Insulin-like growth factor 1 receptor (IGF-1R) is an important therapeutic target for breast cancer treatment. The alteration in the IGF-1R associated signaling network due to various genetic and environmental factors leads the system towards metastasis. The pharmacophore modeling and logical approaches have been applied to analyze the behaviour of complex regulatory network involved in breast cancer. A total of 23 inhibitors were selected to generate ligand based pharmacophore using the tool, Molecular Operating Environment (MOE). The best model consisted of three pharmacophore features: aromatic hydrophobic (HyD/Aro), hydrophobic (HyD) and hydrogen bond acceptor (HBA). This model was validated against World drug bank (WDB) database screening to identify 189 hits with the required pharmacophore features and was further screened by using Lipinski positive compounds. Finally, the most effective drug, fulvestrant, was selected. Fulvestrant is a selective estrogen receptor down regulator (SERD). This inhibitor was further studied by using both in-silico and in-vitro approaches that showed the targeted effect of fulvestrant in ER+ MCF-7 cells. Results suggested that fulvestrant has selective cytotoxic effect and a dose dependent response on IRS-1, IGF-1R, PDZK1 and ER-α in MCF-7 cells. PDZK1 can be an important inhibitory target using fulvestrant because it directly regulates IGF-1R.

Background: Triple-negative breast cancer (TNBC) is more likely to metastasise to the lungs than other breast cancer (BrCa) types, yet the molecular interactions within the tumour microenvironment (TME) at secondary sites remain poorly understood. Methods: This pilot study aimed to explore the metabolic crosstalk between MDA-MB-231 TNBC cells and MRC-5 lung fibroblasts within a co-culture system to replicate the lung metastatic TME. Co-cultures were also treated with Vitamin D or Vitamin E to evaluate the effects of these nutraceuticals on the metabolic crosstalk between TNBC cells and fibroblasts. Results: Our findings demonstrate that co-culture induced the activation of fibroblasts into cancer-associated fibroblasts (CAFs), evidenced by increased α-SMA and FAP expression. Metabolic profiling revealed that TNBC cells in co-culture displayed increased expression of enzymes associated with oxidative phosphorylation (OXPHOS) and glutamine metabolism, while fibroblasts exhibited a metabolic profile consistent with glycolysis and lactate metabolism. Vitamin D inhibited lactate metabolism and HIF-1α expression in fibroblasts while suppressing TCA cycle activity in cancer cells, suggesting a potential role in disrupting oncogenic metabolic crosstalk. Conversely, Vitamin E treatment was associated with increased expression of TCA cycle and oxidative metabolism-related markers in BrCa cells without significantly affecting fibroblast glycolysis. Such differential metabolic responses may contribute to metabolic heterogeneity within the tumour microenvironment. Conclusions: These results provide valuable insights into the metabolic dynamics of TNBC metastases in the lung TME and demonstrate that Vitamins D and E exert distinct effects on metabolic crosstalk between cancer cells and fibroblasts. These findings may have significant implications for the potential supplementation of Vitamins D and E in patients with metastatic TNBC and justify further in-depth analysis.

Background: Breast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER-α) associated Biological Regulatory Network (BRN) for a small part of complex events that leads to BC metastasis.Methods: A discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN) to analyze the logical parameters of the involved entities.Results: In-silicobased discrete and continuous modeling of ER-αassociated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER-α, IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs) such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC.Conclusion: Thein-silicoapproaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER-α, IGF-1R and EGFR) for wet lab experiments as well as provided valuable insights in the treatment of cancers such as BC.

Background: C-X-C chemokine ligand 12 (CXCL12) has important implications in breast cancer (BC) pathogenesis. It is selectively expressed on B and T lymphocytes and is involved in hematopoiesis, thymocyte trafficking, stem cell motility, neovascularization, and tumorigenesis. The single nucleotide polymorphism (SNP)rs1801157of CXCL12 gene has been found to be associated with higher risk of BC.Methods: Our study focuses on the genotypic and allelic distribution of SNP (rs1801157; G/A) in Pakistani population as well as its association with the clinico-pathological features. The association betweenrs1801157genotypes (G/A) and BC risks was assessed by a multivariate logistic regression (MLR) analysis. Genotyping was performed in both healthy individuals and patients of BC using PCR-restriction fragment length polymorphism (PCR-RFLP) method. Furthermore,in-silicoapproaches were adapted to investigate the association of CXCL12 and its receptor CXCR4 with genes/proteins involved in BC signalling.Results: Significant differences in allelic and genotypic distribution between BC patients and healthy individuals of genotype (G/G) and (A/G) (p < 0.05) were observed. The frequency of the allele G in the BC group (77%) was significantly higher as compared to control group (61%) (p = 0.01). The association of genotype GG with clinico-pathological features including age, stages of cancer and organ (lung, liver, bones and brain) metastasis (p > 0.05) was assessed. In a MLR analysis, a number of variables including age, weight of an individual, affected lymph nodes, hormonal status (estrogen and progesterone receptor), alcohol consumption and family history associated with the GG genotype (GG:AA, odds ratio (OR) = 1.30, 95% CI [1.06–1.60]) were found to be independent risk factors for BC. Ourin-vitroresults suggest that genotype GG is possibly increasing the risk of BC in Pakistani cohorts.in-silicoanalysis finds that CXCL12–CXCR4 is associated with an increased expression of PDZK1, PI3k and Akt which lead the breast tumor towards metastasis.Conclusion: Multiple targets such as CXCL12, CXCR4, PDZK1, PI3k and Akt can be inhibited in combined strategies to treat BC metastasis.