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Tumor mutational burden presents limiting effects on predicting the efficacy of immune checkpoint inhibitors and prognostic assessment in adrenocortical carcinoma
Fangshi Xu1,2, Yibing Guan1,2, Peng Zhang2, Li Xue2, Yubo Ma1,2, Mei Gao2, Tie Chong2 and Bin-Cheng Ren3*
Abstract
Background: Adrenocortical carcinoma (ACC) is a highly malignant urologic cancer and tends to metastasize. Although immune checkpoint inhibitors (ICIs) bring a glimmer of light to conquer ACC, only a fraction of patients have benefit from ICIs treatment. It is well known that tumor mutational burden (TMB) is closely associated with the efficacy and response rate of immunotherapy. However, its roles in ACC were not investigated.
Methods: Using somatic mutations data of 92 ACC samples in TCGA database, we calculated their TMB values by the ‘maftools’ package in R software (Ver 3.6.3). To explore the roles of TMB in ICIs therapy, we have addressed this issue from three perspectives. First, the effects of TMB levels on tumor immune microenvironment (TIM) were analyzed through CIBERSORT algorithm, ssGSEA method and TIMER web server. Second, we investigated the expressive cor- relations between TMB level and five pivotal immune checkpoints based on Pearson coefficient. Third, the difference in TIDE score between high- and low-TMB groups was compared. The prognostic value of TMB was also evaluated. Besides, GSEA was performed to determine the changes in the activities of signaling pathways caused by TMB.
Results: TMB values in ACC samples were not high. The average of total mutation counts in each sample was only 21.5. High TMB could lead metabolic reprogramming and poor survival outcomes. However, it was unable to affect the infiltration levels of lymphocytes, and failed to facilitate the activities of immune-related pathways. Regarding immune checkpoints (ICs), only PD-L1 upregulation could result in a good prognosis, and TMB level did not correlate with the expressions of other ICs except for LAG3. There was no significant difference in TIDE score between high- and low-TMB groups. Combining the present results and previous study, we speculated that inadequate stimulation for neoantigens formation, intrinsic immune-resistance and special genomic alterations were three possible reasons for TMB limiting functions in TIM and ICIs. Besides, TMB was toughly applied in clinical practice due to its high cost of determination and non-universal definition of high TMB.
Conclusions: TMB presents limiting effects on prediction for ICIs efficacy and prognostic assessment for ACC patients.
*Correspondence: renbincheng7@163.com
3 Department of Rheumatology and Immunology, The Second Affiliated Hospital of Xi’an Jiaotong University, No. 157, West Five Road, Xi’an 710000, Shaanxi Province, China
Full list of author information is available at the end of the article
☒ BMC
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Keywords: Tumor mutational burden, Adrenocortical carcinoma, Immune checkpoint inhibitors, Immune microenvironment, Prognosis
Background
Adrenocortical carcinoma (ACC) is a highly malignant urologic cancer and tends to metastasize. The overall survival (OS) time of ACC patients is commonly less than 30 months, while the 5-year overall survival rate (OSR) of patients with clinical stage III-IV is only 13% [1]. Although surgery is the most effective therapeutic approach, the resectable cases only account for 16% in all ACC patients [2]. Even after complete surgical resection, approximately 40% of patients will progress to metastatic disease within 2 year [3]. As the basic adjuvant therapy for advanced ACC, the response rate of mitotane plus chemotherapy was less than 30% [4]. Thus, the treatment of ACC is extremely challenging.
Recently, immune checkpoint inhibitors (ICIs) lead a significant breakthrough in cancer treatment and bring a glimmer of light to conquer ACC. Since 2016, five agents targeting the programmed death-1/ligand-1 (PD-1/L1) have been approved to treat urothelial car- cinoma, especially for metastatic cancer [5]. However, only a fraction of patients can present a good thera- peutic response. A Phase 2 study revealed that, for ACC patients treated with pembrolizumab, their clini- cal benefit rate reached up to 54%, but their objective response rate (ORR) was just 15% [6]. Therefore, it is urgent and meaningful to identify an effective predic- tive biomarker for the efficacy of ICIs.
Tumor mutational burden (TMB) refers to the total- ity of somatic mutations per million bases, including single nucleotide polymorphisms (SNPs) and variations of copy number. High number of mutations in somatic exonic regions commonly induces the formation of neoantigen, which activates T cell immunogenicity to inhibit tumor cells and brings a better therapeutic response [7]. Hence, TMB is regarded as a potential biomarker for predicting the efficacy and response rate of immunotherapy [7]. In the phase III CheckMate 227 study, nivolumab plus ipilimumab significantly pro- longed progression-free survival (PFS) of non-small- cell lung cancer (NSCLC) patients with high TMB (≥10 mutations per megabase, MB) comparing with chemo- therapy [8]. Given that cancer patients with high TMB commonly have a higher response rate to anti-PD-1/L-1 immunotherapy, the US Food and Drug Administration (FDA) even have approved pembrolizumab for patients with malignant solid tumors of any histologic type with high TMB (≥10MB) in June 2020 [9]. However, the roles of TMB in ACC has not been fully elucidated.
In the present study, we investigated the relationships between TMB, immune microenvironment and ICIs therapy in ACC using bioinformatics method, and found a dramatically different outcome. High TMB may not affect the ACC immune microenvironment and not serve as a predictive biomarker for ICIs efficacy.
