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Drug response prediction model using a hierarchical structural component modeling method

Cited 1 time in Web of Science Cited 1 time in Scopus
Authors
Kim, Sungtae; Choi, Sungkyoung; Yoon, Jung-Hwan; Kim, Youngsoo; Lee, Seungyeoun; Park, Taesung
Issue Date
2018-08-13
Publisher
BioMed Central
Citation
BMC Bioinformatics, 19(Suppl 9):288
Keywords
BiomarkersComponent-based structural equation modelingDrug responseLiver cancerMultiple reaction monitoring mass spectrometryMRM-MSPrediction modelSorafenib
Abstract
Background
Component-based structural equation modeling methods are now widely used in science, business, education, and other fields. This method uses unobservable variables, i.e., “latent” variables, and structural equation model relationships between observable variables. Here, we applied this structural equation modeling method to biologically structured data. To identify candidate drug-response biomarkers, we first used proteomic peptide-level data, as measured by multiple reaction monitoring mass spectrometry (MRM-MS), for liver cancer patients. MRM-MS is a highly sensitive and selective method for proteomic targeted quantitation of peptide abundances in complex biological samples.

Results
We developed a component-based drug response prediction model, having the advantage that it first combines collapsed peptide-level data into protein-level information, facilitating subsequent biological interpretation. Our model also uses an alternating least squares algorithm, to efficiently estimate both coefficients of peptides and proteins. This approach also considers correlations between variables, without constraint, by a multiple testing problem. Using estimated peptide and protein coefficients, we selected significant protein biomarkers by permutation testing, resulting in our model for predicting liver cancer response to the tyrosine kinase inhibitor sorafenib.

Conclusions
Using data from a cohort of liver cancer patients, we then “fine-tuned” our model to successfully predict drug responses, as demonstrated by a high area under the curve (AUC) score. Such drug response prediction models may eventually find clinical translation in identifying individual patients likely to respond to specific therapies.
ISSN
1471-2105
Language
English
URI
http://hdl.handle.net/10371/143530
DOI
https://doi.org/10.1186/s12859-018-2270-7
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College of Natural Sciences (자연과학대학)Dept. of Statistics (통계학과)Journal Papers (저널논문_통계학과)
College of Natural Sciences (자연과학대학)Program in Bioinformatics (협동과정-생물정보학전공)Journal Papers (저널논문_협동과정-생물정보학전공)
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