Project description:Colorectal cancer (CRC) is a significant contributor to global cancer-related mortality, emphasizing the critical need for advanced predictive biomarkers to guide treatment decisions. In our pursuit of identifying novel biomarkers, as part of an international consortium, we have previously categorized CRCs into four transcriptome-based consensus molecular subtypes (CMS1-4). These subtypes show promise for predicting outcomes and facilitating biological investigations. To streamline the integration of CMS classification into clinical practice, where routinely formalin-fixed paraffin-embedded (FFPE) samples are used, we developed NanoCMSer. This NanoString-based CMS classifier reliably stratifies both fresh-frozen (FF) and FFPE samples using a concise panel of just 55 genes. Employing domain adaptation methods during training enhanced the generalizability of the classifier, resulting in high accuracy for both FF and FFPE samples. For FF NanoString-based datasets, an accuracy of 95% and for FFPE NanoString-based datasets an accuracy of 92% was achieved, which is the highest reported for FFPE tissues. Furthermore, using the NanoCMSer, a 96% accuracy across a comprehensive collection of 1,976 RNAseq-based samples from 23 datasets aggregated in this study was determined. Additionally, our findings highlight that even with a limited number of genes, the biological relevance of predictions at the individual sample level can be still identified, with distinct CMS biology readily recognizable upon conducting enrichment analysis. Furthermore, our observations revealed a substantial difference in 5-year recurrence free survival (RFS) for CMS2 and CMS3 patients in stage III compared to II (CMS2 21% more recurrences, CMS3 31% more recurrences), while this difference is less pronounced for CMS1 and CMS4 with 11% and 9%, respectively. We posit NanoCMSer as a robust tool with potential applications to assess the CMS status of CRCs in both tumor biology and clinical practice, accessible through the NanoCMSer R package.

Project description:Purpose: The goals of this study is to determine the best method of gene expression quantification (RNA-seq, Microarray, NanoString) and amplification kits adapted to low-input and/or low-quality RNA samples (FFPE samples) Methods: Mouse bladder cancer cell line (mouse bladder cancer cell line, BC57) and mouse normal mouse normal urothelium were fixed in formalin and embedded in paraffin (FFPE), andfesh frozen (FF) in liquid nitrogen. The total RNA of these 4 samples were tested by 3 technologies (NanoString, RNA-seq and Microarray) and the results were compared to its reference (high-quality and high-input RNA of mouse bladder cancer cell line and mouse normal mouse normal urothelium). For NanoString with low-input RNA samples, each sample was tested by NanoString quantification after amplification by SMARTer Stranded Total RNA-Seq Kit - Pico Input mammelian and Ovation SoLo NuGEN RNA-seq System, and NanoString based on PCR approach with three input quantities: 50pg, 250pg and 2ng of total RNA, except for NanoString quantification after amplification by SMARTer Stranded Total RNA-Seq Kit - Pico Input mammelian kit for which the minimum recommended quantity was 250pg of total RNA. NanoString direct quantification was also done for FF and FFPE samples at high amount (50ng of total RNA) and results obtained from FF samples were considered as the reference. To determine which is the method for NanoString technology, low-input and low-quality RNA samples, we performed NanoString control quality metrics, principal component analysis, and a differential analysis between the mouse bladder cancer cell lines and the mouse normal mouse normal urothelium for each input quantity, amplification method and method of sample preservation (FF or FFPE). Results: The NanoString based PCR based approach is recommended for quantification of gene expression of FFPE and FF samples from 250pg of total RNA. However, NanoString quantification after amplification by SMARTer Stranded Total RNA-Seq Kit - Pico Input mammelian and Ovation SoLo NuGEN RNA-seq System is not recommended for FF and FFPE from low-input 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.

Dataset Information

NanoCMSer: A Clinically Applicable Stratification Tool for Stratification of Colorectal Cancer Samples [II]

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