Modeling the Cellular Response of Lung Cancer to Radiation Therapy for a Broad Range of Fractionation Schedules

Purpose: To demonstrate that a mathematical model can be used to quantitatively understand tumor cellular dynamics during a course of radiotherapy, and to predict the likelihood of local control as a function of dose and treatment fractions.<br /><br />Experimental Design: We model outcomes for early-stage, localized non-small cell lung cancer (NSCLC), by fitting a mechanistic, cellular dynamics-based tumor control probability that assumes a constant local supply of oxygen and glucose. In addition to standard radiobiological effects such as repair of sub-lethal damage and the impact of hypoxia, we also accounted for proliferation as well as radiosensitivity variability within the cell cycle. We applied the model to 36 published and 2 unpublished early stage patient cohorts, totaling 2701 patients.<br /><br />Results: Precise likelihood best-fit values were derived for the radiobiological parameters: α (0.305 Gy^-1; 95% CI: 0.120-0.365), the α/β ratio (2.80 Gy; 95% CI: 0.40-4.40), and the oxygen enhancement ratio (OER) value for intermediately hypoxic cells receiving glucose but not oxygen (1.70; 95% CI: 1.55-2.25). All fractionation groups are well-fitted by a single dose-response curve with a high ² p-value, indicating consistency with the fitted model. The analysis was further validated with additional 23 patient cohorts (n=1628). The model indicates that hypofractionation regimes overcome hypoxia (and cell-cycle radiosensitivity variations) by the sheer impact of high doses per fraction, whereas lower dose-per-fraction regimes allow for reoxygenation and corresponding sensitization, but lose effectiveness for prolonged treatments due to proliferation.<br /><br />Conclusions: The mechanistic tumor response modeling can accurately predict over-treatment or under-treatment for various treatment regimes.

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