Molecular and clinicopathological implications of PRAME expression in adult glioma

Minh-Khang Le; Huy Gia Vuong; Ian F Dunn; Tetsuo Kondo

doi:10.1371/journal.pone.0290542

. 2023 Oct 5;18(10):e0290542. doi: 10.1371/journal.pone.0290542

Molecular and clinicopathological implications of PRAME expression in adult glioma

Minh-Khang Le ¹, Huy Gia Vuong ², Ian F Dunn ³, Tetsuo Kondo ^1,^*

Editor: Syed M Faisal⁴

PMCID: PMC10553321 PMID: 37796789

Abstract

Background

PRAME (PReferentially expressed Antigen in MElanoma) is a biomarker studied in various human cancers. Little is known about the biological implications of PRAME in glioma. We aimed to perform a comprehensive analysis to explore PRAME gene expression and its biological and clinicopathological significance in gliomas.

Methods and materials

We accessed the human cancer atlas (TCGA) database to collect glioma patients (n = 668) with primary tumors and gene expression data. Single nucleotide variants, copy number variation, DNA methylation data, and other clinicopathological factors were also extracted for the analysis.

Results

Overall, 170, 484, and 14 tumors showed no expression, low expression (FPKM≤1), and overexpression (FPKM>1) of the PRAME gene, respectively. The principal component analysis and pathway analyses showed that PRAME-positive gliomas (n = 498), which consisted of tumors with PRAME low expression and overexpression, expressed different oncogenic profiles, possessing higher activity of Hedgehog, P3IK-AKT-mTOR, and Wnt/β-catenin pathways (p<0.001). DNA methylation analysis also illustrated that PRAME-positive tumors were distributed more densely within a grade 4-related cluster (p<0.001). PRAME positivity was an independent prognostic factor for poor outcomes in a multivariate cox analysis adjusted for clinical characteristics and genetic events. Kaplan-Meier analysis stratified by revised classification showed that PRAME positivity was solely associated with IDH-wildtype glioblastoma, grade 4. Finally, PRAME-overexpressing cases (n = 14) had the worst clinical outcome compared to the PRAME-negative and PRAME-low cohorts (adjusted p<0.001) in pairwise comparisons.

Conclusion

PRAME expression statuses may dictate different biological and clinicopathological profiles in IDH-wildtype glioblastoma.

Introduction

PRAME (PReferentially expressed Antigen in MElanoma) is a cancer-testis antigen that is expressed by melanoma cells and was isolated by autologous T cells in a melanoma patient [1]. PRAME expression is used to support the diagnosis of melanoma over nevus in combination with histopathological features and other findings. The expression of the PRAME gene and PRAME protein can be practically evaluated by pigmented lesion assay (PLA) and immunohistochemistry (IHC), respectively [2, 3]. Although PRAME has its primary application in the diagnosis of melanoma, PRAME was also found to be expressed by various epithelial and non-epithelial cancers [4], including uterine carcinoma, uterine carcinosarcoma, ovarian carcinoma, adenoid cystic carcinoma, seminoma, thymic carcinoma, basal cell carcinoma, synovial sarcoma, myxoid liposarcoma, and neuroblastoma. The biological and clinicopathological implications of PRAME expression have been unknown in adult gliomas.

Adult gliomas are a heterogeneous and common group of brain cancers with unclear cell-of-origin [5]. The biological profile of gliomas has been studied with respect to histology, epigenetic, genetic characteristics, cell-of-origin, and tumor microenvironment [6, 7]. There are highly diverse oncogenic mechanisms contributing to gliomagenesis and tumor progression, including Wnt/β-catenin [8], PI3K/Akt/mTOR [9], TGF-β [10], and mesenchymal transition, among many others [11]. Important genetic abnormalities affecting the prognosis of glioma patients consist of IDH1/2 mutations, CDKN2A/B homozygous deletion, EGFR amplification, TP53 mutations, ATRX mutations, TERT promoter mutations, and 7 gain 10 loss chromosomal abnormalities [12]. The recent World Health Organization (WHO) classification of Tumours of the Central Nervous System (CNS) emphasizes that glioma can be divided by IDH mutation and 1p/19q codeletion status. IDH-wildtype astrocytoma has more advanced clinicopathological progression and tumor with grade 4 is referred to as the “glioblastoma” category. Glioblastoma is diagnosed by the absence of IDH mutation and one of the high-grade features, including high-grade morphology, TERT promoter mutation, 7 gain/10 loss chromosomal abnormality, or EGFR amplification. However, the prognostic factors of glioma are still under investigation.

In the present study, we first examined how PRAME gene expression was related to biological profiles. Secondly, we investigated whether PRAME expression patterns were related to the DNA methylation landscape. Finally, clinicopathological characteristics and survivorship were compared between PRAME-low and PRAME-high glioma patients.

Materials and methods

Data processing

The Human Cancer Atlas (TCGA) database consists of many datasets. We extracted cases from the TCGA-GBM and TCGA-LGG projects. Only cases with available gene expression profiles (GEP) and primary tumors (no recurrent or metastatic tumors) were included in the study. To adapt to the new WHO classification, cases with grades 2/3 in the previous studies [13, 14] were reclassified into grade 4 as follows: (1) the presence of both IDH1/2 mutation and CDKN2A/B homozygous deletion, or (2) the absence of IDH1/2 mutations and the presence of at least one of the following abnormalities: TERT promoter mutation, EGFR amplification, and 7 gain 10 loss chromosomal abnormality. Other histopathological grading features such as microvascular proliferation and pseudopalisading necrosis were assumed to be included in previously evaluated grade IV gliomas of the original studies [13, 14]. Tumors with the absence of IDH1/2 mutations and no other high-grade morphological and genetic features mentioned above were reclassified as Astrocytoma, Not Otherwise Specified (NOS). Tumors with the presence of both IDH1/2 mutations and 1p/19q codeletion were categorized as Oligodendroglioma. Mixed glioma was re-distributed into new categories based on IDH1/2 mutation and 1p/19q codeletion status. This reclassification was published in our previous paper [15]. The difference in data processing between this study and our previous one was that we used cbioportal for cancer genomics (https://www.cbioportal.org) datasets that are related to TCGA-GBM and TCGA-LGG projects, including (1) Brain Lower Grade Glioma (TCGA, Firehose Legacy), (2) Glioblastoma Multiforme (TCGA, Firehose Legacy), and (3) Merged Cohort of LGG and GBM (TCGA, Cell 2016). This difference led to a slight inconsistency in the total number of glioma patients.

Gene set variation analysis (GSVA)

GSVA is a type of single-sample gene set enrichment analysis (ssGSEA) [16], which is a variant of conventional GSEA [17]. The rank of genes was based on an expression-level statistic, which is a Gaussian or Poisson kernel estimation of the cumulative density function of each gene across the samples. Each value of kernel estimation was calculated, given each gene expression of each sample. As a result, the enrichment score (ES) of a gene set can be calculated for each sample. Therefore, we can investigate the activity of signaling pathways, using GSVA. In this study, we employed the gene sets of the hallmark pathways (“H” category) in the MSigDB database (https://www.gsea-msigdb.org) and excluded cancer-irrelevant pathways.

