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Liang JH, Wu YF, Shen HR, Li Y, Liang JH, Gao R, Hua W, Shang CY, Du KX, Xing TY, Zhang XY, Wang CX, Zhu LQ, Shao YW, Li JY, Wu JZ, Yin H, Wang L, Xu W. Clinical implications of CSF-ctDNA positivity in newly diagnosed diffuse large B cell lymphoma. Leukemia 2024; 38:1541-1552. [PMID: 38750139 DOI: 10.1038/s41375-024-02279-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 05/01/2024] [Accepted: 05/03/2024] [Indexed: 07/03/2024]
Abstract
The clinical implications of CSF-ctDNA positivity in newly diagnosed diffuse large B cell lymphoma (ND-DLBCL) remains largely unexplored. One hundred ND-DLBCL patients were consecutively enrolled as training cohort and another 26 ND-DLBCL patients were prospectively enrolled in validation cohort. CSF-ctDNA positivity (CSF(+)) was identified in 25 patients (25.0%) in the training cohort and 7 patients (26.9%) in the validation cohort, extremely higher than CNS involvement rate detected by conventional methods. Patients with mutations of CARD11, JAK2, ID3, and PLCG2 were more predominant with CSF(+) while FAT4 mutations were negatively correlated with CSF(+). The downregulation of PI3K-AKT signaling, focal adhesion, actin cytoskeleton, and tight junction pathways were enriched in CSF(+) ND-DLBCL. Furthermore, pretreatment CSF(+) was significantly associated with poor outcomes. Three risk factors, including high CSF protein level, high plasma ctDNA burden, and involvement of high-risk sites were used to predict the risk of CSF(+) in ND-DLBCL. The sensitivity and specificity of pretreatment CSF-ctDNA to predict CNS relapse were 100% and 77.3%. Taken together, we firstly present the prevalence and the genomic and transcriptomic landscape for CSF-ctDNA(+) DLBCL and highlight the importance of CSF-ctDNA as a noninvasive biomarker in detecting and monitoring of CSF infiltration and predicting CNS relapse in DLBCL.
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Affiliation(s)
- Jin-Hua Liang
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China
| | - Yi-Fan Wu
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China
| | - Hao-Rui Shen
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China
| | - Yue Li
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China
| | - Jun-Heng Liang
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, China
| | - Rui Gao
- Department of Endocrinology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford, OX3 7LE, UK
| | - Wei Hua
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China
| | - Chun-Yu Shang
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China
| | - Kai-Xin Du
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China
| | - Tong-Yao Xing
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China
| | - Xin-Yu Zhang
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China
| | - Chen-Xuan Wang
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, China
| | - Liu-Qing Zhu
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, China
| | - Yang W Shao
- Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu, China
| | - Jian-Yong Li
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China
| | - Jia-Zhu Wu
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China
| | - Hua Yin
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China
| | - Li Wang
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China
| | - Wei Xu
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, 210029, China.
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, 210029, China.
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210029, China.
