Literature DB >> 32245463

Expression patterns of immune checkpoints in acute myeloid leukemia.

Cunte Chen1, Chaofeng Liang1, Shunqing Wang2, Chi Leong Chio1, Yuping Zhang2, Chengwu Zeng1, Shaohua Chen3, Caixia Wang4, Yangqiu Li5.   

Abstract

Immunotherapy with immune checkpoint inhibitors (ICIs) for solid tumors had significantly improved overall survival. This type of therapy is still not available for acute myeloid leukemia (AML). One major issue is the lack of knowledge for the expression patterns of immune checkpoints (IC) in AML. In this study, we first explored the prognostic value of ICs for AML patients by analyzing RNA-seq and mutation data from 176 AML patients from the Cancer Genome Atlas (TCGA) database. We further validated the results of the database analysis by analyzing bone marrow (BM) samples from 62 patients with de novo AML. Both TCGA data and validation results indicated that high expression of PD-1, PD-L1, and PD-L2 was associated with poor overall survival (OS) in AML patients. In addition, increased co-expression of PD-1/CTLA-4 or PD-L2/CTLA-4 correlated with poor OS in AML patients (3-year OS: TGCA data 30% vs 0% and 20% vs 0%, validation group 57% vs 31% and 57% vs 33%, respectively) (P < 0.05). Moreover, co-expression of PD-1/PD-L1, PD-1/PD-L1/PD-L2, and PD-1/LAG-3 was found to correlate with poor OS in AML patients with FLT3mut, RUNX1mut, and TET2mut, respectively. In conclusion, high expression of ICs in the BM leukemia cells of AML patients correlated with poor outcome. The co-expression patterns of PD-1/CTLA-4, PD-L2/CTLA-4, PD-1/PD-L1, PD-1/PD-L1/PD-L2, and PD-1/LAG-3 might be potential immune biomarkers for designing novel AML therapy.

Entities:  

Keywords:  AML; Immune checkpoint; PD-1; PD-L1; PD-L2; Prognosis

Mesh:

Substances:

Year:  2020        PMID: 32245463      PMCID: PMC7118887          DOI: 10.1186/s13045-020-00853-x

Source DB:  PubMed          Journal:  J Hematol Oncol        ISSN: 1756-8722            Impact factor:   17.388


To the Editor, Immune checkpoint (IC) blockade by inhibitors of the programmed cell death 1 (PD-1) and PD-1 ligand 1 (PD-L1) has significantly improved clinical outcome for a variety of solid tumors [1, 2], while little is known about the role of ICs in leukemia [3]. Previous reports have shown that higher numbers of PD-1 + T cells are related to poor outcome for patients with acute myeloid leukemia (AML) [3]. Clinical trials using PD-1 inhibitors are ongoing to treat patients with a high risk for AML relapse [4]. However, the response rate varies widely, ranging from 22 to 72% [4], which may be due to heterogeneity in the IC expression level as well as distinct dominant IC expression patterns in different AML cases [5]. Therefore, it is worth studying the expression patterns of ICs in AML. In this study, we first explored the prognostic value of ICs in AML patients through analyzing RNA-seq and mutation data from the Cancer Genome Atlas (TCGA) database [6] and further validated the results by quantitative real-time PCR analysis of AML bone marrow (BM) samples from our clinical center. A total of 176 de novo AML patients from the TCGA database and 62 AML BM samples were used for overall survival (OS) analysis and validation. Higher expression of PD-1, PD-L1, and PD-L2 correlated with poor OS in the TCGA database analysis (3-year OS 23% vs 38%, 19% vs 46%, and 15% vs 40%, respectively, P < 0.05). This result was confirmed in the validation group (3-year OS 40% vs 68%, 22% vs 64%, and 42% vs 68%, respectively, P < 0.05, Fig. 1a, b). We further analyzed the expression patterns of PD-1, PD-L1, and PD-L2 with other important ICs [7-9]. Subsequently, with Pearson’s correlation analysis, we found that the expression of PD-1, PD-L1, or PD-L2 was positively associated with the expression of cytotoxic T-lymphocyte associated protein 4 (CTLA-4) (r = 0.259, P < 0.001; r = 0.435, P < 0.001; r = 0.269, P < 0.001, respectively) and lymphocyte activation gene-3 (LAG-3) (r = 0.275, P < 0.001; r = 0.276, P < 0.001; r = 0.160, P = 0.033, respectively) in the TCGA group (Fig. 1c). This concomitant expression pattern was again confirmed in the validation group (Fig. 1e), showing the possibility of concomitant expression of PD-1, PD-L1, or PD-L2 with CTLA-4 (r = 0.373, P = 0.003; r = 0.998, P < 0.001; r = 0.998, P < 0.001, respectively) and LAG3 (r = 0.372, P = 0.003; r = 0.994, P < 0.001; r = 0.994, P < 0.001, respectively). AML patients with high expression of CTLA-4 and LAG-3 were found to have poor OS (3-year OS 9% vs 36% and 13% vs 40% respectively) (Fig. 1d). This result was again confirmed in the validation group (Fig. 1f) (3-year OS: CTLA-4 34% vs 66%, LAG-3 33% vs 70%).
Fig. 1

