skip to main content
Language:

A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin

Miniere, Hugo J M ; Lima, Ernesto A B F ; Lorenzo, Guillermo ; Hormuth, 2nd, David A ; Ty, Sophia ; Brock, Amy ; Yankeelov, Thomas E

Cancer biology & therapy, 2024-12, Vol.25 (1), p.2321769-2321769 [Peer Reviewed Journal]

United States: Taylor & Francis

Full text available

Citations Cited by
  • Title:
    A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin
  • Author: Miniere, Hugo J M ; Lima, Ernesto A B F ; Lorenzo, Guillermo ; Hormuth, 2nd, David A ; Ty, Sophia ; Brock, Amy ; Yankeelov, Thomas E
  • Subjects: Breast Neoplasms - drug therapy ; Cell Cycle ; Cell Proliferation ; data assimilation ; Doxorubicin - pharmacology ; Female ; Humans ; mathematical oncology ; MCF-7 Cells ; mechanism-based modeling ; Research Paper ; Time-resolved microscopy
  • Is Part Of: Cancer biology & therapy, 2024-12, Vol.25 (1), p.2321769-2321769
  • Notes: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Description: Tumor heterogeneity contributes significantly to chemoresistance, a leading cause of treatment failure. To better personalize therapies, it is essential to develop tools capable of identifying and predicting intra- and inter-tumor heterogeneities. Biology-inspired mathematical models are capable of attacking this problem, but tumor heterogeneity is often overlooked in modeling studies, while phenotypic considerations capturing spatial dynamics are not typically included in modeling studies. We present a data assimilation-prediction pipeline with a two-phenotype model that includes a spatiotemporal component to characterize and predict the evolution of breast cancer cells and their heterogeneous response to chemotherapy. Our model assumes that the cells can be divided into two subpopulations: surviving cells unaffected by the treatment, and irreversibly damaged cells undergoing treatment-induced death. MCF7 breast cancer cells were previously cultivated in wells for up to 1000 hours, treated with various concentrations of doxorubicin and imaged with time-resolved microscopy to record spatiotemporally-resolved cell count data. Images were used to generate cell density maps. Treatment response predictions were initialized by a training set and updated by weekly measurements. Our mathematical model successfully calibrated the spatiotemporal cell growth dynamics, achieving median [range] concordance correlation coefficients of > .99 [.88, >.99] and .73 [.58, .85] across the whole well and individual pixels, respectively. Our proposed data assimilation-prediction approach achieved values of .97 [.44, >.99] and .69 [.35, .79] for the whole well and individual pixels, respectively. Thus, our model can capture and predict the spatiotemporal dynamics of MCF7 cells treated with doxorubicin in an setting.
  • Publisher: United States: Taylor & Francis
  • Language: English
Links
Back to results list
Go to Previous pagePrevious Result  9 NextGo to Next page

  • Implemented by: ABCD-USP USP   Social Network Logos: Freepik

Searching Remote Databases, Please Wait

  • Searching for
  • inscope:(USP_VIDEOS),scope:("PRIMO"),scope:(USP_FISICO),scope:(USP_EREVISTAS),scope:(USP),scope:(USP_EBOOKS),scope:(USP_PRODUCAO),primo_central_multiple_fe
  • Show me what you have so far