Prediction of immune checkpoint inhibition with immune oncology-related gene expression in gastrointestinal cancer using a machine learning classifier

Abstract

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of gastrointestinal cancer. However, biomarkers correlated with the efficacy of ICIs in gastrointestinal cancer are still lacking. In this study, we performed 395-plex immune oncology (IO)-related gene target sequencing in tumor samples from 96 patients with metastatic gastrointestinal cancer patients treated with ICIs, and a linear support vector machine learning strategy was applied to construct a predictive model. ResultsAll 96 patients were randomly assigned into the discovery (n=72) and validation (n=24) cohorts. A 24-gene RNA signature (termed the IO-score) was constructed from 395 immune-related gene expression profiling using a machine learning strategy to identify patients who might benefit from ICIs. The durable clinical benefit rate was higher in patients with a high IO-score than in patients with a low IO-score (discovery cohort: 92.0% vs 4.3%, p<0.001; validation cohort: 85.7% vs 17.6%, p=0.004). The IO-score may exhibit a higher predictive value in the discovery (area under the receiver operating characteristic curve (AUC)=0.97)) and validation (AUC=0.74) cohorts compared with the programmed death ligand 1 positivity (AUC=0.52), tumor mutational burden (AUC=0.69) and microsatellite instability status (AUC=0.59) in the combined cohort. Moreover, patients with a high IO-score also exhibited a prolonged overall survival compared with patients with a low IO-score (discovery cohort: HR, 0.29; 95% CI 0.15 to 0.56; p=0.003; validation cohort: HR, 0.32; 95% CI 0.10 to 1.05; p=0.04). Taken together, our results indicated the potential of IO-score as a biomarker for immunotherapy in patients with gastrointestinal cancers.

Immunotherapy Predictive Molecular Markers in Advanced Gastroesophageal Cancer: MSI and Beyond

“…Immune checkpoint inhibitor treated patients were stratified into either high or low scoring groups based on the Youden index of a receiver operating curve (ROC) curve. By comparison of the area under the curve of the ROC curve (AUC), the RNA signature outperformed PD-L1, TMB, and MSI status [ 69 ]. In contrast, in a phase Ib/II trial of second line durvalumab with/without tremelimumab in GEC patients, a tumor RNA-based IFNγ signature was not associated with improved clinical response [ 24 ].…”

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

“…In addition, there is interest in defining the role of the number of somatic mutations (tumor mutational burden) as a biomarker for ICI [ 48 , 101 , 102 ]. Many prediction models of response to ICI consider the frequency of tumor-infiltrating lymphocytes (TILs) [ 4 , 103 , 104 , 105 , 106 ]. Moreover, integrative diagnostic approaches that combine several omics techniques have been shown to increase prediction to response to ICI therapy, including tumor-mutational burden (TMB) or neoantigen burden, to identify tumors with pro-immunogenic properties [ 102 , 103 , 104 , 107 , 108 , 109 , 110 , 111 ].…”

An Integrative Prognostic and Immune Analysis of PTPRD in Pan-Cancer

“…However, despite the remarkable performance of ICIs, long-lasting therapeutic responses differ among patients [10]. It has recently been found that high microsatellite instability (MSI-H), PD-L1 expression, tumor mutation burden (TMB), gene expression pro les (GEPs), tumor immune microenvironment (TIME), and some speci c gene mutations are associated with immunotherapy response [11][12][13]. Even those biomarkers that have been identi ed and validated have certain clinical implementation restrictions.…”