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Mu K, Zhang J, Gu Y, Huang G. Development and validation of a nomogram for predicting cardiovascular mortality risk for diffuse large B-cell lymphoma in children, adolescents, and adults. Front Pediatr 2024; 12:1346006. [PMID: 38384660 PMCID: PMC10879433 DOI: 10.3389/fped.2024.1346006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
Objective This study aimed to construct and validate a nomogram for predicting cardiovascular mortality (CVM) for child, adolescent, and adult patients with diffuse large B-cell lymphoma (DLBCL). Materials and methods Patients with only one primary tumor of DLBCL first diagnosed between 2000 and 2019 in the SEER database were extracted. We used the cumulative incidence function (CIF) to evaluate the cumulative rate of CVM. The outcome of interest was CVM, which was analyzed using a competing risk model, accounting for death due to other causes. The total database was randomly divided into a training cohort and an internal validation cohort at a ratio of 7:3. Adjustments were for demographics, tumor characteristics, and treatment modalities. Nomograms were constructed according to these risk factors to predict CVM risk at 5, 10, and 15 years. Validation included receiver operating characteristic (ROC) curves, time-dependent ROC, C-index, calibration curves, and decision curve analysis. Results One hundred four thousand six hundred six patients following initial diagnosis of DLBCL were included (58.3% male, median age 64 years, range 0-80, White 83.98%). Among them, 5.02% died of CVM, with a median follow-up time of 61 (31-98) months. Nomograms based on the seven risk factors (age at diagnosis, gender, race, tumor grade, Ann Arbor stage, radiation, chemotherapy) with hazard ratios ranging from 0.19-1.17 showed excellent discrimination, and calibration plots demonstrated satisfactory prediction. The 5-, 10-, and 15-year AUC and C-index of CVM in the training set were 0.716 (0.714-0.718), 0.713 (0.711-0.715), 0.706 (0.704-0.708), 0.731, 0.727, and 0.719; the corresponding figures for the validation set were 0.705 (0.688-0.722), 0.704 (0.689-0.718), 0.707 (0.693-0.722), 0.698, 0.698, and 0.699. Decision curve analysis revealed a clinically beneficial net benefit. Conclusions We first built the nomogram model for DLBCL patients with satisfactory prediction and excellent discrimination, which might play an essential role in helping physicians enact better treatment strategies at the time of initial diagnosis.
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Affiliation(s)
- Kai Mu
- Pediatric Heart Center, Children’s Hospital of Fudan University, Shanghai, China
- Department of Pediatric, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Jing Zhang
- Department of Pediatric, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Yan Gu
- Department of Pediatric, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Guoying Huang
- Pediatric Heart Center, Children’s Hospital of Fudan University, Shanghai, China
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Perry TA, Masand N, Vrzalikova K, Pugh M, Wei W, Hollows R, Bouchalova K, Nohtani M, Fennell E, Bouchal J, Kearns P, Murray PG. The Oncogenic Lipid Sphingosine-1-Phosphate Impedes the Phagocytosis of Tumor Cells by M1 Macrophages in Diffuse Large B Cell Lymphoma. Cancers (Basel) 2024; 16:574. [PMID: 38339325 PMCID: PMC10854869 DOI: 10.3390/cancers16030574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/21/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND A total of 30-40% of diffuse large B cell lymphoma (DLBCL) patients will either not respond to the standard therapy or their disease will recur. The first-line treatment for DLBCL is rituximab and combination chemotherapy. This treatment involves the chemotherapy-induced recruitment of tumor-associated macrophages that recognize and kill rituximab-opsonized DLBCL cells. However, we lack insights into the factors responsible for the recruitment and functionality of macrophages in DLBCL tumors. METHODS We have studied the effects of the immunomodulatory lipid sphingosine-1-phosphate (S1P) on macrophage activity in DLBCL, both in vitro and in animal models. RESULTS We show that tumor-derived S1P mediates the chemoattraction of both monocytes and macrophages in vitro and in animal models, an effect that is dependent upon the S1P receptor S1PR1. However, S1P inhibited M1 macrophage-mediated phagocytosis of DLBCL tumor cells opsonized with the CD20 monoclonal antibodies rituximab and ofatumumab, an effect that could be reversed by an S1PR1 inhibitor. CONCLUSIONS Our data show that S1P signaling can modulate macrophage recruitment and tumor cell killing by anti-CD20 monoclonal antibodies in DLBCL. The administration of S1PR1 inhibitors could enhance the phagocytosis of tumor cells and improve outcomes for patients.
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Affiliation(s)
- Tracey A. Perry
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK; (N.M.); (W.W.); (R.H.); (P.K.)
| | - Navta Masand
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK; (N.M.); (W.W.); (R.H.); (P.K.)
| | - Katerina Vrzalikova
- Institute of Immunology & Immunotherapy, University of Birmingham, Birmingham B15 2TT, UK; (K.V.); (M.P.)
- Royal College of Surgeons in Ireland Medical University of Bahrain, Manama P.O. Box 15503, Bahrain
| | - Matthew Pugh
- Institute of Immunology & Immunotherapy, University of Birmingham, Birmingham B15 2TT, UK; (K.V.); (M.P.)
| | - Wenbin Wei
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK; (N.M.); (W.W.); (R.H.); (P.K.)
