TY - JOUR
T1 - Modeling the cellular response of lung cancer to radiation therapy for a broad range of fractionation schedules
AU - Jeong, Jeho
AU - Oh, Jung Hun
AU - Sonke, Jan Jakob
AU - Belderbos, Jose
AU - Bradley, Jeffrey D.
AU - Fontanella, Andrew N.
AU - Rao, Shyam S.
AU - Deasy, Joseph O.
N1 - Publisher Copyright:
©2017 AACR.
PY - 2017/9/15
Y1 - 2017/9/15
N2 - 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. 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 We applied the model to 36 published and two unpublished early-stage patient cohorts, totaling 2,701 patients. Results: Precise likelihood best-fit values were derived for the radiobiological parameters: a [0.305 Gy1; 95% confidence interval (CI), 0.120–0.365], the a/b 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 x2 P value, indicating consistency with the fitted model. The analysis was further validated with an additional 23 patient cohorts (n ¼ 1,628). The model indicates that hypofractionation regimens overcome hypoxia (and cell-cycle radiosensitivity variations) by the sheer impact of high doses per fraction, whereas lower dose-per-fraction regimens allow for reoxygenation and corresponding sensitization, but lose effectiveness for prolonged treatments due to proliferation. Conclusions: This proposed mechanistic tumor-response model can accurately predict overtreatment or undertreatment for various treatment regimens.
AB - 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. 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 two unpublished early-stage patient cohorts, totaling 2,701 patients. Results: Precise likelihood best-fit values were derived for the radiobiological parameters: a [0.305 Gy1; 95% confidence interval (CI), 0.120–0.365], the a/b 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 x2 P value, indicating consistency with the fitted model. The analysis was further validated with an additional 23 patient cohorts (n ¼ 1,628). The model indicates that hypofractionation regimens overcome hypoxia (and cell-cycle radiosensitivity variations) by the sheer impact of high doses per fraction, whereas lower dose-per-fraction regimens allow for reoxygenation and corresponding sensitization, but lose effectiveness for prolonged treatments due to proliferation. Conclusions: This proposed mechanistic tumor-response model can accurately predict overtreatment or undertreatment for various treatment regimens.
UR - http://www.scopus.com/inward/record.url?scp=85029488217&partnerID=8YFLogxK
U2 - 10.1158/1078-0432.CCR-16-3277
DO - 10.1158/1078-0432.CCR-16-3277
M3 - Article
C2 - 28539466
AN - SCOPUS:85029488217
SN - 1078-0432
VL - 23
SP - 5469
EP - 5479
JO - Clinical Cancer Research
JF - Clinical Cancer Research
IS - 18
ER -