Novel concept to personalize radiation oncology: Predicting cell-specific survival prior to treatment

Novel concept to personalize radiation oncology: Predicting cell-specific survival prior to treatment

Oesten, H.; von Neubeck, C.; Jakob, A.; Loeck, S.; Enghardt, W.; Krause, M.; Mcmahon, S. J.; Grassberger, C.; Paganetti, H.; Lühr, A.

(1) Purpose/Objective
To enhance tumor response and thus treatment outcome in radiation therapy, a dose prescription strategy prior to treatment is necessary to individualize radiation oncology.
However, prediction of cell-specific survival prior to treatment is currently unavailable. Thus, we developed an approach to stratify patients prior to therapy by predicting individual radiation response based on cell survival.

(2) Material/methods
Based on a previously developed mechanistic radiation response model of DNA repair and cell survival (S_cell) prediction for normal tissue cells, we simulated measured ∝- and β-values of 19 in vitro cancer cell lines (skin, lung, brain). The radiation model incorporates four cell-specific parameters: number of chromosomes, p53-mutation-status, cell-cycle distribution and the effective genome size (eGS). The first three are only experimentally available; the latter was obtained through minimizing the difference between the simulated and measured ∝- and β-values. A parametrization of eGS as a function of the cells’ chromosome number was proposed. The correct choice of all parameters was validated by an independent dataset of time-dependent γ-H2AX data over 24h.

(3) Results
Overall good agreement between simulated and measured in vitro cancer S_cell curves was achieved (Fig. 1). The measured β values were found to increase quadratically with the obtained eGS (R^2=0.81) irrespectively of other cell-specific parameters (Fig. 2b). The measured ∝ values increased linearly with the eGS manifesting different slopes distinguishable into the cells’ p53-mutation-status (Fig. 2a). Measured ∝ and β were predictable based on eGS with an uncertainty of one sigma: σ=0.04Gy^(-1) for ∝ and σ=0.01Gy^(-2) for β. The eGS was found to correlate (R^2=0.70) with the number of chromosomes for all but four cell lines. The detailed cell-specific cell cycle distributions were found to have a negligible impact on the radiobiological parameters. Measured time-dependent γ-H2AX data was predictable through repair kinetics simulations.

(4) Conclusion
A mechanistic model for radiation response of normal human cells was successfully modified to allow for simulations of measured in vitro S_cell of 19 cancer cell lines. Independent of cancer entity, the radiobiological value β was predictable only by the eGS while the prediction of ∝ required in addition at least knowledge of the p53-mutation-status. An enhanced correlation of the eGS with a clinically accessible parameter, as suggested, may facilitate a stratification strategy based on cell-specific survival prediction for individualized patient treatment in radiotherapy.

  • Lecture (Conference)
    ESTRO 37 - Innovation for Value and Access, 20.-24.04.2018, Barcelona, Spanien

Publ.-Id: 26204