Development of a simple artificial intelligence method to accurately subtype breast cancers based on gene expression barcodes

INTRODUCTION: Breast cancer is a heterogeneous disease with an ever-growing increase in the biological subtypes being recognized. Along with molecular subtypes, are metastatic and primary cancers, where molecular profile, tumour histology and grade collectively contribute to subtype diversity. Accurate subtype classification has been shown to coincide with improved diagnosis, prognosis and aetiology; imparting a comprehensive patient status with strong correlation to clinical and 5year survival outcomes. Histopathological examinations of tumours, which are mostly inaccurate, is unfortunately still the classification method of choice. The use of gene expression profiling has been studied extensively for implementation in breast cancer subtyping. These profiles include classification, prognosis and in the case of MammaPrintTM, chemotherapy sensitivity, and breast cancer recurrence with Oncotype DXTM. Despite the success of MammaPrintTM and OncotypeTM DX, significant advances in diagnosis and treatment by gene expression profiling, diagnostic gene signatures need to be further explored. The Gene Expression Barcode was developed to overcome the constraints of microarray expression data, such as probe effects, noisy data and the relationship between intensity and actual expression. The algorithm shows clear demarcation of low and high expression measurements to classify genes as silenced and expressed by means of a binary ‘barcode’. As signatures derived from absolute expression calls would simplify implementation in a laboratory setting, we explored the potential of expression barcodes as features for machine learning based classification. We present a simple method, which combines biologically relevant feature selection with the K-means clustering algorithm to accurately classify breast tissue samples as being normal or malignant. METHODOLOGY: Carefully selected and curated normal and tumour samples were obtained from the NCBI’s Gene Expression Omnibus database. We developed a filter to produce a minimal discriminating feature set/barcode by selecting probes which were http://etd.uwc. ac.za/ 26 stably expressed within tissue types, yet differentially expressed between the two tissue types. K-means clustering of tissues based on the minimal feature set was performed to ensure that the barcode signature was able to correctly classify the training set. Unseen samples from both the original and unrelated experiments were then classified to ascertain the predictive accuracy of the signature and its ability to generalize to the classification of unseen samples. RESULTS: The optimized feature selection filter of binary data reduced the feature set from 22215 to 85 informative probes. K-means clustering showed clear separation of normal epithelial breast tissue and primary tumour samples. A 100% accuracy in tissue classification was observed, even for samples from tumour classes not represented in the training set. DISCUSSION: With a simplistic filtering and clustering technique, we were able to classify unseen normal breast and tumour samples with 100% accuracy based on gene expression data that has been converted to ‘absence/presence calls’. We propose that such signatures, which may be easily translated to a PCRor hybridizationbased laboratory test, shows promise for reliable classification of tissues of ambiguous malignancy status. Furthermore, we predict that pairing our barcode and filtering approach with more powerful classification techniques such as multicategory support vector machines could produce robust expression-based classifiers that have potential for clinical application. 2.