Materials and methods
Data source
The analytical data were obtained from the Cancer Genome Atlas (TCGA) public database (https://portal. gdc.cancer.gov/repository). There were three data catego- ries were downloaded, namely gene expression data (240 ACC samples), somatic mutations data (92 ACC sam- ples) and clinical data (90 ACC samples). The data type of gene expression was ‘Transcriptome profiling’, and that of somatic mutations was ‘Simple nucleotide variation’ The workflow type of somatic mutations was chosen as ‘Var- Scan2 Annotation’ The data format of clinical data was
| Variables | Number (percentage) |
|---|---|
| Vital status | |
| Alive | 57 (63.3%) |
| Dead | 33 (36.7%) |
| Age | NA |
| Gender | |
| Male | 30 (33.3%) |
| Female | 60 (66.7%) |
| Tumor Grade | NA |
| Clinical Stage | |
| Stage I | 9 (10.0%) |
| Stage II | 44 (48.9%) |
| Stage III | 19 (21.1%) |
| Stage IV | 18 (20.0%) |
| T stage | |
| T1 | 9 (10.0%) |
| T2 | 49 (54.4%) |
| T3 | 11 (12.2%) |
| T4 | 21 (23.4%) |
| M stage | NA |
| N stage | |
| N0 | 80 (88.9%) |
| N1 | 10 (11.1%) |
ACC Adrenocortical carcinoma, NA not available
‘BCR-XML’ The clinical features of 90 ACC samples were shown in Table 1.
The calculation of TMB value
The visualization of somatic mutations data was per- formed via the ‘maftools’ package in R software (Ver 3.6.3) [10]. The concurrent and exclusive associations across mutated genes were determined by using ‘corrplot’ package in R software. TMB represents the total number of mutations per megabyte in tumor samples, which was defined as the average number of somatic mutations in tumor genome, including gene coding errors, base sub- stitution insertions and deletions [11]. Due to the length of human exon being commonly set as 38 MB (million bases), the TMB value of each ACC sample was calcu- lated as the total mutation frequency/38 [12].
GSEA
Gene-set enrichment analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant and concordant differences between two biological phenotypes [13]. In the present study, GSEA was performed to compare the differences in signal pathways between different TMB levels. Enrichment statistics was based on weighted method. Phenotype labels were set as high TMB level versus low TMB level. Permutation Type was phenotype. The number of permutations was set as 1000, and ‘c2.cp. Kegg.v7.4 symbols’ was chosen as the gene set. When the normalized enrichment score (NES) ≥1, nominal (NOM) p-value ≤0.05 and false discovery rate (FDR) q-value ≤0.25 were simultaneously satisfied, the pathways were considered to significantly enriched.
Survival analysis and clinical correlation analysis
According to the median of TMB value, 90 ACC sam- ples were divided into high- and low-TMB groups. The ‘survival’ package in R software was employed to analyze the prognostic differences between high- and low-TMB groups. Receiver operating characteristic curve (ROC) was applied to assess the predictive accuracy of TMB. Cox univariate and multivariate analyses were succes- sively conducted to identify whether TMB was an inde- pendent prognostic factor of ACC. The relationships between TMB and clinicopathological features of ACC were determined based on Kolmogorov-Smirnov test.
Immune analysis
CIBERSORT algorithm can estimate the abundances of various kinds of human leukocyte subtypes through the gene expression data [14]. This method is based on deconvolution algorithm, and relies on a LM22 refer- ence set which provides a set of gene expression features
of 22 leukocyte subtypes [15]. 1000 permutations and p-value<0.05 were set during CIBERSORT analysis. Then, the immune abundances of 22 leukocyte subtypes in each ACC sample were calculated and their visualiza- tions were displayed through ‘barplot’ package in R soft- ware. To evaluate the effect of TMB on ACC immune microenvironment, the difference in infiltration levels of multiple immune cells between high- and low-TMB groups was compared. Its visualization was performed via ‘vioplot’ package. Moreover, the active scores of 13 immune-related pathways were calculated based on sin- gle-sample gene set enrichment analysis (ssGSEA).
TIMER web server is a comprehensive resource for systematical analysis of immune infiltrates across diverse cancer types. The ‘Survival’ module was applied to con- firm the associations of infiltration levels of immune cells with survival outcomes in ACC [16].
Correlation analysis of immune checkpoints and TMB
To explore the relationships between TMB and the effi- cacy of ICIs, we demonstrated this issue in two ways. First, we analyzed the expressive correlations between TMB level and five pivotal immune checkpoints (ICs) (including PD-L1, CTLA4, LAG3, HAVCR2 and TIGIT), which was quantized by Pearson coefficient. Besides, the differences in the expressions of five ICs between high- and low-TMB levels were calculated through ‘ggplot2’ and ‘reshape2’ R packages.
Second, we also employed TIDE score to shed light on this issue. In 2018, Jiang P’s team developed a novel algorithm, tumor immune dysfunction and exclusion sys- tem (TIDE), to predict the outcome of cancer patients treated with PD-1/L1 inhibitors [17]. The higher TIDE score commonly indicates a greater propensity for dys- function of T cells, therefore leading a worse efficacy of PD-1/L1 inhibitors. Therefore, we compared the differ- ence in TIDE score between high- and low-TMB levels via ‘Limma’ R package.
