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Dynamic treatment effect (DTE) curves reveal the mode of action for standard and experimental cancer therapies.

Choudhury KR, Keir ST, Ashcraft KA, Boss MK, Dewhirst MW - Oncotarget (2015)

Bottom Line: The methodology doesn't presuppose any prior form for the treatment effect dynamics and is shown to give consistent estimates with missing data.Second, we demonstrate that a combination of temozolomide and an experimental therapy in a glioma PDX model yields an effect, similar to an additive version of the DTE curves for the mono-therapies, except that there is a 30 day delay in peak inhibition.We show that resulting DTE curves are flat.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Bioinformatics, Duke University Medical Center, NC, USA.

ABSTRACT
We present a method for estimating the empirical dynamic treatment effect (DTE) curves from tumor growth delay (TGD) studies. This improves on current common methods of TGD analysis, such as T/C ratio and doubling times, by providing a more detailed treatment effect and overcomes their lack of reproducibility. The methodology doesn't presuppose any prior form for the treatment effect dynamics and is shown to give consistent estimates with missing data. The method is illustrated by application to real data from TGD studies involving three types of therapy. Firstly, we demonstrate that radiotherapy induces a sharp peak in inhibition in a FaDu model. The height, duration and timing of the peak increase linearly with radiation dose. Second, we demonstrate that a combination of temozolomide and an experimental therapy in a glioma PDX model yields an effect, similar to an additive version of the DTE curves for the mono-therapies, except that there is a 30 day delay in peak inhibition. In the third study, we consider the DTE of anti-angiogenic therapy in glioma. We show that resulting DTE curves are flat. We discuss how features of the DTE curves should be interpreted and potentially used to improve therapy.

No MeSH data available.


Related in: MedlinePlus

Estimated rates of control growth and inhibition due to Bevacizumab treatment, derived from the data in Figure 8
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Figure 9: Estimated rates of control growth and inhibition due to Bevacizumab treatment, derived from the data in Figure 8

Mentions: Growth rates in the control arm ranged between 5.4 %/day to 20.1%/day (Table 2). The estimated treatment effect curves were approximately flat for all 6 studies, indicating a relatively constant rate of inhibition throughout the study (Figure 9). Peak growth rates in the treated arm were negative across studies, implying that on average no tumor shrinkage took place at any point in the study. As might be expected, tumors in studies exhibiting lower growth rates lasted longer (Figure 8).


Dynamic treatment effect (DTE) curves reveal the mode of action for standard and experimental cancer therapies.

Choudhury KR, Keir ST, Ashcraft KA, Boss MK, Dewhirst MW - Oncotarget (2015)

Estimated rates of control growth and inhibition due to Bevacizumab treatment, derived from the data in Figure 8
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4546495&req=5

Figure 9: Estimated rates of control growth and inhibition due to Bevacizumab treatment, derived from the data in Figure 8
Mentions: Growth rates in the control arm ranged between 5.4 %/day to 20.1%/day (Table 2). The estimated treatment effect curves were approximately flat for all 6 studies, indicating a relatively constant rate of inhibition throughout the study (Figure 9). Peak growth rates in the treated arm were negative across studies, implying that on average no tumor shrinkage took place at any point in the study. As might be expected, tumors in studies exhibiting lower growth rates lasted longer (Figure 8).

Bottom Line: The methodology doesn't presuppose any prior form for the treatment effect dynamics and is shown to give consistent estimates with missing data.Second, we demonstrate that a combination of temozolomide and an experimental therapy in a glioma PDX model yields an effect, similar to an additive version of the DTE curves for the mono-therapies, except that there is a 30 day delay in peak inhibition.We show that resulting DTE curves are flat.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Bioinformatics, Duke University Medical Center, NC, USA.

ABSTRACT
We present a method for estimating the empirical dynamic treatment effect (DTE) curves from tumor growth delay (TGD) studies. This improves on current common methods of TGD analysis, such as T/C ratio and doubling times, by providing a more detailed treatment effect and overcomes their lack of reproducibility. The methodology doesn't presuppose any prior form for the treatment effect dynamics and is shown to give consistent estimates with missing data. The method is illustrated by application to real data from TGD studies involving three types of therapy. Firstly, we demonstrate that radiotherapy induces a sharp peak in inhibition in a FaDu model. The height, duration and timing of the peak increase linearly with radiation dose. Second, we demonstrate that a combination of temozolomide and an experimental therapy in a glioma PDX model yields an effect, similar to an additive version of the DTE curves for the mono-therapies, except that there is a 30 day delay in peak inhibition. In the third study, we consider the DTE of anti-angiogenic therapy in glioma. We show that resulting DTE curves are flat. We discuss how features of the DTE curves should be interpreted and potentially used to improve therapy.

No MeSH data available.


Related in: MedlinePlus