Data analysis

Continuous and categorical variables were described by median (range) and the number of cases (percentage), respectively. Chi-square tests and Wilcoxon’s test were performed to compare categorical and continuous variables by default. Pathway activity was calculated by GSVA. For survival analysis, we performed Kaplan-Meier analysis and univariate and multivariate Cox analysis. Heatmap was created, using the Biokit package, run on Python 3.9. Other data analyses were conducted, using R version 4.2.1 (The R Foundation, Austria).

Results

Investigating PRAME expression and pathway activities

A total of 668 primary tumors in 668 glioma patients, were included in our study. We explored the normalized read count value of these 668 gliomas and found that the PRAME gene was not expressed in 170 gliomas (PRAME-negative; FPKM = 0). Fig 1A shows the normalized read counts of the PRAME expression. Therefore, the entire cohort was divided into two groups, PRAME-negative and PRAME-positive, based on the PRAME expression status. Mean PRAME gene expression in the PRAME-positive cohort was 0.98 FPKM, which was relatively low. Most cases (n = 654) had PRAME gene expression < 1 FPKM, while only 14 cases had high PRAME expression (Fig 1A, inset). Next, we conducted principal component analysis (PCA) over 730 genes within the nCounter PanCancer Pathways panel, published by Nanostring Technology (https://nanostring.com/, excluding 40 internal reference genes). This panel included genes within 13 cancer-associated canonical pathways, which supports the understanding of basic cancer biology. S1 Table shows the symbol and ID of the genes within the panel. A dimension reduction plot (Fig 1B) was created, using PC1 and PC2 components, which explained the highest variance in the data. We found that PRAME-negative tumors clustered together with some PRAME-positive counterparts. However, there was a higher percentage of PRAME-positive gliomas outside of this cluster. Therefore, there were differences in the distributions of PRAME-negative and PRAME-positive gliomas in the 730-gene cancer-related space. Finally, we created a heatmap to show the pathway-level comparisons between the two groups, using two-sample independent t-tests (Fig 1C). The PRAME-positive tumors expressed higher activity of Hedgehog (p<0.001), P3IK-AKT-mTOR (p<0.001), P53 (p<0.001), apoptosis (p<0.001), IL2-STAT5 (p<0.001), and Wnt/β-catenin (p<0.001) signaling pathways while these tumors reduced other biological signals, including E2F targets (p<0.001), G2M mitotic checkpoint (p = 0.005), reactive oxidative oxygen species (p<0.001), TNF-α (p<0.001), IL6-JAK-STAT6 (p<0.001), inflammatory response (p<0.001), angiogenesis (p<0.001), epithelial-mesenchymal transition (p<0.001), mTORC1 (p<0.001), glycolysis (p<0.001), and hypoxia (p<0.001). Fig 1D summarizes the results of pathway analysis. In addition, Fig 1E illustrates the PRAME expression in each revised category. In general, gliomas grade 4 had higher PRAME expression compared to other categories.

Fig 1 — (A) Histogram shows the distribution of the *PRAME* expression read counts (FPKM). The inset only shows the distribution of tumors with FPKM>1. (B) Principal component analysis (PCA) plot created by PC1 and PC2 of the nCounter Nanostring PanCancer Pathways gene panel. (C) The activity heatmap of cancer-related pathways illustrates the activity of each pathway in each sample. The p-values in the left column are calculated by the two-sample independent t-tests to compare the enrichment score (ES) of each pathway between *PRAME*-negative and *PRAME*-positive tumors. (D) The summary plot of upregulated and downregulated pathways in *PRAME*-positive gliomas. (E) The boxplot compares *PRAME* expression between different revised categories of the new WHO classification.

PRAME positivity was densely distributed within a distinct DNA methylation cluster

In the study cohort, there were a total of 476 tumors with available data about 450K DNA methylation. We performed t-SNE dimension reduction to explore the differences in the distribution of PRAME-negative and PRAME-positive glioma in DNA methylation hyperspace. This DNA methylation space can be interpreted as the reduced representation of the CpG methylation landscape. We also included the Glioma CpG Island Methylator Phenotype (G-CIMP), which was published in a previous paper [33941250]. In this study, 476 tumors in DNA methylation space can be relatively divided into two unsupervised tSNE clusters, small (right, lower corner) and large clusters (left and upper part) (Fig 2A–2D). The small cluster densely consisted of PRAME-low and PRAME-overexpressing samples while the larger cluster had a significant portion of PRAME-negative samples (Fig 2A). The revised subtype (Fig 2B), CIMP clusters (Fig 2C), and IDH status (Fig 2D) were strongly associated with these 2 clusters. Fig 2E shows a heatmap of distribution of PRAME expression status within CIMP clusters. There were significant difference in the distribution of PRAME expression status (chi-square test, p<0.001). This discrimination can be seen in LGm6-GBM (6/12 vs. 7/484 vs. 0/170), classic-like (1/12 vs. 63/484 vs. 4/170), and mesenchymal-like (2/12 vs. 87/484 vs. 9/170).

Fig 2 — t-SNE dimension reduction plots show the distribution of DNA methylation landscapes of 474 tumors, characterized by *PRAME* expression status (A), re-classified WHO grades (B), and *IDH* mutation status (C).

PRAME positivity was associated with IDH-wildtype glioblastoma and adverse outcomes

Table 1 summarizes the clinicopathological characteristics of PRAME-negative and PRAME-positive gliomas. Clinically, patients with PRAME-positive gliomas were older (p<0.001). There were no differences in gender (p = 0.419) and race (p = 0.382). Comparisons of revised classification between PRAME-negative and PRAME-positive cohorts showed that IDH-mutant astrocytoma, grade 2 (26.5% vs. 13.1%) and oligodendroglioma (35.9% vs. 21.7%) dominated PRAME-negative glioma while the incidence of IDH-wildtype glioblastoma, grade 4 (32.9% vs. 4.1%) was much higher in PRAME-positive glioma. These differences were significant (p<0.001). Regarding genetic abnormalities, PRAME-positive tumors more frequently acquired EGFR amplification (20.4% vs. 4.7%; p<0.001), CDKN2A/B homozygous deletion (25.5% vs. 6.0%; p<0.001), and 7 gain 10 loss chromosomal aberrations (28.4% vs. 7.1%; p<0.001) than PRAME-negative tumors. Conversely, IDH1/2 mutations (87.6% vs. 56.7%; p<0.001) and ATRX mutations (40.0% vs. 27.4%; p = 0.005) were significantly more common in PRAME-negative cases.

Table 1. Comparison of clinicopathological characteristics of PRAME-negative and PRAME-positive cohorts.

