Huang, KunDong, TianhanSafa, Ahmad R.Jerde, Travis J.Lu, TaoLu, Xiongbin2023-05-232023-05-232023-05https://hdl.handle.net/1805/33192http://dx.doi.org/10.7912/C2/3140Indiana University-Purdue University Indianapolis (IUPUI)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.en-USThesis