Practical prediction model of the clinical response to programmed death-ligand 1 inhibitors in advanced gastric cancer

Abstract

The identification of predictive biomarkers or models is necessary for the selection of patients who might benefit the most from immunotherapy. Seven histological features (signet ring cell [SRC], fibrous stroma, myxoid stroma, tumor-infiltrating lymphocytes [TILs], necrosis, tertiary lymphoid follicles, and ulceration) detected in surgically resected tissues (N = 44) were used to train a model. The presence of SRC became an optimal decision parameter for pathology alone (AUC = 0.78). Analysis of differentially expressed genes (DEGs) for the prediction of genomic markers showed that C-X-C motif chemokine ligand 11 (CXCL11) was high in responders (P < 0.001). Immunohistochemistry (IHC) was performed to verify its potential as a biomarker. IHC revealed that the expression of CXCL11 was associated with responsiveness (P = 0.003). The response prediction model was trained by integrating the results of the analysis of pathological factors and RNA sequencing (RNA-seq). When trained with the C5.0 decision tree model, the categorical level of the expression of CXCL11, a single variable, was shown to be the best model (AUC = 0.812). The AUC of the model trained with the random forest was 0.944. Survival analysis revealed that the C5.0-trained model (log-rank P = 0.01 for progression-free survival [PFS]; log-rank P = 0.012 for overall survival [OS]) and the random forest-trained model (log-rank P < 0.001 for PFS; log-rank P = 0.001 for OS) predicted prognosis more accurately than the PD-L1 test (log-rank P = 0.031 for PFS; log-rank P = 0.107 for OS).

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

“…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].…”

“…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 Immune-related Gene Signature of Predicting Lymph Node Metastasis and Responses to Immune Checkpoint Inhibitor Treatment in Gastric Cancer Patients

“…Next, other four datasets, a metastatic GC dataset, a IMvigor210 dataset, a bladder cancer dataset and a melanomas dataset were used to assess the e cacy of the LGSGC predicting response of cancer patients to the PD-1 inhibition [21][22][23][24][25]. Based on the standardized expression of all genes, tumor immune dysfunction and exclusion (TIDE) scores were gained by visiting to the Dana Farber Cancer Institute & Harvard University(HTTP://tide.dfci.harvard.edu/).…”

Evolution of the Tumor Microenvironment toward Immune-Suppressive Seclusion during Brain Metastasis of Breast Cancer: Implications for Targeted Therapy

“…Immune checkpoint inhibitors (ICIs), such as anti-programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) antibodies, have revolutionized cancer therapy [ 6 ]. Treatment with ICIs is associated with improved response rates in many malignancies, including melanoma, non-small cell lung cancer (NSCLC), head-and-neck squamous cell cancer, advanced gastric cancer, and renal cell carcinoma [ 7 , 8 ]. Unfortunately, BC is less amenable to treatment with ICIs due to its inherently low immunogenicity [ 9 ].…”