Variable	PRAME-negative (n = 170)	PRAME-positive (n = 498)	p-value
Age	39 (17–74)	4 (14–89)	<0.001
Gender			0.419
Female	68 (44.7%)	185 (40.6%)
Male	84 (55.3%)	271 (59.4%)
Race			0.382
White	155 (91.2%)	455 (91.4%)
Asian	2 (1.2%)	11 (2.2%)
Black	7 (4.1%)	24 (4.8%)
Not reported	6 (3.5%)	8 (1.6%)
Revised classification			<0.001
Astrocytoma NOS, grade 2	8 (4.7%)	12 (2.4%)
Astrocytoma NOS, grade 3	7 (4.1%)	28 (5.6%)
Astrocytoma NOS, grade 4	0 (0.0%)	8 (1.6%)
IDH-mutant astrocytoma, grade 2	45 (26.5%)	65 (13.1%)
IDH-mutant astrocytoma, grade 3	26 (15.3%)	69 (13.9%)
IDH-mutant astrocytoma, grade 4	3 (1.8%)	18 (3.6%)
IDH-wildtype glioblastoma, grade 4	7 (4.1%)	164 (32.9%)
Oligodendroglioma	61 (35.9%)	108 (21.7%)
Unknown	13 (7.6%)	26 (5.2%)
IDH1/2 mutation	148/169 (87.6%)	279/492 (56.7%)	<0.001
TP53 mutation	76/170 (44.7%)	207/497 (41.6%)	0.545
ATRX mutation	68/170 (40.0%)	136/497 (27.4%)	0.005
TERT promoter mutation	37/91 (40.7%)	117/227 (51.5%)	0.103
EGFR amplification	8/169 (4.7%)	100/490 (20.4%)	<0.001
CDKN2A/B homozygous deletion	11/169 (6.0%)	125/490 (25.5%)	<0.001
7 gain 10 loss	12/169 (7.1%)	139/490 (28.4%)	<0.001
Vital status			<0.001
Alive	129 (84.9%)	298 (65.4%)
Dead	23 (15.1%)	158 (34.6%)
Overall survival time (months)	15.2 (0.0–134.0)	11.5 (0.0–211.0)	0.087

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NOS: Not otherwise specified.

In survival analysis, PRAME was a general marker of prognosis in the entire studied cohort (p<0.001, Fig 3A). Stratified by the new WHO classification, there were no significant results in IDH-mutant astrocytoma grade 2 (p = 0.891, Fig 3B), grade 3 (p = 0.502, Fig 3C), and grade 4 (p = 0.160, Fig 3D). However, PRAME positivity was of prognostic significance in IDH-wildtype glioblastoma grade 4 (p = 0.018, Fig 3E). There was no obvious survival difference between PRAME-positive and PRAME-negative oligodendrogliomas. We also compared the survival outcomes of IDH-mutant/1p19q codeletion and IDH-mutant/non-1p19q codeletion (S1 Fig) but PRAME positivity was not related to the prognosis. Table 2 shows multivariate analyses adjusted for clinical characteristics, whole genome sequencing (WGS) (IDH1/2 mutation, ATRX mutation, and TP53 mutation), whole exome sequencing (WES) (TERT promoter mutaton), and copy number variation information (CDKN2A/B homozygous deletion, EGFR amplification, and 7 gain/10 loss). The prognostic effect of PRAME positivity was significant and independent to clinical characteristics (HR = 2.73; 95%CI = 1.66–4.15; p<0.001), WGS (HR = 2.03; 95%CI = 1.28–3.22; p = 0.003), and CNV (HR = 2.11; 95%CI = 1.35–3.29; p = 0.001) features.

Table 2. Multivariate Cox survival analyses with overall survival and vital status as the outcome, adjusted for clinical characteristics, WGS, WES, and CNV information.

Variable	HR	95%CI	p-value
Clinical characteristics
PRAME positive	2.73	1.66–4.50	<0.001
Age (years)	1.08	1.06–1.09	<0.001
Men	1.05	0.76–1.45	0.761
Race
Asian	1
Black	0.79	0.16–3.93	0.769
White	0.73	0.18–2.98	0.662
WGS-available data
PRAME positive	2.03	1.28–3.22	0.003
IDH1/2 mutation	0.07	0.05–0.12	<0.001
ATRX mutation	1.56	0.85–2.87	0.151
TP53 mutation	1.09	0.75–1.59	0.657
WES-available data
PRAME positive	1.61	0.94–2.77	0.083
TERT promoter mutation	2.02	1.28–3.20	0.003
CNV-available data
PRAME positive	2.11	1.35–3.29	0.001
EGFR amplification	1.36	0.90–2.06	0.143
CDKN2A/B homozygous deletion	2.55	1.70–3.81	<0.001
7 gain/10 loss	3.61	2.35–5.55	<0.001

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HR: hazard ratio; 95%CI: 95% confidence interval; WGS: whole genome sequencing; WES: whole exome sequencing; CNV: copy number variation.

PRAME overexpression was associated with a worse prognosis than PRAME positivity

First, we defined PRAME overexpression when FPKM > 1, which was observed in only 14 gliomas within the entire studied cohort. Table 3 shows the clinicopathological characteristics of these 14 patients. Notably, only 1 case (7.1%) of this cohort was IDH-mutant. Next, we performed KM analysis to compare the overall survival of PRAME-negative (n = 170), PRAME-low (n = 481), and PRAME-overexpressing (n = 14) cases (PRAME-low and PRAME-overexpressing comprise PRAME-positive cohort) (Fig 4). To avoid false-positive results, pairwise log-rank test comparisons were conducted, using Benjamini-Hochberg correction to calculate the adjusted p-values. Even with a small sample (n = 14), PRAME-overexpressing glioma showed a significantly worse outcome than PRAME-low (adjusted p<0.001), and PRAME-negative (adjusted p<0.001). 2-year overall survival rates of PRAME-negative, PRAME-low, and PRAME-overexpressing cohorts were 92.8% (95%CI = 88.1% - 97.8%), 64.0% (95%CI = 59.2% - 69.1%), and 13.8% (95%CI = 2.6% - 73.3%), respectively.

Table 3. Clinicopathological data of 14 patients with PRAME-overexpressing gliomas.

						Mutations
Patient ID	Age (yo)	Gender	Race	Revised classification	PRAME expression (FPKM)	IDH1/2	TP53	ATRX	TERT	CHD	EGFRamp	7+/10-	OS time (months)	Vital Status
TCGA-02-0047	79	M	White	IDH-wt Glioblastoma, G4	37.9	No	No	No	No	Yes	No	No	14.9	Dead
TCGA-06-0168	60	F	White	IDH-wt Glioblastoma, G4	1.3	No	No	No	No	No	Yes	No	19.9	Dead
TCGA-06-0646	61	M	White	IDH-wt Glioblastoma, G4	2.6	No	No	No	No	Yes	Yes	No	5.8	Dead
TCGA-06-2569	24	F	Black	IDH-wt Glioblastoma, G4	211.7	No	Yes	No	No	No	Yes	No	0.4	Alive
TCGA-06-5411	52	M	White	IDH-wt Glioblastoma, G4	1.1	No	No	No	No	Yes	Yes	No	8.5	Dead
TCGA-12-0821	63	M	White	IDH-wt Glioblastoma, G4	41.9	No	No	No	No	Yes	Yes	No	10.8	Dead
TCGA-14-0871	75	F	White	IDH-wt Glioblastoma, G4	32.2	No	Yes	No	No	n/a	n/a	n/a	29.3	Dead
TCGA-26-5133	59	M	White	IDH-wt Glioblastoma, G4	1.5	No	Yes	No	No	No	No	Yes	15.1	Alive
TCGA-28-5218	63	M	White	Astrocytoma NOS, G3	17.3	No	No	No	No	Yes	Yes	No	5.2	Dead
TCGA-DH-5140	38	F	White	IDH-wt Glioblastoma, G4	6.5	No	Yes	No	No	No	Yes	No	20.2	Dead
TCGA-DU-6403	60	F	White	IDH-wt Glioblastoma, G4	6.9	No	No	No	No	No	Yes	Yes	11.8	Dead
TCGA-E1-A7YD	58	M	White	IDH-wt Glioblastoma, G4	3.9	No	Yes	No	No	No	Yes	No	14.5	Dead
TCGA-FG-5963	23	M	White	IDH-wt Glioblastoma, G4	79.5	No	Yes	Yes	No	Yes	No	No	25.8	Dead
TCGA-S9-A7IY	40	M	White	Oligodendroglioma	1.3	Yes	No	No	No	No	No	No	23.8	Alive

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CHD: CDKN2A/B homozygous deletion; EGFRamp: EGFR gene amplification; 7+/10-: 7 gain 10 loss chromosomal abnormalities; OS: overall survival.

