Abstract
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.
Highlights
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
IntClust subtypes are reproducible in a large meta-analysis, show clinical validity and best capture variation in genomic drivers
Using the largest sample collection with extensive genomic, transcriptomic and clinical annotation in existence, we previously described a scheme for classifying breast tumors into 10 subtypes based on the pattern of copy number alterations (CNAs) which exert a concordant effect on gene expression in cis
Summary
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. Attempts to improve this situation by using genomic technology focused on data-driven methods including unsupervised transcriptome-based classification [1,2,3] and gene signatures trained against a specific clinical outcome [4,5,6]. The strategy for discriminating between driver and passenger events amongst these somatic alterations has, for non-synonymous mutations, focused on identification of genes more frequently mutated than expected by chance in a given collection of tumor samples This approach has required some adjustment owing to the non-random background mutation rates in cancer genomes [13] and may be complemented by accounting for the pattern of mutational distribution within genes [14], it does provide a roadmap for the comprehensive identification of all driver mutations if a sufficiently large sample size is interrogated [15].
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