Integrative computational immunogenomic profiling of cortisol-secreting adrenocortical carcinoma
Jordan J. Baechle1 İD W. Kimryn Rathmell4
David N. Hanna2 Konjeti R. Sekhar2 Jeffrey C. Rathmell3
Naira Baregamian2
1School of Medicine, Meharry Medical College, Nashville, TN, USA
2Division of Surgical Oncology & Endocrine Surgery, Department of Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
3Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
4Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
Correspondence
Naira Baregamian, Vanderbilt University Medical Center, Division of Surgical Oncology & Endocrine Surgery, 2220 Pierce Avenue, 597 Preston Research Building, Nashville, TN 37232, USA. Email: naira.baregamian@vumc.org
Abstract
Adrenocortical carcinoma (ACC) is a rare but highly aggressive malignancy. Nearly half of ACC tumours overproduce and secrete adrenal steroids. Excess cortisol se- cretion, in particular, has been associated with poor prognosis among ACC patients. Furthermore, recent immunotherapy clinical trials have demonstrated significant im- munoresistance among cortisol-secreting ACC (CS-ACC) patients when compared to their non-cortisol-secreting (nonCS-ACC) counterparts. The immunosuppressive role of excess glucocorticoid therapies and hypersecretion is known; however, the impact of the cortisol hypersecretion on ACC tumour microenvironment (TME), im- mune expression profiles and immune cell responses remain largely undefined. In this study, we characterized the TME of ACC patients and compared the immunogenomic profiles of nonCS-ACC and CS-ACC tumours to assess the impact of differentially expressed genes (DEGs) by utilizing The Cancer Genome Atlas (TCGA) database. Immunogenomic comparison (CS- vs. nonCS-ACC tumour TMEs) demonstrated an immunosuppressive expression profile with a direct impact on patient survival. We identified several primary prognostic indicators and potential targets within ACC tu- mour immune landscape. Differentially expressed immune genes with prognostic sig- nificance provide additional insight into the understanding of potential contributory mechanisms underlying failure of initial immunotherapeutic trials and poor prognosis of patients with CS-ACC.
KEYWORDS
adrenocortical carcinoma, cortisol secreting adrenocortical carcinoma, cushing’s syndrome, immunometabolism, tumour immunology, tumour microenvironment
|
1 INTRODUCTION
Adrenocortical carcinoma (ACC) is among the rarest and most aggressive cancers. Although the current prognostication of pa- tients with ACC primarily hinges on the presence or absence of
metastases and tumour resectability, over a third of patients present with an advanced, unresectable disease.1-6 Patients with fully re- sectable (R0) disease have a reported 5-year survival rate of approx- imately 50%, whereas patients with the unresectable disease have a 5-year survival rate near 0% and a median survival of shorter than
@ 2021 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.
12 months.4,7,8 Although large collaborative studies have greatly en- hanced the molecular characterization of ACCs9,10 aside from the ad- vent of mitotane therapy in the treatment of ACC since 1959, there has been little improvement in overall mortality over the past sev- eral decades.11,12 Due to the limited therapeutic options for patients with unresectable ACC, several immunotherapies are currently under evaluation;13-16 thus, having a comprehensive understanding of the ACC tumour microenvironment is important for guiding future therapeutic directions.
Nearly half of patients presenting with ACC have been shown to exhibit steroid hormone hypersecretion with excess cortisol secre- tion being the most predominant hormone and often considered a strong risk factor for poor prognosis.6,17 Glucocorticoids, including cortisol, are small lipid hormones produced by the adrenal glands that exert their effects through glucocorticoid receptors modu- lating gene expression to perform a variety of functions, including arresting immune cell growth and maturation, inhibiting activation signalling and inducing lymphocyte apoptosis.18,19 Glucocorticoids have proven so effective in this role that they are the cornerstone of treatment for many hypersensitive immune reactions and auto- immune diseases.20,21 However, the immunosuppressive effects of excess glucocorticoid therapy and hypersecretion have also been shown to hinder the immune system’s capacity to ward off infec- tions and malignancy and have been associated with a variety of other effects, including muscle wasting, osteoporosis and meta- bolic derangements.22,23 A recent in vivo study by Landwehr et al.24 demonstrated cortisol excess to be associated with T-cell depletion and anergy in ACC TME, while a recent immunotherapy clinical trial revealed a pattern of immune resistance among cortisol-secreting ACC (CS-ACC) tumours, with higher rates of immunotherapeu- tic failure among CS-ACC patients compared to the patients with nonCS-ACC.13,25-27 In this study, we utilized The Cancer Genome Atlas (TCGA) ACC cohort28 to characterize the TME of ACC by com- paring TME immunogenomic profiles of CS and nonCS-ACCs. We have also investigated the correlations and prognostic significance of differentially expressed immune genes (DEIGs) and tumour- infiltrating immune cell (TIIC) profiles.