Fig 4 — (***), (**), and (*) indicate adjusted p < 0.001, adjusted p = < 0.01, and adjusted p < 0.05, respectively. The risk table illustrates the number of cases that survived across the timeline in each cohort of *PRAME* expression status.

Analysis of the association between tumor microenvironment (TME) and PRAME expression

Given that PRAME is associated with cytotoxic T-cell activation and killing in glioblastoma [18], we compared the immunologic cell population between PRAME-negative and PRAME-positive gliomas. GSVA of cell type-specific gene sets [19] was performed to calculate the activity (ES) of 17 cell types, including B cells, T cells, T helper, Th1, Th2, TFH, Th17, Treg, CD8 T cells, T gamma delta, cytotoxic cells, NK cells, dendritic cells, eosinophils, macrophages, mast cells, and neutrophils (Fig 5). There were increased activities of T cells (p<0.001), Th2 (p<0.001), Th17 (p<0.001), cytotoxic cells (p = 0.028), macrophages (p<0.001), and neutrophils (p<0.001) in PRAME-positive gliomas while there was reduced activities of TFH (p<0.001) and CD8 T cell (p<0.001).

Discussion

In the present study, we showed that PRAME expression status was significantly correlated with biological and clinicopathological characteristics of adult glioma grade 4, IDH-wildtype (IDH-wildtype glioblastoma). In gene expression analysis, there was a large number of gliomas showing no PRAME expression while a few numbers of tumors possessed high levels of PRAME expression. The remaining tumors generally showed low PRAME expression. Therefore, the included gliomas were divided into PRAME-negative and PRAME-positive subgroups. We then compare biological profiles and clinicopathological characteristics between PRAME-negative and PRAME-positive cases. The PCA of the PanCancer Pathways panel showed that different PRAME expression status was relatively different in their distributions, indicating that PRAME positivity may be related to oncogenic mechanisms in adult glioma. In pathway analysis, we illustrated that PRAME-positive gliomas possessed higher activity of Hedgehog, P3IK-AKT-mTOR, P53, apoptosis, IL2-STAT5, and Wnt/β-catenin signaling pathways and lower expression of E2F targets, G2M mitotic checkpoint, reactive oxidative oxygen species, TNF-α, IL6-JAK-STAT6, inflammatory response, angiogenesis, epithelial-mesenchymal transition, mTORC1, glycolysis, and hypoxia. In DNA methylation analysis, PRAME-positive gliomas were distributed more densely in a distinct, grade 4-related cluster, which implied that PRAME expression can be an indicator of the CpG methylation landscape. Clinicopathologically, PRAME positivity was associated with older age, higher grades, EGFR amplification, CDKN2A/B homozygous deletion, and 7 gain 10 loss. This association was also related to IDH-wildtype glioblastoma in the present study. Finally, PRAME expression status was identified as an independent prognostic factor of IDH-wildtype glioblastoma.

The significance of PRAME expression has been mentioned previously. Wu et al. [20] developed a PRAME-containing formula for risk score, which was inferred from the regression model. This score quantified the risk of Karnofsky performance score, but not the prognosis itself. Therefore, the inference of PRAME prognostic value is plausible but weak. The other study by Zhang et al. [21] mainly compared the PRAME expression between different types of brain tumors, including subtypes of astrocytic and non-astrocytic tumors. However, it can be difficult to conclude that PRAME expression is an independent prognostic factor by the current evidence. On the other hand, the goal of our study is to focus on the biological and clinicopathological characteristics of PRAME. Multiple analyses were performed to provide more concrete proof of the significance of this gene in glioma.

PRAME has been recently introduced as a prognostic and/or oncogenic biomarker of various cancer types, including melanocytic neoplasms [22], invasive breast carcinoma [23], lung adenocarcinoma [24], lung squamous cell carcinoma [25], and hematological malignancies [26]. PRAME has also been found to be expressed by various types of neoplasms as mentioned earlier [4]. Regarding CNS tumors, the significance of PRAME expression has been investigated in medulloblastoma [27, 28] as a biomarker for immunotherapy. However, little is known about the biological and clinical significance of the PRAME protein and its corresponding gene in glioma. To the best of our knowledge, the intensity and pattern of PRAME and its gene expression in glioma are still under investigation. In the present study, we found that most of these tumors still expressed PRAME at a low level and a minority of them, however, showed gene overexpression. Even with low expression, the PRAME-expressing glioma still had distinct biological characteristics, which was shown in subsequent analyses. Further studies to validate PRAME protein expression in glioma, using western blot analysis, immunohistochemistry, immunofluorescence, or other techniques, are needed because it is not clear whether PRAME gene expression can be an indicator of its protein status.

The nCounter Nanostring PanCancer Pathways panel was used to evaluate the biological profiles of human cancers in previous studies [29, 30]. Although the experimental pipeline of Nanostring technology was not performed in the present study, the biological value of the genes should be similar in principle. Using this panel, our PCA analysis illustrated that PRAME expression status can be a biomarker of glioma biology although further interpretations are not available in such general results. Pathway analysis showed more details in the biological difference among PRAME expression statuses. In glioma, the Wnt/β-catenin signal promotes neurogenesis and cell proliferation while the PI3K/AKT/mTOR pathway is associated with growth, metabolism, autophagy, survival, and chemotherapy resistance of glioblastoma [31]. The hedgehog signaling pathway is also required for glioma-initiating cell proliferation and tumorigenesis [32]. These pathways were increased in PRAME-positive gliomas. However, various oncogenic processes or signals in PRAME-positive gliomas such as E2F targets, G2M mitotic checkpoint, reactive oxidative oxygen species, IL6-JAK-STAT6, angiogenesis, epithelial-mesenchymal transition, and mTORC1 were activated at the lower levels compared to PRAME-negative tumors. These results were controversial, suggesting biological heterogeneity in PRAME-positive tumors.