Results
TMB level in ACC is not high
Using 92 ACC samples in TCGA database, we exhibited the landscape of somatic mutations in ACC (Fig. 1). Mis- sense mutation was the most common mutational form. Single nucleotide polymorphism (SNP) accounts for the largest part of all variant types (Fig. 1A). C>T and C>A transitions were both major classes of single nucleotide variants (SNV) (Fig. 1A). In addition, the waterfall plot demonstrated the profile of somatic mutation informa- tion in each ACC sample (Fig. 1B). Somatic mutations occurred in 73.91% of all ACC samples. However, the average of total mutation counts in each sample was only 21.5 (Fig. 1A), and the highest gene mutation frequency
A
Variant Classification
Variant Type
SNV Class
Missense_Mutation
T>G
250
Nonsense_Mutation
SNP
Frame_Shift_Del
T>A
672
Splice_Site
T>C
989
Frame_Shift_Ins
INS
In_Frame_Del
C>T
3758
Nonstop_Mutation
C>G
1123
In_Frame_Ins
DEL
Translation_Start_Site
C>A
3220
0
1000
2000
3000
4000
5000
6000
0
1000
2000
3000
4000
5000
6000
7000
0.00
0.25
0.50
0.75
1.00
Variants per sample
Variant Classification summary
Median: 21.5
Top 10 mutated genes
1776
72
TTN
12%
TP53
18%
MUC4
14%
1184
48
MUC16
14%
CTNNB1
15%
PKHD1
9%
592
24
NF1
9%
CNTNAP5
9%
PCDH15
8%
0
ASXL3
8%
0
0
A
=
B
2
Altered in 68 (73.91%) of 92 samples.
1776
· Frame_Shift_Del
· Frame_Shift_Ins
Missense_Mutation
· Splice_Site
= In_Frame_Del
= Multi_Hit
# Nonsense_Mutation
0
17
0
TP53
CTNNB1
18%
MUC16
15%
MUC4
14%
TTN
14%
CNTNAP5
12%
NF1
9%
PKHD1
9%
9%
ASXL3
8%
DST
HMCN1
8%
PCDH15
8%
PRKAR1A
8%
ANK2
8%
CCDC168
7%
CMYA5
7%
FBN2
7%
MEN1
7%
SVEP1
7%
7%
ADGRG4
5%
AHNAK2
5%
ATRX
EYS
5%
FAT3
5%
FAT4
5%
HUWE1
5%
5%
KMT2B
5%
LRP1
OBSCN
5%
WDFY4
5%
5%
Fig. 1 The landscape of somatic mutations in ACC. A The summary of mutational type. B The waterfall plot of top 20 mutated genes. ACC, adrenocortical carcinoma; INS, insertion; DEL, deletion; SNV, single nucleotide variant; SNP, single nucleotide polymorphism
was just 18% (TP53) (Fig. 1B). These results indicated that although somatic mutations frequently occurred in ACC samples, the mutation burden was not high.
Alterations of TMB levels lead metabolic reprogramming in ACC
The GSEA results indicated that different TMB lev- els had a dramatic impact on the activities of signaling pathways (Fig. 2A-J). In high TMB group, cell prolif- eration-related pathways, including ‘cell cycle’ ‘Base excision repair’ and ‘DNA replication’ pathways were significantly enriched, which revealed that high TMB
could promote ACC progression (Fig. 2ADE). Never- theless, the enrichments of ‘homologous recombina- tion’ and ‘p53’ pathways caused by high TMB would bring an inhibitory effect on malignant progression. As shown in Table 2, the deficiency of homologous recom- bination can impede the process of DNA double-strand break repair, which commonly promote tumorigenesis (Fig. 2B) [18]. p53, a famous tumor suppressor pro- tein, exerts its anti-tumor functions through promoting apoptosis and DNA repair [19]. Hence, its enrichment is unfavorable for cancer development.
A
Enrichment plot: KEGG_CELL_CYCLE
B
Enrichment plot:
C
Enrichment plot: KEGG_P53_SIGNALING_PATHWAY
D
Enrichment plot: KEGG_BASE_EXCISION_REPAIR
Đ
Enrichment plot: KEGG_DNA_REPLICATION
Enrichment score (ES)
0.7
KEGG_HOMOLOGOUS_RECOMBINATION
0.6
Enrichment score (ES)
Enrichment score (ES)
Enrichment score (ES)
0.7
8
0.5
07
5
0.6
Enrichment score (ES)
16
.5
0.6
.A
1
15
1.4
1.3
4
3
.2
0.3
.2
.1
17
1
R.O
Ranked list metric (Signal2Noise)
Ranked list metric (Signal2Noise)
Ranked list metric (Signa(2Noise)
Ranked list metric (Signal2Noise)
Ranked list metric (Signal2Noise)
0.5
55
9 5
0.5
#
0
=
0.0
0.5
Y (negutuely comelate di
65
as
” (negatively completed)
Tinegurtunely comelated)
0.5
Tinegatively conwanted)
20.000
40,000
50.000
.
10,000
20,000
30.000
€0.000
50.000
·
10,000
20,000
30,000
40,000
50.000
0
10.000
20.000
30.000
48,000
50.000
.
10,000
20,000
10,000
10.000
80,000
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
F
Enrichment profile - Hits
Ranking metric scores
G
Enrichment profile - Hits
Ranking metric scores
H
Enrichment profile - Hits
Ranking metric scores
Enrichment plot: KEGG_BUTANOATE_METABOLISM
Enrichment plot:
Enrichment plot: KEGG_TYROSINE_METABOLISM
I
Enrichment profile -Hits
Ranking metric scores
J
Enrichment profile - Hits
Ranking metic scores
Enrichment plot: KEGG_HISTIDINE_METABOLISM
Enrichment score (ES)
Enrichment score (ES)
Enrichment score (ES)
Enrichment plot: KEGG_PRIMARY_BILE_ACID_BIOSYNTHESIS
KEGG_DRUG_METABOLISM_CYTOCHROME_P450
OU
O.
