Can drugs enhance hypofractionated radiotherapy? A novel method of modeling radiosensitization using in vitro data

Abstract

Purpose: Hypofractionated radiotherapy (hRT) is being explored for a number of malignancies. The potential benefit of giving concurrent chemotherapy with hRT is not known. We sought to predict the effects of combined modality treatments by using mathematical models derived from laboratory data.

Methods and materials: Data from 26 published clonogenic survival assays for cancer cell lines with and without the use of radiosensitizing chemotherapy were collected. The first three data points of the RT arm of each assay were used to derive parameters for the linear quadratic (LQ) model, the multitarget (MT) model, and the generalized linear quadratic (gLQ) model. For each assay and model, the difference between the predicted and observed surviving fractions at the highest tested RT dose was calculated. The gLQ model was fitted to all the data from each RT cell survival assay, and the biologically equivalent doses in 2-Gy fractions (EQD2s) of clinically relevant hRT regimens were calculated. The increase in cell kill conferred by the addition of chemotherapy was used to estimate the EQD2 of hRT along with a radiosensitizing agent. For comparison, this was repeated using conventionally fractionated RT regimens.

Results: At a mean RT dose of 8.0 Gy, the average errors for the LQ, MT, and gLQ models were 1.63, 0.83, and 0.56 log units, respectively, favoring the gLQ model (p < 0.05). Radiosensitizing chemotherapy increased the EQD2 of hRT schedules by an average of 28% to 82%, depending on disease site. This increase was similar to the gains predicted for the addition of chemotherapy to conventionally fractionated RT.

Conclusions: Based on published in vitro assays, the gLQ equation is superior to the LQ and MT models in predicting cell kill at high doses of RT. Modeling exercises demonstrate that significant increases in biologically equivalent dose may be achieved with the addition of radiosensitizing agents to hRT. Clinical study of this approach is warranted.

Copyright © 2012 Elsevier Inc. All rights reserved.

Figures

Figure 1

Example (data from van Bree, et al [46]) of Determining Model Errors: Each cell survival model is fit to only the first three data points from a clonogenic cell assay (radiotherapy only). At the highest tested dose (8 Gy), the Linear Quadratic (LQ), Multi-target, and Generalized Linear Quadratic (gLQ) models overestimate cell kill by 1.6, 0.9, and 0.4 log units, respectively.

Figure 2

Example of determining biologically equivalent dose (EQD2) and dose enhancement factor (DEF): After fitting the Generalized Linear Quadratic (gLQ) model to survival assay data, estimates of log cell kill at 2 Gy and 10 Gy are 0.2 and 2.0, respectively. The EQD2 of a single fraction of 10 Gy is therefore 20 Gy. To determine EQD2 for chemoradiation, the DEF is defined as the increase in log cell kill at the closest lower tested radiotherapy (RT) dose. For example, a DEF of 1.0 would be used for fraction sizes between 7.5 and 10 Gy. For fraction sizes of 10 Gy or higher, a DEF of 1.7 is used. The EQD2 of a single fraction of 10 Gy with a radiosensitizing agent in this example is 37 Gy.

Figure 3

EQD2 for GBM cell lines treated with 2.0 Gy x 30 (top) and 6 Gy x 5 (bottom), with and without concurrent temozolomide. Error bars indicate 95% confidence intervals. denotes a significant (p

Figure 4

EQD2 for HNC cell lines treated with 2.0 Gy x 35 (top) and 5.0 Gy x 6 (bottom), with and without concurrent cisplatin. * denotes addition of cetuximab. Error bars indicate 95% confidence intervals. denotes a significant (p

Figure 5

EQD2 for NSCLC cell lines treated with 2.0 Gy x 30 (top) and 10.0 Gy x 5 (bottom). Car = Carboplatin, Doc = Docetaxel, Pac = Paclitaxel, Gem = Gemcitabine, Vin = Vinorelbine. Error bars indicate 95% confidence intervals. denotes a significant (p

Figure 6

EQD2 for Pancreatic cancer cell lines treated with 1.8 Gy x 28 (top) and 10 Gy x 3 (bottom), with and without concurrent gemcitabine. * denotes addition of oxaliplatin. ** denotes addition of erlotinib. Error bars indicate 95% confidence intervals. denotes a significant (p