key: cord-0022977-8uv3pmbc
authors: Chen, Cunte; Chen, Zhuowen; Chio, Chi Leong; Zhao, Ying; Li, Yongsheng; Liu, Zhipeng; Jin, Zhenyi; Wu, Xiuli; Wei, Wei; Zhao, Qi; Li, Yangqiu
title: Higher Expression of WT1 With Lower CD58 Expression may be Biomarkers for Risk Stratification of Patients With Cytogenetically Normal Acute Myeloid Leukemia
date: 2021-11-05
journal: Technol Cancer Res Treat
DOI: 10.1177/15330338211052152
sha: 248b47b171c56670de4058995cc59e67d8efdbe2
doc_id: 22977
cord_uid: 8uv3pmbc

Background: Cytogenetics at diagnosis is the most important prognostic factor for adult acute myeloid leukemia (AML), but nearly 50% of AML patients who exhibit cytogenetically normal AML (CN-AML) do not undergo effective risk stratification. Therefore, the development of potential biomarkers to further define risk stratification for CN-AML patients is worth exploring. Methods: Transcriptome data from 163 cases in the GSE12417-GPL96 dataset and 104 CN-AML patient cases in the GSE71014-GPL10558 dataset were downloaded from the Gene Expression Omnibus database for overall survival (OS) analysis and validation. Results: The combination of Wilms tumor 1 (WT1) and cluster of diffraction 58 (CD58) can predict the prognosis of CN-AML patients. High expression of WT1 and low expression of CD58 were associated with poor OS in CN-AML. Notably, when WT1 and CD58 were used to concurrently predict OS, CN-AML patients were divided into three groups: low risk, WT1(low) CD58(high); intermediate risk, WT1(high)CD58(high) or WT1(low)CD58(low); and high risk, WT1(high) CD58(low). Compared with low-risk patients, intermediate- and high-risk patients had shorter survival time and worse OS. Furthermore, a nomogram model constructed with WT1 and CD58 may personalize and reveal the 1-, 2-, 3-, 4-, and 5-year OS rate of CN-AML patients. Both time-dependent receiver operating characteristics and calibration curves suggested that the nomogram model demonstrated good performance. Conclusion: Higher expression of WT1 with lower CD58 expression may be a potential biomarker for risk stratification of CN-AML patients. Moreover, a nomogram model constructed with WT1 and CD58 may personalize and reveal the 1-, 2-, 3-, 4-, and 5-year OS rates of CN-AML patients.

Acute myeloid leukemia (AML) is a genetically heterogeneous disease in which the accumulation of somatic mutations leads to uncontrolled cell proliferation and differentiation. Cytogenetics at diagnosis provides the most important prognostic information for adult AML, but 40% to 50% of AML patients present as cytogenetically normal AML (CN-AML) and do not have biomarkers for effective risk stratification. [1] [2] [3] [4] Currently, all such CN-AML cases are classified as intermediate risk. 5, 6 However, this group is quite heterogeneous, and the 4-year rate of overall survival (OS) is only 43%. 1, 7, 8 With the development of transcriptome sequencing, many biomarkers are emerging that can also be used to further refine the molecular risk definition for CN-AML.

The Wilms tumor 1 (WT1) gene, located on chromosome 11p13, plays an important role in development, tissue homeostasis, and disease. 9 Various studies have suggested that high expression of WT1 is significantly associated with the prognosis and relapse of AML patients, which has an important clinical value in guiding AML treatment. 10, 11 Recently, vaccines targeting WT1 and T-cells engineered to express a receptor specific for WT1 could stimulate specific immune responses and help prevent relapse in AML patients. However, not all patients benefit from the WT1 vaccine where only 64% of patients have an immune response. 11, 12 It is thought that there are other factors that may influence the effects of WT1 on immunotherapy and lead to a poor prognosis for AML patients. Recent studies have suggested that AML patients with high expression of immune checkpoint (IC) proteins predict poor prognosis. [13] [14] [15] Moreover, AML patients have an immune response to IC inhibitors. 16 In addition, various studies indicated that down-regulation of cluster of diffraction 3ζ, a T-cell costimulatory molecule (CM), is a reason for the decreased level of T-cell activation in leukemia patients. Thus, whether the pattern of higher WT1 and IC genes or lower CMs may contribute to the OS of CN-AML is worth exploring.

In this study, 2 large datasets in the Gene Expression Omnibus (GEO) database were used to explore the combination of WT1 and IC proteins or CMs as a potential biomarker for further refining risk stratification in CN-AML. In addition, we constructed a nomogram model to personalize and visualize the prediction of OS rates for CN-AML patients.

