Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2017

Open Access 01-12-2017 | Research article

Multiscalar cellular automaton simulates in-vivo tumour-stroma patterns calibrated from in-vitro assay data

Authors: J. A. Delgado-SanMartin, J. I. Hare, E. J. Davies, J. W. T. Yates

Published in: BMC Medical Informatics and Decision Making | Issue 1/2017

Login to get access

Abstract

Background

The tumour stroma -or tumour microenvironment- is an important constituent of solid cancers and it is thought to be one of the main obstacles to quantitative translation of drug activity between the preclinical and clinical phases of drug development. The tumour-stroma relationship has been described as being both pro- and antitumour in multiple studies. However, the causality of this complex biological relationship between the tumour and stroma has not yet been explored in a quantitative manner in complex tumour morphologies.

Methods

To understand how these stromal and microenvironmental factors contribute to tumour physiology and how oxygen distributes within them, we have developed a lattice-based multiscalar cellular automaton model. This model uses principles of cytokine and oxygen diffusion as well as cell motility and plasticity to describe tumour-stroma landscapes. Furthermore, to calibrate the model, we propose an innovative modelling platform to extract model parameters from multiple in-vitro assays. This platform provides a novel way to extract meta-data that can be used to complement in-vivo studies and can be further applied in other contexts.

Results

Here we show the necessity of the tumour-stroma opposing relationship for the model simulations to successfully describe the in-vivo stromal patterns of the human lung cancer cell lines Calu3 and Calu6, as models of clinical and preclinical tumour-stromal topologies. This is especially relevant to drugs that target the tumour microenvironment, such as antiangiogenics, compounds targeting the hedgehog pathway or immune checkpoint inhibitors, and is potentially a key platform to understand the mechanistic drivers for these drugs.

