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.

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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