Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease

Abstract

Background: Recent studies reported the responses of ustekinumab (UST) for the treatment of Crohn's disease (CD) differ among patients, while the cause was unrevealed. The study aimed to develop a prediction model based on the gene transcription profiling of patients with CD in response to UST.Methods: The GSE112366 dataset, which contains 86 CD and 26 normal samples, was downloaded for analysis. Differentially expressed genes (DEGs) were identified first. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were administered. Least absolute shrinkage and selection operator regression analysis was performed to build a model for UST response prediction. Results: A total of 122 DEGs were identified. GO and KEGG analyses revealed that immune response pathways are significantly enriched in patients with CD. A multivariate logistic regression equation that comprises four genes (HSD3B1, MUC4, CF1, and CCL11) for UST response prediction was built. The area under the receiver operator characteristic curve for patients in training set and testing set were 0.746 and 0.734, respectively.Conclusions: This study is the first to build a gene expression prediction model for UST response in patients with CD and provides valuable data sources for further studies.

Precision medicine and drug optimization in adult inflammatory bowel disease patients

“…277 Several machine learning predictive models have already been suggested such as (1) a machine learning algorithm to predict clinical remission with thiopurines 278 ; (2) a machine learning to predict non-durable response to anti-TNF therapy in CD patients using transcriptome imputed from genotypes 279 ; (3) a machine learning to identify, in CD patients, predictive factors of remission and drug durability with ustekinumab 280 ; or (4) a machine learning gene expression to predict response to ustekinumab in CD patients. 281 Prediction of side effects. If in precision medicine, biomarkers can be used to predict response to treatment, they can also predict the occurrence of side effects.…”

Machine learning applications for transcription level and phenotype predictions

“…To this end, a multivariate regression model was developed to predict sensitivity and specificity of the antibody therapy, ustekinumab, using gene transcription profiling of patients with Crohn's disease. 73 By applying SVM on gene expression profiles of 395 immune-related genes, Lu et al identified predictive 24-gene RNA signatures to discriminate between the metastatic gastrointestinal cancer patients who might or might not benefit from immune checkpoint inhibition treatments. 74 Along the same line, RF was used to classify responders and nonresponders to two antitumor necrosis factors, adalimumab or etanercept, in rheumatoid arthritis patients using transcriptomic and DNA-methylation profiles of blood samples.…”

“…With more targeted treatment options becoming available, it is of important interest for clinicians to predetermine if, and to what extent, certain patients will benefit from specific treatments. To this end, a multivariate regression model was developed to predict sensitivity and specificity of the antibody therapy, ustekinumab, using gene transcription profiling of patients with Crohn's disease 73 . By applying SVM on gene expression profiles of 395 immune‐related genes, Lu et al identified predictive 24‐gene RNA signatures to discriminate between the metastatic gastrointestinal cancer patients who might or might not benefit from immune checkpoint inhibition treatments 74 .…”

Making Medical Predictions about Diseases with Gene Expression Data

“…Researchers also built prediction models for specific drugs. For instance, He Manrong et al [24] created a model for the response of ustekinumab (UST) for patients of Crohn's disease (CD) under treatment based on data from gene transcription profiling. UST is not the main treatment of CD, but when traditional therapies are not well responded to or they bring severe side effects to some patients, UST becomes a preferred alternative.…”