| Autor: |
Rapier-Sharman N; Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA., Spendlove MD; Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA., Poulsen JB; Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA., Appel AE; Infectious Diseases and Genomic Medicine Group, J. Craig Venter Institute, Rockville, MD 20850, USA., Wiscovitch-Russo R; Infectious Diseases and Genomic Medicine Group, J. Craig Venter Institute, Rockville, MD 20850, USA., Vashee S; Synthetic Biology and Bioenergy Group, J. Craig Venter Institute, Rockville, MD 20850, USA., Gonzalez-Juarbe N; Infectious Diseases and Genomic Medicine Group, J. Craig Venter Institute, Rockville, MD 20850, USA., Pickett BE; Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT 84602, USA. |
| Abstrakt: |
Background/Objectives : Breast cancer is diagnosed in 2.3 million women each year and kills 685,000 (~30% of patients) worldwide. The prognosis for many breast cancer subtypes has improved due to treatments targeting the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). In contrast, patients with triple-negative breast cancer (TNBC) tumors, which lack all three commonly targeted membrane markers, more frequently relapse and have lower survival rates due to a lack of tumor-selective TNBC treatments. We aim to investigate TNBC mechanistic markers that could be targeted for treatment. Methods: We performed a secondary TNBC analysis of 196 samples across 10 publicly available bulk RNA-sequencing studies to better understand the molecular mechanism(s) of disease and predict robust mechanistic markers that could be used to improve the mechanistic understanding of and diagnostic capabilities for TNBC. Results: Our analysis identified ~12,500 significant differentially expressed genes (FDR-adjusted p -value < 0.05), including KIF14 and ELMOD3, and two significantly modulated pathways. Additionally, our novel findings include highly accurate mechanistic markers identified using machine learning methods, including CIDEC (97.1% accuracy alone), CD300LG, ASPM, and RGS1 (98.9% combined accuracy), as well as TNBC subtype-differentiating mechanistic markers, including the targets PDE3B, CFD, IFNG, and ADM, which have associated therapeutics that can potentially be repurposed to improve treatment options. We then experimentally and computationally validated a subset of these findings. Conclusions: The results of our analyses can be used to better understand the mechanism(s) of disease and contribute to the development of improved diagnostics and/or treatments for TNBC. |