Overall survival (OS) of ICs in AML patients. a The OS probability in AML patients with high or low PD-1, PD-L1, or PD-L2 expression in TCGA group. (left panel) X-tile software (version 3.6.1) was used to define the optimal cutoff value for gene expression levels for prognosis, which is represented by the highest intensity pixel. Black dots represent the optimal cutoff value. The black to red or green in the color scale indicates that the range of pixels was from low to high. (right panel) Kaplan–Meier curves based on the optimal cutoff values. b The OS probability in AML patients with high or low PD-1, PD-L1, or PD-L2 expression in the validation group (n = 62). c Relationship between PD-1, PD-L1, and PD-L2 and other immune checkpoints in TCGA group. The outermost circle indicates 1 to 22, X and Y chromosomes; the second layer shows the location of the genes in the chromosomes; the third layer shows the IC genes; the innermost layer represents the average expression levels of the genes, which is shown by the height of the column; the lines in the center of the circle show the co-expression network of the PD-1, PD-L1, and PD-L2 and other ICs. The red font in the center of the circle displays the Pearson’s coefficient with a P value < 0.05 for the correlation of two IC genes. d, f The OS probability in AML patients with high or low CTLA-4 and LAG-3 based on the optimal cutoff values provided by the X-tile software (version 3.6.1) in TCGA group (d) and in the validation group (f). e The chord diagram shows the co-expression network between PD-1, PD-L1, PD-L2, CTLA-4, and LAG-3 in BM samples from AML patients in the validation group (n = 62). The band represents a positive correlation between the two IC genes, and the thickness indicates the magnitude of the Pearson’s correlation coefficient (the P value for testing the correlation coefficient was < 0.05)

Overall survival (OS) of ICs in AML patients. a The OS probability in AML patients with high or low PD-1, PD-L1, or PD-L2 expression in TCGA group. (left panel) X-tile software (version 3.6.1) was used to define the optimal cutoff value for gene expression levels for prognosis, which is represented by the highest intensity pixel. Black dots represent the optimal cutoff value. The black to red or green in the color scale indicates that the range of pixels was from low to high. (right panel) Kaplan–Meier curves based on the optimal cutoff values. b The OS probability in AML patients with high or low PD-1, PD-L1, or PD-L2 expression in the validation group (n = 62). c Relationship between PD-1, PD-L1, and PD-L2 and other immune checkpoints in TCGA group. The outermost circle indicates 1 to 22, X and Y chromosomes; the second layer shows the location of the genes in the chromosomes; the third layer shows the IC genes; the innermost layer represents the average expression levels of the genes, which is shown by the height of the column; the lines in the center of the circle show the co-expression network of the PD-1, PD-L1, and PD-L2 and other ICs. The red font in the center of the circle displays the Pearson’s coefficient with a P value < 0.05 for the correlation of two IC genes. d, f The OS probability in AML patients with high or low CTLA-4 and LAG-3 based on the optimal cutoff values provided by the X-tile software (version 3.6.1) in TCGA group (d) and in the validation group (f). e The chord diagram shows the co-expression network between PD-1, PD-L1, PD-L2, CTLA-4, and LAG-3 in BM samples from AML patients in the validation group (n = 62). The band represents a positive correlation between the two IC genes, and the thickness indicates the magnitude of the Pearson’s correlation coefficient (the P value for testing the correlation coefficient was < 0.05) Combination of IC inhibitors (ICIs) has the potential to improve responses [4, 10]. We analyzed expression patterns of ICs and found that pairwise combinations of PD-1, PD-L1, and PD-L2 and CTLA-4 and LAG-3 correlated with poor OS in AML patients (P < 0.05, Figure S1). Furthermore, among AML patients with high expressions of PD-1 or PD-L2, concomitant high expression of CTLA-4 correlated with poor OS in both the TCGA database (3-year OS: PD-1highCTLA-4low vs PD-1highCTLA-4high 30% vs 0%, 20% vs 0%) and validation group (3-year OS: PD-L2highCTLA-4low vs PD-L2highCTLA-4high 57% vs 31%, 57% vs 33%) (P < 0.05, Fig. 2a, b). AML with PD-L1highCTLA-4high correlated with poor OS in the TCGA dataset (3-year OS 24% vs 0%, P < 0.001); however, OS was not significantly different in the validation group (3-year OS 33% vs 20%, P = 0.353, Fig. 2a, b). In addition, high expression of LAG-3 with PD-1high, PD-L1high, or PD-L2high failed to correlate with OS in the TCGA and validation groups (Figures S2A - B).
Fig. 2

