Predicting patient tumor responses to radiation treatment using computational models

Nov, 2021 - By SMI

Predicting patient tumor responses to radiation treatment using computational models

Moffitt Cancer Center researchers are using computer modeling to increase the customization of radiation treatment. They analyze how connections between cancer cells and antibodies, as well as their subsequent reactions to radiation, influence the tumor cells. They believe their model might help anticipate how patients will react to radiation treatment.

Radiation therapy is one of the most often utilized cancer treatments. Furthermore, despite its lengthy and widespread usage, radiation therapy is more of a one-size-fits-all treatment that pays little attention to the biology of a patient's tumor. People with cancer respond differentially to radiation therapy depending on the kind of tumor and individual genetic variables. Furthermore, tumors comprise a wide range of immune cells, vascular networks, and surrounding tissue architecture, all of which have a significant impact on treatment results.

A large number of suppressor immune cells, for instance, permits tumor cells to avoid cell death, but a large number of immune effector cells can induce cancer cell death. Many new treatments are attempting to enhance radiation therapy by increasing the impact of immune effector cells; however, given the vast range of radiation dosages, schedules, and timing situations, determining the optimal therapeutic strategy is difficult.

Moffitt researchers chose to use improvements in computer modeling to better understand cellular reactions to radiation treatment. They created a model to investigate the link between the tumor-immune environment and a tumor's reaction to radiation in the presence and absence of particular patient variables. They added migration, cell proliferation, cell death, cell-to-cell interactions, immune cell motility, and the cytotoxic effect of radiation in their model.

According to their concept, tumor cells would either avoid immune predation or be eliminated by the immune system depending on the amount of immune suppressor and effector cells available. They studied trials from 10,469 individuals with 31 different types of tumor cells and they were able to determine the number of tumor cells, immune suppressor, and immune effector cells in each tumor. They included these findings into their model, which revealed that radiation therapy can alter the immunological environment in favor of tumor eradication, but other cancers that do not undergo this shift will most likely regenerate. The researchers created an individual Radiation Immune Score by combining the parameters that predicted these radiation-induced changes (iRIS). It could be able to anticipate with this model how patients will react to radiation and the lowest radiation dosage necessary to attain tumor control with or without the assistance of immune targeting drugs.

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