Recent studies showed dozens of clinicopathological risk factors with prognostic significance in adult gliomas. Clinically, age, tumor size, and tumor location within CNS are predictive factors of glioma patient outcomes [33, 34]. Pathologically, histological glioma subtypes and WHO grade are also related to glioma prognosis. Regarding genetic abnormalities, CDKN2A/B homozygous deletion, EGFR amplification, TP53 mutations, ATRX mutations, TERT promoter mutations, and 7 gain 10 loss chromosomal abnormalities are associated with poor prognosis while IDH1/2 mutations are closely related to superior outcomes [12]. Regarding gene expression, a previous study developed a stemness index from the regularized cox model to predict the prognosis of glioma patients [35]. Therefore, it is important to show the prognostic significance of a biomarker by adjusting such confounders in a multivariate analysis. In the present study, we found that PRAME positivity was an independent prognostic factor to other clinicopathological factors. Interestingly, we found that PRAME gene overexpression, which is more likely visualized by the protein expression detection methods, was related to a subgroup with a significantly worse prognosis than PRAME-low gliomas despite its small sample size.

DNA methylation profile was proven to be pathologically associated with CNS tumors. A methylation-based random forest classifier was developed to provide a novel biological fingerprint of CNS tumors in addition to other identifiers such as histopathology and genetic abnormalities [36]. Another study also argued that methylation profiling can be a reliable biomarker for further low-grade glioma subtyping [37]. Therefore, examining whether there is a relationship between PRAME expression status and DNA methylation characteristics can further consolidate PRAME value in glioma biology. Our study showed that PRAME-positive gliomas were distributed more densely in the IDH-wildtype-related methylation cluster compared to the other cluster. Although this specific distribution of PRAME-positive tumors can be attributed to the dense clustering of grade 4 tumors, we believe that PRAME positivity and negativity can still be an indicator of DNA methylation profile, regardless of the causal relationships.

PRAME can also be associated with glioma TME. A previous study showed that Decitabine can increase PRAME expression and, thus, enhance the T-cell-mediated cytotoxicity, which makes PRAME an interesting target for immunotherapy [18]. However, TME of cell line can be difficult to interpret because the stromal or microenvironment context of cancer in vivo is different from that of cell line condition. Our study showed that PRAME higher expression was related to increased cytotoxic cell, macrophage, and neutrophil activity but it was also associated with many immune modulating cells such as Th2, Th17, and TFH. Therefore, PRAME expression is in a complicated relationship with many immunologic cell populations, not only cytotoxic T cells.

However, there were limitations in the present study. First, selection bias was a potential problem because this study used a public database. Second, our findings of PRAME positivity in IDH-mutant glioma were not significant potentially due to small samples, and, thus, sampling error. Therefore, a larger study of PRAME expression on IDH-mutant glioma can be helpful to examine the biological and clinicopathological relevance of PRAME positivity in these brain tumors. Third, protein expression data was not fully available and, therefore, cannot be analyzed. PRAME gene expression may be different from PRAME protein expression, which can be practically evaluated by immunohistochemistry. Hence, immunohistochemical studies are required to validate PRAME prognostic significance at the protein level. Additionally, data about histone modification is not available in TCGA-LGG and TCGA-GBM projects. Therefore, we were not able to analyze the relationship between PRAME expression and this epigenetic regulation. Finally, to our knowledge, there was no available information of chemotherapy and radiotherapy resistance in TCGA datasets. Hence, we were not able to investigate the relationship between PRAME expression and treatment response.

Conclusion

Our study illustrated that a proportion of glioma did not express PRAME while the majority of glioma expressed PRAME, among which a few tumors possessed high PRAME expression. PRAME-positive tumors had different biological (gene expression, DNA methylation, and pathway) and clinicopathological characteristics, which were related to IDH-wildtype glioblastoma. In survival analysis, PRAME positivity, especially PRAME overexpression, was related to poor prognosis.

Supporting information

S1 Fig

Kaplan-Meier curves compare the survivorship of PRAME-positive and PRAME-negative tumors in IDH-mutant gliomas with (A) and without (B) 1p/19q co-deletion.

(TIF)

Click here for additional data file.^{(529.7KB, tif)}

S1 Table. Nanostring PanCancer pathways panel consists of 770 genes.

(XLSX)

Click here for additional data file.^{(25.2KB, xlsx)}

List of abbreviations

PRAME: Preferentially expressed antigen in melanoma
TCGA: The human cancer atlas
FPKM: fragments per kilobase of exon per million mapped fragments
IDH: isocitrate dehydrogenase
PLA: pigmented lesion assay
IHC: immunohistochemistry
WHO: World Health Organization
GSEA: gene set enrichment analysis
ssGSEA: single-sample gene set enrichment analysis
GSVA: gene set variation analysis

Data Availability

The mRNA, methylation data underlying the results presented in the study are available from GDC (https://portal.gdc.cancer.gov/). The clinical, WES, and WGS data underlying the results presented in the study are available from cBioPortal (https://www.cbioportal.org/).

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0290542.r001

Decision Letter 0

Syed M Faisal

26 Jun 2023

PONE-D-23-03723Molecular and clinicopathological implications of PRAME expression in adult gliomaPLOS ONE

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Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

The study has enormous translational importance, as they justified the prognostic signature in potential study cohorts for further clinical applications.

The authors should address the following issues to consider this manuscript, primarily standard. However, the description is sometimes concise, and the authors should provide more details.

Minor comments:

1.The general description of pertaining to glioma vs glioblastoma should be mentioned in

introduction section of the manuscript.

2. The author should discuss the role of IDH1-mutation in the prognosis of glioma.

3. Should make a diagram of signaling pathways that are upregulated and downregulated

in PRAME-positive gliomas and PRAME-negative gliomas.

Reviewer #2: In this manuscript, the authors investigated and claimed that PRAME positivity is an independent prognostic factor for poor outcomes. They suggested that its over-expression can indicate different biological and clinicopathological profiles, such as age, grade, and EGFR amplification, in adult glioma. High PRAME positivity was found to be solely associated with wtIDH glioma, glioma DNA methylation landscape, and poor outcome. While PRAME is an important molecular and clinicopathological marker for different cancers including adult glioma, the reviewer believes that the present work lacks novelty as similar conclusions have already been established in previous publications. Furthermore, the present work lacks a mechanistic approach, and several aspects of the manuscript still need to be properly addressed."

Major concerns:

1. It has already been established in previous publications that PRAME is expressed in adult glioma and is associated with poor prognosis. [Wei et al., J. Neurooncol. 2019 Apr; 142(2):375-384. doi: 10.1007/s11060-019-03110-5.] [Zhang et al., J. Neurooncol. 2008 May; 88(1): 65-76. doi: 10.1007/s11060-008-9534-4.] [Zhao et al., Ann Clin Lab Sci. 2022 Mar; 52(2): 185-195.] The present study primarily utilized public databases, and no immunohistochemical data were provided to support their claim. Please provide reasons to support the uniqueness and novelty of this article compared to previous publications.

2. According to the WHO 2021 guidelines, wtIDH gliomas are clustered together in grade 4, including diffuse astrocytoma, anaplastic astrocytoma, glioblastoma, and glioblastoma NOS. The present study claims a positive correlation between PRAME and wtIDH glioma, but it doesn't clearly explain the correlation between PRAME and these wtIDH gliomas. It is important to mention the expression status of PRAME in these specific groups of gliomas.

