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McLaughlin N, Wang Y, Witzig T, Villasboas J, Habermann T, Inwards D, Bennani N, Thanarajasingam G, Nowakowski G, Porrata L, Thompson C, Micallef I, Johnston P, Ansell S, Paludo J. Central nervous system involvement by mantle cell lymphoma. Leuk Lymphoma 2023; 64:371-377. [PMID: 36416595 DOI: 10.1080/10428194.2022.2148211] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Involvement of the central nervous system (CNS) is a rare complication of mantle cell lymphoma (MCL) with limited treatment options. We report the outcomes of 36 patients with CNS involvement compared to 72 matched control MCL patients without CNS involvement. Four patients (11%) with CNS MCL were diagnosed with CNS involvement at time of MCL diagnosis. Median OS from MCL diagnosis was 50.3 months (95% CI: 22.8-79.6) for the CNS MCL group compared to 97.1 months (95% CI: 82.8-NR; p= <0.001) for the control group. Median OS from CNS involvement was 4.7 months (95% CI: 2.3-6.7). CNS involvement by MCL has dismal outcomes as evident by a short median OS and PFS after CNS involvement. Advanced stage, blastoid variant, elevated LDH, and elevated Ki67 at MCL diagnosis were features more commonly seen in the CNS MCL cohort.
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Affiliation(s)
| | - Yucai Wang
- Division of Hematology, Mayo Clinic, Rochester, MN, USA
| | - Thomas Witzig
- Division of Hematology, Mayo Clinic, Rochester, MN, USA
| | | | | | - David Inwards
- Division of Hematology, Mayo Clinic, Rochester, MN, USA
| | - Nora Bennani
- Division of Hematology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Luis Porrata
- Division of Hematology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | - Jonas Paludo
- Division of Hematology, Mayo Clinic, Rochester, MN, USA
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Yang ZZ, Kim HJ, Wu H, Tang X, Krull J, Mondello P, Villasboas J, Novak A, Ansell S. Abstract 2529: T-cell phenotype and differentiation vary in the tumor microenvironment of follicular lymphoma and are associated with patient outcome. Cancer Res 2022. [DOI: 10.1158/1538-7445.am2022-2529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Previous studies have found that the prevalence of subtypes of T cells may be associated with patient outcomes, but a comprehensive analysis of the immune profile in the TM of FL has not been done. In the present study, we identified groups of FL patients with discretely unique TMEs and determined whether the T-cell phenotypes in the TME differed among groups. Using a cohort of 82 FL patients with biopsy specimens collected before treatment, we defined the type of TME based on the content of major lineages (T, B, monocytes/macrophages and NK cells) determined by CyTOF analysis. Hierarchical clustering of this cohort stratified patients into 4 groups with different TME: group 1 (G1) included patients with a high percentage of monocyte/macrophages/NK cells; patients from G2 and G3 were enriched for intratumoral T and B cells, respectively. Patients with intermediate numbers of T and B cells were included in G4. CITRUS analysis revealed that T-cell clusters with phenotypes expressing KLRG1, CD57, PD-1dim and rich in TEMRA cells were significantly more abundant in G1 when compared to G2, G3 or G4. In contrast, T-cell clusters with phenotypes containing CD127, CD45RA, CCR7 or PD-1high cells were significantly less abundant in G1 when compared to other groups. When compared to G3 or G4, 2 classes of T cell clusters, all from CD4+ T cells, were significantly more (CD127+KLRG1-) or less (CD57+PD-1high) abundant in G2, respectively. Clusters with a phenotype rich in short-lived effector cells (SLEC) (from CD8+) were upregulated in G3 when compared to G4. These results suggest that patient groups with distinct TME exhibited variable T-cell phenotypes. To determine the role of T-cell differentiation in predicting patient outcome in FL, we identified T-cell subsets using tSNE plots based on the expression of T-cell maturation markers. We identified 18 subsets (S1-S18) of T cells (11 from CD4, 6 from CD8 and 1 from CD4-CD8-) in each sample. Four subsets - S4 (CD4+TN), S5 (CD4+TCM), S7 (CD8+TN) and S10 (CD8+MEPCs) - were considered to be naïve cells or cells in the early stages of differentiation. Four additional subsets - S8 (CD57+TFH), S9 (CD4+TEXH), S13 (CD8+SLECs) and S17 (CD4+PD-1+Treg) - were memory cells with expression of surface markers indicting late-stage differentiation. We found that the 4 subsets (S4: p=0.08, S5: p=0.01, S7: p=0.04 and S10: p<0.01) with an early-stage phenotype were associated with a favorable clinical outcome. In contrast, subsets (S8: p=0.02, S9: p=0.04, S13: p=0.07 and S17: p=0.06) with a late-stage differentiation phenotype had an unfavorable survival. Supporting this finding, we observed that increased numbers of CD45RA+ T cells correlated with a favorable survival. These results indicate that the differentiation stage may determine the role of T cells in predicting patient outcome in FL.