The transcriptome data of 163 cases (see Figure 1 ) in the GSE12417-GPL96 dataset 17 and 104 cases of CN-AML patients in the GSE71014-GPL10558 dataset 18 were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/ ), [19, 20] which were designated as training and validation cohorts, respectively. The corresponding clinical information including age, sample source, survival time, and events were downloaded and listed in Supplemental Table S1 . Moreover, the transcriptome data for WT1, IC genes, and CMs were obtained for data mining (Supplemental Table S2 ). Because the GEO database is publicly available, approval from the local ethics committee was not required.

A nomogram model is applied to individualize and visualize the clinical outcome of cancer patients. [21] [22] [23] [24] [25] The "foreign" and "rms" packages in R software (version 4.0.2, https://www.rproject.org/) were used to construct a nomogram model to visualize the OS rate of CN-AML patients. 26 First, each variable in the nomogram was given a weighted point. Then, the total points of all variables for each patient were summed and located on the total point scale. Finally, the OS rate was determined by drawing a vertical line on the total point scale. The time-dependent receiver operating characteristic (ROC) and calibration curves were used to evaluate the prediction performance of the nomogram model, and the judgment criterion included the following: (i) area under curve (AUC) >0.5 and (ii) the OS rate predicted by the nomogram model was significantly close to the actual OS rate.

The transcriptome data of 163 and 104 CN-AML patients in the GSE12417-GPL96 and GSE71014-GPL10558 datasets was downloaded from the GEO database, which was designated as training and validation cohorts, respectively. The expression patterns of WT1, IC, and CM genes were characterized, and the Spearman correlation between WT1 and IC and CM genes was further analyzed. Then, genes with P < .05 of Spearman correlation were selected for Kaplan-Meier curve analysis. Notably, genes with consistent prognostic characteristics in both the training and validation cohorts were used for the construction of risk stratification. Furthermore, univariate and multivariate Cox regression models were used to confirm that the combination of two genes had a better prediction of OS in CN-AML patients than a gene alone. Finally, the identified genes were used to visualize the 1-, 2-, 3-, 4-, and 5-year OS rates of CN-AML patients ( Figure 1 ).

All statistical analysis was conducted by R software (version 4.0.2, https://www.r-project.org/). The optimal cut-off for gene expression was determined by maximally selected rank statistics in the "maxstat" package. The "survival" package was used to plot the Kaplan-Meier curves, and comparison between groups was performed by the log-rank test. The AUC in the time-dependent ROC curve was obtained by the "survival ROC" package. The correlation coefficient between the two genes was determined by Spearman's method. A twotailed P value <.05 was considered statistically significant.

The relationship between WT1 and IC or CM gene expression was evaluated based on the molecular profiles of the patients both in the training and validation datasets. Interestingly, the expression pattern of WT1, 5 IC genes programmed cell death 1, programmed cell death 1 ligand 2 (PD-L2), lymphocyte activating 3, indoleamine 2,3-dioxygenase 1 (IDO1), and human endogenous retrovirus-subfamily H long-terminal repeat-associating protein 2, and 12 CMs in both the training and validation cohorts were examined (Figure 2a OS Analysis of WT1, CMs, and ICs in CN-AML As shown in Figure 3a , compared with the low expression of the WT1 group, high expression of WT1 was associated with poor OS for CN-AML patients in the training cohort (3-year OS rate: 19% vs 38%, P = .007). A similar result could be found in the validation cohort (3-year OS rate: 46% vs 68%, P = .026) (Figure 3b ). Interestingly, CN-AML patients with high CD58 expression had favorable OS compared to those with low CD58 expression in the training cohort (3-year OS: 41% vs 22%, P = .012) (Figure 3c ). This result could be confirmed in the validation cohort (3-year OS: 70% vs 43%, P = .018) (Figure 3d ). However, the expression levels of CD2, CD86, ICAM1, and ITGAL had no significant correlation with OS in CN-AML (Supplemental Figure S1 ).

To better understand the combination of WT1 and CD58 in predicting the OS of CN-AML patients, Spearman correlation (Figure 4b) . The results could be confirmed in the validation cohort (3-year OS rate, high vs intermediate vs low: 40% vs 50% vs 73%, P = .004) (Figure 4d) .