- The Palatine Centre, Durham University, Durham DH1 3LE, UK
| | - Robert Hollows
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK; (N.M.); (W.W.); (R.H.); (P.K.)
| | - Katerina Bouchalova
- Department of Pediatrics, Faculty of Medicine and Dentistry, Palacky University and University Hospital Olomouc, 77900 Olomouc, Czech Republic;
| | - Mahdi Nohtani
- Limerick Digital Cancer Research Centre, Health Research Institute and Bernal Institute and School of Medicine, University of Limerick, Limerick V94 T9PX, Ireland; (M.N.); (E.F.)
| | - Eanna Fennell
- Limerick Digital Cancer Research Centre, Health Research Institute and Bernal Institute and School of Medicine, University of Limerick, Limerick V94 T9PX, Ireland; (M.N.); (E.F.)
| | - Jan Bouchal
- Department of Clinical and Molecular Pathology, Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University and University Hospital Olomouc, 77900 Olomouc, Czech Republic;
| | - Pamela Kearns
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK; (N.M.); (W.W.); (R.H.); (P.K.)
- National Institute for Health Research (NIHR), Birmingham Biomedical Research Centre, University of Birmingham, Birmingham B15 2TT, UK
| | - Paul G. Murray
- Institute of Immunology & Immunotherapy, University of Birmingham, Birmingham B15 2TT, UK; (K.V.); (M.P.)
- Royal College of Surgeons in Ireland Medical University of Bahrain, Manama P.O. Box 15503, Bahrain
- Limerick Digital Cancer Research Centre, Health Research Institute and Bernal Institute and School of Medicine, University of Limerick, Limerick V94 T9PX, Ireland; (M.N.); (E.F.)
- Department of Clinical and Molecular Pathology, Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University and University Hospital Olomouc, 77900 Olomouc, Czech Republic;
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Oluwole OO, Patel AR, Vadgama S, Smith NJ, Blissett R, Feng C, Dickinson M, Johnston PB, Perales MA. An updated cost-effectiveness analysis of axicabtagene ciloleucel in second-line large B-cell lymphoma patients in the United States. J Med Econ 2024; 27:77-83. [PMID: 38053517 PMCID: PMC11254511 DOI: 10.1080/13696998.2023.2290832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 11/30/2023] [Indexed: 12/07/2023]
Abstract
AIMS This economic evaluation of axicabtagene ciloleucel (axi-cel) versus previous standard of care (SOC; salvage chemotherapy followed by high-dose therapy with autologous stem cell rescue) in the second line (2L) large B-cell lymphoma population is an update of previous economic models that contained immature survival data. METHODS This analysis is based on primary overall survival (OS) ZUMA-7 clinical trial data (median follow-up of 47.2 months), from a United States (US) payer perspective, with a model time horizon of 50 years. Mixture cure models were used to extrapolate updated survival data; subsequent treatment data and costs were updated. Patients who remained in the event-free survival state by 5 years were assumed to have achieved long-term remission and not require subsequent treatment. RESULTS Substantial survival and quality of life benefits were observed despite 57% of patients in the SOC arm receiving subsequent cellular therapy: median model-projected (ZUMA-7 trial Kaplan-Meier estimated) OS was 78 months (median not reached) for axi-cel versus 25 months (31 months) for SOC, resulting in incremental quality-adjusted life year (QALY) difference of 1.63 in favor of axi-cel. Incrementally higher subsequent treatment costs were observed in the SOC arm due to substantial crossover to cellular therapies, thus, when considering the generally accepted willingness to pay threshold of $150,000 per QALY in the US, axi-cel was cost-effective with an incremental cost-effectiveness ratio of $98,040 per QALY. CONCLUSIONS Results remained consistent across a wide range of sensitivity and scenario analysis, including a crossover adjusted analysis, suggesting that the mature OS data has significantly reduced the uncertainty of axi-cel's cost-effectiveness in the 2L setting in the US. Deferring treatment with CAR T therapies after attempting a path to transplant may result in excess mortality, lower quality of life and would be an inefficient use of resources relative to 2L axi-cel.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Miguel-Angel Perales
- Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
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