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

Noh, Myung-Giun; Yoon, Youngmin; Kim, Gihyeon; Kim, Hyun; Lee, Eulgi; Kim, Yeongmin; Park, Changho; Lee, Kyung-Hwa; Park, Hansoo

doi:10.1038/s12276-021-00559-1

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

Experimental & molecular medicine : EMM, v.53 no.2, 2021년, pp.223 - 234

Noh, Myung-Giun (Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), Cheomdangwagi-ro 123, Buk-gu, Gwangju Korea) , Yoon, Youngmin (Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), Cheomdangwagi-ro 123, Buk-gu, Gwangju Korea) , Kim, Gihyeon (Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), Cheomdangwagi-ro 123, Buk-gu, Gwangju Korea) , Kim, Hyun (Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), Cheomdangwagi-ro 123, Buk-gu, Gwangju Korea) , Lee, Eulgi (Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), Cheomdangwagi-ro 123, Buk-gu, Gwangju Korea) , Kim, Yeongmin (Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology (GIST), Cheomdangwagi-ro 123, Buk-gu, Gwangju Korea) , Park, Changho (Genome and Company, Pangyo-ro 253, Bundang-gu. Seoungnam-si, Gyeonggi-do Korea) , Lee, Kyung-Hwa (Department of Pathology, Chonna) , Park, Hansoo

Abstract ▼ AI-Helper

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).Gastric cancer: Promising prediction model built on biomarker dataA prediction model that identifies patients with gastric cancer who are likely to respond well to immunotherapy has been developed by researchers in South Korea. Hansoo Park at the Gwangju Institute of Science and Technology and co-workers identified several biomarkers in gastric cancer tissues that were associated with how well patients may respond to immunotherapy treatment. They found that patients with malignant cells known as signet ring cells were least likely to respond well to immune checkpoint inhibitor drugs. Conversely, high expression levels of a gene called CXCL11 was associated with a strong positive response to the drugs. The researchers used these and other biomarker data to build a model for selecting appropriate candidates for immunotherapy. Further research will refine this initial biomarker list for gastric cancer and help improve the model.

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