2 METHODS |
2.1 Data acquisition, patient demographics & tumour pathology |
We utilized the RNA sequencing count table data of Adrenal Cortical Carcinomas (N = 92) from The Cancer Genome Atlas (TCGA) Firehose Legacy Cohort.28 Of the 92 patients in the TCGA cohort, 67 (73%) patients, with common type ACCs (non-myxoid/ non-oncocytic), did not undergo neoadjuvant therapy and had re- ported hormone hypersecretion and mRNA expression values were included in the study cohort. The American Joint Commission on Cancer Staging Manual, 8th edition, was used to determine TNM classification. Categorical variables were presented as frequency
and percentages and compared using chi-square or Fisher’s exact test, as appropriate. Continuous variables were reported as me- dian values with interquartile range (IQR) and compared using the Kruskal-Wallis test.
2.2 Computational immunogenomic deconvolution |
The Cancer Genome Atlas (TCGA, Firehose Legacy) was accessed through cBioPortal (https://www.cbioportal.org/). CIBERSORTx was used to estimate tumour-infiltrating immune subsets (includ- ing B cells, CD4+T cells, CD8+T cells, dendritic cells, macrophages, natural killer cells and neutrophils). CIBERSORTx is a computational immunogenomic platform, a publicly available web-based deconvo- lution program (https://cibersortx.stanford.edu)29. All genes with quantified mRNA expression (log RNA Seq V2 RSEM) in TCGA database (n > 19,000) were compared between CS- and nonCS- ACCs. The significance criteria for DEG were set at a p-value and q-value < 0.05. After characterizing the relationships between DEGs and comparing the expression profiles between CS-ACC and nonCS- ACC TMEs, DEGs were categorized according to biological function using Panther Gene Classification.3º All DEIGs and TIIC associa- tions were constructed in heatmap format to represent all potential associations and analysed in their relation to patient OS and DFS. Additionally, the mRNA expression of genes involved in steroid me- tabolism was analysed for their correlations with TIICs and prognos- tic DEIGs. Gene expression signatures were compiled by normalizing the sum of the gene mRNA Z-scores (log RNA Seq V2 RSEM) relative to the median on a scale of -5 to 5. Patients with positive cumula- tive normalized expression levels (≥0.00) were assigned to the high signature expression group and those with negative cumulative nor- malized expression levels (<0.00) were assigned to the low signature expression group.
2.3 Patient outcomes analysis |
Survival analysis was analysed by time-to-event Cox regression mod- els for overall survival (OS) and disease-free survival (DFS). OS was defined as the time from the date of index operation to the date of death. DFS was defined as the time from index operation to the date of documented disease recurrence or death. Kaplan-Meier method and log-rank test were used to compare OS and DFS of ACC patients according to mRNA expression signature profiles. Significance for OS and DFS analysis was set at a p-value < 0.05.
2.4 Data availability |
The Cancer Genome Atlas (TCGA, Firehose Legacy) was accessed through cBioPortal (https://www.cbioportal.org/) (https://www. cbioportal.org/study?id=605903f6e4b0242bd5d4433b).
2.5 Statistics
All quantitative comparison, correlation and survival analyses were performed using the 1.1.383 R statistics software (R Core Team Vienna).
3 RESULTS
|
3.1 Patient demographic, tumour pathology and treatment parameters of adrenocortical carcinoma |
We identified 67 individuals in the TCGA ACC cohort with patho- logically confirmed common type ACC with reported mRNA expres- sion data and who did not undergo neoadjuvant therapy.28 Of these, 32 (47.8%) had CS-ACC and 35 (52.2%) were nonCS-ACC tumours. Excess cortisol secretion was diagnosed by biochemical assessment in 7 (21.9%) CS-ACC patients (subclinical Cushing’s syndrome) and by both clinical and biochemical assessment in 25 (78.1%) CS-ACC pa- tients (clinical Cushing’s syndrome). The groups were similar in age at diagnosis (p = 0.37), race (p = 0.26), tumour stage T (p = 0.81), nodal status N (p= 0.14), metastasis M (p= 1.00) and clinical stage (p=0.43). The CS-ACC group was female-predominant compared to the nonCS- ACC group (81.2 vs. 45.7%, p < 0.01). CS- and nonCS-ACC tumours demonstrated similar fractions of genome alteration (p = 0.97), muta- tion count (p = 0.193), mitotic count (p = 0.08) and rate (p = 0.72), tu- mour necrosis (p = 0.67), Weiss Score31 (p = 0.77) and rates of vascular invasion (p = 0.60). Both groups reported similar resection margins (p = 0.675) and underwent similar rates of adjuvant (p = 0.66) therapy, as well as mitotane (p = 0.12) and radiation therapy (p = 0.40). CS-ACC patients experienced higher rates of ACC recurrence (62.5 vs. 31.2%, p = 0.02). Demographic, clinical and pathologic features of the study cohort by cortisol secretion are further summarized in Table S1.