0.1
Enrichment score (ES)
0.1
5.1
Enrichment score (ES)
0.2
2.1
02
0.1
.3
1.2
03
0.2
0.3
a
DA
0.4
0.6
0.5
0.5
0.5
0.5
0.6
0.6
0.6
Ranked list metric (Signal2Noise)
Ranked list metric (Signal2Noise)
Ranked list metric (Signal2Noise)
Ranked list metric (Signal2Noise)
Ranked list metric (Signal2Noise)
s
0.0
00
3 4
0.5
5
T (Pepelively deswwf)
0.5
“(negatively convicted)
” (negatively completed)
5
3
18.000
20.000
30.000
+0,000
98.000
9
20,000
30.000
48,000
50.000
A
10.000
20.000
30.000
00.000
a
18,000
20.000
30.000
10.000
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a
20,000
30.000
48,000
50.000
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
Enrichment profile - Hit
Ranking metric scores
Enrichment profile - Hits
Ranking metric scores
Enrichment profile - Hits
Ranking metric scores
Enrichment profile - Hit
Ranking metric scores
Enrichment profile - Hits Ranking metric scores
H
cerebral cortex development
cellular component assembly involved in
morphogenesis
histone lysine methylation
U
I
regulation of neuroblast proliferation
p.adjust
0.05
Proteoglycans in cancer
p.adjust
0.04
myofibril
0.03
0.09
0.02
Colorectal cancer -
contractile fiber part
0.01
0.08
CC
KEGG
sarcomere
Counts
Basal cell carcinoma -
0.07
☐ 2
A band
☐
4
Counts
☐
7
Endometrial cancer -
☐
2
extracellular matrix structural
☐
2
constituent
☐
protease binding
Thyroid cancer -
3
disordered domain specific binding
MF
I
U
T
T
I
structural molecule activity conferring
0.16 0.18 0.20 0.22 0.24 0.26
elasticity
GeneRatio
T
T
T
I
0.10
0.15
0.20
0.25
GeneRatio
| Pathway | Variation trend | NOM p-value | Functions in cancer |
|---|---|---|---|
| Homologous recombination | Enriched in high TMB | 0.008 | Main steps in DNA double-strand break repair. |
| P53 | Enriched in high TMB | 0.000 | Tumor suppressor protein. |
| Butanoate metabolism | Enriched in low TMB | 0.017 | 1. An anticancer ingredient. 2. Butanoate metabolism is disrupted in breast cancer. |
| Tyrosine metabolism | Enriched in low TMB level | 0.008 | Tyrosine kinase family encompasses the greatest number of oncoproteins. |
| Histidine metabolism | Enriched in low TMB level | 0.028 | Histidine metabolism can increase the effectiveness of methotrexate treatment. |
| Bile acid metabolism | Enriched in low TMB level | 0.035 | Bile acids have been proposed to promote colon carcinogenesis. |
| Cytochrome P450 | Enriched in low TMB level | 0.002 | Cytochrome P450 enzymes play key roles in bile acid synthesis. |
TMB tumor mutational burden
Metabolic reprogramming is a major hallmark of can- cer. In ACC, we found that five metabolic signaling pathways enriched in low-TMB group (Table 2). Among that, butanoate has served as an anticancer ingredi- ent [20], and its metabolism pathway has been found to be disrupted in breast cancer patients [21]. There- fore, butanoate metabolism has been regarded as an approach to differentiate cancer patients from healthy individuals [21]. Histidine metabolism can boost cancer therapy through increasing the effectiveness of metho- trexate treatment [22]. Bile metabolism has been proven to regulate tumorigenesis, and high level of bile acid could promote colon proliferation [23, 24]. In a word, the alterations of TMB levels lead metabolic reprogramming in ACC, which may be involved in the regulation of ACC progression.
Besides, we investigated the biological functions of 30 most frequently mutated genes in ACC samples, includ- ing TP53, CTNNB1, MUC16, MUC4 and TTN etc. (Fig. 1B). These genes were closely involved in the pro- liferation- and invasion-related process, such as ‘cel- lular component assembly involved in morphogenesis’, ‘extracellular matrix structural constituent’ and ‘struc- tural molecule activity conferring elasticity’ (Fig. 2H). For instance, extracellular matrix (ECM) is the dynamic foundation of tumorigenesis and cancer progression [25]. The active assemblage of ECM undoubtedly contributes to cancer development. Meanwhile, these mutated genes also profoundly affected the regulation of multiple can- cers (Fig. 2I). Briefly, frequently mutated genes in ACC were tightly associated with tumor malignant behavior.
High TMB results in poor prognosis and unfavorable clinical stages
According to the median of TMB (2.19/MB), 90 ACC samples were classified into high- and low-TMB groups. The prognostic analysis revealed that high TMB con- ferred an adverse survival outcome (HR=5.82, P<0.001)
(Fig. 3A). The 5-year OSRs of high- and low-TMB groups were 34.7 and 82%, respectively. Meanwhile, the TMB was found to have a preponderance of prognostic pre- diction over other clinical indicators (Area under curve, AUC=0.813) (Fig. 3BC). Interestingly, despite the great predicting performance of TMB, it was not an independ- ent prognostic factor of ACC (HR=1.055, P=0.26) (Fig. 3DE). In addition, High TMB was significantly asso- ciated with advanced clinical stage (Fig. 2F) and T stage (Fig. 2G).