Co-expression patterns of ICs related to poor OS in AML patients. a, b Comparison of OS curves in AML patients with PD-1high, PD-L1high, or PD-L2high co-expressed with CTLA-4low or CTLA-4high in TCGA group (a) and the validation group (n = 62) (b), respectively. c Comparison of OS curves in AML patients with or without FLT3, RUNX1, or TET2 mutation in TCGA group. mut mutation, wt wildtype. d Schematic summary of optimal IC combination detection for OS analysis in AML patients with genetic mutations

Co-expression patterns of ICs related to poor OS in AML patients. a, b Comparison of OS curves in AML patients with PD-1high, PD-L1high, or PD-L2high co-expressed with CTLA-4low or CTLA-4high in TCGA group (a) and the validation group (n = 62) (b), respectively. c Comparison of OS curves in AML patients with or without FLT3, RUNX1, or TET2 mutation in TCGA group. mut mutation, wt wildtype. d Schematic summary of optimal IC combination detection for OS analysis in AML patients with genetic mutations To obtain the effects of PD-1, PD-L1, and PD-L2 on the prognosis of AML patients with genetic mutations, we analyzed OS of the top ten AML patients with a recurrent mutation (Figures S3), including FLT3mut, RUNX1mut, or TET2mut [11-13]. Interestingly, in comparison with AML patients without such mutations, high co-expressions of PD-1/PD-L1 (P = 0.029), PD-1/PD-L1/PD-L2 (P = 0.003), and PD-1/LAG-3 (P < 0.001) were found to be associated with poor OS in AML patients with FLT3mut, RUNX1mut, or TET2mut (1-year OS 0% vs 58% vs 49%, 0% vs 56% vs 100%, and 0% vs100% vs 63%, respectively) (Fig. 2c). To the best of our knowledge, we for the first time described that high co-expressions of PD-1/CTLA-4 and PD-L2/CTLA-4 correlated with poor OS of AML patients. Moreover, high co-expressions of PD-1/PD-L1, PD-1/PD-L1/PD-L2, and PD-1/LAG-3 were associated with poor OS of AML patients with FLT3mut, RUNX1mut, or TET2mut, respectively (Fig. 2d). These co-expression patterns might be potential immune biomarkers for designing novel AML therapy. Additional file 1: Figure S1. Combination of IC detection for OS analysis in patients with AML. A and B: Comparison of OS in patients with high or low expression of PD-1, PD-L1, or PD-L2 co-expressed with high or low CTLA-4 or LAG-3 in the TCGA group (A) and in the validation group (n = 62) (B), respectively. Additional file 2: Figure S2. Comparison of OS in AML patients with high expression PD-1, PD-L1 or PD-L2 co-expressed with high or low LAG3 in the TCGA group (A) and the validation group (B). Additional file 3: Figure S3. Mutation landscape of the top 10 genes in 176 AML patients in the TCGA database. Additional file 4: Table S1. Clinical information for the AML patients. Additional file 5: Table S2. The primers for qRT-PCR. Additional file 6: Materials and Method
  13 in total