Cortisol secretion was not significantly associated with short- ened overall survival (OS) (hazard ratio [HR] 1.83; 95% confidence interval [CI] 0.82 - 4.07, p = 0.14) but was significantly associated with shortened disease-free survival (DFS) (HR 2.34; 95% CI 1.13 - 4.85, p = 0.02). The 5-year OS was 59.6% for nonCS-ACCs and 51.6% for CS-ACCs. The 5-year DFS was 59.5% and 30.1% for nonCS- and CS-ACC tumours respectively. The poor DFS prognosis associated with CS-ACC despite similar patient demographics, tumour pathol- ogy and treatment protocols commonly associated with prognosis (cancer stage, Weiss Score, adjuvant therapy) is suggestive of a pos- sible direct impact of cortisol secretion on ACC biology or TME im- mune opposition underlying patient DFS.
3.2 Differential Immunologic Gene mRNA Expression (DEGs) of cortisol-secreting and non- cortisol-secreting adrenocortical carcinoma |
Analysis of all genes (n > 19,000) with quantified mRNA expression in TCGA database demonstrated 1,612 differentially expressed genes (DEGs) between CS- and nonCS-ACC tumours. Of these DEGs, 1,021
were classifiable using Panther Genomic Classification (Figure 1A). Forty-four (4.3%) genes of those classifiable were identified to be di- rectly related to immunological processes and termed differentially expressed immune genes (DEIGs). On subcategorization of immu- nological processes using Panther Genomic grouping, DEIGs were primarily involved in immune response and leucocyte activation and maturation. The distribution of immunological processes is summarized in Figure 1B. Expression profiles of the 44 DEIGs stratified according to cortisol secretion and all mRNA expression intercorrelations repre- sented in heatmap format are shown in Figure 1C. Forty-three (97.7%) of the categorizable DEIGs identified showed decreased mRNA ex- pression levels in CS-ACC compared to nonCS-ACC tumours. Uniquely, CCRL2 showed elevated mRNA expression levels within CS-ACC TME compared to nonCS-ACC. Aside from CCRL2, all DEIGs showed posi- tive mRNA expression correlations with one another (r ≥ 0.00), sug- gesting common or related transcription factors and/or cell processes, with CCRL2 as an exception (Figure 1C). CCRL2 mRNA expression was negatively associated with that of several other DEIGs, including CCR6 (r =- 0.28), JAK3(r =- 0.38), NKAP(r =- 0.21), RNF135 (r =- 0.27), SIRPA (r = - 0.27) and TLR5 (r = - 0.25) (Figure 1D). CCRL2 mRNA expression was negatively associated with resting CD4 memory T cells (r = - 29).
3.3 Tumour-Infiltrating Immune Cell (TIIC) profiles of adrenocortical carcinoma
The immunogenomic TME deconvolution using the CIBERSORTx plat- form elucidated a distinct TIIC landscape among CS-ACC compared to nonCS-ACC based absolute TIIC estimations (Figure 2A). Median proportional TIICs profiles for CS- and nonCS-ACCs are shown in Figure 2A. CS-ACC tumours demonstrated depletion of CD8+ T cells (p = 0.02), activated natural killer cells (NK2) (p = 0.04), as well as M1 macrophages (p = 0.04), and increased infiltration of activated dendritic cells (DC2) (p = 0.02). Of these four differentially infiltrated immune cell types that are identified in CS- and nonCS-ACC TMEs, DC2 was the only TIIC population with significant prognostic asso- ciation. Increased DC2 infiltration was the only TIIC population with significant prognostic association. Increased DC2 infiltration associ- ated with poor DFS (HR 78.9, 95% CI 7.51 - 829, p < 0.01) (Figure 2B).
