NanoCMSer: A Clinically Applicable Stratification Tool for Stratification of Colorectal Cancer Samples
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ABSTRACT:
PROVIDER: PRJNA1084749 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:NanoCMSer: A Clinically Applicable Stratification Tool for Stratification of Colorectal Cancer Samples [II]
| PRJNA1085294 | ENA
Project description:NanoCMSer: A Clinically Applicable Stratification Tool for Stratification of Colorectal Cancer Samples [III]
| PRJNA1085295 | ENA
Project description:NanoCMSer: A Clinically Applicable Stratification Tool for Stratification of Colorectal Cancer Samples [IV]
| PRJNA1085297 | ENA
Project description:NanoCMSer: A Clinically Applicable Tool for Stratification of Colorectal Cancer Samples [March2024]
| PRJNA1092887 | ENA
Project description:NanoCMSer: A Clinically Applicable Tool for Stratification of Colorectal Cancer Samples [March2024.CODE]
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Project description:NanoCMSer: A Clinically Applicable Tool for Stratification of Colorectal Cancer Samples [March2024.MATCH]
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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 routine clinical practice, particularly on low RNA-quality archival data, we developed NanoCMSer. This NanoString-based CMS classifier reliably stratifies both fresh-frozen (FF) and formalin-fixed paraffin-embedded (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 decline in recurrence free survival (RFS) for CMS2 and CMS3 patients as they progress from stage II to III, while this decline is less pronounced for CMS1 and CMS4. We posit NanoCMSer as a robust tool with potential applications in both tumor biology and clinical practice, accessible through the NanoCMSer R package.
2025-05-22 | GSE260968 | GEO
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 routine clinical practice, particularly on low RNA-quality archival data, we developed NanoCMSer. This NanoString-based CMS classifier reliably stratifies both fresh-frozen (FF) and formalin-fixed paraffin-embedded (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 decline in recurrence free survival (RFS) for CMS2 and CMS3 patients as they progress from stage II to III, while this decline is less pronounced for CMS1 and CMS4. We posit NanoCMSer as a robust tool with potential applications in both tumor biology and clinical practice, accessible through the NanoCMSer R package.
2025-05-22 | GSE261108 | GEO
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 routine clinical practice, particularly on low RNA-quality archival data, we developed NanoCMSer. This NanoString-based CMS classifier reliably stratifies both fresh-frozen (FF) and formalin-fixed paraffin-embedded (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 decline in recurrence free survival (RFS) for CMS2 and CMS3 patients as they progress from stage II to III, while this decline is less pronounced for CMS1 and CMS4. We posit NanoCMSer as a robust tool with potential applications in both tumor biology and clinical practice, accessible through the NanoCMSer R package.
2025-05-22 | GSE261107 | GEO
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 routine clinical practice, particularly on low RNA-quality archival data, we developed NanoCMSer. This NanoString-based CMS classifier reliably stratifies both fresh-frozen (FF) and formalin-fixed paraffin-embedded (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 decline in recurrence free survival (RFS) for CMS2 and CMS3 patients as they progress from stage II to III, while this decline is less pronounced for CMS1 and CMS4. We posit NanoCMSer as a robust tool with potential applications in both tumor biology and clinical practice, accessible through the NanoCMSer R package.
2025-05-22 | GSE261104 | GEO