1.  A skewed distribution and increased PD-1+Vβ+CD4+/CD8+ T cells in patients with acute myeloid leukemia.

Authors:  Jingying Huang; Jiaxiong Tan; Youchun Chen; Shuxin Huang; Ling Xu; Yikai Zhang; Yuhong Lu; Zhi Yu; Shaohua Chen; Yangqiu Li
Journal:  J Leukoc Biol       Date:  2019-05-28       Impact factor: 4.962

2.  Higher frequency of the CTLA-4+ LAG-3+ T-cell subset in patients with newly diagnosed acute myeloid leukemia.

Authors:  Youchun Chen; Jiaxiong Tan; Shuxin Huang; Xin Huang; Jingying Huang; Jie Chen; Zhi Yu; Yuhong Lu; Jianyu Weng; Xin Du; Yangqiu Li; Xianfeng Zha; Shaohua Chen
Journal:  Asia Pac J Clin Oncol       Date:  2019-10-15       Impact factor: 2.601

Review 3.  Current concepts of non-coding RNA regulation of immune checkpoints in cancer.

Authors:  Maria Anna Smolle; Felix Prinz; George Adrian Calin; Martin Pichler
Journal:  Mol Aspects Med       Date:  2019-09-30

4.  Higher PD-1 expression concurrent with exhausted CD8+ T cells in patients with de novo acute myeloid leukemia.

Authors:  Jiaxiong Tan; Shaohua Chen; Yuhong Lu; Danlin Yao; Ling Xu; Yikai Zhang; Lijian Yang; Jie Chen; Jing Lai; Zhi Yu; Kanger Zhu; Yangqiu Li
Journal:  Chin J Cancer Res       Date:  2017-10       Impact factor: 5.087

5.  The distribution of T-cell subsets and the expression of immune checkpoint receptors and ligands in patients with newly diagnosed and relapsed acute myeloid leukemia.

Authors:  Patrick Williams; Sreyashi Basu; Guillermo Garcia-Manero; Christopher S Hourigan; Karolyn A Oetjen; Jorge E Cortes; Farhad Ravandi; Elias J Jabbour; Zainab Al-Hamal; Marina Konopleva; Jing Ning; Lianchun Xiao; Juliana Hidalgo Lopez; Steve M Kornblau; Michael Andreeff; Wilmer Flores; Carlos Bueso-Ramos; Jorge Blando; Pallavi Galera; Katherine R Calvo; Gheath Al-Atrash; James P Allison; Hagop M Kantarjian; Padmanee Sharma; Naval G Daver
Journal:  Cancer       Date:  2018-11-30       Impact factor: 6.860

Review 6.  Targeting FLT3 mutations in AML: review of current knowledge and evidence.

Authors:  Naval Daver; Richard F Schlenk; Nigel H Russell; Mark J Levis
Journal:  Leukemia       Date:  2019-01-16       Impact factor: 11.528

Review 7.  Achievements and futures of immune checkpoint inhibitors in non-small cell lung cancer.

Authors:  Zhenbin Qiu; Zihao Chen; Chao Zhang; Wenzhao Zhong
Journal:  Exp Hematol Oncol       Date:  2019-08-22

8.  CAR-T "the living drugs", immune checkpoint inhibitors, and precision medicine: a new era of cancer therapy.

Authors:  Delong Liu
Journal:  J Hematol Oncol       Date:  2019-11-08       Impact factor: 17.388

Review 9.  Molecular landscape and targeted therapy of acute myeloid leukemia.

Authors:  Runxia Gu; Xue Yang; Hui Wei
Journal:  Biomark Res       Date:  2018-11-08

Review 10.  Gilteritinib: a novel FLT3 inhibitor for acute myeloid leukemia.

Authors:  Juanjuan Zhao; Yongping Song; Delong Liu
Journal:  Biomark Res       Date:  2019-09-11
View more
  31 in total

1.  Immune-Based Therapeutic Interventions for Acute Myeloid Leukemia.

Authors:  Fabiana Perna; Manuel R Espinoza-Gutarra; Giuseppe Bombaci; Sherif S Farag; Jennifer E Schwartz
Journal:  Cancer Treat Res       Date:  2022

2.  Tumor mutation burden determined by a 645-cancer gene panel and compared with microsatellite instability and mismatch repair genes in colorectal cancer.

