Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer
Background Only a subset of patients with gastric cancer experience long-term benefits from immune checkpoint inhibitors (ICIs). Currently, there is a deficiency in precise predictive biomarkers for ICI efficacy. The aim of this study was to develop and validate a pathomics-driven ensemble model for...
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BMJ Publishing Group
2024-05-01
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| Series: | Journal for ImmunoTherapy of Cancer |
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| author | Jing Wang Zhe Li Wei Wang Md Tauhidul Islam Xiaoyan Wang Zhen Han Zihan Li Guoxin Li Yuming Jiang Taojun Zhang Wenjun Xiong Zepang Sun Lequan Yu Zhicheng Zhang Xianqi Yang Shengtian Sang Alyssa A Guo |
| author_facet | Jing Wang Zhe Li Wei Wang Md Tauhidul Islam Xiaoyan Wang Zhen Han Zihan Li Guoxin Li Yuming Jiang Taojun Zhang Wenjun Xiong Zepang Sun Lequan Yu Zhicheng Zhang Xianqi Yang Shengtian Sang Alyssa A Guo |
| author_sort | Jing Wang |
| collection | DOAJ |
| description | Background Only a subset of patients with gastric cancer experience long-term benefits from immune checkpoint inhibitors (ICIs). Currently, there is a deficiency in precise predictive biomarkers for ICI efficacy. The aim of this study was to develop and validate a pathomics-driven ensemble model for predicting the response to ICIs in gastric cancer, using H&E-stained whole slide images (WSI).Methods This multicenter study retrospectively collected and analyzed H&E-stained WSIs and clinical data from 584 patients with gastric cancer. An ensemble model, integrating four classifiers: least absolute shrinkage and selection operator, k-nearest neighbors, decision trees, and random forests, was developed and validated using pathomics features, with the objective of predicting the therapeutic efficacy of immune checkpoint inhibition. Model performance was evaluated using metrics including the area under the curve (AUC), sensitivity, and specificity. Additionally, SHAP (SHapley Additive exPlanations) analysis was used to explain the model’s predicted values as the sum of the attribution values for each input feature. Pathogenomics analysis was employed to explain the molecular mechanisms underlying the model’s predictions.Results Our pathomics-driven ensemble model effectively stratified the response to ICIs in training cohort (AUC 0.985 (95% CI 0.971 to 0.999)), which was further validated in internal validation cohort (AUC 0.921 (95% CI 0.839 to 0.999)), as well as in external validation cohort 1 (AUC 0.914 (95% CI 0.837 to 0.990)), and external validation cohort 2 (0.927 (95% CI 0.802 to 0.999)). The univariate Cox regression analysis revealed that the prediction signature of pathomics-driven ensemble model was a prognostic factor for progression-free survival in patients with gastric cancer who underwent immunotherapy (p<0.001, HR 0.35 (95% CI 0.24 to 0.50)), and remained an independent predictor after multivariable Cox regression adjusted for clinicopathological variables, (including sex, age, carcinoembryonic antigen, carbohydrate antigen 19-9, therapy regime, line of therapy, differentiation, location and programmed death ligand 1 (PD-L1) expression in all patients (p<0.001, HR 0.34 (95% CI 0.24 to 0.50)). Pathogenomics analysis suggested that the ensemble model is driven by molecular-level immune, cancer, metabolism-related pathways, and was correlated with the immune-related characteristics, including immune score, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data score, and tumor purity.Conclusions Our pathomics-driven ensemble model exhibited high accuracy and robustness in predicting the response to ICIs using WSIs. Therefore, it could serve as a novel and valuable tool to facilitate precision immunotherapy. |
| format | Article |
| id | doaj-art-fbc3fe4ae1334e78b508e20a0570d965 |
| institution | Kabale University |
| issn | 2051-1426 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | BMJ Publishing Group |
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| series | Journal for ImmunoTherapy of Cancer |
