机构:[1]Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA.[2]Department of Hematology, The Second Affiliated Hospital of Soochow University, Suzhou, China.[3]Department of Cell Biology, School of Biology & Basic Medical Sciences, Soochow University, Suzhou, China.[4]Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
Background: Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. Methods: It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. Results: We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. Conclusions: The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.
基金:
This work was supported in part by research grants from USA National
Institutes of Health (R03-DE025646), National Natural Science Foundation of
China (81673448), Natural Science Foundation of Jiangsu Province China (BK
20161218), and The Applied Basic Research Programs of Suzhou City
(SYS201546).
第一作者机构:[1]Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA.
共同第一作者:
通讯作者:
通讯机构:[3]Department of Cell Biology, School of Biology & Basic Medical Sciences, Soochow University, Suzhou, China.[4]Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
推荐引用方式(GB/T 7714):
Xinyan Zhang,Bingzong Li,Huiying Han,et al.Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression[J].BMC CANCER.2018,18(1):551.doi:10.1186/s12885-018-4483-6.
APA:
Xinyan Zhang,Bingzong Li,Huiying Han,Sha Song,Hongxia Xu...&Wenzhuo Zhuang.(2018).Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.BMC CANCER,18,(1)
MLA:
Xinyan Zhang,et al."Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression".BMC CANCER 18..1(2018):551