3.4 Steroid metabolism gene expression comparison and tumour-infiltrating immune cells in cortisol-secreting adrenocortical carcinoma
The mRNA expression of all genes underlying steroid metabolism en- zymes, including cortisol synthesis, and influential transcription fac- tors are depicted in Figure 3A. The mRNA expression levels of the steroid synthesis genes and transcriptions levels of ACC tumours are compared according to cortisol secretion in Figure 3B. CS-ACCs dem- onstrated decreased mRNA expression of StAR and HSD17B5 and in- creased mRNA expression of CYP11A1, CYP17A1, HSD3B1, HSD3B2, HSD11B2, PBX1 and NR5A1 compared to nonCS-ACC (p-values <0.05). Steroid metabolism gene expression correlations with TIIC subtypes
(A)
(B)
Immune Effector Process
Compartment Organization/Biogenesis
Cellular Processes
Cell Signaling
Immune Response Activation
Immune Response
Cell Localization
Developmental Process
Immune System Process
Cell Growth/Locomotion
Metabolic Process
Bilogical Regulation
Bilogical Adhesion
Multi-organism Process Multicellular Organismal Process
Leukocyte Activation/Maturation
Stimulus Response
Immune Development
CCL5
CCRL2
CD1C
CD1E
CD226
CD274
CD3D
CD3E
CD3G
CD27
CD40LG
CD40
CX3CR1
CXCR3 EOMES CXCR6
(C)
FYB1
GAPT
GBP2
GBP4 GBP5
GBP7
HDC
HLAA
ILAB HLAF
HLAH
IL 16
KLRK1
NKAP
PYHIN1
RNF135
SIRPA
THEMIS
ITK
JAK3
TIGIT TLR10
TLR5
UBASH3A
nonCS-ACC
CS-ACC
(D)
CO
XCL1 XCL2
CCL5
CCL5
CCR2
CCR2
CCR6
CCR6
CCRL2
CCRL2
CD1C
CD1C
CD1E
CD1E
CD226
CD226
CD274
CD274
1
CD3D
CD3D
CD3E
CD3E
CD3G
CD3G
0.8
CD27
CD27
CD40LG
CD40LG
CD40
CX3CR1
CD40
CX3CR1
0.6
CXCR3
CXCR3
CXCR6
CXCR6
EOMES
EOMES
0.4
FYB1
GAPT
5.0
FYB1
2.5
GAPT
GBP2
GBP2
0.2
GBP4
0.0
GBP4
GBP5
GBP5
GBP7
-2.5
GBP7
0
HDC
HLAA
HLAB
-5.0
HDC
HLAA
HLAB
-0.5
HLAF
HLAH
HLAF
IL16
HLAH
IL 16
-0.4
ITK
JAK3
ITK
KLRK1
JAK3
KLRK1
NKAP
NKAP
-QUE
PYHIN1
RNF135
PYHIN1
SIRPA
RNF135
THEMIS
SIRPA
TIGIT
THEMIS
TLR10
TIGIT
TLR10
-1
TLR5
UBASH3A
TLR5
XCL1
UBASH3A
XCL2
XCL1 XCL2
among nonCS-ACC and CS-ACC are represented in heatmap format in Figure 3C,D. StAR, NR0B1 and NR5A1 mRNA expressions were nega- tively associated with activated mast cell in both nonCS-ACC and CS- ACC (r ≤-0.70) in CS- and nonCS-ACCs. Among CS-ACC, the mRNA expression of genes coding for enzymes contributing to cortisol syn- thesis StAR, CYP11A1, CYP17A1, HSD3B2 and CYP21A2 and steroid metabolism transcription factors NR0B1 and NR5A1 were associated with decreased plasma B cell, CD8 T cell, M1 macrophage, activated mast cell and neutrophil infiltration (r ≤ -0.50).
