Simulating Interclonal Interactions in Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is one of the most common types of cancers, accounting for 37% of B-cell tumor cases globally. DLBCL is known to be a heterogeneous disease, resulting in variable clinical presentations and the development of drug resistance. One underexplored aspect of drug res...

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Bibliographic Details
Published inBioengineering (Basel) Vol. 10; no. 12; p. 1360
Main Authors Ganesh, Siddarth R, Roth, Charles M, Parekkadan, Biju
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 27.11.2023
Subjects
Antibodies
B cells
B-cell lymphoma
Behavior
Bioengineering
Cancer
Cancer therapies
Cells
Clinical outcomes
clonal interaction
Cloning
Cytotoxicity
Development and progression
diffuse large B-cell lymphoma
Drug resistance
Genetic aspects
Growth models
Health aspects
Health services
Localization
Lymphocytes B
Lymphoma
Lymphomas
mathematical model
Mathematical models
Microenvironments
Mutation
Patients
Plasmids
Populations
Prostate cancer
Simulation
Tumors
United States
United States > US
mathematical model
diffuse large B-cell lymphoma
clonal interaction
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Summary:Diffuse large B-cell lymphoma (DLBCL) is one of the most common types of cancers, accounting for 37% of B-cell tumor cases globally. DLBCL is known to be a heterogeneous disease, resulting in variable clinical presentations and the development of drug resistance. One underexplored aspect of drug resistance is the evolving dynamics between parental and drug-resistant clones within the same microenvironment. In this work, the effects of interclonal interactions between two cell populations-one sensitive to treatment and the other resistant to treatment-on tumor growth behaviors were explored through a mathematical model. In vitro cultures of mixed DLBCL populations demonstrated cooperative interactions and revealed the need for modifying the model to account for complex interactions. Multiple best-fit models derived from in vitro data indicated a difference in steady-state behaviors based on therapy administrations in simulations. The model and methods may serve as a tool for understanding the behaviors of heterogeneous tumors and identifying the optimal therapeutic regimen to eliminate cancer cell populations using computer-guided simulations.
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ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering10121360