Data driven mathematical model of colon cancer progression

Kirshtein, Arkadz; Akbarinejad, Shaya; Hao, Wenrui; Le, Trang; Aronow, Rachel A.; Shahriyari, Leili

doi:10.1101/2020.11.02.365668

Cited by 7 publications

(2 citation statements)

References 132 publications

(211 reference statements)

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“…Following [ 58 ], to prove that the system with positive coefficients and positive initial conditions has a positive solution, let us consider the set of integrating factors, one for each variable:

…”

Section: Appendix A1 System Of Odesmentioning

confidence: 99%

“…Although some studies include bone modeling, osteoblast cells, or osteosarcoma treatments [ 50 , 51 , 52 , 53 , 54 ], to the best of our knowledge, there is currently no mathematical model explaining the progression of osteosarcoma tumors. The relationship between immune cells and tumor cells have been used as an alternative approach in the mathematical modeling of different cancers types in some studies [ 55 , 56 , 57 , 58 ]. Objective of this study is to build a data-driven model for the progression of osteosarcoma tumors that considers immune cell interactions with tumor cells.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Mathematical Model of Osteosarcoma

Kirshtein

et al. 2021

Cancers

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As the immune system has a significant role in tumor progression, in this paper, we develop a data-driven mathematical model to study the interactions between immune cells and the osteosarcoma microenvironment. Osteosarcoma tumors are divided into three clusters based on their relative abundance of immune cells as estimated from their gene expression profiles. We then analyze the tumor progression and effects of the immune system on cancer growth in each cluster. Cluster 3, which had approximately the same number of naive and M2 macrophages, had the slowest tumor growth, and cluster 2, with the highest population of naive macrophages, had the highest cancer population at the steady states. We also found that the fastest growth of cancer occurred when the anti-tumor immune cells and cytokines, including dendritic cells, helper T cells, cytotoxic cells, and IFN-γ, switched from increasing to decreasing, while the dynamics of regulatory T cells switched from decreasing to increasing. Importantly, the most impactful immune parameters on the number of cancer and total cells were the activation and decay rates of the macrophages and regulatory T cells for all clusters. This work presents the first osteosarcoma progression model, which can be later extended to investigate the effectiveness of various osteosarcoma treatments.

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…”

Section: Appendix A1 System Of Odesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Mathematical Model of Osteosarcoma

Kirshtein

et al. 2021

Cancers

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Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review

Sofia,

Zhou,

Shahriyari

2023

Bioengineering

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This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients’ gene expression and clinical data through a variety of techniques to predict patients’ outcomes, mechanistic models focus on investigating cells’ and molecules’ interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors’ microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes.

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Data Driven Mathematical Model of FOLFIRI Treatment for Colon Cancer

Budithi

Kirshtein

et al. 2021

Cancers

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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.

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Data driven mathematical model of colon cancer progression

Cited by 7 publications

References 132 publications

Data-Driven Mathematical Model of Osteosarcoma

Data-Driven Mathematical Model of Osteosarcoma

Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review

Data Driven Mathematical Model of FOLFIRI Treatment for Colon Cancer

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