3.5 Differential tumour immune cell infiltration, immune-related differentially expressed genes patient outcomes in cortisol-secreting adrenocortical carcinoma |
The mRNA expression of 14 (31.8%) of the 44 DEIGs emerged as sig- nificant prognostic indicators (HR > 1.00, OS and DFS, p < 0.05) and
made up the cumulative prognostic immune signature (CCR6, CD1C, CD1E, CD40, EOMES, GBP2, HLAA, HLAB, HLAH, JAK3, NKAP, SIRPA, TLR5, XCL1). DEIGs contributing to this prognostic immune signature were suppressed in CS-ACCs and can be grouped into several sub- categories according to immune function, including chemokine and cytokine signalling (CCR6, XCL1), macrophage signalling (GBP2, TLR5), leucocyte antigen proteins (HLA-A, B, H), T-cell signalling (CD1C, CD1E, EOMES, NKAP), B-cell signalling (CD40), DC signalling (SIRPA) and global immune development and response (JAK3) (Figure 4A-C). The distribu- tion of DEIGs by cortisol secretion and the impact of DEIGs on OS and DFS are summarized in Figure 4 and Table S1. Nine DEIGs were posi- tively associated with DFS only and included CD40, CX3CR1, CXCR6, GAPT, HDC, HLAF, IL16, RNF135 and XCL2. Functionally, these genes can be grouped into chemotactic signalling (XCL2), innate immune re- sponse (GAPT, HDC, IL16) and other (RNF135). The mRNA expression of CCL5, CCR2, CD226, CD274, CD3D, CD3E, CD3G, CD27, CD40LG, CXCR3, FYB1, GBP4, GBP5, GBP7, ITK, KLRK1, PHYIN1, THEMIS, TIGIT, TLR10 and UBASH3A showed no prognostic significance in DFS or OS
WILEY
1.5
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
Tumor-infiltrating immune cells (au)
(A)
1.0
0.5
. …
…
. ..
0.0
B cells naive
B cells memory
Plasma cells
T cells CD8
T cells CD4 naive
T cells CD4 memory resting
T cells follicular helper
T cells regulatory (Tregs)
Natural killer cells resting
Natural killer cells activated
Monocytes
Macrophages MO
Macrophages M1
Macrophages M2
Dendritic cells resting
Dendritic cells activated
Mast cells resting
Mast cells activated
Eosinophils
Neutrophils
nonCS-ACC
CS-ACC
| (B) | Overall Survival | Disease-free Survival |
|---|---|---|
| T cells CD8 | 0.18 [0.00 - 9.15] p=0.388 | 0.06 [0.00 - 3.13] p=0.165 |
| Natural killer cells activated | 0.00 [0.00 - 8.07] p=0.162 | 0.03 [0.00 - 18.3] p=0.292 |
| Macrophages M1 | 27.4 [0.14-525] p=0.217 | 0.98 [0.00 - 666] p=0.217 |
| Dendritic cells activated | 9.17 [0.44 - 190] p=0.152 | 52.1 [3.35 - 811] p=0.005 |
Hazard Rartio [95% Confidence Interval]
FIGURE 2 Tumour-infiltrating immune cell (TIIC) profiles of adrenocortical carcinoma (ACC). (A) Scaled absolute value of tumour infiltration by immune cell types estimated by CIBERSORTx in ACC tumours and stratified into subgroups by cortisol secretion, abs, absolute arbitrary units; ns = p-value ≥ 0.05; * p-value < 0.05. (B) Impact of differentially expressed TIIC subtypes (CD8 T cells, activated natural killer cells, M1 macrophages, activated dendritic cells) on overall (OS) and disease-free survival (DFS). Regression analysis, expressed as univariate Cox regression hazard ratio (HR) and 95% confidence interval (95% CI): HR [lower 95% CI - higher 95% CI], bold = p-value < 0.05
(Figure 4A-C). There were no DEIG expression levels that were associ- ated with OS and not DFS. The CCRL2 gene mRNA expression was the only DEIG upregulated in CS-ACC and was associated with poor DFS (HR 1.45, 95% CI 1.05 - 2.02, p = 0.03).
