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On the analysis of clonogenic survival data: Statistical alternatives to the linear-quadratic model

Overview of attention for article published in Radiation Oncology, January 2016
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Title
On the analysis of clonogenic survival data: Statistical alternatives to the linear-quadratic model
Published in
Radiation Oncology, January 2016
DOI 10.1186/s13014-016-0584-z
Pubmed ID
Authors

Steffen Unkel, Claus Belka, Kirsten Lauber

Abstract

The most frequently used method to quantitatively describe the response to ionizing irradiation in terms of clonogenic survival is the linear-quadratic (LQ) model. In the LQ model, the logarithm of the surviving fraction is regressed linearly on the radiation dose by means of a second-degree polynomial. The ratio of the estimated parameters for the linear and quadratic term, respectively, represents the dose at which both terms have the same weight in the abrogation of clonogenic survival. This ratio is known as the α/β ratio. However, there are plausible scenarios in which the α/β ratio fails to sufficiently reflect differences between dose-response curves, for example when curves with similar α/β ratio but different overall steepness are being compared. In such situations, the interpretation of the LQ model is severely limited. Colony formation assays were performed in order to measure the clonogenic survival of nine human pancreatic cancer cell lines and immortalized human pancreatic ductal epithelial cells upon irradiation at 0-10 Gy. The resulting dataset was subjected to LQ regression and non-linear log-logistic regression. Dimensionality reduction of the data was performed by cluster analysis and principal component analysis. Both the LQ model and the non-linear log-logistic regression model resulted in accurate approximations of the observed dose-response relationships in the dataset of clonogenic survival. However, in contrast to the LQ model the non-linear regression model allowed the discrimination of curves with different overall steepness but similar α/β ratio and revealed an improved goodness-of-fit. Additionally, the estimated parameters in the non-linear model exhibit a more direct interpretation than the α/β ratio. Dimensionality reduction of clonogenic survival data by means of cluster analysis was shown to be a useful tool for classifying radioresistant and sensitive cell lines. More quantitatively, principal component analysis allowed the extraction of scores of radioresistance, which displayed significant correlations with the estimated parameters of the regression models. Undoubtedly, LQ regression is a robust method for the analysis of clonogenic survival data. Nevertheless, alternative approaches including non-linear regression and multivariate techniques such as cluster analysis and principal component analysis represent versatile tools for the extraction of parameters and/or scores of the cellular response towards ionizing irradiation with a more intuitive biological interpretation. The latter are highly informative for correlation analyses with other types of data, including functional genomics data that are increasingly being generated.

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Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Germany 1 2%
Canada 1 2%
Unknown 57 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 20%
Student > Bachelor 9 15%
Student > Ph. D. Student 8 13%
Student > Master 7 12%
Other 6 10%
Other 9 15%
Unknown 9 15%
Readers by discipline Count As %
Medicine and Dentistry 14 23%
Biochemistry, Genetics and Molecular Biology 10 17%
Engineering 7 12%
Physics and Astronomy 5 8%
Agricultural and Biological Sciences 3 5%
Other 7 12%
Unknown 14 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 January 2016.
All research outputs
#6,067,894
of 7,059,888 outputs
Outputs from Radiation Oncology
#913
of 1,023 outputs
Outputs of similar age
#262,728
of 319,092 outputs
Outputs of similar age from Radiation Oncology
#37
of 50 outputs
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