High TMB cannot affect the immune microenvironment of ACC
Using the CIBERSORT algorithm, the immune abun- dances of 22 leukocyte subtypes in each ACC sample were exhibited in Fig. 4A. Interestingly, high TMB not only cannot affect the infiltration levels of lympho- cytes (Fig. 4B), but also cannot facilitate the activities of immune-related pathways (Fig. 4C). Particularly, as the pivotal immune cells for targeting cancer, the immune abundances of CD8+ T, CD4+ T and NK cells did not present significantly differences between high- and low- TMB groups (Fig. 4B). Moreover, the infiltration levels of six hub immune cells also could not affect ACC progno- sis (Fig. 4D), which suggested that ACC may equip with a certain degree of immune resistance.
There were no observable differences in the activities of 13 immune-related pathways between different TMB lev- els (Fig. 4C). It is well known that ICIs therapy is tightly related to ‘antigen-presenting process’ [26], ‘immune checkpoint expression’ [27] and ‘cytolytic effect’ [28]. Nevertheless, these immune pathways all failed to be affected by high TMB (Fig. 3C).
TMB may not serve as a biomarker for predicting the efficacy of ICIs
In the present study, we investigated the potential linkages between TMB and ICIs therapy from three
| Characteristics | HR (95% CI) | P value |
|---|---|---|
| Gender | 0.818(0.389-1.719) | 0.598 |
| Clinical stage | 3.230(2.118-4.926) | <0.001 |
| T | 3.390(2.207-5.209) | <0.001 |
| N | 2.175(0.894-5.292) | 0.087 |
| TMB | 1.179(1.091-1.275) | <0.001 |
A
TMB prognostic value
B
TCGA cohort (n=91)
C
TMB Time-dependent ROC
1.0
1.0
Low
1.0
Survival probability
0.8
High
0.8
0.8
0.6
Sensitivity (TPR)
Sensitivity (TPR)
0.4
0.6
0.6
0.2
HR = 5.82 (2.49-13.60)
0.4
0.4
0.0
P < 0.001
TMB (AUC = 0.813)
Gender (AUC = 0.524)
0
2.5
5
7.5
10
12.5
0.2
clinical stage (AUC = 0.790)
0.2
1-Year (AUC = 0.609)
Overall survival time (Year)
T (AUC = 0.786)
3-Year (AUC = 0.780)
Low
45
32
N (AUC = 0.556)
21
9
4
2
0.0
0.0
5-Year (AUC = 0.836)
High
45
22
8
2
0
0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
1-Specificity (FPR)
1-Specificity (FPR)
D
F
5
**
4
TMB value
3
2
1
0
Stage I
Stage II
Stage III
Stage IV
1
2
3
4
5
E
Characteristics
HR (95% CI)
P value
G
**
Gender
0.734(0.344-1.566)
0.424
4
Clinical stage
1.345(0.572-3.166)
0.497
TMB value
3
T
2.595(1.239-5.435)
0.011
2
N
1.573(0.504-4.904)
0.435
1
TMB
1.055(0.961-1.159)
0.26
0
T1
T2
T3
T4
1
2
3
4
5
aspects. First, the impacts of immune checkpoints (ICs) on ACC prognosis. Overexpression of ICs in tumor cells is implicated in immune exemption and immune escape through suppressing the functions of T cells [29], which commonly will lead an adverse progno- sis for cancer patients. Through survival analyses, we
found that only PD-L1(CD274) upregulation could result in a good prognosis (Fig. 5A), whereas other ICs were unable to affect the survival outcomes of ACC patients (Fig. 5B-E). Similarly, Billon et al. also con- firmed that high expression of PD-L1 was a favorable prognostic marker in ACC [30]. These findings indi- cated that the changes in ICs expressions was weakly
A
100%
B cells naive
B cells memory
. Plasma cells
= T cells CD8
80%
T cells CD4 naive
T cells CD4 memory resting
T cells CD4 memory activated
. T cells follicular helper
= T cells regulatory (Tregs)
Relative Percent
T cells gamma delta
60%
. NK cells resting
NK cells activated
= Monocytes
· Macrophages MO
Macrophages M1
· Macrophages M2
40%
Dendritic cells resting
Dendritic cells activated
· Mast cells resting
. Mast cells activated
. Eosinophils
. Neutrophils
20%
0%
co-ssey ait
High TMB
B
Low TMB
0.6
p=0.930
C
APC co-inhibition
Type-II-IFN-Response*
1
APC co-stimulation
0.5
P=0.207
p=0.659
Type-I-IFN-Response
CCR
0.4
0.5
p=0.070
Fraction
0.3
p=0.479
T-cell-co-stimulation
0
Check-point
p=0.070
0.2
p=0.479
p=0.287
P=0.151
p=0.149
0.1
p=0.455
P=0.147P=0.629
p=0.612p=0.252
p=0.076
p=0.562
p=0.740
T-cell-co-inhibition
Cytolytic-activity
p=0.131
0.0
p=0.803
p=0.740p=0,492
B cells naive
B cells memory
Plasma cells
T cells CD8
T cells CD4 naive
T cells CD4 memory resting
T cells CD4 memory activated
T cells follicular helper
T cells regulatory (Tregs)
T cells gamma delta
NK cells resting
NK cells activated
Monocytes
Macrophages MO
Macrophages M1
Macrophages M2
Dendritic cells resting
Dendritic cells activated
Mast cells resting
Mast cells activated
Eosinophils
Neutrophils
Parainflammation
HLA
MHC-class-I
Inflammation-promoting
D
B Cell
CDB+ T Cell
CD4+ T Cell
Macrophage
Neutrophil
Dendritic Cel
1.0
Log-rank P = 0.396
Log-rank P = 0.055
Log-rank P = 0.724
Log-rank P = 0.175
Log-rank P = 0.475
Log-rank P = 0.601
Cumulative Survival
0.8
Level
ACC
Low (Bottom 50%)
0.6
High (Top 50%)
0.4
+
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
Time to Follow-Up (months)
related to ACC prognosis, therefore targeting ICs ther- apy was not obviously beneficial for ACC patients.
