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Wang S, Rong R, Yang DM, Fujimoto J, Bishop JA, Yan S, Cai L, Behrens C, Berry LD, Wilhelm C, Aisner D, Sholl L, Johnson BE, Kwiatkowski DJ, Wistuba II, Bunn PA, Minna J, Xiao G, Kris MG, Xie Y. Features of tumor-microenvironment images predict targeted therapy survival benefit in patients with EGFR-mutant lung cancer. J Clin Invest 2023; 133:e160330. [PMID: 36647832 PMCID: PMC9843059 DOI: 10.1172/jci160330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 11/08/2022] [Indexed: 01/18/2023] Open
Abstract
Tyrosine kinase inhibitors (TKIs) targeting epidermal growth factor receptor (EGFR) are effective for many patients with lung cancer with EGFR mutations. However, not all patients are responsive to EGFR TKIs, including even those harboring EGFR-sensitizing mutations. In this study, we quantified the cells and cellular interaction features of the tumor microenvironment (TME) using routine H&E-stained biopsy sections. These TME features were used to develop a prediction model for survival benefit from EGFR TKI therapy in patients with lung adenocarcinoma and EGFR-sensitizing mutations in the Lung Cancer Mutation Consortium 1 (LCMC1) and validated in an independent LCMC2 cohort. In the validation data set, EGFR TKI treatment prolonged survival in the predicted-to-benefit group but not in the predicted-not-to-benefit group. Among patients treated with EGFR TKIs, the predicted-to-benefit group had prolonged survival outcomes compared with the predicted not-to-benefit group. The EGFR TKI survival benefit positively correlated with tumor-tumor interaction image features and negatively correlated with tumor-stroma interaction. Moreover, the tumor-stroma interaction was associated with higher activation of the hepatocyte growth factor/MET-mediated PI3K/AKT signaling pathway and epithelial-mesenchymal transition process, supporting the hypothesis of fibroblast-involved resistance to EGFR TKI treatment.
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Affiliation(s)
- Shidan Wang
- Quantitative Biomedical Research Center, The Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Ruichen Rong
- Quantitative Biomedical Research Center, The Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Donghan M. Yang
- Quantitative Biomedical Research Center, The Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, Division of Pathology/Lab Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Justin A. Bishop
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Shirley Yan
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Ling Cai
- Quantitative Biomedical Research Center, The Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Carmen Behrens
- Department of Translational Molecular Pathology, Division of Pathology/Lab Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Lynne D. Berry
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Clare Wilhelm
- Department of Thoracic Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Dara Aisner
- Department of Pathology, University of Colorado, Denver, Colorado, USA
| | - Lynette Sholl
- Department of Pathology, Brigham and Women’s Hospital, Harvard University, Boston, Massachusetts, USA
| | - Bruce E. Johnson
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - David J. Kwiatkowski
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard University, Boston, Massachusetts, USA
| | - Ignacio I. Wistuba
- Department of Translational Molecular Pathology, Division of Pathology/Lab Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Paul A. Bunn
- Division of Medical Oncology, School of Medicine, University of Colorado, Aurora, Colorado, USA
| | - John Minna
- Hamon Center for Therapeutic Oncology Research
- Departments of Internal Medicine and Pharmacology
- Simmons Comprehensive Cancer Center, and
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, The Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Simmons Comprehensive Cancer Center, and
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Mark G. Kris
- Department of Thoracic Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Yang Xie
- Quantitative Biomedical Research Center, The Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Simmons Comprehensive Cancer Center, and
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas, USA
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Nam JG, Park S, Park CM, Jeon YK, Chung DH, Goo JM, Kim YT, Kim H. Histopathologic Basis for a Chest CT Deep Learning Survival Prediction Model in Patients with Lung Adenocarcinoma. Radiology 2022; 305:441-451. [PMID: 35787198 DOI: 10.1148/radiol.213262] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background A preoperative CT-based deep learning (DL) prediction model was proposed to estimate disease-free survival in patients with resected lung adenocarcinoma. However, the black-box nature of DL hinders interpretation of its results. Purpose To provide histopathologic evidence underpinning the DL survival prediction model and to demonstrate the feasibility of the model in identifying patients with histopathologic risk factors through unsupervised clustering and a series of regression analyses. Materials and Methods For this retrospective study, data from patients who underwent curative resection for lung adenocarcinoma without neoadjuvant therapy from January 2016 to September 2020 were collected from a tertiary care center. Seven histopathologic risk factors for the resected adenocarcinoma were documented: the aggressive adenocarcinoma subtype (cribriform, morular, solid, or micropapillary-predominant subtype); mediastinal nodal metastasis (pN2); presence of lymphatic, venous, and perineural invasion; visceral pleural invasion (VPI); and EGFR mutation status. Unsupervised clustering using 80 DL model-driven CT features was performed, and associations between the patient clusters and the histopathologic features were analyzed. Multivariable regression analyses were performed to investigate the added value of the DL model output to the semantic CT features (clinical T category and radiologic nodule type [ie, solid or subsolid]) for histopathologic associations. Results A total of 1667 patients (median age, 64 years [IQR, 57-71 years]; 975 women) were evaluated. Unsupervised patient clusters 3 and 4 were associated with all histopathologic risk factors (P < .01) except for EGFR mutation status (P = .30 for cluster 3). After multivariable adjustment, model output was associated with the aggressive adenocarcinoma subtype (odds ratio [OR], 1.03; 95% CI: 1.002, 1.05; P = .03), venous invasion (OR, 1.03; 95% CI: 1.004, 1.06; P = .02), and VPI (OR, 1.08; 95% CI: 1.06, 1.10; P < .001), independently of the semantic CT features. Conclusion The deep learning model extracted CT imaging surrogates for the histopathologic profiles of lung adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Yanagawa in this issue.
