Data Driven Mathematical Model of FOLFIRI Treatment for Colon Cancer

Abstract

Many colon cancer patients show resistance to their treatments. Therefore, it is important to consider unique characteristic of each tumor to find the best treatment options for each patient. In this study, we develop a data driven mathematical model for interaction between the tumor microenvironment and FOLFIRI drug agents in colon cancer. Patients are divided into five distinct clusters based on their estimated immune cell fractions obtained from their primary tumors’ gene expression data. We then analyze the effects of drugs on cancer cells and immune cells in each group, and we observe different responses to the FOLFIRI drugs between patients in different immune groups. For instance, patients in cluster 3 with the highest T-reg/T-helper ratio respond better to the FOLFIRI treatment, while patients in cluster 2 with the lowest T-reg/T-helper ratio resist the treatment. Moreover, we use ROC curve to validate the model using the tumor status of the patients at their follow up, and the model predicts well for the earlier follow up days.

Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model

“…The most desirable features of mathematical models of cancer, including stochastic [ 53 – 55 ] and deterministic models [ 56 – 58 ], are their ability to make good predictions, testing plausible biological hypotheses or generating clinically testable hypothesis. For example, a multiscale model of prostate cancer shows that low androgen levels may increase resistance to hormonal therapy and that treatment with 5 α -reductase inhibitors may lead to more therapy-resistant cancer cells [ 59 ], and a data driven mathematical model predicts the response to FOLFIRI treatment for colon cancer patients [ 60 ]. Moreover, an agent-based model [ 61 ] of tumor progression indicate that while macrophages existence can increase the size of the tumor, an increase in their infiltration has a reverse effect.…”

Identification of autophagy related genes in predicting the prognosis and aiding 5- fluorouracil therapy of colorectal cancer

“…Recently, immune cells were reported to have a great affection on CRC patients’ response to treatment [ 10 ]. Thus immune marker sets, which were widely accepted as corresponding symbols of different immunocytes were applied to explore their association with HSPB8 expression in TIMER and GEPIA databases.…”

Investigating Optimal Chemotherapy Options for Osteosarcoma Patients through a Mathematical Model

“…With the increasing availability of gene expression data for many cancer types and the growing accuracy of tumor deconvolution methods, utilizing a deconvolution method on the gene expression data of a tumor to study the various components of the tumor microenvironment has become a more and more attractive option. Many recent studies applied the currently best performing deconvolution method, CIBERSORTx, to study the dynamics of cancer growth or to explore the relationships between clinical information and immune infiltrates [33,[144][145][146]. In this study, we extended our previous work [58], which used CIBERSORTx to obtain immune abundances of osteosarcoma tumors and studied the tumor growth while considering its interactions with immune cells, to investigate the effects of chemotherapy on the osteosarcoma microenvironment.…”