Second, although appointing a reliable marker for pre- dicting ICIs efficacy is not conclusive, the patients with PD-1/L1 positive expression commonly presented a bet- ter therapeutic response [31]. Except for LAG3, TMB level did not correlate with the expressions of PD-L1, CTLA4, HAVCR2 and TIGIT (Fig. 5F-J). Furthermore, the expressive levels of all ICs in high TMB group was comparable to those in low TMB group (Fig. 5K).
Third, TIDE score can quantify the possibility that patients will benefit from anti-PD-1/L1 or anti-CTLA4
treatment [17]. Nevertheless, there was no significant difference in TIDE score between high- and low-TMB groups (Fig. 5L). This observation revealed that TMB level was not competent to play as a biomarker for pre- dicting the efficacy of anti-PD-1/L1 therapy.
Discussion
ACC is highly malignant and tends to metastasize. Nei- ther surgical excision nor mitotane cannot achieve a satisfactory therapeutic effect. In recent years, immune checkpoint inhibitors (ICIs) provides a promising option for metastatic ACC, and have been initially applied in
A
PD-L1 (CD274)
B
CTLA4
C
HAVCR2
1.0
CD274
1.0
CTLA4
1.0
HAVCR2
Survival probability
Low
Low
Low
0.8
High
Survival probability
0.8
High
Survival probability
0.8
High
0.6
0.6
0.6
0.4
0.4
0.4
0.2
Overall Survival
0.2
Overall Survival HR = 0.75 (0.35-1.58)
0.2
Overall Survival
HR = 0.26 (0.11-0.61)
HR = 0.62 (0.29-1.30)
0.0
P = 0.002
0.0
P = 0.444
0.0
P = 0.205
0
50
100
150
0
50
100
150
0
50
100
150
Overall survival time (Year)
Overall survival time (Year)
Overall survival time (Year)
Low
39
11
3
1
Low
39
14
3
1
Low
39
13
3
1
High
40
17
5
1
High
40
14
5
1
High
40
15
5
1
D
LAG3
TIGIT
1.0
LAG3
E
1.0
TIGIT
F
A =- 0.21 , p=0.062
Survival probability
Low
Survival probability
Low
30
0.8
High
0.8
High
20
0.6
0.6
TMB
0.4
0.4
10
0.2
Overall Survival
Overall Survival
HR = 1.36 (0.64-2.87)
0.2
HR = 0.75 (0.36-1.58)
0
0.0
P = 0.419
0.0
P = 0.451
0.0
2.5
5.0
7.5
10.0
12.5
PD-L1
0
50
100
150
0
50
100
150
Overall survival time (Year)
Overall survival time (Year)
G
R =- 0.18,p=0.11
30
Low
39
15
5
1
Low
39
12
4
2
High
40
13
3
High
40
16
4
0
20
K
TMB
10
The relative expressions of ICs
4
ns
ns
ns
0
0.0
0.5
1.0
1.5
2.0
3
H
CTLA4
Low-TMB
R =- 0.085 , p=0.45
30
2
ns
ns
High-TMB
20
1
TMB
10
0
I
0
:
PD-L1 (CD274) CTLA4
HAVCR2
LAG3
TIGIT
0
5
HAVCR2
10
TIDE score
R =- 0.23, p=0.041
30
ITS
L
1.0
20
ns
TMB
TMB value
10
J
0.5
0
R=0.046 , p=0.69
30
0.0
0.5
1.0
1.5
2.0
ns
TIGIT
0.0
20
TMB
Low-TMB
10
-0.5
High-TMB
Dysfunction Exclusion
TIDE
0
0
5
LAG3
10
15
Fig. 5 TMB may not serve as a biomarker for predicting the efficacy of ICIs. A-E The effects of five immune checkpoints on ACC prognosis. F-J The expressive correlations between TMB and immune checkpoints. K The expressive differences of immune checkpoints between high- and low-TMB groups. L The difference in TIDE score between high- and low-TMB groups
urothelial carcinoma [5]. However, only a small propor- tion of patients can benefit from ICIs therapy. TMB is considered as a potential biomarker for predicting ICIs efficacy [32], and has been validated in lung cancer and hepatocellular carcinoma [8, 33]. Nonetheless, in the present study, we found that TMB has limiting effects on predicting ICIs efficacy and prognostic assessment in ACC through preliminary bioinformatic analyses (Fig. 6AB).
Why TMB is not conducive to predicting the efficacy of ICIs in ACC?
Immune checkpoint, especially the overexpression of PD-1/L1 and CTLA4 can generate inhibitory signals of
T-cell function, thus suppressing anti-tumor immune process. From the perspective mechanisms, CTLA4 has higher avidity and affinity to ligands B7-1 and B7-2 so that it can competitively inhibit molecule T-cell recep- tor (TCR) signaling [34]. As for PD-1/L1, it is capable of attenuating T-cell activation through transmitting the tyrosine phosphatase SHP2 [34]. In consequence, the expression status of immune checkpoint is strongly associated with efficacy of ICIs [35]. Although it is so far inconclusive about identifying a reliable biomarker for predicting the efficacy of ICIs, many clinical trials have reported that PD-1/L1 positive expression com- monly implied a better response to ICIs. For instance, in NCT01693562 trial referring to metastatic urothelial
A
The efficacy of ICIs
Low TMB
Intrinsic immune-resistance
Special genomic alterations
2.1MB (ACC) VS 14MB (NSCLC)
Steroid phenotype
TP53 and CTNNB1
15-
Altered in ZT (100%) of 27 samples.
