Software Packages

TMEImmune: estimates the immune score of tumors based on their gene expression data, helping to identify which tumors are likely to respond to the checkpoint inhibitors and to estimate patient survival time.

First version released in Nov 2024 https://pypi.org/project/TMEImmune/1.3.2/.  

TumorDecon: provides an estimation of the relative number of cell types, including various immune cells, in a mixed cell population of tumors using gene expression profiles of tumor. 

First version released in June 2020 https://pypi.org/project/TumorDecon/.  Supported by NCI ITCR program.

If you are using any parts of this code please cite: 

• T. Le, R. Aronow, A. Kirshtein, L. Shahriyari, A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells,  Briefing in Bioinformatics, 2020.

Data-driven mathematical model for colon cancer: Every colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we developed a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimated the relative abundance of each immune cell from gene expression profiles of tumors, and group patients based on their immune patterns. Then we compared the tumor sensitivity and progression in each of these groups of patients, and observe differences in the patterns of tumor growth between the groups. Supported by NCI ITCR program.

If you are using any parts of this code please cite: 

• A. Kirshtein, S. Akbarinejad, W. Hao, T. Le, S. Su, R. Aronow, L. Shahriyari, Data driven mathematical model of colon cancer progression, Journal of Clinical Medicine, 2020.

Data-driven mathematical model of FOLFIRI treatment for colon cancer: 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. Supported by NCI ITCR program.

If you are using any parts of this code please cite:

• Budithi, A., Su, S., Kirshtein, A., Shahriyari L. Data driven mathematical model of FOLFIRI treatment for colon cancer, Cancers, 2021. 

Data driven mathematical model of osteosarcom: In this work, we develop a data driven mathematical model to study the key interactions between the immune system and osteosarcoma microenvironment. Patients with childhood osteosarcoma are divided into three clusters based on their relative abundance of immune cells estimated from their gene expression data. We then analyze the progression of tumor and the effects of the immune system on caner growth in each cluster. Supported by NCI ITCR program.

If using any parts of this code please cite:

• Le,T., Su, S., Kirshtein, A., Shahriyari L. Data driven mathematical model of osteosarcoma. Cancers, 2021.

Investigating optimal chemotherapy options for osteosarcoma patients through a data-driven mathematical model: All tumors are unique, so they might respond differently to the same treatment. In this work, we develop a mathematical model of the interactions between key players in osteosarcoma microenvironment and the most common chemotherapy drugs in three clusters of tumors with distinct immune compositions. We then study the behaviors of cells and cytokines in the tumor microenvironment under the effects of chemotherapy, investigate the responses in each cluster to different treatment regimens and various treatment start times, as well as suggest optimal dosages for tumors of each cluster. Supported by NCI ITCR program.

If using any parts of this code please cite: 

• Le, T; Su, S; Shahriyari, L. Investigating optimal chemotherapy options for osteosarcoma patients through a mathematical model. Cells, 2021.