Conclusion

The tumour-stroma automaton model presented here enables the interpretation of complex in-vitro data and uses it to parametrise a model for in-vivo tumour-stromal relationships.
Appendix
Available only for authorised users
Literature
1.
Mueller MM, Fusenig NE. Friends or foes—bipolar effects of the tumour stroma in cancer. Nat Rev Cancer. 2004;4(11):839–49.CrossRefPubMed
2.
Whipple CA. Tumor talk: understanding the conversation between the tumor and its microenvironment. Cancer Cell Microenviron. 2015;2(2):e773.PubMedPubMedCentral
3.
Smith NR, Baker D, Farren M, Pommier A, Swann R, Wang X, Mistry S, McDaid K, Kendrew J, Womack C. Tumor stromal architecture can define the intrinsic tumor response to VEGF-targeted therapy. Clin Cancer Res. 2013;19(24):6943–56.CrossRefPubMed
4.
McMillin DW, Negri JM, Mitsiades CS. The role of tumour–stromal interactions in modifying drug response: challenges and opportunities. Nat Rev Drug Discov. 2013;12(3):217–28.CrossRefPubMed
5.
Rampias T, Favicchio R, Stebbing J, Giamas G. Targeting tumor–stroma crosstalk: the example of the NT157 inhibitor. Oncogene. 2015;35(20):2562-4.
6.
Knowlton S, Joshi A, Yenilmez B, Ozbolat IT, Chua CK, Khademhosseini A, Tasoglu S. Advancing cancer research using bioprinting for tumor-on-a-chip platforms. Int J Bioprinting. 2016;2(2):3–8.
7.
Das V, Bruzzese F, Konečný P, Iannelli F, Budillon A, Hajdúch M. Pathophysiologically relevant in vitro tumor models for drug screening. Drug Discov Today. 2015;20(7):848–55.CrossRefPubMed
8.
Choi SYC, Lin D, Gout PW, Collins CC, Xu Y, Wang Y. Lessons from patient-derived xenografts for better in vitro modeling of human cancer. Adv Drug Deliv Rev. 2014;79:222–37.
9.
Delgado San Martin J, Hare J, Yates J, Barry S. Tumour stromal morphology impacts nanomedicine cytotoxicity in patient-derived xenografts. Nanomedicine. 2015;11(5):1247–52.CrossRefPubMed
10.
Pérez JL, Puente ET, Kulich EI, MAr JAB, Nistal M, Peramato PG, García MR, Villar JMN, De Miguel M. Relationship between tumor grade and geometrical complexity in prostate cancer. bioRxiv. 2015:015016.
11.
12.
Unger C, Kramer N, Walzl A, Scherzer M, Hengstschläger M, Dolznig H. Modeling human carcinomas: physiologically relevant 3D models to improve anti-cancer drug development. Adv Drug Deliv Rev. 2014;79:50–67.CrossRefPubMed
13.
Robertson-Tessi M, Gillies RJ, Gatenby RA, Anderson AR. Non-linear tumor-immune interactions arising from spatial metabolic heterogeneity. bioRxiv. 2016:038273.
14.
Liang S, Hoskins M, Khanna P, Kunz RF, Dong C. Effects of the tumor-leukocyte microenvironment on melanoma–neutrophil adhesion to the endothelium in a shear flow. Cell Mol Bioeng. 2008;1(2–3):189–200.CrossRefPubMedPubMedCentral
15.
Starruß J, de Back W, Brusch L, Deutsch A. Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology. Bioinformatics. 2014; 30(9):1331-2
16.
Wang Z, Butner JD, Kerketta R, Cristini V, Deisboeck TS. Simulating cancer growth with multiscale agent-based modeling. Semin Cancer Biol. 2015;30:70–78.
17.
Chakrabarti A, Verbridge S, Stroock AD, Fischbach C, Varner JD. Multiscale models of breast cancer progression. Ann Biomed Eng. 2012;40(11):2488–500.CrossRefPubMed
18.
Picco N, Gatenby R, Anderson A. Niche-driven stem cell plasticity and its role in cancer progression. bioRxiv. 2016:056762.
19.
Delgado-SanMartin J, Hare J, Moura A, Yates J. Oxygen-driven model: a pathology-relevant model of tumour growth in xenografts. PLoS Comput Biol. 2015;11(10):e1004550.CrossRefPubMedPubMedCentral
20.
Weber TS, Jaehnert I, Schichor C, Or-Guil M, Carneiro J. Quantifying the length and variance of the eukaryotic cell cycle phases by a stochastic model and dual nucleoside pulse labelling. 2014.
21.
Sutherland RL, Hall RE, Taylor IW. Cell proliferation kinetics of MCF-7 human mammary carcinoma cells in culture and effects of tamoxifen on exponentially growing and plateau-phase cells. Cancer Res. 1983;43(9):3998–4006.PubMed
22.
Davies EJ, Dong M, Gutekunst M, Närhi K, van Zoggel HJ, Blom S, Nagaraj A, Metsalu T, Oswald E, Erkens-Schulze S. Capturing complex tumour biology in vitro: histological and molecular characterisation of precision cut slices. Sci Rep. 2015;5:17187.CrossRefPubMedPubMedCentral
23.
Russell J, Carlin S, Burke SA, Wen B, Yang KM, Ling CC. Immunohistochemical detection of changes in tumor hypoxia. Int J Radiat Oncol Biol Phys. 2009;73(4):1177–86.
24.
25.
Pittman RN. Regulation of tissue oxygenation. Colloquium series on integrated systems physiology: from molecule to function. 2011;3(3):1–100.
26.
Brower M, Carney DN, Oie HK, Gazdar AF, Minna JD. Growth of cell lines and clinical specimens of human non-small cell lung cancer in a serum-free defined medium. Cancer Res. 1986;46(2):798–806.PubMed
27.
Ribba B, Watkin E, Tod M, Girard P, Grenier E, You B, Giraudo E, Freyer G. A model of vascular tumour growth in mice combining longitudinal tumour size data with histological biomarkers. Eur J Cancer. 2011;47(3):479–90.CrossRefPubMed
28.
Evans ND, Dimelow RJ, Yates JW. Modelling of tumour growth and cytotoxic effect of docetaxel in xenografts. Comput Methods Prog Biomed. 2014;114(3):e3–13.CrossRef
29.
Netti PA, Berk DA, Swartz MA, Grodzinsky AJ, Jain RK. Role of extracellular matrix assembly in interstitial transport in solid tumors. Cancer Res. 2000;60(9):2497–503.PubMed
30.
Van Liedekerke P, Palm M, Jagiella N, Drasdo D. Simulating tissue mechanics with agent-based models: concepts, perspectives and some novel results. Comput Part Mech. 2015;2(4):401–44.CrossRef
31.
Fridman WH, Pages F, Sautes-Fridman C, Galon J. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer. 2012;12(4):298–306.CrossRefPubMed
Metadata
Title
Multiscalar cellular automaton simulates in-vivo tumour-stroma patterns calibrated from in-vitro assay data
Authors
J. A. Delgado-SanMartin
J. I. Hare
E. J. Davies
J. W. T. Yates
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2017
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-017-0461-1

Other articles of this Issue 1/2017

BMC Medical Informatics and Decision Making 1/2017 Go to the issue

Research article

Completeness and timeliness of notifiable disease reporting: a comparison of laboratory and provider reports submitted to a large county health department

Research article

Routine health information system utilization and factors associated thereof among health workers at government health institutions in East Gojjam Zone, Northwest Ethiopia

Research article

Assessing the usability by clinicians of VISION: A hierarchical display of patient-collected physiological information to clinicians

Research article

A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease

Research article

How can information systems provide support to nurses’ hand hygiene performance? Using gamification and indoor location to improve hand hygiene awareness and reduce hospital infections

Research article

Factors influencing the development of primary care data collection projects from electronic health records: a systematic review of the literature