Median TMB
10-
-
-
5-
0
ACC
NSCLC
Et
B
Limiting Prognostic value
Difficult detection methods
Uncertain definition of high TMB
Whole exome sequencing
Different cutoff value
1.0
Low
Survival probability
0.8 -
High
0.6
0.4
0.2
HR = 5.82 (2.49-13.60)
0.0
P < 0.001
0
2.5
5
7.5
10
12.5
Overall survival time (Year)
Fig. 6 TMB has limiting effects on predicting the efficacy of ICIs and prognostic assessment in ACC. TMB, tumor mutational burden; ACC, adrenocortical carcinoma; ICIs, immune checkpoints inhibitors; NSCLC, non-small-cell lung cancer
carcinoma (UC), the objective response rate (ORR) in PD-L1 positive patients was up to 46.4%, by contrast, that in PD-L1 negative patients was even 0% [36]. Given that the therapeutic advantage brought by PD-1/L1 overex- pression, many researchers supported PD-1/L1 expres- sion as a predictive biomarker for ICIs response [31]. Moreover, it is now well recognized that tumor immune microenvironment (TIM) has a great influence on tumor progression and response to immunotherapy [37]. For example, as the pivotal immune cells for retarding cancer, dysfunction of CD8+ T cells will promote immunologic tolerance, thereby induce immunotherapy resistance [38]. The activation of CD8+ T cells requires the assis- tances of antigen-presenting cells (APCs) and CD4+ T cells. Conversely, macrophage M2 cells and regulatory T cells (Tregs) can make immunologic barriers against the process [38]. Therefore, difference in TIM will create the difference in response to ICIs treatment.
In the present study, we speculated that TMB was weakly associated with ICIs therapy based on threefold findings. First, TMB could not lead any changes in infil- tration levels of immune cells in ACC. Patients with high- or low-TMB seem to possess similar TIM. Meanwhile, some ICIs-related pathways such as ‘APC process’, ‘check- point’ and ‘cytolytic activity’ could not be motivated by high TMB level (Fig. 4C). Second, there was no expres- sive correlations between TMB and immune checkpoints (Fig. 5F-K). TMB level was not capable of affecting the expressive status of ICs. Third, TIDE score indicated that there were no significant differences in the occurrence probabilities of immune exclusion and T cells dysfunc- tion between high- and low-TMB groups (Fig. 5L). These findings reiterated that ACC patients with high TMB could not benefit from ICIs therapy. Naturally, TMB may not serve as an effective biomarker for predicting ICIs efficacy. Referring to some prior research, we hypothe- sized that there were three possible reasons for the inter- esting phenomenon (Fig. 6A).
First, the level of TMB in ACC is too low to propel the formation of neoantigens. As acknowledged, the accu- mulation of TMB induces the production of neoantigens, thus stimulating the activity of T cell and promoting anti- tumor immune effect. Nevertheless, not all mutations result in immunogenic neoantigens and determining which somatic mutations are likely to induce neoantigens remains extremely challenging. Therefore, only a certain level of TMB accumulation can trigger the formation of neoantigens. Therefore, TMB represents a quantifi- able approach to measure the probability of neoantigen production [39]. The higher TMB, the greater chance to produce immunogenic neoantigens. Hellmann MD et al. found that PFS of patients receiving nivolumab plus ipilimumab with a high TMB (≥10MB) was significantly
longer than that of patients receiving conventional chem- otherapy (42.6% versus 13.2%) [8]. Based on POPLAR (NCT01903993) and OAK (NCT02008227) trials, Gan- dara DR et al. defined the high TMB as larger than 14 MB and regarded high TMB as a predictor of clinical benefit in NSCLC patients with atezolizumab treatment [40, 41]. By contrast, in our study, the median of TMB in ACC was only 2.1 MB, which was far lower than that in lung cancer (10 or 14 MB). As a result, we speculated that low accumulation of TMB in ACC was not enough to induce the formation of tumor neoantigens, therefore, it cannot affect the immune microenvironment of ACC. Second, ACC is provided with intrinsic immune-resistance. It has been proven that ACC patients with cortisol secret- ing usually accompanied with the suppression of T cell activity [42]. This subtype of ACC, so called steroid phe- notype, commonly displays the low pathological immune scores, revealing its immune-resistance [42]. Meanwhile, tumor-related glucocorticoid excess occurs in about 60% of all ACC patients, which also cause the depletion of T cells and undesirable prognosis [42]. Therefore, the negligible impact of TMB on immune microenviron- ment of ACC may result from the immune-resistance of ACC itself. Third, some characteristic genomic altera- tions of ACC also mediated the immunological toler- ance in ACC. In the present study, TP53 and CTNNB1 were the most mutated genes (Fig. 1B). Ragazzon B et al. confirmed that 52% of invasive ACC patients possessed the mutations of TP53 or CTNNB1, suggesting that the mutations of two genes were involved in the malignant progression of ACC [43]. More importantly, TP53 muta- tion can lead the decreased lymphocytic infiltration lev- els and thereby induce the occurrence of tumor immune escape. In addition, overexpression of CTTNB1 was also proven to reduce the infiltration levels of tumor-infil- trating lymphocytes (TILs) and CD8+ T cells, and was associated with immunosuppression and poor prognosis in ACC [44]. Therefore, the frequently mutated TP53 and CNNB1 in ACC also bring a suppression on infiltration levels of immune cells and retard the anti-tumor immu- nological process. This may be the genomic element that makes TMB fail to affect immune microenvironment of ACC.
