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A model of NSCLC microenvironment predicts optimal receptor targets

  • Chuang Han
  • Yu WuEmail author
Research Article
  • 10 Downloads

Abstract

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Keywords

non-small-cell lung cancer tumor microenvironment intercellular signaling network logistic population dynamics drug resistance multi-target therapy 

Notes

Acknowledgements

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.

Supplementary material

40484_2019_171_MOESM1_ESM.pdf (139 kb)
Supplementary Table S1. Three patients with difference in secretion rate parameters
40484_2019_171_MOESM2_ESM.pdf (17 kb)
Supplementary Table S2. x-axis parameter panels of Fig. 7
40484_2019_171_MOESM3_ESM.pdf (197 kb)
Supplementary Table S3. The dynamic patient with difference in regulation ratio parameters and secretion rate parameters
40484_2019_171_MOESM4_ESM.pdf (269 kb)
Supplementary Table S4
40484_2019_171_MOESM5_ESM.pdf (750 kb)
Supplementary Table S5. Deterministic Parameters
40484_2019_171_MOESM6_ESM.pdf (656 kb)
I. Supporting Methods

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Engineering MechanicsZhejiang UniversityHangzhouChina
  2. 2.Key Laboratory of Soft Machines and Smart Devices of Zhejiang ProvinceZhejiang UniversityHangzhouChina
  3. 3.Soft Matter Research CenterZhejiang UniversityHangzhouChina

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