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Jung HH, Kim JY, Cho EY, Lee JE, Kim SW, Nam SJ, Park YH, Ahn JS, Im YH. A Retrospective Exploratory Analysis for Serum Extracellular Vesicles Reveals APRIL (TNFSF13), CXCL13, and VEGF-A as Prognostic Biomarkers for Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer. Int J Mol Sci 2023; 24:15576. [PMID: 37958571 PMCID: PMC10647725 DOI: 10.3390/ijms242115576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023] Open
Abstract
Neoadjuvant chemotherapy (NAC) is widely used as a standard treatment for early-stage triple-negative breast cancer (TNBC). While patients who achieve pathologic complete response (pCR) have a highly favorable outcome, patients who do not achieve pCR have variable prognoses. It is important to identify patients who are most likely to have poor survival outcomes to identify candidates for more aggressive therapeutic approaches after NAC. Many studies have demonstrated that cytokines and growth factors packaged into extracellular vesicles (EVs) have an essential role in tumor progression and drug resistance. In this study, we examined the role of serum-derived EV-associated cytokines as prognostic biomarkers for long-term outcomes in patients who underwent anthracycline-taxane-based NAC. We isolated extracellular vesicles from the serum of 190 TNBC patients who underwent NAC between 2015 and 2018 at Samsung Medical Center. EV-associated cytokine concentrations were measured with ProcartaPlex Immune Monitoring 65-plex panels. The prognostic value of EV-associated cytokines was studied. We found that patients with high EV_APRIL, EV_CXCL13, and EV_VEGF-A levels had shorter overall survival (OS). We further evaluated the role of these selected biomarkers as prognostic factors in patients with residual disease (RD) after NAC. Even in patients with RD, high levels of EV_APRIL, EV_CXCL13, and EV_VEGF-A were correlated with poor OS. In all subgroup analyses, EV_CXCL13 overexpression was significantly associated with poor overall survival. Moreover, multivariate analysis indicated that a high level of EV_CXCL13 was an independent predictor of poor OS. Correlation analysis between biomarker levels in EVs and serum showed that EV_VEGF-A positively correlated with soluble VEGF-A but not CXCL13. An elevated level of soluble VEGF-A was also associated with poor OS. These findings suggest that EV_APRIL, EV_CXCL13, and EV_VEGF-A may be useful in identifying TNBC patients at risk of poor survival outcomes after NAC.
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Affiliation(s)
- Hae Hyun Jung
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul 06351, Republic of Korea; (H.H.J.); (J.-Y.K.); (Y.H.P.)
- Biomedical Research Institute, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Ji-Yeon Kim
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul 06351, Republic of Korea; (H.H.J.); (J.-Y.K.); (Y.H.P.)
- Biomedical Research Institute, Samsung Medical Center, Seoul 06351, Republic of Korea
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Seoul 06351, Republic of Korea;
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea; (E.Y.C.); (J.E.L.); (S.W.K.); (S.J.N.)
| | - Eun Yoon Cho
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea; (E.Y.C.); (J.E.L.); (S.W.K.); (S.J.N.)
- Department of Pathology, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Jeong Eon Lee
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea; (E.Y.C.); (J.E.L.); (S.W.K.); (S.J.N.)
- Department of Surgery, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Seok Won Kim
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea; (E.Y.C.); (J.E.L.); (S.W.K.); (S.J.N.)
- Department of Surgery, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Seok Jin Nam
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea; (E.Y.C.); (J.E.L.); (S.W.K.); (S.J.N.)
- Department of Surgery, Samsung Medical Center, Seoul 06351, Republic of Korea
| | - Yeon Hee Park
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul 06351, Republic of Korea; (H.H.J.); (J.-Y.K.); (Y.H.P.)
- Biomedical Research Institute, Samsung Medical Center, Seoul 06351, Republic of Korea
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Seoul 06351, Republic of Korea;
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea; (E.Y.C.); (J.E.L.); (S.W.K.); (S.J.N.)
| | - Jin Seok Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Seoul 06351, Republic of Korea;
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea; (E.Y.C.); (J.E.L.); (S.W.K.); (S.J.N.)
| | - Young-Hyuck Im
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul 06351, Republic of Korea; (H.H.J.); (J.-Y.K.); (Y.H.P.)
- Biomedical Research Institute, Samsung Medical Center, Seoul 06351, Republic of Korea
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Seoul 06351, Republic of Korea;
- School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea; (E.Y.C.); (J.E.L.); (S.W.K.); (S.J.N.)
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Liang J, He T, Li H, Guo X, Zhang Z. Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma. J Transl Med 2022; 20:293. [PMID: 35765031 PMCID: PMC9238034 DOI: 10.1186/s12967-022-03491-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 06/18/2022] [Indexed: 01/13/2023] Open
Abstract
Purpose The current study aimed to construct a novel cancer artificial intelligence survival analysis system for predicting the individual mortality risk curves for cervical carcinoma patients receiving different treatments. Methods Study dataset (n = 14,946) was downloaded from Surveillance Epidemiology and End Results database. Accelerated failure time algorithm, multi-task logistic regression algorithm, and Cox proportional hazard regression algorithm were used to develop prognostic models for cancer specific survival of cervical carcinoma patients. Results Multivariate Cox regression identified stage, PM, chemotherapy, Age, PT, and radiation_surgery as independent influence factors for cervical carcinoma patients. The concordance indexes of Cox model were 0.860, 0.849, and 0.848 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.881, 0.845, and 0.841 in validation dataset. The concordance indexes of accelerated failure time model were 0.861, 0.852, and 0.851 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.882, 0.847, and 0.846 in validation dataset. The concordance indexes of multi-task logistic regression model were 0.860, 0.863, and 0.861 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.880, 0.860, and 0.861 in validation dataset. Brier score indicated that these three prognostic models have good diagnostic accuracy for cervical carcinoma patients. The current research lacked independent external validation study. Conclusion The current study developed a novel cancer artificial intelligence survival analysis system to provide individual mortality risk predictive curves for cervical carcinoma patients based on three different artificial intelligence algorithms. Cancer artificial intelligence survival analysis system could provide mortality percentage at specific time points and explore the actual treatment benefits under different treatments in four stages, which could help patient determine the best individualized treatment. Cancer artificial intelligence survival analysis system was available at: https://zhangzhiqiao15.shinyapps.io/Tumor_Artificial_Intelligence_Survival_Analysis_System/. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03491-8.
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Affiliation(s)
- Jieyi Liang
- Department of Gynaecology, Shunde Hospital, Southern Medical University, Shunde, 528303, Guangdong, China
| | - Tingshan He
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, 528303, Guangdong, China
| | - Hong Li
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, 528303, Guangdong, China
| | - Xueqing Guo
- Department of Gynaecology, Shunde Hospital, Southern Medical University, Shunde, 528303, Guangdong, China
| | - Zhiqiao Zhang
- Department of Infectious Diseases, Shunde Hospital, Southern Medical University, Shunde, 528303, Guangdong, China.
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