| spelling | doaj-art-fbc3fe4ae1334e78b508e20a0570d9652025-02-04T01:05:09ZengBMJ Publishing GroupJournal for ImmunoTherapy of Cancer2051-14262024-05-0112510.1136/jitc-2024-008927Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancerJing Wang0Zhe Li1Wei Wang2Md Tauhidul Islam3Xiaoyan Wang4Zhen Han5Zihan Li6Guoxin Li7Yuming Jiang8Taojun Zhang9Wenjun Xiong10Zepang Sun11Lequan Yu12Zhicheng Zhang13Xianqi Yang14Shengtian Sang15Alyssa A Guo16Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China1 China-Japan Friendship Hospital, Beijing, Beijing, ChinaVaccinex, Inc., Rochester, NY, USADepartment of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USADepartment of Gastroenterology, Central South University Third Xiangya Hospital, Changsha, Hunan, ChinaDepartment of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine,Southern Medical University, Guangzhou, Guangdong, ChinaDepartment of Bioengineering, University of Washington, Seattle, Washington, USA6 Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, ChinaDepartment of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USADepartment of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine,Southern Medical University, Guangzhou, Guangdong, ChinaDepartment of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, ChinaDepartment of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine,Southern Medical University, Guangzhou, Guangdong, ChinaDepartment of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong KongDepartment of Gastroenterology, The First Hospital of Jilin University, Changchun, Jilin, ChinaDepartment of Gastric Surgery, and State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, ChinaDepartment of Radiology, Stanford University School of Medicine, Stanford, California, USADepartment of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USABackground Only a subset of patients with gastric cancer experience long-term benefits from immune checkpoint inhibitors (ICIs). Currently, there is a deficiency in precise predictive biomarkers for ICI efficacy. The aim of this study was to develop and validate a pathomics-driven ensemble model for predicting the response to ICIs in gastric cancer, using H&E-stained whole slide images (WSI).Methods This multicenter study retrospectively collected and analyzed H&E-stained WSIs and clinical data from 584 patients with gastric cancer. An ensemble model, integrating four classifiers: least absolute shrinkage and selection operator, k-nearest neighbors, decision trees, and random forests, was developed and validated using pathomics features, with the objective of predicting the therapeutic efficacy of immune checkpoint inhibition. Model performance was evaluated using metrics including the area under the curve (AUC), sensitivity, and specificity. Additionally, SHAP (SHapley Additive exPlanations) analysis was used to explain the model’s predicted values as the sum of the attribution values for each input feature. Pathogenomics analysis was employed to explain the molecular mechanisms underlying the model’s predictions.Results Our pathomics-driven ensemble model effectively stratified the response to ICIs in training cohort (AUC 0.985 (95% CI 0.971 to 0.999)), which was further validated in internal validation cohort (AUC 0.921 (95% CI 0.839 to 0.999)), as well as in external validation cohort 1 (AUC 0.914 (95% CI 0.837 to 0.990)), and external validation cohort 2 (0.927 (95% CI 0.802 to 0.999)). The univariate Cox regression analysis revealed that the prediction signature of pathomics-driven ensemble model was a prognostic factor for progression-free survival in patients with gastric cancer who underwent immunotherapy (p<0.001, HR 0.35 (95% CI 0.24 to 0.50)), and remained an independent predictor after multivariable Cox regression adjusted for clinicopathological variables, (including sex, age, carcinoembryonic antigen, carbohydrate antigen 19-9, therapy regime, line of therapy, differentiation, location and programmed death ligand 1 (PD-L1) expression in all patients (p<0.001, HR 0.34 (95% CI 0.24 to 0.50)). Pathogenomics analysis suggested that the ensemble model is driven by molecular-level immune, cancer, metabolism-related pathways, and was correlated with the immune-related characteristics, including immune score, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data score, and tumor purity.Conclusions Our pathomics-driven ensemble model exhibited high accuracy and robustness in predicting the response to ICIs using WSIs. Therefore, it could serve as a novel and valuable tool to facilitate precision immunotherapy.https://jitc.bmj.com/content/12/5/e008927.full |
| spellingShingle | Jing Wang Zhe Li Wei Wang Md Tauhidul Islam Xiaoyan Wang Zhen Han Zihan Li Guoxin Li Yuming Jiang Taojun Zhang Wenjun Xiong Zepang Sun Lequan Yu Zhicheng Zhang Xianqi Yang Shengtian Sang Alyssa A Guo Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer Journal for ImmunoTherapy of Cancer |
| title | Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer |
| title_full | Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer |
| title_fullStr | Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer |
| title_full_unstemmed | Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer |
| title_short | Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer |
| title_sort | development and interpretation of a pathomics driven ensemble model for predicting the response to immunotherapy in gastric cancer |
| url | https://jitc.bmj.com/content/12/5/e008927.full |
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