Lu Peng,1 Liyang Wang,2 Ling Chen,3 Zhu Shen1 

1Department of Dermatology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, People’s Republic of China; 2School of Biomedical Engineering, Tsinghua University, Beijing, 100084, People’s Republic of China; 3Department of Dermatology, Daping Hospital, Army Medical University, Chongqing, 400042, People’s Republic of China

Correspondence: Zhu Shen, Department of Dermatology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No. 106, Zhongshan 2nd Road, Guangzhou, Guangdong, 510080, People’s Republic of China, Email zhushencq@hotmail.com

Purpose: Biologic therapies have transformed plaque psoriasis treatment, but patient responses remain variable, neces+sitating machine prediction model for personalized therapy.
Patients and Methods: Transcriptomic and clinical data from moderate-to-severe psoriatic patient biopsies were sourced from GSE117468. Differential gene analysis identified Brodalumab treatment-associated genes. Lasso regression selected response-related genes, and LightGBM was used to build machine learning models. Model robustness was assessed using five-fold cross-validation.
Results: Biopsies (n=491) from 116 patients’ lesional (LS) and non-lesional (NL) tissues were analyzed, divided into Brodalumab (140 mg or 210 mg) and placebo groups. Responders were defined as achieving ≥ 75% improvement in Psoriasis Area and Severity Index at week 12. Lasso identified genes from classical psoriasis pathways (IL-17, PPAR signaling, HLA-D alleles) and novel targets (WIF1, SLC44A5, LOC441528, SAA1). Six LightGBM models were trained to predict 12-week treatment response and 4-week response speed using LS, NL, and combined (LS_&_NL) data. LS_&_NL models showed superior performance, achieving AUC-ROC values of 95.14% (140 mg) and 92.83% (210 mg) for 12-week response prediction and 98.70% (140 mg) and 97.51% (210 mg) for 4-week response speed prediction.
Conclusion: These models provide robust tools for predicting Brodalumab response, supporting precision medicine and optimizing resource allocation in plaque psoriasis management.
Plain Language Summary: Why was this study done?
Biologic drugs have improved the management of with moderate-to-severe plaque psoriasis, but responses vary. Predicting who will benefit most could aid clinicians in selecting optimal treatments, reduce trial-and-error approaches, and save healthcare resources.
What did the researchers do?
Scientists analyzed lesional and non-lesional skin biopsies from 116 psoriatic patients treated with Brodalumab (an IL-17RA inhibitor) or a placebo. Utilizing transcriptomic data and computer models, they:
Identify genes linked to treatment success.
Built tools to predict patients achieving significant improvement (≥ 75% clearer lesions) by week 12, or early respond by week 4.
What did they find?
Integration of data from both lesional (LS) and non-lesional (NL) specimens yielded the highest predictive accuracy. Key genes included known inflammation markers (eg, IL-17) and novel targets (eg, WIF1).
Models developed using LS_&_NL gene signatures predicted outcomes with high AUC-ROC:
12-week treatment success: 95.14% value for Brodalumab 140 mg dose, and 92.83% for 210 mg.
4-week early response: 98.70% value for Brodalumab 140 mg, and 97.51% for 210 mg.
What do these results mean?
These tools could aid clinicians in personalizing treatment regimens, giving patients faster relief and avoiding ineffective therapies. This approach may improve care quality and reduce healthcare costs. The study also uncovers new genetic clues that could inspire future psoriasis treatments.

Keywords: machine learning, treatment response prediction, plaque psoriasis