Clinical, epidemiological and experimental data identified the insulin-like growth factor-1 receptor (IGF1R) as a candidate therapeutic target in oncology. While this paradigm is based on well-established biological facts, including the potent antiapoptotic and cell survival capabilities of the receptor, most Phase III clinical trials designed to target the IGF1R led to disappointing results. The present study was aimed at evaluating the hypothesis that combined treatment composed of selective IGF1R inhibitor along with classical chemotherapy might be more effective than individual monotherapies in breast cancer treatment. Analyses included comprehensive measurements of the synergism achieved by various combination regimens using the CompuSyn software. In addition, proteomic analyses were conducted to identify the proteins involved in the synergistic killing effect at a global level. Data presented here demonstrates that co-treatment of IGF1R inhibitor along with chemotherapeutic drugs markedly improves the therapeutic efficiency in breast cancer cells. Of clinical relevance, our analyses indicate that high IGF1R baseline expression may serve as a predictive biomarker for IGF1R targeted therapy. In addition, we identified a tengenes signature with potential predictive value. In conclusion, the use of a series of bioinformatics tools shed light on some of the biological pathways that might be responsible for synergysm in cancer therapy.

Insulin and insulin-like growth factor-1 (IGF1), acting respectively via the insulin (INSR) and IGF1 (IGF1R) receptors, play key developmental and metabolic roles throughout life. In addition, both signaling pathways fulfill important roles in cancer initiation and progression. The present study was aimed at identifying mechanistic differences between INSR and IGF1R using a recently developed bioinformatics tool, the Biological Network Simulator (BioNSi). This application allows to import and merge multiple pathways and interaction information from the KEGG database into a single network representation. The BioNsi network simulation tool allowed us to exploit the availability of gene expression data derived from breast cancer cell lines with specific disruptions of the INSR or IGF1R genes in order to investigate potential differences in protein expression that might be linked to biological attributes of the specific receptor networks. Modeling-generated information was corroborated by experimental and biological assays. BioNSi analyses revealed that the expression of 75 and 71 genes changed during simulation of IGF1R-KD and INSR-KD, compared to control cells, respectively. Out of 16 proteins that BioNSi analysis was based on, validated by Western blotting, nine were shown to be involved in DNA repair, eight in cell cycle checkpoints, six in proliferation, eight in apoptosis, seven in oxidative stress, six in cell migration, two in energy homeostasis, and three in senescence. Taken together, analyses identified a number of commonalities and, most importantly, dissimilarities between the IGF1R and INSR pathways that might help explain the basis for the biological differences between these networks.