3. PRAME is highly immunogenic and serves as a robust target for immunotherapy. Certain drugs, such as Decitabine, can increase PRAME expression to enhance T-cell-mediated toxicity against GBM, which contradicts the findings of the present study. [Ma et al., Neuro-oncology. 2022 Dec; 24(12), 2093-2106]. The present study lacks an exploration of the immunological aspects of PRAME in gliomas. It is important to investigate the correlation between different immunological populations, pro-inflammatory signaling cascades, and PRAME.

4. Among 665 patients, 170 patients were PRAME-negative, and 495 patients were PRAME-positive. Moreover, within the PRAME-positive population, 481 had low PRAME expression, and only 14 had high PRAME expression. The study compared the PRAME-positive and -negative populations for various biological and clinicopathological parameters. However, the study didn't explain the biological and clinicopathological differences between the low and high PRAME-expressing populations and their relationship with patient survival. It would be worthwhile to compare the biological parameters between the 481 low and 14 high PRAME-expressing sample sets. Additionally, the number of samples in the high PRAME-expressing set is too small to draw conclusions. Increasing the sample size for the high PRAME-positive population is advisable.

5. In the Results section, the authors mentioned that PRAME-positive tumors expressed higher activity in the PI3K-AKT-mTOR, P53, and IL2-STAT5 pathways in gliomas. However, Fig 1C doesn't correlate with this statement, as it shows upregulation of KRAS, TGFb, and Notch signaling pathways in PRAME-positive tumors. Additionally, in Fig 3A, 3B, and 3C, it seems that the PRAME-positive population has longer survival than the PRAME-negative population, although statistical significance is lacking possibly due to the small sample size. Similarly, in Fig 4 and Supplementary Fig A & B, the PRAME low-expressing population tends to have longer survival than the PRAME-negative population. In Fig 3D, PRAME-positive mutant IDH1/2 shows higher survival than PRAME-negative wt-IDH1/2 population. Please provide a proper explanation of the data.

6. It has already been established that PRAME overexpression can inhibit the growth of breast cancer and leukemia. However, PRAME overexpression in glioma is associated with poor outcomes. The present study compared the hallmarks of apoptosis between PRAME-positive and -negative adult glioma but found no differences in apoptotic hallmarks between the two groups. Please explain these findings in detail with underlying mechanisms. Also, the specific role of PRAME in retinoic acid signaling in glioma malignancy is unknown. Since the present study lacks a mechanistic approach, it is important to consider the correlation of PRAME expression with retinoic acid signaling status in wtIDH gliomas.

7. Histone methylation or acetylation status, along with other epigenetic modifications, are important hallmarks of glioma development and progression. The present study did not establish any correlation between PRAME expression and histone methylation or acetylation status in glioma. Additionally, what is the correlation between G-CIMP and PRAME in glioma?

8. Radioresistance and chemoresistance are major obstacles in the successful treatment of adult glioma. Therefore, it would be advisable to investigate the correlation between PRAME expression and treatment resistance properties in adult gliomas.

Minor concerns:

1. Please add the references at the end of the third sentence in the Introduction section.

2. Please mention the figure/table numbers in the Results section at the end of the statements (a) "The association between PRAME expression and WHO grades was statistically significant" and (b) "Conversely, IDH1/2 mutations (87.9%) and ATRX mutations (39.8%) were significantly more common in PRAME-negative cases" under the sub-heading entitled "PRAME-positive gliomas had more advanced grades and adverse outcomes."

3. Properly mention the title of the X-axis in Fig 3A-D.

4. On the title page, it should be ORCID, not ORCHID. Please correct this typo error.

5. Please thoroughly check the manuscript for grammatical and typographical errors.

6. Please rephrase the second sentence of the Discussion section, as it appears confusing to readers."

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Reviewer #1: Yes: Shadab Kazmi

Reviewer #2: No

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PLoS One. 2023 Oct 5;18(10):e0290542. doi: 10.1371/journal.pone.0290542.r002

Author response to Decision Letter 0

17 Jul 2023

To the editors:

Thank you for processing our manuscript. In this revised version, we made modifications to the author board and analyses. Although there many alterations added, there are 3 main changes.

First, we regret to inform you that we removed our co-author, Kathryn Eschbacher, from the author board. It should be noted that there was no conflict of interest in this project and this removal was made solely based on the personal reasons of this author.

Secondly, we re-run the analyses by using our published preprocessing pipeline [PMID: 37253389] and additionally used data cbioportal for a higher quality of data. The results were similar but the data presentation can be different due to different random seeds of analysis. All the results related to Figures and Tables were revised accordingly.

Thirdly, we modified Table 2, focusing on 4 distinct multivariate analyses of PRAME status adjusted for clinical characteristics, genetic mutations (WGS-available data), TERT promoter mutations (WES-available data), and copy number alterations (7+/10-, EGFR amplification, and CDKN2A/B homozygous deletion). The reason for these modifications is due to the heterogeneity of data availability of different data types (clinical information, WGS, WES, and copy number array). Stratified survival analysis of PRAME status by new classifications was done independently by Kaplan-Meier analyses.

Reviewer #1: In this manuscript, Khang Le et al. explore the publicly available human cancer atlas to reveal the role of PRAME gene expression and its biological and clinicopathological significance in gliomas and established it as a potential prognostic biomarker for glioma. The study has enormous translational importance, as they justified the prognostic signature in potential study cohorts for further clinical applications.

Thank you for reviewing our manuscript.

The authors should address the following issues to consider this manuscript, primarily standard. However, the description is sometimes concise, and the authors should provide more details.

Minor comments:

1. The general description of pertaining to glioma vs glioblastoma should be mentioned in introduction section of the manuscript.

We added a few sentences describing the context of WHO classification regarding glioma and glioblastoma in Introduction as follows:

“Adult gliomas are a heterogeneous and common group of brain cancers with unclear cell-of-origin [5]. The biological profile of gliomas has been studied with respect to histology, epigenetic, genetic characteristics, cell-of-origin, and tumor microenvironment [6,7]. There are highly diverse oncogenic mechanisms contributing to gliomagenesis and tumor progression, including Wnt/𝛽-catenin [8], PI3K/Akt/mTOR [9], TGF-𝛽 [10], and mesenchymal transition, among many others [11]. Important genetic abnormalities affecting the prognosis of glioma patients consist of IDH1/2 mutations, CDKN2A/B homozygous deletion, EGFR amplification, TP53 mutations, ATRX mutations, TERT promoter mutations, and 7 gain 10 loss chromosomal abnormalities [12]. The recent World Health Organization (WHO) classification of Tumours of the Central Nervous System (CNS) emphasizes that glioma can be divided by IDH mutation and 1p/19q codeletion status. IDH-wildtype astrocytoma has more advanced clinicopathological progression and tumor with grade 4 is referred to as the “glioblastoma” category. Glioblastoma is diagnosed by the absence of IDH mutation and one of the high-grade features, including high-grade morphology, TERT promoter mutation, 7 gain/10 loss chromosomal abnormality, or EGFR amplification. However, the prognostic factors of glioma are still under investigation.”

2. The author should discuss the role of IDH1-mutation in the prognosis of glioma.

Thank you for your comments.

We added more details about IDH mutation in Introduction to clarify the background in the previous comment.

3. Should make a diagram of signaling pathways that are upregulated and downregulated

in PRAME-positive gliomas and PRAME-negative gliomas.