The CCRL2 gene codes for the C-C Motif Chemokine Receptor- Like 2, a non-signalling seven-transmembrane domain receptor re- lated to the atypical chemokine receptor (ACKR) family, however, and its role of this receptor in TME is elusive. ACKRs typically bind chemokines without G protein signalling activation to promote li- gand internalization and degradation; however, more importantly, they regulate immune functions by scavenging chemokines from the local environment.32 Previous studies have demonstrated CCRL2 re- ceptors to act as decoy receptors scavenging chemokines from the TME and their expression to be associated with poor dendritic cell trafficking.33 Elevated CCRL2 expression has been shown in primary neutrophils relative to other immune cell types and further upregu- lated on neutrophil activation.33
Genes with mRNA expression found to be significantly associated with OS and DFS (n = 14, [CCR6, CD1C, CD1E, CD40, EOMES, GBP2, HLAA, HLAB, HLAH, JAK3, NKAP, SIRPA, TLR5, XCL1]) were compiled to create a composite immune mRNA expression signature charac- teristically suppressed in CS-ACC tumours compared to nonCS-ACC (Table S2). The bulk of the genes contributing to the prognostic mRNA signature downregulated in CS-ACC were identified to code for
interactive proteins crucial in the stepwise process of lymphocyte- mediated.34 These steps include membrane and intercellular signal- ling proteins involved in T-cell and NK cell activation (CD1C, CD1E, NKAP), recruitment (CCR6, XCL1), tumour recognition (CD1C, CD1E, HLAA, HLAB, HLAH) and CD8 T-cell differentiation (EOMES).35,36 Other gene products, including those of GBP2 and TLR5, have been shown to contribute to the innate immune response through macro- phage activation and enhanced phagocytic and oxidative killing. 37,38 Signal regulatory protein alpha (SIRPA) gene codes for the cell surface receptor for CD47. The SIRPA-CD47 has been shown to prevent the maturation of dendritic cells and promote immune tolerance of ma- ture dendritic cells.39 Janus kinase (JAK) family of tyrosine kinases involved in cytokine receptor-mediated intracellular signal transduc- tion of the innate and adaptive immune system and mutations of this gene are characteristic of severe combined immunodeficiency.40
3.6 Prognostic differentially expressed immune gene and steroid metabolism gene correlations in adrenocortical carcinoma |
Expression correlations between prognostic DEIGs and steroid metabolism genes are shown in Figure 5. The mRNA expression of genes coding for enzymes specific to cortisol metabolism (including
|
(A)
Cholesterol
Cortisol Metabolism
Steroid Metabolism
StAR
CYP11A1
HSD3B2 HSD3B1
Pregnenolone
Progesterone
CYP21A2
11-Deoxy corticosterone
CYP11B1
Corticosterone
CYP11B2
Aldosterone
CYP17A1
CYP17A1
HSD3B2 HSD3B1
17OH-Pregnenolone
17OH-Pregesterone
CYP21A2
11-Deoxycortiosol
CYP11B2
Cortisol
HSD11B2
Cortisone
CYP17A1
HSD3B2 HSD3B1
DHEA
Androstenedione
CYP19A1
Estrone
Transciption Factors
HSD17B5
HSD17B5
HSD17B1
PBX1
HSD3B2 HSD3B1
CREB1 CES1
NR4A1 NR5A1
NROB1
Androstenediol
Testosterone
CYP19A1
Estradiol
mRNA Expression (Log RNA Seq V2 RSEM) @
6
ns
ns
ns
ns
ns
ns
ns
ns
ns
3
8
.
:
.
·
.
.
.
.
nonCS-ACC
0
8
.
…
CS-ACC
.
8
.
8
.
.
·
-3
6
-6
StAR
CYP11A1
CYP17A1
HSD3B1
HSD3B2
CYP21A2
CYP11B2
HSD11B2
CYP11B1
HSD17B5
CYP19A1
HSD17B1
PBX1
CREB1
CES1
NROB1
NR4A1
NR5A1
Cortisol Metabolism Enzymes
Transcription Factors
Steroid Metabolism Enzymes
CS-ACC
(C)
nonCS-ACC
StAR
CYP11A1
CYP17A1
HSD3B1
HSD3B2
CYP21A2
CYP11B2
HSD11B2
CYP11B1
HSD17B5
CYP19A1
HSD17B1
PBX1
CREB1
CES1
NROB1
NR4A1
NR5A1
StAR
CYP11A1
CYP17A1
HSD3B1
HSD3B2
CYP21A2
CYP11B2
HSD11B2
CYP11B1
HSD17B5
CYP19A1
HSD17B1
PBX1
CREB1
CES1
NROB1
NR4A1
NR5A1
B cells naive
1
B cells naive
1
B cells memory
Plasma cells
