Research On The Rationality Of A New Cancer Biomarker Algorithm And Combinatorial Drivers Of Triple Negative Breast Cancer

Cancer is a complex disease,which is often driven by combinatorial activities of multiple cancer genes.With the development of bioinformatics and high-throughput technologies,a central task of precision medicine is to develop novel algorithms to enable highly effective treatment of cancer patients based on their individual genetic background.Breast cancer is the most prevalent cancer in women,and triple negative breast cancer(TNBC)is the focus of breast cancer research owing to its prognosis and the lack of targeted therapies.In order to facilitate precision medicine for TNBC,we identified and validated no vel drivers for TNBC using the newly de veloped algorithm,State specific network Inference via Mutually Exclusive Bimodality Analysis(SIMBA).We first applied it to prostate cancer to identify key transcription factors regulating metastatic micro RNAs.Moreover,we applied the new algorithm to identify novel drivers of TNBC.We successfully discovered the key transcription factors EN1 and FOXC1,which play synergistic roles in triple negative breast cancer,and validate the importance of transcription factors as new drivers for Triple Negative Breast Cancer by biology experiments.In summary,we developed a new algorithm for finding combinatorial drivers for cancer based on the concept of precision medicine,and applied it to prostate cancer and TNBC.We identified the key transcription factors EN1 and FOXC1,which play important synergistic roles in triple negative breast cancer.The discovery of key transcription factors EN1 and FOXC1 provides new ideas for the subsequent clinical development of anti-cancer molecule target drugs.