We added Figure 1D that describes pathways that are down-regulated or up-regulated regarding the PRAME expression status.

Thank you for reviewing our manuscript.

Major concerns:

Thank you for bringing these studies to our attention. We believe that the purpose of the aforementioned studies is different from our present work. We focused on the biological and clinicopathological investigation of PRAME expression in a more comprehensive way, including epigenetic, genetic, and gene expression.

We added a paragraph to discuss this issue as follows:

“The significance of PRAME expression has been mentioned previously. Wu et al [20] developed a PRAME-containing formula for risk score, which was inferred from the regression model. This score quantified the risk of Karnofsky performance score, but not the prognosis itself. Therefore, the inference of PRAME prognostic value is plausible but weak. The other study by Zhang et al [21] mainly compared the PRAME expression between different types of brain tumors, including subtypes of astrocytic and non-astrocytic tumors. However, it can be difficult to conclude that PRAME expression is an independent prognostic factor by the current evidence. On the other hand, the goal of our study is to focus on the biological and clinicopathological characteristics of PRAME. Multiple analyses were performed to provide more concrete proof of the significance of this gene in glioma.”

2. According to the WHO 2021 guidelines, wtIDH gliomas are clustered together in grade 4, including diffuse astrocytoma, anaplastic astrocytoma, glioblastoma, and glioblastoma NOS.

We believe that Glioblastoma, IDH-wildtype is defined when there is one or more of the following features: microvascular proliferation, necrosis, TERT promoter mutation, EGFR gene amplification, and 7+/10- chromosome copy-number changes according to WHO classification 5th edition. Therefore, astrocytic tumors with IDH wildtype status and no other feature are ill-defined and should be classified as Astrocytoma, NOS. Therefore, we revised the classification in a more detailed way, following our recently published paper [PMID: 37253389].

We added details about our revised data processing pipeline as follows:

“Data processing

The Human Cancer Atlas (TCGA) database consists of many datasets. We extracted cases from the TCGA-GBM and TCGA-LGG projects. Only cases with available gene expression profiles (GEP) and primary tumors (no recurrent or metastatic tumors) were included in the study. To adapt to the new WHO classification, cases with grades 2/3 in the previous studies [13,14] were reclassified into grade 4 as follows: (1) the presence of both IDH1/2 mutation and CDKN2A/B homozygous deletion, or (2) the absence of IDH1/2 mutations and the presence of at least one of the following abnormalities: TERT promoter mutation, EGFR amplification, and 7 gain 10 loss chromosomal abnormality. Other histopathological grading features such as microvascular proliferation and pseudopalisading necrosis were assumed to be included in previously evaluated grade IV gliomas of the original studies [13,14]. Tumors with the absence of IDH1/2 mutations and no other high-grade morphological and genetic features mentioned above were reclassified as Astrocytoma, Not Otherwise Specified (NOS). Tumors with the presence of both IDH1/2 mutations and 1p/19q codeletion were categorized as Oligodendroglioma. Mixed glioma was re-distributed into new categories based on IDH1/2 mutation and 1p/19q codeletion status. This reclassification was published in our previous paper [15]. The difference in data processing between this study and our previous one was that we used cbioportal for cancer genomics (https://www.cbioportal.org) datasets that are related to TCGA-GBM and TCGA-LGG projects, including (1) Brain Lower Grade Glioma (TCGA, Firehose Legacy), (2) Glioblastoma Multiforme (TCGA, Firehose Legacy), and (3) Merged Cohort of LGG and GBM (TCGA, Cell 2016). This difference led to a slight inconsistency in the total number of glioma patients.”

The present study claims a positive correlation between PRAME and wtIDH glioma, but it doesn't clearly explain the correlation between PRAME and these wtIDH gliomas. It is important to mention the expression status of PRAME in these specific groups of gliomas.

We added a boxplot (Figure 1E) to illustrate the PRAME expression status in each revised category of glioma.

Thank you for your information.

We added a section of analysis named “Analysis of the association between tumor microenvironment (TME) and PRAME expression” as follows:

“Analysis of the association between tumor microenvironment (TME) and PRAME expression

Given that PRAME is associated with cytotoxic T-cell activation and killing in glioblastoma [15], we compared the immunologic cell population between PRAME-negative and PRAME¬-positive gliomas. GSVA of cell type-specific gene sets [16] was performed to calculate the activity (ES) of 17 cell types, including B cells, T cells, T helper, Th1, Th2, TFH, Th17, Treg, CD8 T cells, T gamma delta, cytotoxic cells, NK cells, dendritic cells, eosinophils, macrophages, mast cells, and neutrophils (Figure 5). There were increased activities of T cells (p<0.001), Th2 (p<0.001), Th17 (p<0.001), cytotoxic cells (p=0.028), macrophages (p<0.001), and neutrophils (p<0.001) in PRAME-positive gliomas while there was reduced activities of TFH (p<0.001) and CD8 T cell (p<0.001).”

We also added a corresponding discussion paragraph:

“PRAME can also be associated with glioma TME. A previous study showed that Decitabine can increase PRAME expression and, thus, enhance the T-cell-mediated cytotoxicity, which makes PRAME an interesting target for immunotherapy [15]. However, TME of cell line can be difficult to interpret because the stromal or microenvironment context of cancer in vivo is different from that of cell line condition. Our study showed that PRAME higher expression was related to increased cytotoxic cell, macrophage, and neutrophil activity but it was also associated with many immune modulating cells such as Th2, Th17, and TFH. Therefore, PRAME expression is in a complicated relationship with many immunologic cell populations, not only cytotoxic T cells.”

Thank you for your suggestion. However, it is quite difficult to define a cutoff of PRAME overexpression. This is emperically determined by the data at hand and defined by >1, >3, or >5 FPKM. For example, a previous study used FPKM>1 to define higher expression [PMID: 27577089]. Therefore, we believe that it is better to follow these recommendations. In our data, a vast number of cases had PRAME read count<1. Therefore, >1 FPKM is an appropriate cutoff for determining high expression or overexpression.

As the reviewer mentioned, the sample size of PRAME-overexpressing cohort was small to create reliable results. Therefore, to avoid multiple hypothesis problem, we only performed survival analysis for this cohort. We additionally applied Benjamini-Hochberg correction for the pairwise comparison in this survival analysis to solidify the result. The analysis of other biological parameters, which requires a lot of hypothesis testing, can be performed but it can lead to false positive results and misleading conclusions. Therefore, we believe that it is better not to over-do the investigation of the PRAME-overexpressing cohort.

Thank you for pointing out this observation. In Figure 3A, PRAME-positive and PRAME-negative glioma patients had difference in survival patterns (p<0.001). We concur with you that the PRAME-negative cohort survived better in the >10 years of follow-up. Unfortunately, we do not have a clear explanation for this observation. Although it seems that PRAME-negative cohort deceased sooner than PRAME-positive cohort in the period of >10 years of follow-up, there may be many confounding factors because of the long follow-up time. Another confounding factor is sample size. The size of PRAME-positive cohort was much larger than PRAME-negative one. Therefore, the positive cohort was likely to have more outliers than the negative cohort. Generally, first years of follow-up are more reliable prognostic indicator compared to longer term of follow-up. To avoid this confusing phenomenon, we limited the x-axis from 0 to 10 years of follow-up, which eliminates such observation of the outliers.

