to ~159 million reads). Both NanoString and ScriptSeq RNA-Seq technologies yield reliable gene expression data for degraded and FFPE material. The high degree of correlation between NanoString and RNA-Seq platforms suggests discovery based whole transciptome studies from FFPE material will produce reliable expression data. The RiboZeroGold ScriptSeq protocol performed particularly well for lincRNA expression from FFPE libraries but detection of eSNV and fusion transcripts was less sensitive. We performed RNASeq on RNA from nine matched pairs of fresh-frozen and FFPE tissues from breast cancer patients. The goal was to test the RiboZeroGold ScriptSeq complete low input library preparation kit for degraded RNA samples.

Project description:Advantages of RNA-Seq over array based platforms are quantitative gene expression and discovery of expressed single nucleotide variants (eSNVs) and fusion transcripts from a single platform, but the sensitivity for each of these characteristics is unknown. We measured gene expression in a set of manually degraded RNAs, nine pairs of matched fresh-frozen, and FFPE RNA isolated from breast tumor with the hybridization based, NanoString nCounter, (226 gene panel) and with whole transcriptome RNA-Seq using RiboZeroGold ScriptSeq V2 library preparation kits. We performed correlation analyses of gene expression between samples and across platforms. We then specifically assessed whole transcriptome expression of lincRNA and discovery of eSNVs and fusion transcripts in the FFPE RNA-Seq data. For gene expression in the manually degraded samples, we observed Pearson correlation of >0.94 and >0.80 with NanoString and ScriptSeq protocols respectively. Gene expression data for matched fresh-frozen and FFPE samples yielded mean Pearson correlations of 0.874 and 0.783 for NanoString (226 genes) and ScriptSeq whole transcriptome protocols respectively. Specifically for lincRNAs, we observed superb Pearson correlation (0.988) between matched fresh-frozen and FFPE pairs. FFPE samples across NanoString and RNA-Seq platforms gave a mean Pearson correlation of 0.838. In FFPE libraries, we detected 53.4% of high confidence SNVs and 24% of high confidence fusion transcripts. Sensitivity of fusion transcript detection was not overcome by an increase in depth of sequencing up to 3-fold (increase from ~56 to ~159 million reads). Both NanoString and ScriptSeq RNA-Seq technologies yield reliable gene expression data for degraded and FFPE material. The high degree of correlation between NanoString and RNA-Seq platforms suggests discovery based whole transciptome studies from FFPE material will produce reliable expression data. The RiboZeroGold ScriptSeq protocol performed particularly well for lincRNA expression from FFPE libraries but detection of eSNV and fusion transcripts was less sensitive.

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.

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.

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Comparison of Nanostring nCounter data on FFPE colon cancer samples and Affymetrix microarray data on matched frozen tissues

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