Impact of genetic dynamics and single-cell heterogeneity on development of nonstandard personalized medicine strategies for cancer

Abstract

Cancers are heterogeneous and genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type. However, it does not yet systematically address heterogeneity at the single-cell level within a single individual's cancer or the dynamic nature of cancer due to genetic and epigenetic change as well as transient functional changes. We have developed a mathematical model of personalized cancer therapy incorporating genetic evolutionary dynamics and single-cell heterogeneity, and have examined simulated clinical outcomes. Analyses of an illustrative case and a virtual clinical trial of over 3 million evaluable "patients" demonstrate that augmented (and sometimes counterintuitive) nonstandard personalized medicine strategies may lead to superior patient outcomes compared with the current personalized medicine approach. Current personalized medicine matches therapy to a tumor molecular profile at diagnosis and at tumor relapse or progression, generally focusing on the average, static, and current properties of the sample. Nonstandard strategies also consider minor subclones, dynamics, and predicted future tumor states. Our methods allow systematic study and evaluation of nonstandard personalized medicine strategies. These findings may, in turn, suggest global adjustments and enhancements to translational oncology research paradigms.

Fig. 1.

Illustrative example contrasting current practice of personalized medicine (A) and nonstandard personalized medicine (B). Time (months) is on the x axis, and cell number is on the y axis. The total number of cells (N) is shown in blue (multiplied by 1.5 to create separation from the predominant population for clarity), S cells are shown in green, R₁ cells are shown in red, R₂ cells are shown in light blue, and R_1–2 cells are shown in magenta. Treatments are indicated by the solid bars at the top: green is drug-1, blue is drug-2, and both colors indicate a combination. In the current personalized medicine strategy (A), the patient is treated with drug-1 and experiences a complete response, only to relapse 14 mo after diagnosis with R₁ cells. He/she is then treated with drug-2, experiencing a second complete response before he/she relapses with incurable R_1–2 cells 28 mo after initial diagnosis. In the nonstandard personalized medicine strategy (B), the patient is treated with drug-2 for 4 mo to suppress a possible R₁ subpopulation even though it has not been detected. The bulk tumor slowly grows under observation. At 4 mo, treatment continues with an equal mixture of drug-1 and drug-2, resulting first in a complete response and then in an apparent cure. Note that initial treatment with an equal drug mixture would have been less effective in immediate eradication of R₁ cells, allowing more time for incurable R_1–2 cells to evolve. Parameter values are provided in SI Methods.

Fig. 2.

Kaplan–Meier survival curves of a virtual clinical trial incorporating different strategies. Approximately 3 million evaluable virtual patients were treated with each of the strategies. The x axis shows time (weeks), and the y axis shows the surviving patient fraction. Strategy 0 (dark blue) is the current personalized medicine strategy: treatment with the best drug for the observed predominant cell type and switching to the alternative drug on tumor progression or relapse. Strategy 1 (green) minimizes total cell numbers at the next time point. Strategy 2.1 (red) minimizes the chance of developing incurable R_1–2 cells at the next time point unless the patient has detectable disease (10⁹ cells); at that point, total cell number is minimized. Strategy 2.2 (light blue) minimizes the chance of developing incurable R_1–2 cells at the next time point unless the patient has a high disease burden (10¹¹ cells); at that point, the total number of cells is minimized. Strategy 3 (magenta) minimizes the total cells at the next time point unless the predicted R_1–2 cell population at that time point is ≥1; in that case, the R_1–2 cell population is minimized. Strategy 4 (olive) predicts the time to mortality from each cell population and the time to “incurability” from forming R_1–2 cells, prioritizing treatment of the most imminent threat at the next time point.