Citation Format: Zhi-Zhang Yang, Hyo Jin Kim, Hongyan Wu, Xinyi Tang, Jordan Krull, Patrizia Mondello, Jose Villasboas, Anne Novak, Stephen Ansell. T-cell phenotype and differentiation vary in the tumor microenvironment of follicular lymphoma and are associated with patient outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2529.
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Affiliation(s)
| | | | - Hongyan Wu
- 2China Three Gorges University, Yichang, China
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Yang ZZ, Kim HJ, Wu H, Tang X, Krull J, Mondello P, Villasboas J, Novak A, Ansell S. T-cell phenotype varies in distinct tumor microenvironments and CD57+ TFH cells are associated with disease progression and inferior survival in follicular lymphoma. The Journal of Immunology 2022. [DOI: 10.4049/jimmunol.208.supp.177.05] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Abstract
The tumor microenvironment (TME) plays a crucial role in mediating the tumor immune response, thereby affecting patient outcomes in follicular lymphoma (FL). Using CyTOF, we analyzed a cohort of 82 FL patients with biopsy specimens collected before the first treatment regimen was administered. Our results showed that there was a difference in the T-cell phenotype in patients with various TMEs. Patients with TMEs rich in monocyte/macrophages/NK cells had more CD8+ cells and tended to have fewer TN, but more TEMRA and TEXH cells. Patients enriched with T cells exhibited a T-cell phenotype skewed toward to TCM and patients with B cell dominance tended to have significant higher number of TFH cells. T-cell phenotypes with more terminally differentiated, senescent and exhausted cells were highly represented in patients with less T cells when compared to patients with more T cells. We observed that CD57+TFH cells was significantly more abundant in patients who had disease progression than patients who had complete response to therapy, suggesting a role for CD57+ TFH cells in promoting malignant cell growth in FL. Consistent with this finding, we observed that increased numbers of CD57+ TFH cells correlated with an inferior survival in FL. Using CITE-seq technology, we found that CD57+ TFH cells exhibited a substantially different transcriptome from CD57− TFH cells. Genes that were differentially upregulated in CD57+ TFH cells when compared to CD57− TFH cells included genes involved in cell survival, compromised inflammatory response, and metabolic activation. Taken together, our results indicate different tumor microenvironments among patient groups that is associated with variable T-cell phenotypes.