0.8
B cells memory
Plasma cells
0.8
T cells CD8
T cells CD4 naive
0.6
T cells CD8
T cells CD4 naive
0.6
T cells CD4 memory resting
T cells CD4 memory resting
T cells follicular helper
0.4
T cells follicular helper
0.4
T cells regulatory (Tregs)
0.2
T cells regulatory (Tregs)
Natural killer cells resting
Natural killer cells resting
0.2
Natural killer cells activated
Natural killer cells activated
Monocytes
0
Monocytes
0
Macrophages MO
Macrophages M1
-0.2
Macrophages MO
Macrophages M1
-0.2
Macrophages M2
Dendritic cells resting
-0.4
Macrophages M2
Dendritic cells resting
-0.4
Dendritic cells activated
Mast cells resting
-0.6
Dendritic cells activated
Mast cells resting
-0.6
Mast cells activated
Mast cells activated
Eosinophils
-0.8
Eosinophils
-0.8
Neutrophils
Neutrophils
-1
-1
(A)
(B)
(C)
CCL5
CCR2
CCR6
CCRL2
CD1C
CD1E
CD226
CD274
CD3D
CD3E
CD3G
CD27
CD40LG
CD40
CX3CR1
CXCR3
CXCR6
EOMES
FYB1
GAPT
GBP2
CS-ACC
GBP4
GBP5
nonCS-ACC
GBP7
HDC
HLAA
HLAB
HLAF
HLAH
IL 16
ITK
JAK3
KLRK1
NKAP
PYHIN1
RNF135
SIRPA
THEMIS
TIGIT
TLR10
TLR5
UBASH3A
XCL1
XCL2
-5.0
-2.5
0.0
2.5
5.0
0.5
1.0
1.5
0.5
1.0
1.5
2.0
mRNA Expression Distribution
Overall Survival Hazard Ratio
Disease-free Survival Hazard Ratio
CYP11A1, CYP17A1, HSD3B1 HSD3B2, CYP21A2 and CYP11B2) and transcription factors PBX1 and NR5A1 were negatively associated with the prognostic DEIGs. The roles of these genes in steroid me- tabolism can be found in Figure 3A. PBX1 and NR5A1 belong to the PBX homeobox and the nuclear receptor families of transcription factors respectively. PBX1 and NR5A1 govern the transcription of cortisol and sex hormone biosynthesis. 41
3.7 Immunosuppressive signature of cortisol- secreting adrenocortical carcinoma |
Immunogenomic deconvolution of ACC TME revealed an immuno- suppressive signature with multiple intercorrelated DEIG mRNA ex- pression sub-clusters. The HLA sub-cluster (HLA-A, B, H) (r ≥ 0.70) showed strong positive intercorrelations. Furthermore, CD1C and CD1E (r = 0.84) were found to be positively correlated (Figure 6A).
The strong associations between the HLA mRNA expression values would also suggest a decreased MHC class I surface expression, which would result in decreased antigen presentation and T-cell ac- tivation. Supportively, HLA sub-cluster mRNA expression was posi- tively associated with CD8+ T-cell infiltration.
Cortisol-secreting ACC tumours from patients with clinical and subclinical Cushing’s syndrome showed significantly increased infil- tration of CD8+T cells and resting mast cells in CS-ACCs. Further sub-analysis comparing the immunogenomic and TIIC profile was performed (Figure S1) to compare CS-ACC patients diagnosed by biochemical and clinical evaluation (clinical Cushing’s syndrome) with those diagnosed by biochemical evaluation alone (subclinical Cushing’s ACC patients).
The relationships between the individual gene expression con- tributing to CS-ACC signature showed many positive and negative correlations with TIICs (Figure 6B). Messenger RNA expression of CD1C and CD1E, a TCR contributory gene, was associated with
(A)
All ACCs
StAR
CYP11A1 CYP17A1
HSD3B1
HSD3B2
CYP21A2
CYP11B2
HSD11B2
CYP11B1
HSD17B5
CYP19A1
HSD17B1
PBX1
CREB1
CES1
NROB1
NR4A1
NR5A1
CCR6
1
CD1C
0.8
CD1E
CD40
0.6
EOMES
0.4
GBP2
HLAA
0.2
HLAB
0
HLAH
JAK3
-0.2
NKAP
-0.4
SIRPA
TLR5
-0.6
XCL1
-0.8
Signature
-1
nonCS-ACCs
(B)
StAR
CYP11A1
CYP17A1
HSD3B1
HSD3B2
CYP21A2
CYP11B2
HSD11B2
CYP11B1
HSD17B5
CYP19A1
HSD17B1
PBX1
CREB1
CES1
NROB1
NR4A1
NR5A1
CCR6
1
CD1C
0.8
CD1E
CD40
0.6
EOMES
0.4
GBP2
HLAA
0.2
HLAB
0
HLAH
JAK3
-0.2
NKAP
-0.4
SIRPA
TLR5
-0.6
XCL1
-0.8
Signature
-1
CS-ACCs
(C)
StAR
CYP11A1
CYP17A1
HSD3B1
HSD3B2
CYP21A2
CYP11B2
HSD11B2
CYP11B1
HSD17B5
CYP19A1
HSD17B1
PBX1
CREB1
CES1
NROB1
NR4A1
NR5A1
CCR6
1
CD1C
0.8
CD1E
CD40