Why TMB is not helpful to prognostic assessment in ACC? Except for predicting ICIs efficacy, TMB has been found to strongly associated with cancer prognosis. Nevertheless, TMB presents different prognostic char- acteristics in different cancers. It was a poor prognostic marker for neuroblastoma [45] and glioma [46], but a good prognostic marker for NSCLC [47]. Meanwhile, Hwang, W. L et al. argued that TMB was beneficial to optimize prognosis stratification and guide treatment
decisions [45]. Although we also found that high TMB led to a poor survival outcome (Fig. 3A), we still thought that the suggestive effect of TMB on prognos- tic assessment was fairly limiting at the present stage. First, the determination of TMB value needs to per- form the whole exome sequencing (WES) on tumour DNA and matching normal DNA [48]. High costs and substantial turnaround time for analysis and sequenc- ing are obviously not applicable to routine detection and screening. Second, we lacked one universal defi- nition of high TMB. A pan-cancer research revealed that a specific TMB cutoff can be always found to dis- tinguish the prognosis of patients for most cancers, but the specific cutoff points markedly varied across different cancer types [49]. Altogether, due to the dif- ficult detection process and the inconsistent standards of high TMB, it fails to effectively to contribute to the prognostic assessment of ACC (Fig. 6B).
The mutations of titin and mucin do not seem to work in ACC
Due to the large size of exon, the mutations of titin (TTN) and mucin (MUC) are frequently detected in multiple solid tumors, and closely correlated with TMB and the objective response to ICIs [50]. For example, MUC16 was mutated in 38.4% of gastric cancer (GC) patients, and was associated with higher TMB and good immune response [51]. Therefore, Li X et al. con- sidered that its mutation could guide immunotherapy treatment for GC patients [51]. Besides, a pan-can- cer research revealed that a positive correlation was observed between the ORR of ICIs therapy and the fre- quency of TTN mutation, which also heralded that high TTN mutation predicted a better response rate to ICIs [50]. However, although TTN (12%), MUC4 (14%) and MUC16 (14%) commonly mutated in ACC (Fig. 1B), these mutations did not seem to be correlated with the efficacy of ICIs for ACC cases. There are two possible reasons to make such phenomenon.
First, the mutation frequency of MUC16 in ACC (14%) was much lower than that in other solid tumors, such as GC (38.4%), lung adenocarcinoma (42.76%) and melanoma (73.86%) [52]. This directly leads that low mutation of MUC16 is not competent to bring a higher TMB to trigger a strong immune response [49]. Second, the roles of MUC16 and TTN mutations in the anti- tumor immune process were still unclear. For example, MUC16 could hinder the targeting of NK cells to tumor cells [53]. Curiously, MUC16 mutations was commonly accompanied with better prognosis and therapeu- tic response [51]. The underlying mechanisms remain elusive.
Limitations and conclusions
Naturally, there are some limitations in our research. First, the conclusion of this study is based on bioinfor- matics analyses and has not been verified by real clinical cohorts. Second, insufficient ACC data in TCGA data- base may lead biased conclusions.
Collectively, according to our findings, TMB may not be appropriate to predict ICIs efficacy in ACC. Inad- equate stimulation for neoantigens formation, intrin- sic immune-resistance and special genomic alterations were three possible reasons. Moreover, although TMB could affect the survival outcomes of ACC patients, it was toughly applied in clinical practice because of its high cost of determination and non-universal definition of high TMB. In a word, TMB presents limiting effects on predicting ICIs efficacy and prognostic assessment for ACC patients. Our findings provided an interest- ing insight into ACC immunotherapy and prognosis analyses.
Abbreviations
ACC: Adrenocortical carcinoma; TMB: Tumor mutational burden; ICIs: Immune checkpoint inhibitors; PD-1: Programmed death-1; CTLA4: Cytotoxic T-Lymphocyte-Associated Antigen 4; SNPs: Single nucleotide polymorphisms; PFS: Progression-free survival; NSCLC: Non-small-cell lung cancer; MB: Muta- tions per megabase; SNV: Single nucleotide variant; TIM: Tumor immune microenvironment; APCs: Antigen-presenting cells; TCR: T-cell receptor; TILs: Tumor-infiltrating lymphocytes; WES: Whole exome sequencing.
Acknowledgements
The authors would like to thank ‘HOME for Researchers’ website (https://www. home-for-researchers.com/) for its linguistic assistance. Dr. Fangshi Xu would like to thank his girlfriend Dr. Danrui Cai for her encourage.
Authors’ contributions
BC R, TC and FS X conceived and designed the study. FS X, YB G, PZ and LX analyzed and interpreted the data. FS X, YB G, PZ, LX, YB M and MG wrote the manuscript. BC R, PZ, LX, YB M and MG made a revised version. The author(s) read and approved the final manuscript.
Funding
This work was supported by the Free Exploration Fund of the second Affiliated Hospital of Xi’an Jiaotong University.
Availability of data and materials
The datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
Our study did not require an ethical board approval because it did not contain human or animal trials.
Consent for publication Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Department of Medicine, Xi’an Jiaotong University, No. 76, Yanta West Road, Xi’an 710061, Shaanxi, China. 2 Department of Urology, The Second Affiliated Hospital of Xi’an Jiaotong University, No. 157, West Five Road, Xi’an 710000, Shaanxi Province, China. 3 Department of Rheumatology and Immunology, The Second Affiliated Hospital of Xi’an Jiaotong University, No. 157, West Five Road, Xi’an 710000, Shaanxi Province, China.
Received: 15 June 2021 Accepted: 8 April 2022 Published online: 14 May 2022
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