Drug Target Identification in Triple Negative Breast Cancer Stem Cell Pathways: a computational study of gene regulatory pathways using Boolean networks

Aditya Lahiri, Haswanth Vundavilli, Madhurima Mondal, Pranabesh Bhattacharjee, Brian Decker, Giuseppe Del Priore, N. Peter Reeves, Aniruddha Datta
2023 · doi:10.1101/2023.05.03.539160
preprint OA: closed CC-BY-NC-ND-4.0
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Abstract

ABSTRACT Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer associated with an early age of onset, greater propensity towards metastasis, and poorer clinical outcomes. It accounts for 10% to 20% of newly diagnosed breast cancer cases and disproportionately affects individuals from the African American race. While TNBC is sensitive to chemotherapy, it is also prone to relapse. This is because chemotherapy successfully targets the primary TNBC tumor cell but often fails to target the subpopulation of TNBC stem cells. TNBC stem cells display cancerous traits such as cell cycle progression, survival, proliferation, apoptosis inhibition, and epithelial-mesenchymal transition. To study the cancer initiating behavior of the TNBC stem cells, we studied their underlying signaling pathways using Boolean networks(BN). BNs are effective in capturing the causal interactions taking place in signaling pathways. We built the BN from the pathway literature and used it to evaluate the efficacies of eleven targeted inhibitory drugs in suppressing cancer-promoting genes. We simulated the BN when the pathways had single or multiple mutations, with a maximum of three mutations at a time. Our findings indicated that STAT3, GLI , and NF-κB are the most optimal targets for inhibition. These genes are known regulators of the cancer-promoting genes in the pathway,hence our model agrees with the existing biological literature. Therefore inhibiting these three genes has the potential to prevent TNBC relapse. Additionally, our studies found that drug efficacies decreased as mutations increased in the pathway. Furthermore, we noticed that combinations of drugs performed better than single drugs.

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License: CC-BY-NC-ND-4.0