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Keywords {🔍}

intclust, cancer, breast, expression, subtypes, gene, classification, subtype, pubmed, study, data, genes, studies, additional, file, article, metabric, google, scholar, samples, tumors, number, survival, pam, copy, based, clinical, models, cas, classifier, external, cell, molecular, genomic, scmgene, analysis, tumor, figure, method, dataset, central, explained, comparison, lines, nature, profiles, patterns, tcga, patients, size,

Topics {✒️}

genome-driven integrated classification genome-wide driver-based classification org/web/packages/boot/index european genome-phenome archive distant metastasis-free survival org/web/packages/] waldron receiver-operating-characteristic curves kaplan-meier survival plots copy number-driven genes long-term clinical management long-term clinical benefit data-driven approaches designed article download pdf luminal immune-positive subgroup heterogeneity search search receive neo-adjuvant chemotherapy mutations deemed ‘m-class adjusted r-squared statistic driver-based molecular taxonomy high-resolution copy-number er-negative breast cancer studies received neo-adjuvant adjusted r-squared statistics gene expression profile average gene-expression profiles node-negative breast cancer carlos caldas r-squared values explained er-positive breast cancer cancer research uk copy number-based classifier subgroups comprise high-risk /pubs/sbtpaper] copy number profiles copy-number profiles author information authors receiver operating characteristic shah sp privacy choices/manage cookies relapse-free survival gene expression-based classifier expression-based intclust classification cancer genome atlas cancer genome landscapes late-stage ovarian cancer lawrence ms view cross-platform normalization methods long-term survival [30] long term survival

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         headline:Genome-driven integrated classification of breast cancer validated in over 7,500 samples
         description:IntClust is a classification of breast cancer comprising 10 subtypes based on molecular drivers identified through the integration of genomic and transcriptomic data from 1,000 breast tumors and validated in a further 1,000. We present a reliable method for subtyping breast tumors into the IntClust subtypes based on gene expression and demonstrate the clinical and biological validity of the IntClust classification. We developed a gene expression-based approach for classifying breast tumors into the ten IntClust subtypes by using the ensemble profile of the index discovery dataset. We evaluate this approach in 983 independent samples for which the combined copy-number and gene expression IntClust classification was available. Only 24 samples are discordantly classified. Next, we compile a consolidated external dataset composed of a further 7,544 breast tumors. We use our approach to classify all samples into the IntClust subtypes. All ten subtypes are observable in most studies at comparable frequencies. The IntClust subtypes are significantly associated with relapse-free survival and recapitulate patterns of survival observed previously. In studies of neo-adjuvant chemotherapy, IntClust reveals distinct patterns of chemosensitivity. Finally, patterns of expression of genomic drivers reported by TCGA (The Cancer Genome Atlas) are better explained by IntClust as compared to the PAM50 classifier. IntClust subtypes are reproducible in a large meta-analysis, show clinical validity and best capture variation in genomic drivers. IntClust is a driver-based breast cancer classification and is likely to become increasingly relevant as more targeted biological therapies become available.
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      headline:Genome-driven integrated classification of breast cancer validated in over 7,500 samples
      description:IntClust is a classification of breast cancer comprising 10 subtypes based on molecular drivers identified through the integration of genomic and transcriptomic data from 1,000 breast tumors and validated in a further 1,000. We present a reliable method for subtyping breast tumors into the IntClust subtypes based on gene expression and demonstrate the clinical and biological validity of the IntClust classification. We developed a gene expression-based approach for classifying breast tumors into the ten IntClust subtypes by using the ensemble profile of the index discovery dataset. We evaluate this approach in 983 independent samples for which the combined copy-number and gene expression IntClust classification was available. Only 24 samples are discordantly classified. Next, we compile a consolidated external dataset composed of a further 7,544 breast tumors. We use our approach to classify all samples into the IntClust subtypes. All ten subtypes are observable in most studies at comparable frequencies. The IntClust subtypes are significantly associated with relapse-free survival and recapitulate patterns of survival observed previously. In studies of neo-adjuvant chemotherapy, IntClust reveals distinct patterns of chemosensitivity. Finally, patterns of expression of genomic drivers reported by TCGA (The Cancer Genome Atlas) are better explained by IntClust as compared to the PAM50 classifier. IntClust subtypes are reproducible in a large meta-analysis, show clinical validity and best capture variation in genomic drivers. IntClust is a driver-based breast cancer classification and is likely to become increasingly relevant as more targeted biological therapies become available.
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      name:Department of Oncology, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
      name:Cambridge Experimental Cancer Medicine Centre and NIHR Cambridge Biomedical, Research Centre, Cambridge University Hospitals NHS, Cambridge, UK

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