Prediction of the Immune Phenotypes of Bladder Cancer Patients for Precision Oncology
Bladder cancer (BC) is the most common urinary malignancy; however accurate diagnosis and prediction of recurrence after therapies remain elusive. This study aimed to develop a biosignature of immunotherapy-based responses using gene expression data. Publicly available BC datasets were collected, an...
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/9758656/ |
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| author | Hyuna Cho Feng Tong Sungyong You Sungyoung Jung Won Hwa Kim Jayoung Kim |
| author_facet | Hyuna Cho Feng Tong Sungyong You Sungyoung Jung Won Hwa Kim Jayoung Kim |
| author_sort | Hyuna Cho |
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| description | Bladder cancer (BC) is the most common urinary malignancy; however accurate diagnosis and prediction of recurrence after therapies remain elusive. This study aimed to develop a biosignature of immunotherapy-based responses using gene expression data. Publicly available BC datasets were collected, and machine learning (ML) approaches were applied to identify a novel biosignature to differentiate patient subgroups. Immune phenotyping of BC in the IMvigor210 dataset included three subtypes: inflamed, excluded, and desert immune. Immune phenotypes were analyzed with gene expressions using traditional but powerful classification methods such as random forests, Deep Neural Networks (DNN), Support Vector Machines (SVM) together with boosting and feature selection methods. Specifically, DNN yielded the highest area under the curve (AUC) with precision and recall (PR) curves and receiver operating characteristic (ROC) curves for each phenotype (<inline-formula><tex-math notation="LaTeX">$0.711 \pm 0.092$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$0.86 \pm 0.039$</tex-math></inline-formula>, respectively) resulting in the identification of gene expression features useful for immune phenotype classification. Our results suggest significant potential to further develop and utilize machine learning algorithms for analysis of BC and its precaution. In conclusion, the findings from this study present a novel gene expression assay that can accurately discriminate BC patients from controls. Upon further validation in independent cohorts, this gene signature could be developed into a predictive test that can support clinical evaluation and patient care. |
| format | Article |
| id | doaj-art-858d39bc761746ad9acf1ecf21e14bb8 |
| institution | Matheson Library |
| issn | 2644-1276 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
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| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-858d39bc761746ad9acf1ecf21e14bb82025-07-02T00:07:04ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762022-01-013475710.1109/OJEMB.2022.31635339758656Prediction of the Immune Phenotypes of Bladder Cancer Patients for Precision OncologyHyuna Cho0Feng Tong1https://orcid.org/0000-0002-3693-5700Sungyong You2https://orcid.org/0000-0003-3513-1783Sungyoung Jung3https://orcid.org/0000-0002-0749-0394Won Hwa Kim4Jayoung Kim5https://orcid.org/0000-0002-3683-4627Graduate School of Artificial Intelligence (GSAI), Pohang University of Science and Technology, Pohang, South KoreaDepartment of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USADepartment of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USADepartment of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USAGraduate School of Artificial Intelligence (GSAI), Pohang University of Science and Technology, Pohang, South KoreaDepartment of Surgery and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USABladder cancer (BC) is the most common urinary malignancy; however accurate diagnosis and prediction of recurrence after therapies remain elusive. This study aimed to develop a biosignature of immunotherapy-based responses using gene expression data. Publicly available BC datasets were collected, and machine learning (ML) approaches were applied to identify a novel biosignature to differentiate patient subgroups. Immune phenotyping of BC in the IMvigor210 dataset included three subtypes: inflamed, excluded, and desert immune. Immune phenotypes were analyzed with gene expressions using traditional but powerful classification methods such as random forests, Deep Neural Networks (DNN), Support Vector Machines (SVM) together with boosting and feature selection methods. Specifically, DNN yielded the highest area under the curve (AUC) with precision and recall (PR) curves and receiver operating characteristic (ROC) curves for each phenotype (<inline-formula><tex-math notation="LaTeX">$0.711 \pm 0.092$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$0.86 \pm 0.039$</tex-math></inline-formula>, respectively) resulting in the identification of gene expression features useful for immune phenotype classification. Our results suggest significant potential to further develop and utilize machine learning algorithms for analysis of BC and its precaution. In conclusion, the findings from this study present a novel gene expression assay that can accurately discriminate BC patients from controls. Upon further validation in independent cohorts, this gene signature could be developed into a predictive test that can support clinical evaluation and patient care.https://ieeexplore.ieee.org/document/9758656/Artificial algorithmbiomarkerbladder cancergene expressionimmunotherapymachine learning |
| spellingShingle | Hyuna Cho Feng Tong Sungyong You Sungyoung Jung Won Hwa Kim Jayoung Kim Prediction of the Immune Phenotypes of Bladder Cancer Patients for Precision Oncology IEEE Open Journal of Engineering in Medicine and Biology Artificial algorithm biomarker bladder cancer gene expression immunotherapy machine learning |
| title | Prediction of the Immune Phenotypes of Bladder Cancer Patients for Precision Oncology |
| title_full | Prediction of the Immune Phenotypes of Bladder Cancer Patients for Precision Oncology |
| title_fullStr | Prediction of the Immune Phenotypes of Bladder Cancer Patients for Precision Oncology |
| title_full_unstemmed | Prediction of the Immune Phenotypes of Bladder Cancer Patients for Precision Oncology |
| title_short | Prediction of the Immune Phenotypes of Bladder Cancer Patients for Precision Oncology |
| title_sort | prediction of the immune phenotypes of bladder cancer patients for precision oncology |
| topic | Artificial algorithm biomarker bladder cancer gene expression immunotherapy machine learning |
| url | https://ieeexplore.ieee.org/document/9758656/ |
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