Connecting Cancer Pathways to Tumor Engines: A Stratification Tool for Colorectal Cancer Combining Human In Vitro Tissue Models with Boolean In Silico Models
Abstract
:1. Introduction
2. Results
2.1. Characterization of Low Passage Cell Lines and Comparison of 3D Tissue Morphology with Cancerous Specimens
2.2. Proliferation and Apoptosis Responses upon Treatment
2.3. Comparison of Signaling Changes upon Treatment between Different Models
2.4. In silico System Responses Reflecting 3D In Vitro Data by Integrating HROC87 Specific Mutations and Signaling Cascades
2.5. Breaking Resistance: In Silico Mode-of-Action Analysis in p53 and BRAF Mutation Background
3. Discussion
4. Materials and Methods
4.1. SISmuc Preparation
4.2. 2D Cell Culture and Cell Lines
4.3. 3D Cell Culture of In Vitro Models
4.4. Animal Models
4.5. Fluorescence Immunohistochemistry
4.6. Total Cell Number and Proliferation Rate
4.7. Protein Lysate Preparation and WB Analysis
4.8. M30 CytoDeathTM ELISA
4.9. Statistical Analysis
4.10. In Silico Simulations
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
2D | ||||||||
Figure 5 | EGFR | Erk | pEGFR | pErk | HGFR | Akt | pHGFR | pAkt |
Ctrl. | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
G | 70 | 96 | 49 | 32 | 44 | 76 | 1 | 227 |
V | 75 | 77 | 80 | 18 | 52 | 73 | 86 | 130 |
GV | 63 | 94 | 53 | 2 | 26 | 86 | 2 | 314 |
3D | ||||||||
Figure 5 | EGFR | Erk | pEGFR | pErk | HGFR | Akt | pHGFR | pAkt |
Ctrl. | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
G | 111 | 128 | 36 | 40 | 91 | 114 | 67 | 74 |
V | 81 | 83 | 86 | 7 | 72 | 65 | 78 | 75 |
GV | 55 | 91 | 61 | 2 | 55 | 70 | 34 | 93 |
PDX | ||||||||
Figure 5 | EGFR | Erk | pEGFR | pErk | HGFR | Akt | pHGFR | pAkt |
Ctrl. | 100 | 100 | 100 | 100 | 100 | 100 | - | 100 |
G | 70 | 102 | 37 | 38 | 55 | 134 | - | 27 |
V | 53 | 67 | 32 | 10 | 55 | 88 | - | 17 |
GV | 77 | 104 | 22 | 4 | 55 | 89 | - | 18 |
Appendix A.1. Ideal-Typic Transformation from a Healthy Epithelium to a Colon Carcinoma
Stage 0 | Stage 1 | Stage 2 | Stage 3 | Stage 4 | Stage 5 | Stage 6 |
---|---|---|---|---|---|---|
normal epithelium | dysplastic aberrant cryptic foci | early/initial adenoma | intermediate adenoma | late adenoma | carcinoma | metastasis |
APC (β-catenin) | COX-2 | KRAS | DCC/loss of 18q | p53 | E-cadherin/BAX |
Appendix A.2. Bioinformatics Mod$eling and Systems Biology Data
Western Blot Data (in Comparison to Untreated Control) | Proliferation Value in 3D | Apoptosis in 3D Compared to Ctrl | |||||||
---|---|---|---|---|---|---|---|---|---|
Ctrl | Gef | Vem | Gef+Vem | HROC87 | HROC87 | ||||
pEGFR | medium | ↓↓ | ↓ | ↓↓ | Ctrl | 0.5 | Ctrl | weak | |
pErk | strong | ↓ | ↓ | ↓↓ | Gef | 0.5 | Gef | ↑ | |
pHGFR | strong | ↓ | 0 | ↓↓ | Vem | 0.5 | Vem | ↑ | |
pAkt | weak | ↓ | 0 | ↑ | Gef + Vem | 0.2 | Gef+Vem | ↑ |
