Prediction of treatment response to antipsychotic drugs for precision medicine approach to schizophrenia: randomized trials and multiomics analysis
Authors: Guo Liang-Kun , Su Yi , Zhang Yu-Ya-Nan , Yu Hao , Lu Zhe , Li Wen-Qiang , Yang Yong-Feng , Xiao Xiao , Yan Hao , Lu Tian-Lan , Li Jun , Liao Yun-Dan , Kang Zhe-Wei , Wang Li-Fang , Li Yue , Li Ming , Liu Bing , Huang Hai-Liang , Lv Lu-Xian , Yao Yin , Tan Yun-Long , Breen Gerome , Everall Ian , Wang Hong-Xing , Huang Zhuo , Zhang Dai , Yue Wei-Hua 
Published: June 2023

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 (n = 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 (n = 1379), which involved 8 weeks of treatment and equally randomized the participants to the Olanzapine, Risperidone, and Aripiprazole groups. Additionally, healthy controls (n = 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 promoter-anchored 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), R2 = 0.478; external validation cohort: AUC = 0.851 (95% CI 0.841–0.861), R2 = 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—ChiCTR-RNC-09000522 (https://www.chictr.org.cn/showproj.aspx?proj=9013).