Validation of a network-based strategy for the optimization of combinatorial target selection in breast cancer therapy: siRNA knockdown of network targets in MDA-MB-231 cells as an in vitro model for inhibition of tumor development

Abstract

Network-based strategies provided by systems biology are attractive tools for cancer therapy. Modulation of cancer networks by anticancer drugs may alter the response of malignant cells and/or drive network re-organization into the inhibition of cancer progression. Previously, using systems biology approach and cancer signaling networks, we identified top-5 highly expressed and connected proteins (HSP90AB1, CSNK2B, TK1, YWHAB and VIM) in the invasive MDA-MB-231 breast cancer cell line. Here, we have knocked down the expression of these proteins, individually or together using siRNAs. The transfected cell lines were assessed for in vitro cell growth, colony formation, migration and invasion relative to control transfected MDA-MB-231, the non-invasive MCF-7 breast carcinoma cell line and the non-tumoral mammary epithelial cell line MCF-10A. The knockdown of the top-5 upregulated connectivity hubs successfully inhibited the in vitro proliferation, colony formation, anchorage independence, migration and invasion in MDA-MB-231 cells; with minimal effects in the control transfected MDA-MB-231 cells or MCF-7 and MCF-10A cells. The in vitro validation of bioinformatics predictions regarding optimized multi-target selection for therapy suggests that protein expression levels together with protein-protein interaction network analysis may provide an optimized combinatorial target selection for a highly effective anti-metastatic precision therapy in triple-negative breast cancer. This approach increases the ability to identify not only druggable hubs as essential targets for cancer survival, but also interactions most susceptible to synergistic drug action. The data provided in this report constitute a preliminary step toward the personalized clinical application of our strategy to optimize the therapeutic use of anti-cancer drugs.

Modeling Basins of Attraction for Breast Cancer Using Hopfield Networks

“…Specific target combinations were derived from the gene expression profile of each patient, with the potential to mitigate side effects and enhance the treatment outcome. Among the parameters tested to indicate gene priority, the node degree has been indicated as a key factor (Carels et al, 2015;Tilli et al, 2016;Conforte et al, 2019). Other topological measures of gene regulatory networks (GRNs) have also been widely used in the identification of new therapeutic targets (Peng and Schork, 2014;Azevedo and Moreira-Filho, 2015).…”

“…Rather, we identified the key genes responsible for attractor stability. By extension, one could argue that key genes are essential to tumor biology and that their inhibition would lead to cell death (Tilli et al, 2016).…”

“…For this reason, personalized medicine is expected to mitigate the side effects and improve treatment efficacy. In vitro validation of this approach showed that simultaneous inhibition of target combinations exhibited a more substantial disruptive effect on malignant cells than the sum of single inhibitions (Tilli et al, 2016).…”

TK1 in Cancer Progression: elucidating its influence on cellular invasion and survival

“…Increased expression of the TK1 enzyme is associated with cancer aggressiveness and TK1 serum levels progressively increase with cancer grade (T1-T4) in breast tissue (14,17).. The role of TK1 and its impact on tumor progression has recently been evaluated in protein networks (18)(19)(20). In one study, TK1 was evaluated in invasive MDA-MB-231 cells within a protein network that included HSP90AB1, CSNK2B, YWHAB and VIM (18).…”

“…The role of TK1 and its impact on tumor progression has recently been evaluated in protein networks (18)(19)(20). In one study, TK1 was evaluated in invasive MDA-MB-231 cells within a protein network that included HSP90AB1, CSNK2B, YWHAB and VIM (18). Knockdowns of the complete network inhibited cellular invasion during the epithelial-mesenchymal transition when compared to non-invasive MCF7 and non-tumoral MCF-10A cell lines (18).…”