To evaluate whether the combination of WT1 and CD58 is better than WT1 or CD58 alone in predicting OS in CN-AML patients, univariate and multivariate Cox regression analysis was performed. The results of univariate Cox regression analysis suggested that although high expression of CD58 was associated with favorable OS in the training cohort (hazard ratio [HR] = 0.69, 95% confidence interval [CI]: 0.47-0.99, P = .043), this finding could not be confirmed in the validation cohort (HR = 0.69, 95% CI: 0.37-1.27, P = .229). What is more, there was no significant correlation between WT1 and OS in both training and validation cohorts (P>.05). Interestingly, co-occurrence of high WT1 expression and low CD58 expression could predict unfavorable OS in CN-AML patients in the training cohort by univariate and multivariate Cox regression analysis (HR = 2.59, 95% CI: 1.25-5.38, P = .010). This result was confirmed in the validation cohort (HR = 3.01, 95% CI: 1.37-6.60, P = .006) ( Table 1 ). These findings suggest that WT1 and CD58 may be biomarkers for risk stratification of CN-AML patients. 

Based on the above findings, WT1 and CD58 were used to build a nomogram model to personalize and display the 1-, 2-, 3-, 4-, and 5-year OS rate of the 267 CN-AML patients included in the training and validation cohorts (Figure 5a ). According to the nomogram model, high expression of WT1 and low expression of CD58 were assigned points of 73 and 100, respectively, while low expression of WT1 and high expression of CD58 were assigned 0 points. The detailed total points corresponding to the OS rates are shown in Supplemental Table S3 . Next, the performance of the nomogram model was evaluated. The 1-, 2-, 3-, 4-, and 5-year AUC in the time-dependent ROC curve were all >0.60 ( Figure 5b) . Moreover, the calibration curves suggested that the 1-, 2-, 3-, 4-, and 5-year OS rates predicted by the nomogram model were significantly close to the actual OS rates (Figure 5c ). Therefore, the results of the ROC and calibration curves demonstrated that the nomogram model constructed with WT1 and CD58 had better performance in predicting the OS rates of CN-AML patients.

In this study, a total of 267 CN-AML patients from 2 datasets in the GEO database were used for OS analysis and validation. We found that higher WT1 concurrent with lower CD58 expression was significantly associated with poor OS. Interestingly, the combination of WT1 and CD58 may further define risk stratification for CN-AML patients. Moreover, we developed a prediction nomogram of the OS rate for individual CN-AML patients. High expression of WT1 predicts poor prognosis and relapse in AML patients. 10, 11 Furthermore, for CN-AML patients undergoing hematopoietic stem cell transplantation (HSCT), high expression of WT1 before HSCT is associated with a higher relapse rate and poor OS. 27 However, more accurate risk stratification cannot be made for all CN-AML patients based on the expression of WT1 alone, and the development of targeted therapies for WT1 cannot benefit all patients. 11, 12, 27 Although the survival curves suggested that the high expression of WT1 was associated with poor OS in CN-AML patients, there was no significant correlation between WT1 and OS in univariate Cox analysis, which was consistent with the publication of the validation dataset. 18 Therefore, it is important to combine WT1 and other genes for the risk stratification of CN-AML patients.

CMs can enhance the immune response of T-cells to tumor cells, and down-regulating these CMs will lead to immune escape for tumor cells. 28 In several clinical trials, CM agonists have been used as adjuvants for immunotherapy to enhance the antitumor effects of solid tumors and hematological malignancies, and many patients have benefited from them. 29, 30 These findings suggest that CMs may be potential biomarkers for risk stratification and a promising immunotherapy strategy for patients with hematological tumors. One study has found that CD58/CD2 is the main costimulatory pathway that can activate CD28-CD8 + T-cells to exert antitumor effects. 31 In addition, another study indicated that high expression of CD58 predicts favorable clinical outcomes in acute lymphoblastic leukemia. [32] These findings are in line with the results of our study. Furthermore, it is worth noting that WT1 had a negative relationship with CD58, suggesting that these genes may potentially serve as a biomarker for risk stratification of CN-AML patients. Interestingly, when WT1 and CD58 were combined, it was found to predict OS in CN-AML patients. Moreover, the combination of WT1 and CD58 was better than WT1 or CD58 alone in predicting OS in CN-AML patients.