Appendix A.3. Simulation of Different Treatment Scenarios
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Name of Network Node | untr. | +gef | +vem | +combi | +MEK-Inh | +BCL2-Inh |
---|---|---|---|---|---|---|
BRAF(V600)-Act | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 |
EGFR | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 |
HGFR | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 |
ERK-Inh | 0.26 | 0.3 | 0.3 | 0.41 | 0.26 | 0.26 |
p53-Mut | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 |
Bcl2 | 0.31 | 0.3467 | 0.305 | 0.2252 | ||
c-Myc-Inh | 0.17 | 0.097 | 0.097 | 0.17 | 0.17 | |
AKT | 0.2 | 0.16 | 0.2 | 0.24 | 0.2 | 0.2 |
(EGFR) * | 0.3 | 0.4 | 0.3 | |||
(HGFR) * | 0.6 | 0.4 | ||||
c-Myc-Act | 0.09 | |||||
Gefitinib | 1.0 | 1.0 | ||||
Vemurafenib | 1.0 | 1.0 | ||||
MEK-Inhibitor | 1.0 | |||||
Bcl2-Inhibitor | 1.0 |
Name of Network Node | untr. | +gef | +vem | +combi | +MEK-Inh | +BCL2-Inh |
---|---|---|---|---|---|---|
apoptosis | 0.21 | 0.35 | 0.35 | 0.35 | 0.99 | 0.99 |
proliferation | 0.5 | 0.5 | 0.5 | 0.2 | 0.0 | 0.5 |
(HGFR)* | 0.97 | 0.6 | 0.97 | 0.4 | 0.97 | 0.97 |
ERK | 0.71 | 0.59 | 0.59 | 0.3 | 0.0 | 0.71 |
(EGFR)* | 0.5 | 0.3 | 0.4 | 0.3 | 0.5 | 0.5 |
AKT | 0.2 | 0.16 | 0.2 | 0.24 | 0.2 | 0.2 |
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Baur, F.; Nietzer, S.L.; Kunz, M.; Saal, F.; Jeromin, J.; Matschos, S.; Linnebacher, M.; Walles, H.; Dandekar, T.; Dandekar, G. Connecting Cancer Pathways to Tumor Engines: A Stratification Tool for Colorectal Cancer Combining Human In Vitro Tissue Models with Boolean In Silico Models. Cancers 2020, 12, 28. https://doi.org/10.3390/cancers12010028
Baur F, Nietzer SL, Kunz M, Saal F, Jeromin J, Matschos S, Linnebacher M, Walles H, Dandekar T, Dandekar G. Connecting Cancer Pathways to Tumor Engines: A Stratification Tool for Colorectal Cancer Combining Human In Vitro Tissue Models with Boolean In Silico Models. Cancers. 2020; 12(1):28. https://doi.org/10.3390/cancers12010028
Chicago/Turabian StyleBaur, Florentin, Sarah L. Nietzer, Meik Kunz, Fabian Saal, Julian Jeromin, Stephanie Matschos, Michael Linnebacher, Heike Walles, Thomas Dandekar, and Gudrun Dandekar. 2020. "Connecting Cancer Pathways to Tumor Engines: A Stratification Tool for Colorectal Cancer Combining Human In Vitro Tissue Models with Boolean In Silico Models" Cancers 12, no. 1: 28. https://doi.org/10.3390/cancers12010028
APA StyleBaur, F., Nietzer, S. L., Kunz, M., Saal, F., Jeromin, J., Matschos, S., Linnebacher, M., Walles, H., Dandekar, T., & Dandekar, G. (2020). Connecting Cancer Pathways to Tumor Engines: A Stratification Tool for Colorectal Cancer Combining Human In Vitro Tissue Models with Boolean In Silico Models. Cancers, 12(1), 28. https://doi.org/10.3390/cancers12010028