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Affiliation(s)
- Ju G Nam
- From the Department of Radiology (J.G.N., C.M.P., J.M.G., H.K.), Department of Thoracic and Cardiovascular Surgery (S.P., Y.T.K.), and Department of Pathology (Y.K.J., D.H.C.), Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Artificial Intelligence Collaborative Network, Seoul National University Hospital, Seoul, Republic of Korea (J.G.N.); Institute of Radiation Medicine (C.M.P., J.M.G.) and Institute of Medical and Biological Engineering (C.M.P.), Seoul National University Medical Research Center, Seoul, Republic of Korea; and Cancer Research Institute, Seoul National University, Seoul, Republic of Korea (Y.K.J., J.M.G., Y.T.K.)
| | - Samina Park
- From the Department of Radiology (J.G.N., C.M.P., J.M.G., H.K.), Department of Thoracic and Cardiovascular Surgery (S.P., Y.T.K.), and Department of Pathology (Y.K.J., D.H.C.), Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Artificial Intelligence Collaborative Network, Seoul National University Hospital, Seoul, Republic of Korea (J.G.N.); Institute of Radiation Medicine (C.M.P., J.M.G.) and Institute of Medical and Biological Engineering (C.M.P.), Seoul National University Medical Research Center, Seoul, Republic of Korea; and Cancer Research Institute, Seoul National University, Seoul, Republic of Korea (Y.K.J., J.M.G., Y.T.K.)
| | - Chang Min Park
- From the Department of Radiology (J.G.N., C.M.P., J.M.G., H.K.), Department of Thoracic and Cardiovascular Surgery (S.P., Y.T.K.), and Department of Pathology (Y.K.J., D.H.C.), Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Artificial Intelligence Collaborative Network, Seoul National University Hospital, Seoul, Republic of Korea (J.G.N.); Institute of Radiation Medicine (C.M.P., J.M.G.) and Institute of Medical and Biological Engineering (C.M.P.), Seoul National University Medical Research Center, Seoul, Republic of Korea; and Cancer Research Institute, Seoul National University, Seoul, Republic of Korea (Y.K.J., J.M.G., Y.T.K.)
| | - Yoon Kyung Jeon
- From the Department of Radiology (J.G.N., C.M.P., J.M.G., H.K.), Department of Thoracic and Cardiovascular Surgery (S.P., Y.T.K.), and Department of Pathology (Y.K.J., D.H.C.), Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Artificial Intelligence Collaborative Network, Seoul National University Hospital, Seoul, Republic of Korea (J.G.N.); Institute of Radiation Medicine (C.M.P., J.M.G.) and Institute of Medical and Biological Engineering (C.M.P.), Seoul National University Medical Research Center, Seoul, Republic of Korea; and Cancer Research Institute, Seoul National University, Seoul, Republic of Korea (Y.K.J., J.M.G., Y.T.K.)
| | - Doo Hyun Chung
- From the Department of Radiology (J.G.N., C.M.P., J.M.G., H.K.), Department of Thoracic and Cardiovascular Surgery (S.P., Y.T.K.), and Department of Pathology (Y.K.J., D.H.C.), Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Artificial Intelligence Collaborative Network, Seoul National University Hospital, Seoul, Republic of Korea (J.G.N.); Institute of Radiation Medicine (C.M.P., J.M.G.) and Institute of Medical and Biological Engineering (C.M.P.), Seoul National University Medical Research Center, Seoul, Republic of Korea; and Cancer Research Institute, Seoul National University, Seoul, Republic of Korea (Y.K.J., J.M.G., Y.T.K.)
| | - Jin Mo Goo
- From the Department of Radiology (J.G.N., C.M.P., J.M.G., H.K.), Department of Thoracic and Cardiovascular Surgery (S.P., Y.T.K.), and Department of Pathology (Y.K.J., D.H.C.), Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Artificial Intelligence Collaborative Network, Seoul National University Hospital, Seoul, Republic of Korea (J.G.N.); Institute of Radiation Medicine (C.M.P., J.M.G.) and Institute of Medical and Biological Engineering (C.M.P.), Seoul National University Medical Research Center, Seoul, Republic of Korea; and Cancer Research Institute, Seoul National University, Seoul, Republic of Korea (Y.K.J., J.M.G., Y.T.K.)