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Zanwar S, Abeykoon JP, Durot E, King R, Perez Burbano GE, Kumar S, Gertz MA, Quinquenel A, Delmer A, Gonsalves W, Cornillet‐Lefebvre P, He R, Warsame R, Buadi FK, Novak AJ, Greipp PT, Inwards D, Habermann TM, Micallef I, Go R, Muchtar E, Kourelis T, Dispenzieri A, Lacy MQ, Dingli D, Nowakowski G, Thompson CA, Johnston P, Thanarajasingam G, Bennani NN, Witzig TE, Villasboas J, Leung N, Lin Y, Kyle RA, Rajkumar SV, Ansell SM, Le‐Rademacher JG, Kapoor P. Impact of MYD88 L265P mutation status on histological transformation of Waldenström Macroglobulinemia. Am J Hematol 2020; 95:274-281. [PMID: 31814157 DOI: 10.1002/ajh.25697] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Revised: 12/01/2019] [Accepted: 12/04/2019] [Indexed: 12/28/2022]
Abstract
Histological transformation in Waldenström macroglobulinemia (WM) is an uncommon complication, with limited data, particularly regarding the impact of MYD88 L265P mutation on transformation. We examined risk factors and outcomes associated with transformation in WM, highlighting the role of MYD88 L265P mutation. Patients with WM seen at Mayo Clinic, Rochester, USA and University Hospital of Reims, France, between 01/01/1996 and December 31, 2017 were included; 50 (4.3%) of 1147 patients transformed to a high-grade lymphoma, with median time-to-transformation of 4.5 (range 0-21) years in the transformed cohort. The MYD88 L265P mutation status was known in 435/1147 (38%) patients (406 with non-transformed WM and 29 patients in transformed cohort). On multivariate analysis, MYD88 WT status alone was an independent predictor of transformation (odds ratio, 7[95%CI: 2.1-23]; P = .003). Additionally, the MYD88 WT status was independently associated with shorter time-to-transformation (HR 7.9 [95%CI: 2.3-27; P = .001]), with a 5-year transformation rate of 16% for MYD88 WT vs 2.8% with MYD88 L265P mutated patients. Patients with transformation demonstrated a significant increase in risk of death compared to patients who did not transform (HR 5.075; 95%CI: 3.8-6.8; P < .001). In conclusion, the MYD88 WT status is an independent predictor of transformation and associated with a shorter time-to-transformation. Additionally, transformation conferred an inferior overall survival in patients with WM.
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Affiliation(s)
- Saurabh Zanwar
- Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Jithma P. Abeykoon
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Eric Durot
- Department of HematologyUniversity Hospital of Reims and UFR Médecine Reims France
| | - Rebecca King
- Department of Laboratory Medicine and PathologyMayo Clinic Rochester Minnesota
| | - Gabriela E. Perez Burbano
- Division of Biomedical Statistics and Informatics, Department of Health Sciences ResearchMayo Clinic Rochester Minnesota
| | - Shaji Kumar
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Morie A. Gertz
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Anne Quinquenel
- Department of HematologyUniversity Hospital of Reims and UFR Médecine Reims France
| | - Alain Delmer
- Department of HematologyUniversity Hospital of Reims and UFR Médecine Reims France
| | - Wilson Gonsalves
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | | | - Rong He
- Department of Laboratory Medicine and PathologyMayo Clinic Rochester Minnesota
| | - Rahma Warsame
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Francis K. Buadi
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Anne J. Novak
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | | | - David Inwards
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Thomas M. Habermann
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Ivana Micallef
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Ronald Go
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Eli Muchtar
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Taxiarchis Kourelis
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Angela Dispenzieri
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Martha Q. Lacy
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - David Dingli
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Grzegorz Nowakowski
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Carrie A. Thompson
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Patrick Johnston
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Gita Thanarajasingam
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - N. Nora Bennani
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Thomas E. Witzig
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Jose Villasboas
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Nelson Leung
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Yi Lin
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Robert A. Kyle
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - S. Vincent Rajkumar
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Stephen M. Ansell
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
| | - Jennifer G. Le‐Rademacher
- Division of Biomedical Statistics and Informatics, Department of Health Sciences ResearchMayo Clinic Rochester Minnesota
| | - Prashant Kapoor
- Division of Hematology, Department of Internal MedicineMayo Clinic Rochester Minnesota
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