Abstract
Every colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we develop a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimate the relative abundance of each immune cell from gene expression profiles of tumors, and group patients based on their immune patterns. Then we compare the tumor sensitivity and progression in each of these groups of patients, and observe differences in the patterns of tumor growth between the groups. For instance, in tumors with a smaller density of naive macrophages than activated macrophages, a higher activation rate of macrophages leads to an increase in cancer cell density, demonstrating a negative effect of macrophages. Other tumors however, exhibit an opposite trend, showing a positive effect of macrophages in controlling tumor size. Although the results indicate that for all patients, the size of the tumor is sensitive to the parameters related to macrophages such as their activation and death rate, this research demonstrates that no single biomarker could predict the dynamics of tumors.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Abbreviations
- CAC
- colitis-associated cancer
- CCL20
- chemokine (C-C motif) ligand 20
- COAD
- colon adenocarcinoma
- DAMP
- damage-associated molecular pattern
- DCs
- dendritic cells
- FasL
- fas ligand
- GEP
- gene expression profiles
- HMGB1
- high mobility group box 1
- IFN
- interferon
- IL
- interleukin
- NF-κB
- nuclear factor kappa B
- NK
- cells natural killer cells
- ODE
- ordinary differential equation
- RAGE
- receptor for advanced glycation endproducts
- RNA-seq
- ribonucleic acid sequencing
- STAT
- signal transducer and activator of transcription
- TAM
- tumor associated macrophage
- TCGA
- the cancer genome atlas
- TGF
- transforming growth factor
- TNF
- tumor necrosis factor
- TSLP
- thymic stromal lymphopoietin