| | - Young Tae Kim
- From the Department of Radiology (J.G.N., C.M.P., J.M.G., H.K.), Department of Thoracic and Cardiovascular Surgery (S.P., Y.T.K.), and Department of Pathology (Y.K.J., D.H.C.), Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Artificial Intelligence Collaborative Network, Seoul National University Hospital, Seoul, Republic of Korea (J.G.N.); Institute of Radiation Medicine (C.M.P., J.M.G.) and Institute of Medical and Biological Engineering (C.M.P.), Seoul National University Medical Research Center, Seoul, Republic of Korea; and Cancer Research Institute, Seoul National University, Seoul, Republic of Korea (Y.K.J., J.M.G., Y.T.K.)
| | - Hyungjin Kim
- From the Department of Radiology (J.G.N., C.M.P., J.M.G., H.K.), Department of Thoracic and Cardiovascular Surgery (S.P., Y.T.K.), and Department of Pathology (Y.K.J., D.H.C.), Seoul National University Hospital and College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Artificial Intelligence Collaborative Network, Seoul National University Hospital, Seoul, Republic of Korea (J.G.N.); Institute of Radiation Medicine (C.M.P., J.M.G.) and Institute of Medical and Biological Engineering (C.M.P.), Seoul National University Medical Research Center, Seoul, Republic of Korea; and Cancer Research Institute, Seoul National University, Seoul, Republic of Korea (Y.K.J., J.M.G., Y.T.K.)
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Gao ZW, Liu C, Yang L, Chen HC, Yang LF, Zhang HZ, Dong K. CD73 Severed as a Potential Prognostic Marker and Promote Lung Cancer Cells Migration via Enhancing EMT Progression. Front Genet 2021; 12:728200. [PMID: 34868205 PMCID: PMC8635862 DOI: 10.3389/fgene.2021.728200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 10/25/2021] [Indexed: 12/25/2022] Open
Abstract
To investigate the expression levels and prognostic value of CD73 in lung cancer. And moreover, to identify the effect and potential mechanism of CD73 on lung cancer cells proliferation and migration. CD73 expression levels in lung cancer were analyzed base on GEPIA2 and GEO database. GEPIA2 and Kaplan-Meier Plotter (KM Plotter) was used to analyzed the correlation between CD73 expression and prognosis. GEO dataset were analyzed via GEO2R. CD73 overexpression cell model was construction via recombinant lentivirus transfection into A549 and NCI-H520 cells. CCK8 assay were used to investigate cells proliferation. Migration and invasion ability were evaluated by scratch and transwell methods. Base on GEPIA2, GSE32683, GSE116959 and GSE37745 dataset, we found that CD73 expression were significant higher in tumor tissues of lung adenocarcinoma (LUAD) compared with that in non-tumor normal tissues and in lung squamous cell carcinoma (LUSC), while there were no significant difference of CD73 expression between LUSC and normal control tissues. Interestingly, a high CD73 level predict poor overall survival (OS) of LUSC. However, GEPIA2 and KM plotter showed the opposite conclusion of prognostic value of CD73 in LUAD. By using cell experiments, we found that CD73 overexpression promoted proliferation and migration of LUAD A549 cells. However, there was no significant effect of CD73 overexpression on LUSC NCI-H520 cells. Furthermore, CD73 overexpression facilitates epithelial to mesenchymal transition (EMT) progression of A549 cells. In conclusion, our results indicated that CD73 expression were increased in LUAD and might be an poor prognostic marker for LUSC patients. CD73 play an important role in LUAD cells proliferation and migration. These data allowed to support CD73 as a therapeutic target for LUAD.
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Affiliation(s)
- Zhao-Wei Gao
- Department of Clinical Laboratory, The Second Affiliated Hospital, Air Force Medical University, Xi'an, China
| | - Chong Liu
- Department of Clinical Laboratory, The Second Affiliated Hospital, Air Force Medical University, Xi'an, China
| | - Lan Yang
- Department of Clinical Laboratory, The Second Affiliated Hospital, Air Force Medical University, Xi'an, China
| | - Hao-Chuan Chen
- Department of Clinical Laboratory, The Second Affiliated Hospital, Air Force Medical University, Xi'an, China
| | - Long-Fei Yang
- Department of Clinical Laboratory, The Second Affiliated Hospital, Air Force Medical University, Xi'an, China
| | - Hui-Zhong Zhang
- Department of Clinical Laboratory, The Second Affiliated Hospital, Air Force Medical University, Xi'an, China
| | - Ke Dong
- Department of Clinical Laboratory, The Second Affiliated Hospital, Air Force Medical University, Xi'an, China
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