Authors:  Zhaofei Zhou; Kang Li; Qiang Wei; Lingxiang Chen; You Shuai; Yajing Wang; Kang He; Lixiang Si; Yuejiao Zhong; Jianwei Lu
Journal:  J Gastrointest Oncol       Date:  2021-12

3.  Programmed Cell Death Ligand 1 Expression Level and Prognostic Significance in Acute Myeloid Leukemia.

Authors:  Ayfer Geduk; Elif B Atesoglu; Ozgur Mehtap; Esra T Demirsoy; Meral U Menguc; Pinar Tarkun; Abdullah Hacihanefioglu; Sibel Balcı
Journal:  Indian J Hematol Blood Transfus       Date:  2021-07-27       Impact factor: 0.915

Review 4.  Extracellular vesicle-mediated immunoregulation in cancer.

Authors:  Tomofumi Yamamoto; Yusuke Yamamoto; Takahiro Ochiya
Journal:  Int J Hematol       Date:  2022-08-11       Impact factor: 2.319

5.  Phase II Trial of Pembrolizumab after High-Dose Cytarabine in Relapsed/Refractory Acute Myeloid Leukemia.

Authors:  Jonathan S Serody; Ivana Gojo; Joshua F Zeidner; Benjamin G Vincent; Anastasia Ivanova; Dominic Moore; Karen P McKinnon; Alec D Wilkinson; Rupkatha Mukhopadhyay; Francesco Mazziotta; Hanna A Knaus; Matthew C Foster; Catherine C Coombs; Katarzyna Jamieson; Hendrik Van Deventer; Jonathan A Webster; Gabrielle T Prince; Amy E DeZern; B Douglas Smith; Mark J Levis; Nathan D Montgomery; Leo Luznik
Journal:  Blood Cancer Discov       Date:  2021-09-10

6.  Tumor mutation burden estimated by a 69-gene-panel is associated with overall survival in patients with diffuse large B-cell lymphoma.

Authors:  Cunte Chen; Sichu Liu; Xinmiao Jiang; Ling Huang; Feili Chen; Xiaojun Wei; Hanguo Guo; Yang Shao; Yangqiu Li; Wenyu Li
Journal:  Exp Hematol Oncol       Date:  2021-03-15

7.  Nomogram Personalizes and Visualizes the Overall Survival of Patients with Triple-Negative Breast Cancer Based on the Immune Genome.

Authors:  Peipei Wang; Yang Fu; Yueyun Chen; Qing Li; Ye Hong; Ting Liu; Zhenyu Ding
Journal:  Biomed Res Int       Date:  2020-11-24       Impact factor: 3.411

8.  Inhibition of BCL11B induces downregulation of PTK7 and results in growth retardation and apoptosis in T-cell acute lymphoblastic leukemia.

Authors:  Kehan Li; Cunte Chen; Rili Gao; Xibao Yu; Youxue Huang; Zheng Chen; Zhuandi Liu; Shaohua Chen; Gengxin Luo; Xin Huang; Grzegorz K Przybylski; Yangqiu Li; Chengwu Zeng
Journal:  Biomark Res       Date:  2021-03-04

9.  Characteristic of TIGIT and DNAM-1 Expression on Foxp3+ γδ T Cells in AML Patients.

Authors:  Zhenyi Jin; Wanyi Ye; Tianbi Lan; Yun Zhao; Xiaxin Liu; Jie Chen; Jing Lai; Shaohua Chen; Xueyun Zhong; Xiuli Wu
Journal:  Biomed Res Int       Date:  2020-07-27       Impact factor: 3.411

Review 10.  Immunotherapy in Acute Myeloid Leukemia: Where We Stand.

Authors:  Alessandro Isidori; Claudio Cerchione; Naval Daver; Courtney DiNardo; Guillermo Garcia-Manero; Marina Konopleva; Elias Jabbour; Farhad Ravandi; Tapan Kadia; Adolfo de la Fuente Burguera; Alessandra Romano; Federica Loscocco; Giuseppe Visani; Giovanni Martinelli; Hagop Kantarjian; Antonio Curti
Journal:  Front Oncol       Date:  2021-05-10       Impact factor: 6.244

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.