Thank you for your suggestion. We experimentally run pathway analysis to investigate the activity of retinoic acid signaling (the full gene set of this pathway is in the link below). The enrichment score (ES) of PRAME-negative and PRAME-positive glioma were compared by two-sample independent t-test, which was similar to other pathways. There was no significant difference between PRAME-negative and PRAME-positive samples (p=0.446). Therefore, the role of PRAME in glioma may not be related to its suppression of retinoic acid signaling. However, we believe that it is better not to include this analysis into the manuscript because this analysis may not be relevant to the main stream of analysis and it can also cause confusion to readers.

The link to retinoic pathway:

https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/REACTOME_SIGNALING_BY_RETINOIC_ACID.html

Thank you. The lack of histone modification data is the limitation of the present study. We added a few sentences to the Discussion as follows:

“However, there were limitations in the present study. First, selection bias was a potential problem because this study used a public database. Second, our findings of PRAME positivity in IDH-mutant glioma were not significant potentially due to small samples, and, thus, sampling error. Therefore, a larger study of PRAME expression on IDH-mutant glioma can be helpful to examine the biological and clinicopathological relevance of PRAME positivity in these brain tumors. Third, protein expression data was not fully available and, therefore, cannot be analyzed. PRAME gene expression may be different from PRAME protein expression, which can be practically evaluated by immunohistochemistry. Hence, immunohistochemical studies are required to validate PRAME prognostic significance at the protein level. Additionally, data about histone modification is not available in TCGA-LGG and TCGA-GBM projects. Therefore, we were not able to analyze the relationship between PRAME expression and this epigenetic regulation. Finally, to our knowledge, there was no available information of chemotherapy and radiotherapy resistance in TCGA datasets. Hence, we were not able to investigate the relationship between PRAME expression and treatment response.”

In addition, we created Figure 2E to demonstrate the relationship between G-CIMP and PRAME expression in glioma.

To avoid confusion, we eliminate the analysis of t-SNE methylation clusters. We instead used the published methylation CIMP clusters and performed chi-square test. We modified the methylation Results section as follows:

“In the study cohort, there were a total of 476 tumors with available data about 450K DNA methylation. We performed t-SNE dimension reduction to explore the differences in the distribution of PRAME-negative and PRAME-positive glioma in DNA methylation hyperspace. This DNA methylation space can be interpreted as the reduced representation of the CpG methylation landscape. We also included the Glioma CpG Island Methylator Phenotype (G-CIMP), which was published in a previous paper [33941250]. In this study, 476 tumors in DNA methylation space can be relatively divided into two unsupervised tSNE clusters, small (right, lower corner) and large clusters (left and upper part) (Figure 2A-D). The small cluster densely consisted of PRAME-low and PRAME¬-overexpressing samples while the larger cluster had a significant portion of PRAME-negative samples (Figure 2A). The revised subtype (Figure 2B), CIMP clusters (Figure 2C), and IDH status (Figure 2D) were strongly associated with these 2 clusters. Figure 2E shows a heatmap of distribution of PRAME expression status within CIMP clusters. There were significant difference in the distribution of PRAME expression status (chi-square test, p<0.001). This discrimination can be seen in LGm6-GBM (6/12 vs. 7/484 vs. 0/170), classic-like (1/12 vs. 63/484 vs. 4/170), and mesenchymal-like (2/12 vs. 87/484 vs. 9/170).”

Thank you for your comment. However, there was no information about chemotherapy and radiotherapy resistance in the TCGA datasets. Therefore, we listed your comments as a limitation of our study.

Minor concerns:

1. Please add the references at the end of the third sentence in the Introduction section.

We added citations at the end of the third sentence in Introduction section.

Thank you. The referred paragraph was written as a description of Table 1 and it is mentioned in the first sentence of this paragraph:

“Table 1 summarizes the clinicopathological characteristics of PRAME-negative and PRAME-positive gliomas.”

3. Properly mention the title of the X-axis in Fig 3A-D.

We changed the title of the x-axis from “Time” to “Follow-up Time (Years)”.

4. On the title page, it should be ORCID, not ORCHID. Please correct this typo error.

We corrected this error.

5. Please thoroughly check the manuscript for grammatical and typographical errors.

Thank you for your comments.

6. Please rephrase the second sentence of the Discussion section, as it appears confusing to readers.

We modified the sentence “In gene expression analysis, we explored PRAME read counts and found that PRAME expression was generally low in these brain tumors. A significant number of tumors showed no PRAME expression.” into “In gene expression analysis, there was a large number of gliomas showing no PRAME expression while a few numbers of tumors possessed high levels of PRAME expression. The remaining tumors generally showed low PRAME expression.”

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PLoS One. doi: 10.1371/journal.pone.0290542.r003

Decision Letter 1

Syed M Faisal

11 Aug 2023

Molecular and clinicopathological implications of PRAME expression in adult glioma

PONE-D-23-03723R1

Dear Dr. Kondo,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Academic Editor

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Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0290542.r004

Acceptance letter

Syed M Faisal

25 Sep 2023

PONE-D-23-03723R1

Molecular and clinicopathological implications of PRAME expression in adult glioma

Dear Dr. Kondo:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

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on behalf of

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig

Kaplan-Meier curves compare the survivorship of PRAME-positive and PRAME-negative tumors in IDH-mutant gliomas with (A) and without (B) 1p/19q co-deletion.

(TIF)

Click here for additional data file.^{(529.7KB, tif)}

S1 Table. Nanostring PanCancer pathways panel consists of 770 genes.

(XLSX)

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Attachment

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Data Availability Statement

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PERMALINK

Molecular and clinicopathological implications of PRAME expression in adult glioma

Minh-Khang Le

Huy Gia Vuong

Ian F Dunn

Tetsuo Kondo

Roles

Abstract

Background

Methods and materials

Results

Conclusion

Introduction

Materials and methods

Data processing

Gene set variation analysis (GSVA)

Data analysis

Results

Investigating PRAME expression and pathway activities

Fig 1.

PRAME positivity was densely distributed within a distinct DNA methylation cluster

Fig 2.

PRAME positivity was associated with IDH-wildtype glioblastoma and adverse outcomes

Table 1. Comparison of clinicopathological characteristics of PRAME-negative and PRAME-positive cohorts.

Fig 3.

Table 2. Multivariate Cox survival analyses with overall survival and vital status as the outcome, adjusted for clinical characteristics, WGS, WES, and CNV information.

PRAME overexpression was associated with a worse prognosis than PRAME positivity

Table 3. Clinicopathological data of 14 patients with PRAME-overexpressing gliomas.

Fig 4. The Kaplan-Meier curve shows the pairwise comparisons of survivorship between PRAME-negative, PRAME low-expressing, and PRAME-overexpressing tumors.

Analysis of the association between tumor microenvironment (TME) and PRAME expression

Fig 5. The boxplots comparing activity of different immunologic cell populations between PRAME-negative and PRAME-positive gliomas.

Discussion

Conclusion

Supporting information

List of abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Syed M Faisal

Roles

Author response to Decision Letter 0

Decision Letter 1

Syed M Faisal

Roles

Acceptance letter

Syed M Faisal

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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