A Machine Learning-Based Histopathological Image Analysis Reveals Cancer Stemness in TNBCs with 17p Loss
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Abstract
Artificial intelligence and machine learning based methods have incorporated scientific research into clinical decision, leading to great improvement in clinical diagnosis and therapeutics. Here we developed a Convolutional Neural Network based model to identify cancer stem-like cells (CSCs) on H&E-stained histopathological images. Combined with cancer genomics profiles, our analysis revealed that triple negative breast cancers (TNBCs) with heterozygous deletion of chromosome 17p (17p-loss) correlate with higher cancer stemness potential compared to TNBCs with neural copy numbers of 17p (17p-intact). 17p-loss TNBC cells also have an increased percentage of CSCs and are resistant to chemotherapies compared with the 17p-intact TNBC cells. Moreover, we built a bioinformatics pipeline to screen compounds that target the stemness of 17p-loss cancer cells, one of which is FK866. FK866 promoted the antitumor activity of doxorubicin in the treatment of 17p-loss TNBCs. Our study provides a powerful computational tool for cancer image analysis as well as a feasible approach for precision cancer medicine.