Prediction of treatment response to antipsychotic drugs for precision medicine approach to schizophrenia: randomized trials and multiomics analysis

Liang-Kun Guo; Yi Su; Yu-Ya-Nan Zhang; Hao Yu; Zhe Lu; Wen-Qiang Li; Yong-Feng Yang; Xiao Xiao; Hao Yan; Tian-Lan Lu; Jun Li; Yun-Dan Liao; Zhe-Wei Kang; Li-Fang Wang; Yue Li; Ming Li; Bing Liu; Hai-Liang Huang; Lu-Xian Lv; Yin Yao; Yun-Long Tan; Gerome Breen; Ian Everall; Hong-Xing Wang; Zhuo Huang; Dai Zhang; Wei-Hua Yue

doi:10.1186/s40779-023-00459-7

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Prediction of treatment response to antipsychotic drugs for precision medicine approach to schizophrenia: randomized trials and multiomics analysis

RESEARCH | Updated：2025-12-13

- Prediction of treatment response to antipsychotic drugs for precision medicine approach to schizophrenia: randomized trials and multiomics analysis
- Prediction of treatment response to antipsychotic drugs for precision medicine approach to schizophrenia: randomized trials and multiomics analysis
- MMR 2024年11卷第1期页码：19-33
- Affiliations：
  
  1.Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China
  2.National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing 100191, China
  3.NHC Key Laboratory of Mental Health and Research Unit of Diagnosis and Treatment of Mood Cognitive Disorder (2018RU006), Chinese Academy of Medical Sciences, Beijing 100191, China
  4.Peking University Huilongguan Clinical Medical School, Beijing Huilongguan Hospital, Beijing 100096, China
  5.Department of Psychiatry, Jining Medical University, Jining 272067, Shandong, China
  6.Henan Key Lab of Biological Psychiatry, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 435001, Henan, China
  7.Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
  8.Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London WC2R 2LS, UK
  9.State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
  10.Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
  11.Stanley Center for Psychiatric Research, the Broad Institute of MIT and Harvard, Cambridge, MA 02141, USA
  12.Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
  13.Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai 200438, China
  14.Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China
  15.State Key Laboratory of Natural and Biomimetic Drugs, Key Laboratory for Neuroscience for Ministry of Education, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing 100191, China
  16.PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
  17.Chinese Institute for Brain Research, Beijing 102206, China
- Author bio：
  
  * huangz@hsc.pku.edu.cn;
  daizhang@bjmu.edu.cn;
  dryue@bjmu.edu.cn
- Funds：
  
  grants from the National Natural Science Foundation of China(81825009;82071505;81901358);the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences(2021-I2M-C&T-B-099;2019-I2M-5–006);the Program of Chinese Institute for Brain Research Beijing(2020-NKX-XM-12);the King’s College London-Peking University Health Science Center Joint Institute for Medical Research(BMU2020KCL001;BMU2019LCKXJ012);the National Key R&D Program of China(2021YFF1201103;2016YFC1307000)
- DOI：10.1186/s40779-023-00459-7
  中图分类号：
- 纸质出版：2024-02
- Accepted：
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Prediction of treatment response to antipsychotic drugs for precision medicine approach to schizophrenia: randomized trials and multiomics analysis[J]. MMR, 2024,11(1):19-33.

Cite this article as: Guo LK, Su Y, Zhang YYN, Yu H, Lu Z, Li WQ, et al. Prediction of treatment response to antipsychotic drugs for precision medicine approach to schizophrenia: randomized trials and multiomics analysis. Mil Med Res. 2023;10(1):24.
Prediction of treatment response to antipsychotic drugs for precision medicine approach to schizophrenia: randomized trials and multiomics analysis[J]. MMR, 2024,11(1):19-33. DOI： 10.1186/s40779-023-00459-7.

Cite this article as: Guo LK, Su Y, Zhang YYN, Yu H, Lu Z, Li WQ, et al. Prediction of treatment response to antipsychotic drugs for precision medicine approach to schizophrenia: randomized trials and multiomics analysis. Mil Med Res. 2023;10(1):24. DOI： 10.1186/s40779-023-00459-7.

摘要

Abstract

Background:

Choosing the appropriate antipsychotic drug (APD) treatment for patients with schizophrenia (SCZ) can be challenging

as the treatment response to APD is highly variable and difficult to predict due to the lack of effective biomarkers. Previous studies have indicated the association between treatment response and genetic and epigenetic factors

but no effective biomarkers have been identified. Hence

further research is imperative to enhance precision medicine in SCZ treatment.

Methods:

Participants with SCZ were recruited from two randomized trials. The discovery cohort was recruited from the CAPOC trial (

=2307) involved 6 weeks of treatment and equally randomized the participants to the Olanzapine

Risperidone

Quetiapine

Aripiprazole

Ziprasidone

and Haloperidol/Perphenazine (subsequently equally assigned to one or the other) groups. The external validation cohort was recruited from the CAPEC trial (

=1379)

which involved 8 weeks of treatment and equally randomized the participants to the Olanzapine

Risperidone

and Aripiprazole groups. Additionally

healthy controls (

=275) from the local community were utilized as a genetic/epigenetic reference. The genetic and epigenetic (DNA methylation) risks of SCZ were assessed using the polygenic risk score (PRS) and polymethylation score

respectively. The study also examined the genetic-epigenetic interactions with treatment response through differential methylation analysis

methylation quantitative trait loci

colocalization

and promoteranchored chromatin interaction. Machine learning was used to develop a prediction model for treatment response

which was evaluated for accuracy and clinical benefit using the area under curve (AUC) for classification

R2 for regression

and decision curve analysis.

Results:

Six risk genes for SCZ (

LINC01795

DDHD2

SBNO1

KCNG2

SEMA7A

and

RUFY1

) involved in cortical morphology were identified as having a genetic-epigenetic interaction associated with treatment response. The developed and externally validated prediction model

which incorporated clinical information

PRS

genetic risk score (GRS)

and proxy methylation level (proxyDNAm)

demonstrated positive benefits for a wide range of patients receiving different APDs

regardless of sex [discovery cohort: AUC=0.874 (95%CI 0.867–0.881)

=0.478; external validation cohort: AUC=0.851 (95%CI 0.841–0.861)

=0.507].

Conclusions:

This study presents a promising precision medicine approach to evaluate treatment response

which has the potential to aid clinicians in making informed decisions about APD treatment for patients with SCZ.

Trial registration

Chinese Clinical Trial Registry (https://www.chictr.org.cn/)

18 Aug 2009 retrospectively registered: CAPOC-ChiCTR-RNC-09000521 (https://www.chictr.org.cn/showproj.aspx?proj=9014)

CAPEC-ChiCTRRNC-09000522 (https://www.chictr.org.cn/showproj.aspx?proj=9013).

关键词

Keywords

references

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