A Moffitt team suggests mathematical modeling may guide the optimal cancer treatment dosing approach better than MTD.
Certain tumor types, such as breast cancers and melanomas, may be best managed if patients are given treatment holidays. Researchers at Moffitt Cancer have found that mathematical modeling may be beneficial in determining the optimal treatment, compared with the maximum tolerated dose. They report in the journal Cancer Research that treatment resistance can be avoided in many cases by using mathematical modeling based on evolutionary principles.
Standard cancer treatments and most clinical trials are based on the notion to treat patients with the highest dose of a drug possible to kill the most cancer cells in the shortest amount of time. However, tumor cells manage to find ways to thrive by activating survival mechanisms. Study investigator Jill Gallaher, PhD, from the Department of Integrated Mathematical Oncology at Moffitt Cancer Center, Tampa, Florida, said an evolutionary flaw in this maximum-tolerated-dose strategy is the assumption that resistant cancer cell populations are not present prior to therapy.
“We found that tumors with known preexisting treatment resistance will most likely recur with a treatment of continuous maximum tolerated dose. Under adaptive scheduling, the drug dose is modified in response to tumor volume changes,” said Gallaher. She and her colleagues found they were able to control many heterogeneous tumors that would otherwise become resistant and recur. “We tested different types of adaptive therapy strategies, such as turning the drug on and off (treatment vacations) or tuning the dose (dose modulations), and found that when there is a large sensitive population, modulations might provide a lower dose and still control the tumor,” Gallaher told Cancer Network.
The researchers used mathematical modeling based on evolutionary principles and cell culture experiments. They simulated maximum dose strategies against adaptive strategies using different combinations of sensitive and resistant cell populations, and also took into consideration the ability of cells to migrate. In addition, they factored in proliferation inheritance patterns.
The researchers theorize that there is no single treatment approach that works best for all tumors, and that the ability of a tumor to respond to a particular treatment depends on the composition of the tumor. They found that tumors which comprise cells that are similar to one another and sensitive to drug treatments tend to respond better to a continuous, maximum-dose approach. In a clinical setting, this approach may work best for tumors such as testicular cancer and certain lymphomas that tend to be more homogeneous. However, tumors that are made up of a mixture of sensitive and resistant cells tend to respond better to an adaptive treatment approach, according to the researchers. This finding may be highly applicable to melanoma, lung cancer, breast cancer, and a host of other tumor types.
Sandy Anderson, PhD, chair of the Department of Integrated Mathematical Oncology at Moffitt Cancer Center, Tampa, Fla., said these findings show the importance of using treatment response as a key driver of treatment decisions, rather than fixed strategies. “One-size-fits-all fixed treatment strategies for metastatic disease are doomed to fail. Instead, we should modify [an] individual patient’s treatment schedule based on how they are currently responding to the treatment,” said Anderson.