A reliable estimation of the OS rate and risk stratification for patients is important for guiding doctors in choosing a therapeutic strategy. Because nearly half of AML patients have a normal karyotype, which is called CN-AML and classified as an intermediate risk group, 1 it is difficult for clinicians to choose treatment. Thus, it is important to further define risk stratification for these patients. In this study, we show that WT1 and CD58 may be potential biomarkers for further defining CN-AML patients as low-, intermediate-, and high-risk. Moreover, nomogram models constructed with WT1 and CD58 may personalize and display the 1-, 2-, 3-, 4-, and 5-year OS rates for CN-AML patients. Notably, both time-dependent ROC and calibration curves indicated that the nomogram model had good performance in predicting prognosis. However, the limitations of this study include the following: (1) we do not have quantitative real-time polymerase chain reaction and immunohistochemical data for WT1 and CD58 from the clinical center to further validate our findings and (2) construction of the nomogram model was only based on the transcriptome sequencing data in the GEO database. Although the nomogram was internally validated by the calibration and time-dependent ROC curves, further study in a clinical center is needed to externally validate the proposed nomogram.

We demonstrate that higher WT1 concurrent with lower CD58 expression may predict poor OS for CN-AML patients. Importantly, WT1 and CD58 may be potential biomarkers for the risk stratification of CN-AML patients and the construction of a nomogram model that personally and visually predicts the OS rate of each CN-AML patient.

Mutations and treatment outcome in cytogenetically normal acute myeloid leukemia

Cytogenetics in acute leukemia

Pretreatment cytogenetic abnormalities are predictive of induction success, cumulative incidence of relapse, and overall survival in adult patients with de novo acute myeloid leukemia: results from cancer and leukemia group B (CALGB 8461)

High expression of CD56 may be associated with favorable overall survival in intermediate-risk acute myeloid leukemia

Favorable prognostic impact of NPM1 mutations in older patients with cytogenetically normal de novo acute myeloid leukemia and associated gene-and microRNA-expression signatures: a cancer and leukemia group B study

Long-term follow-up of cytogenetically normal CEBPA-mutated AML

A package for survival analysis in R. R package version

Acute myeloid leukemia and acute promyelocytic leukemia

Wilms' tumour 1 (WT1) in development, homeostasis and disease

High WT1 expression is an early predictor for relapse in patients with acute promyelocytic leukemia in first remission with negative PML-RARa after anthracyclinebased chemotherapy: a single-center cohort study

Phase 2 trial of a multivalent WT1 peptide vaccine (galinpepimut-S) in acute myeloid leukemia

TCR Gene therapy improves AML prognosis

Expression patterns of immune checkpoints in acute myeloid leukemia

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

Transcriptome-based co-expression of BRD4 and PD-1/PD-L1 predicts poor overall survival in patients with acute myeloid leukemia

Immune checkpoint inhibitors in acute myeloid leukemia: novel combinations and therapeutic targets

An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia

An mRNA expression signature for prognostication in de novo acute myeloid leukemia patients with normal karyotype

Role of COL3A1 and POSTN on pathologic stages of esophageal cancer

Downregulation of miR-199a-3p in hepatocellular carcinoma and Its relevant molecular mechanism via GEO, TCGA database and in silico analyses

Circulating tumor cells as a new predictive and prognostic factor in patients with small cell lung cancer

Development and validation of a prognostic nomogram based on residual tumor in patients with nondisseminated nasopharyngeal carcinoma

Establishment and validation of nomogram model integrated with inflammation-based factors for the prognosis of advanced non-small cell lung cancer

Nomogram personalizes and visualizes the overall survival of patients with triple-negative breast cancer based on the immune genome

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

Increased coexpression of PD-L1 and TIM3/TIGIT is associated with poor overall survival of patients with esophageal squamous cell carcinoma

Outcome of allogeneic hematopoietic stem cell transplantation for cytogenetically normal AML and identification of high-risk subgroup using WT1 expression in association with NPM1 and FLT3-ITD mutations

Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential

Next generation of immune checkpoint therapy in cancer: new developments and challenges

First in human (FIH) study of an OX40 agonist monoclonal antibody (mAb) PF-04518600 (PF-8600) in adult patients (pts) with select advanced solid tumors: preliminary safety and pharmacokinetic (PK)/pharmacodynamic results

CD58/CD2 is the primary costimulatory pathway in human CD28-CD8 + T cells

Expression of surface adhesion molecules CD54 (ICAM-1) and CD58 (LFA-3) in adult acute leukemia: relationship with initial characteristics and prognosis

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grants from the National Natural Science Foundation of China ( 

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental material for this article is available online.

Yangqiu Li https://orcid.org/0000-0002-0974-4036

The transcriptome data in the GSE12417-GPL96 and GSE71014-GPL10558 datasets were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). The data that support the findings of this study are available from the corresponding author upon reasonable request.