Optimal chemotherapy counteracts cancer adaptive resistance in a cell-based, spatially-extended, evolutionary model

Abstract

Most aggressive cancers are incurable due to their fast evolution of drug resistance. We model cancer growth and adaptive response in a simplified cell-based (CB) setting, assuming a genetic resistance to two chemotherapeutic drugs. We show that optimal administration protocols can steer cells resistance and turned it into a weakness for the disease. Our work extends the population-based (PB) model proposed by Orlando et al. (Physical Biology, 2012), in which a homogeneous population of cancer cells evolves according to a fitness landscape. The landscape models three types of trade-offs, differing on whether the cells are more, less, or equal effective when generalizing resistance to two drugs as opposed to specializing to a single one. The CB framework allows us to include genetic heterogeneity, spatial competition, and drugs diffusion, as well as realistic administration protocols. By calibrating our model on Orlando et al.'s assumptions, we show that dynamical protocols that alternate the two drugs minimize the cancer size at the end of (or at mid-points during) treatment. These results significantly differ from those obtained with the homogeneous model---suggesting static protocols under the pro-generalizing and neutral allocation trade-offs---highlighting the important role of spatial and genetic heterogeneities. Our work is the first attempt to search for optimal treatments in a CB setting, a step forward toward realistic clinical applications.

Mathematical Model of Clonal Evolution Proposes a Personalised Multi-Modal Therapy for High-Risk Neuroblastoma

“…Despite the assumptions we made and the difficulty of calibrating our model, we successfully used it to simulate realistic population dynamics, reproducing experimentally observed trends. The evolutionary principles revealed by us are consistent with the theoretical insights in the literature [ 12 , 58 ]. We integrated them in a classification scheme: concrete steps of applying these principles to treat a neuroblastoma patient in a clinical setting.…”

“…In this paper, we have presented a potentially significant stride in this direction for neuroblastoma, similar to a recent theoretical study [ 58 ] and other previous ones relating to other cancer types such as pancreatic, colorectal, and melanoma cancers [ 59 ].…”

“…Notwithstanding these positives, the model can be improved upon—in addition to the extensions discussed within the context of the decision support system—by considering neuroblastoma’s spatial architecture, as it has been shown to be an important factor in adaptive therapy [ 58 , 64 ]. Specifically, as discussed above, a spatial model can describe the synergistic effects abstracted out in our model, such as the spatial patterns arising from the interactions between neuroblastoma and Schwann cells [ 61 ], and the tumour microenvironment [ 62 ].…”

Coupling Cell Size Regulation and Proliferation Dynamics ofC. glutamicumReveals Cell Division Based on Surface Area

“…The adder, in contrast, does consider the cell size to define the probability of division. Understanding how these models describe the dynamics of the population in colonies of low numbers of cells is especially relevant when studying cell proliferation, such as tumors or infections [61][62][63].…”

A Multiscale Orchestrated Computational Framework to Reveal Emergent Phenomena in Neuroblastoma

“…However, they do not capture individual cells' complex and heterogeneous behaviour, such as the importance of clonal evolution and other evolutionary principles [16], which can have important implications for treatment response and disease progression. Agent-based models, on the other hand, represent the behaviour of cells and their interactions with the microenvironment at a micro scale [11,24,33]. These models represent each cell as a discrete "agent" that can interact with its environment and with other agents.…”