Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer

Carol L. Fischer, Amber M. Bates, Emily A. Lanzel, Janet M. Guthmiller, Georgia K. Johnson, Neeraj Kumar Singh, Ansu Kumar, Robinson Vidva, Taher Abbasi, Shireen Vali, Xian Jin Xie, Erliang Zeng, Kim A. Brogden

Research output: Contribution to journalArticle

Abstract

Individual computational models of single myeloid, lymphoid, epithelial, and cancer cells were created and combined into multi-cell computational models and used to predict the collective chemokine, cytokine, and cellular biomarker profiles often seen in inflamed or cancerous tissues. Predicted chemokine and cytokine output profiles from multi-cell computational models of gingival epithelial keratinocytes (GE KER), dendritic cells (DC), and helper T lymphocytes (HTL) exposed to lipopolysaccharide (LPS) or synthetic triacylated lipopeptide (Pam3CSK4) as well as multi-cell computational models of multiple myeloma (MM) and DC were validated using the observed chemokine and cytokine responses from the same cell type combinations grown in laboratory multi-cell cultures with accuracy. Predicted and observed chemokine and cytokine responses of GE KER + DC + HTL exposed to LPS and Pam3CSK4 matched 75% (15/20, p = 0.02069) and 80% (16/20, P = 0.005909), respectively. Multi-cell computational models became ‘personalized’ when cell line-specific genomic data were included into simulations, again validated with the same cell lines grown in laboratory multi-cell cultures. Here, predicted and observed chemokine and cytokine responses of MM cells lines MM.1S and U266B1 matched 75% (3/4) and MM.1S and U266B1 inhibition of DC marker expression in co-culture matched 100% (6/6). Multi-cell computational models have the potential to identify approaches altering the predicted disease-associated output profiles, particularly as high throughput screening tools for anti-inflammatory or immuno-oncology treatments of inflamed multi-cellular tissues and the tumor microenvironment.

Original languageEnglish (US)
Article number10877
JournalScientific reports
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2019

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Chemokines
Biomarkers
Inflammation
Dendritic Cells
Cytokines
Neoplasms
Helper-Inducer T-Lymphocytes
Multiple Myeloma
Keratinocytes
Cell Line
Lipopolysaccharides
Cell Culture Techniques
Tumor Microenvironment
Coculture Techniques
Anti-Inflammatory Agents
Epithelial Cells

ASJC Scopus subject areas

  • General

Cite this

Fischer, C. L., Bates, A. M., Lanzel, E. A., Guthmiller, J. M., Johnson, G. K., Singh, N. K., ... Brogden, K. A. (2019). Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer. Scientific reports, 9(1), [10877]. https://doi.org/10.1038/s41598-019-47381-4

Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer. / Fischer, Carol L.; Bates, Amber M.; Lanzel, Emily A.; Guthmiller, Janet M.; Johnson, Georgia K.; Singh, Neeraj Kumar; Kumar, Ansu; Vidva, Robinson; Abbasi, Taher; Vali, Shireen; Xie, Xian Jin; Zeng, Erliang; Brogden, Kim A.

In: Scientific reports, Vol. 9, No. 1, 10877, 01.12.2019.

Research output: Contribution to journalArticle

Fischer, CL, Bates, AM, Lanzel, EA, Guthmiller, JM, Johnson, GK, Singh, NK, Kumar, A, Vidva, R, Abbasi, T, Vali, S, Xie, XJ, Zeng, E & Brogden, KA 2019, 'Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer', Scientific reports, vol. 9, no. 1, 10877. https://doi.org/10.1038/s41598-019-47381-4
Fischer, Carol L. ; Bates, Amber M. ; Lanzel, Emily A. ; Guthmiller, Janet M. ; Johnson, Georgia K. ; Singh, Neeraj Kumar ; Kumar, Ansu ; Vidva, Robinson ; Abbasi, Taher ; Vali, Shireen ; Xie, Xian Jin ; Zeng, Erliang ; Brogden, Kim A. / Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer. In: Scientific reports. 2019 ; Vol. 9, No. 1.
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