A model of NSCLC microenvironment predicts optimal receptor targets
- 16 Downloads
Tumor microenvironment plays an essential role in the growth of malignancy. Understanding how tumor cells co-evolve with tumor-associated immune cells and stromal cells is important for tumor treatment.
In this paper, we propose a logistic population dynamics model for quantifying the intercellular signaling network in non-small-cell lung cancer (NSCLC). The model describes the evolutionary dynamics of cells and signaling proteins and was used to predict effective receptor targets through combination strategy analysis. Then, we optimized a multi-target strategy analysis algorithm that was verified by applying it to virtual patients with heterogeneous conditions. Furthermore, to deal with acquired resistance which was commonly observed in patients with NSCLC, we proposed a novel targeting strategy — tracking targeted therapy, to optimize the treatment by improving the therapeutic strategy periodically.
The synergistic effect when inhibiting multiple signaling pathways may help significantly retard carcinogenic processes associated with disease progression, compared with suppression of a single signaling pathway. While traditional treatment (surgery, radiotherapy and chemotherapy) tends to attack tumor cells directly, the multi-target therapy we suggested here is aimed to inhibit the development of tumor by emasculating the relative competitive advantages of tumor cells and promoting that of normal cells.
The combination of traditional and targeted therapy, as an interesting experiment, was significantly more effective in treatment of virtual patients due to a clear complementary relationship between the two therapeutic schemes.
Keywordsnon-small-cell lung cancer tumor microenvironment intercellular signaling network logistic population dynamics drug resistance multi-target therapy
We acknowledge the supports from the National Natural Science Foundation of China (Nos. 11402227, 11621062 and 11432012), the Fundamental Research Funds for the Central Universities of China (No. 2015QNA4034), and the Thousand Young Talents Program of China.
- 10.Saffiotti, U., Daniel, L. N., Mao, Y., Shi, X., Williams, A. O. and Kaighn, M. E. (1994) Mechanisms of carcinogenesis by crystalline silica in relation to oxygen radicals. Environ. Health Perspect., 102, 159–163Google Scholar
- 12.Druker, B. J., Talpaz, M., Resta, D. J., Peng, B., Buchdunger, E., Ford, J. M., Lydon, N. B., Kantarjian, H., Capdeville, R., Ohno-Jones, S., et al. (2001) Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N. Engl. J. Med., 344, 1031–1037CrossRefGoogle Scholar
- 17.Yao, Z., Fenoglio, S., Gao, D. C., Camiolo, M., Stiles, B., Lindsted, T., Schlederer, M., Johns, C., Altorki, N., Mittal, V., et al. (2010) TGF-beta IL-6 axis mediates selective and adaptive mechanisms of resistance to molecular targeted therapy in lung cancer. Proc. Natl. Acad. Sci. USA, 107, 15535–15540CrossRefGoogle Scholar
- 34.Schreiber, H. and Rowley, D. A. (2010) Cancer. Awakening immunity. Science, 330, 761–762Google Scholar
- 43.Wistuba, I. I., Behrens, C., Virmani, A. K., Mele, G., Milchgrub, S., Girard, L., Fondon, J. W. 3rd, Garner, H. R., McKay, B., Latif, F., et al. (2000) High resolution chromosome 3p allelotyping of human lung cancer and preneoplastic/preinvasive bronchial epithelium reveals multiple, discontinuous sites of 3p allele loss and three regions of frequent breakpoints. Cancer Res., 60, 1949–1960Google Scholar
- 46.Tsakiridis, T., Cutz, J.-C., Singh, G., Hirte, H., Okawara, G., Corbett, T., Sur, R., Cai, W., Whelan, T. and Wright, J. R. (2008) Association of phosphorylated epidermal growth factor receptor with survival in patients with locally advanced non-small cell lung cancer treated with radiotherapy. J. Thorac. Oncol., 3, 716–722CrossRefGoogle Scholar
- 48.Marek, L., Ware, K. E., Fritzsche, A., Hercule, P., Helton, W. R., Smith, J. E., McDermott, L. A., Coldren, C. D., Nemenoff, R. A., Merrick, D. T., et al. (2009) Fibroblast growth factor (FGF) and FGF receptor-mediated autocrine signaling in non-small-cell lung cancer cells. Mol. Pharmacol., 75, 196–207CrossRefGoogle Scholar
- 52.Hsu, T. I., Wang, Y. C., Hung, C. Y., Yu, C. H., Su, W. C., Chang, W. C. and Hung, J. J. (2016) Positive feedback regulation between IL10 and EGFR promotes lung cancer formation. Oncotarget, 7, 20840–20854Google Scholar
- 53.Zhong, X., Fan, Y., Ritzenthaler, J. D., Zhang, W., Wang, K., Zhou, Q. and Roman, J. (2015) Novel link between prostaglandin E2 (PGE2) and cholinergic signaling in lung cancer: the role of c-Jun in PGE2-induced α7 nicotinic acetylcholine receptor expression and tumor cell proliferation. Thorac. Cancer, 6, 488–500CrossRefGoogle Scholar
- 55.O’Byrne, K. J., Koukourakis, M. I., Giatromanolaki, A., Cox, G., Turley, H., Steward, W. P., Gatter, K. and Harris, A. L. (2000) Vascular endothelial growth factor, platelet-derived endothelial cell growth factor and angiogenesis in non-small-cell lung cancer. Br. J. Cancer, 82, 1427–1432CrossRefGoogle Scholar
- 56.Pertovaara, L., Kaipainen, A., Mustonen, T., Orpana, A., Ferrara, N., Saksela, O. and Alitalo, K. (1994) Vascular endothelial growth factor is induced in response to transforming growth factor-beta in fibroblastic and epithelial cells. J. Biol. Chem., 269, 6271–6274Google Scholar
- 57.Saijo, Y., Tanaka, M., Miki, M., Usui, K., Suzuki, T., Maemondo, M., Hong, X., Tazawa, R., Kikuchi, T., Matsushima, K., et al. (2002) Proinflammatory cytokine IL-1 beta promotes tumor growth of Lewis lung carcinoma by induction of angiogenic factors: in vivo analysis of tumor-stromal interaction. J. Immunol., 169, 469–475CrossRefGoogle Scholar
- 60.Lynch, T. J., Bell, D. W., Sordella, R., Gurubhagavatula, S., Okimoto, R. A., Brannigan, B. W., Harris, P. L., Haserlat, S. M., Supko, J. G., Haluska, F. G., et al. (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med., 350, 2129–2139CrossRefGoogle Scholar