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A Versatile and Upgraded Version of the LundTax Classification Algorithm Applied to Independent Cohorts

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Názov: A Versatile and Upgraded Version of the LundTax Classification Algorithm Applied to Independent Cohorts
Autori: Cotillas, Elena Aramendía, Bernardo, Carina, Veerla, Srinivas, Liedberg, Fredrik, Sjödahl, Gottfrid, Eriksson, Pontus
Prispievatelia: Lund University, Faculty of Medicine, Department of Clinical Sciences, Lund, Section I, Urothelial cancer, Lunds universitet, Medicinska fakulteten, Institutionen för kliniska vetenskaper, Lund, Sektion I, Urinblåsecancer, Originator, Lund University, Faculty of Medicine, Department of Clinical Sciences, Lund, Section I, Urothelial cancer, Urothelial Cancer Genomics, Lunds universitet, Medicinska fakulteten, Institutionen för kliniska vetenskaper, Lund, Sektion I, Urinblåsecancer, Genomiska analyser av urinblåscancer, Originator, Lund University, Profile areas and other strong research environments, Other Strong Research Environments, LUCC: Lund University Cancer Centre, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Övriga starka forskningsmiljöer, LUCC: Lunds universitets cancercentrum, Originator, Lund University, Faculty of Medicine, Department of Clinical Sciences, Lund, Section I, Breast/lungcancer, Lunds universitet, Medicinska fakulteten, Institutionen för kliniska vetenskaper, Lund, Sektion I, Bröst/lungcancer, Originator, Lund University, Faculty of Medicine, Department of Clinical Sciences, Lund, Section I, Breast/lungcancer, Research Group Lung Cancer, Lunds universitet, Medicinska fakulteten, Institutionen för kliniska vetenskaper, Lund, Sektion I, Bröst/lungcancer, Forskningsgrupp Lungcancer, Originator, Lund University, Faculty of Medicine, Department of Clinical Sciences, Lund, Section I, Breast/ovarian cancer, Breast and Ovarian Cancer Genomics, Lunds universitet, Medicinska fakulteten, Institutionen för kliniska vetenskaper, Lund, Sektion I, Bröst/ovarialcancer, Bröst- och ovarialcancer, Originator, Lund University, Faculty of Medicine, Department of Laboratory Medicine, Division of Translational Cancer Research, Breast/lung cancer, Lunds universitet, Medicinska fakulteten, Institutionen för laboratoriemedicin, Avdelningen för translationell cancerforskning, Bröst/lungcancer, Originator, Lund University, Faculty of Medicine, Department of Translational Medicine, Urology - urothelial cancer, Malmö, Lunds universitet, Medicinska fakulteten, Institutionen för translationell medicin, Urologi - blåscancer, Malmö, Originator
Zdroj: Journal of Molecular Diagnostics. 26(12):1081-1101
Predmety: Medical and Health Sciences, Clinical Medicine, Cancer and Oncology, Medicin och hälsovetenskap, Klinisk medicin, Cancer och onkologi
Popis: Stratification of cancer into biologically and molecularly similar subgroups is a cornerstone of precision medicine. The Lund Taxonomy classification system for urothelial carcinoma aims to be applicable across the whole disease spectrum including both non–muscle-invasive and invasive bladder cancer. A successful classification system is one that can be robustly and reproducibly applied to new samples. However, transcriptomic methods used for subtype classification are affected by analytic platform, data preprocessing, cohort composition, and tumor purity. Furthermore, only limited data have been published evaluating the transferability of existing classification algorithms to external data sets. In this study, a single sample classifier was developed based on in-house microarray and RNA-sequencing data, intended to be broadly applicable across studies and platforms. The new classification algorithm was applied to 10 published external bladder cancer cohorts (n = 2560 cases) to evaluate its ability to capturecharacteristic subtype-associated gene expression signatures and complementary data such as mutations, clinical outcomes, treatment response, or histologic subtypes. The effect of sample purity on the classification results was evaluated by generating low-purity versions of samples in silico. The classifier was robustly applicable across different gene expression profiling platforms and preprocessing methods and was less sensitive to variations in sample purity.
Prístupová URL adresa: https://doi.org/10.1016/j.jmoldx.2024.08.005
Databáza: SwePub
Popis
Abstrakt:Stratification of cancer into biologically and molecularly similar subgroups is a cornerstone of precision medicine. The Lund Taxonomy classification system for urothelial carcinoma aims to be applicable across the whole disease spectrum including both non–muscle-invasive and invasive bladder cancer. A successful classification system is one that can be robustly and reproducibly applied to new samples. However, transcriptomic methods used for subtype classification are affected by analytic platform, data preprocessing, cohort composition, and tumor purity. Furthermore, only limited data have been published evaluating the transferability of existing classification algorithms to external data sets. In this study, a single sample classifier was developed based on in-house microarray and RNA-sequencing data, intended to be broadly applicable across studies and platforms. The new classification algorithm was applied to 10 published external bladder cancer cohorts (n = 2560 cases) to evaluate its ability to capturecharacteristic subtype-associated gene expression signatures and complementary data such as mutations, clinical outcomes, treatment response, or histologic subtypes. The effect of sample purity on the classification results was evaluated by generating low-purity versions of samples in silico. The classifier was robustly applicable across different gene expression profiling platforms and preprocessing methods and was less sensitive to variations in sample purity.
ISSN:15251578
19437811
DOI:10.1016/j.jmoldx.2024.08.005