OBJECTIVE: we present a multiscale agent-based model of Ductal Carcinoma in Situ (DCIS) in order to gain a detailed understanding of the cell-scale population dynamics, phenotypic distributions, and the associated interplay of important molecular signaling pathways that are involved in DCIS ductal invasion into the duct cavity (a process we refer to as duct advance rate here). METHODS: DCIS is modeled mathematically through a hybridized discrete cell-scale model and a continuum molecular scale model, which are explicitly linked through a bidirectional feedback mechanism. RESULTS: we find that duct advance rates occur in two distinct phases, characterized by an early exponential population expansion, followed by a long-term steady linear phase of population expansion, a result that is consistent with other modeling work. We further found that the rates were influenced most strongly by endocrine and paracrine signaling intensity, as well as by the effects of cell density induced quiescence within the DCIS population. CONCLUSION: our model analysis identified a complex interplay between phenotypic diversity that may provide a tumor adaptation mechanism to overcome proliferation limiting conditions, allowing for dynamic shifts in phenotypic populations in response to variation in molecular signaling intensity. Further, sensitivity analysis determined DCIS axial advance rates and calcification rates were most sensitive to cell cycle time variation. SIGNIFICANCE: this model may serve as a useful tool to study the cell-scale dynamics involved in DCIS initiation and intraductal invasion, and may provide insights into promising areas of future experimental research.
OBJECTIVE: we present a multiscale agent-based model of Ductal Carcinoma in Situ (DCIS) in order to gain a detailed understanding of the cell-scale population dynamics, phenotypic distributions, and the associated interplay of important molecular signaling pathways that are involved in DCIS ductal invasion into the duct cavity (a process we refer to as duct advance rate here). METHODS:DCIS is modeled mathematically through a hybridized discrete cell-scale model and a continuum molecular scale model, which are explicitly linked through a bidirectional feedback mechanism. RESULTS: we find that duct advance rates occur in two distinct phases, characterized by an early exponential population expansion, followed by a long-term steady linear phase of population expansion, a result that is consistent with other modeling work. We further found that the rates were influenced most strongly by endocrine and paracrine signaling intensity, as well as by the effects of cell density induced quiescence within the DCIS population. CONCLUSION: our model analysis identified a complex interplay between phenotypic diversity that may provide a tumor adaptation mechanism to overcome proliferation limiting conditions, allowing for dynamic shifts in phenotypic populations in response to variation in molecular signaling intensity. Further, sensitivity analysis determined DCIS axial advance rates and calcification rates were most sensitive to cell cycle time variation. SIGNIFICANCE: this model may serve as a useful tool to study the cell-scale dynamics involved in DCIS initiation and intraductal invasion, and may provide insights into promising areas of future experimental research.
Authors: Joseph D Butner; Prashant Dogra; Caroline Chung; Javier Ruiz-Ramírez; Sara Nizzero; Marija Plodinec; Xiaoxian Li; Ping-Ying Pan; Shu-Hsia Chen; Vittorio Cristini; Bulent Ozpolat; George A Calin; Zhihui Wang Journal: Cell Death Dis Date: 2022-05-21 Impact factor: 9.685
Authors: Gina Reye; Xuan Huang; Larisa M Haupt; Ryan J Murphy; Jason J Northey; Erik W Thompson; Konstantin I Momot; Honor J Hugo Journal: J Mammary Gland Biol Neoplasia Date: 2021-08-27 Impact factor: 2.673
Authors: Prashant Dogra; Javier Ruiz Ramírez; Joseph D Butner; Maria J Peláez; Caroline Chung; Anupama Hooda-Nehra; Renata Pasqualini; Wadih Arap; Vittorio Cristini; George A Calin; Bulent Ozpolat; Zhihui Wang Journal: Pharm Res Date: 2022-03-16 Impact factor: 4.200
Authors: Joseph D Butner; Zhihui Wang; Dalia Elganainy; Karine A Al Feghali; Marija Plodinec; George A Calin; Prashant Dogra; Sara Nizzero; Javier Ruiz-Ramírez; Geoffrey V Martin; Hussein A Tawbi; Caroline Chung; Eugene J Koay; James W Welsh; David S Hong; Vittorio Cristini Journal: Nat Biomed Eng Date: 2021-01-04 Impact factor: 29.234