0.6
EOMES
0.4
GBP2
HLAA
0.2
HLAB
0
HLAH
JAK3
-0.2
NKAP
-0.4
SIRPA
TLR5
-0.6
XCL1
-0.8
Signature
-1
Cortisol Metabolism Enzyme Gene
Steroid Metabolism Enzyme Gene
Steroid Metabolism Transcription Factor Gene
FIGURE 5 Expression correlations between prognostic differentially expressed immune genes (DEIGs) and steroid metabolism genes. (A) Heat map of prognostic differentially expressed immune genes (DEIGs) and steroid metabolism genes in all ACC patients. (B) Heat map of prognostic DEIGs and steroid metabolism genes in all nonCS-ACC patients. (C) Heat map of prognostic DEIGs and steroid metabolism genes in CS-ACC patients
T regulatory (Treg) cell infiltration (r > 0.35) and M2 macrophage (r > 0.43) tumour infiltration. TLR5 expression was positively asso- ciated with M2 macrophage infiltration (r = 0.51) and negatively as- sociated with T follicular helper cells (r = - 0.39) and DCa (r = - 0.41). Expression of the HLA cluster genes (HLA-A,B,H) was positively as- sociated with M2 macrophage infiltration (r > 0.39).
Lastly, the composite mRNA expression signature suppressed in CS-ACCs was positively associated with CD8+ T cell (r = 0.35), Treg cell (r = 0.36) and M2 macrophage (r = 0.49) infiltration and nega- tively associated with DC2 (r = - 0.39) in the ACC TME, suggesting a link between the prognostic DEIG signature expression and prog- nostic TIIC profiles. Univariate Cox regression showed low expres- sion of the mRNA signature was associated with significantly shorter OS (HR 3.43, 95% CI 1.42-8.28, p = 0.016) and DFS (HR 4.82; 95% CI 2.15-10.8, p < 0.001). The 5-year OS for all ACC patients was 77.9% for the high expression group and 36.5% for the low expres- sion group, while the DFS was 71.6% for the high expression group and 18.5% for the low expression group (Figure 6C,D).
4 DISCUSSION |
In this study, we examined and defined the DEIGs and TIIC profiles of ACC tumour microenvironment and immunosuppressive signa- tures through computational immunogenomic deconvolution of the TCGA genomic data. Specifically, we noted differences between CS-ACC and hormonally inactive or non-cortisol-producing hormo- nally active ACC tumours (nonCS-ACC). Our findings strongly sup- port previous studies where CS-ACC was shown to be associated with immune resistant TME and poor patient outcomes compared to nonCS-ACC despite similar pathology and stage.24 Furthermore, we demonstrated immunogenomic differences between CS-ACC and nonCS-ACC TME while identifying distinct mRNA expression profiles associated with immune process genes. The downregulation of many of these DEIGs was associated with poor patient outcomes and differential TIIC profiles. Consistent with a recent independent cohort study,24 CS-ACC tumours demonstrated significantly lower levels of CD8 T cells compared to nonCS-ACC. Additionally, CS-ACC showed decreased infiltration of NK2 cells and M1 macrophages. DC2 tumour infiltration was observed to a greater degree in CS-ACC tumours and associated with a poor DFS prognosis. These findings support the notion that excess cortisol secretion in the ACC TME may not only alter the TIIC abundance, diversity and activity, but also contribute to tumour immune escape, immunotherapeutic fail- ure and adversely impact patient outcomes.
(A)
CCR6
CD1C
CD1E
CD40
EOMES
GBP2
HLAA
HLAB
HLAH
JAK3
NKAP
SIRPA
TLR5
XCL1
Signature
(C)
Overall Survival by Signature Expression mRNA Z-score (log RNA Seq V2 RSEM)
1.00
CCR6
1
CD1C
0.8
CD1E
Survival Probability
0.75
CD40
0.6
EOMES
0.4
GBP2
0.50
HLAA
0.2
HLAB
0
0.25
HLAH
p = 0.0037
JAK3
-0.2
NKAP
-0.4
0.00
SIRPA
0
10
20
30
40
50
60
TLR5
-0.6
Signature Expression
Time (Months)
XCL1
-0.8
Number at risk
Signature
High
34
33
29
21
15
14
13
-1
Low
33
31
21
20
13
9
6
0
10
20
30
40
50
60
Time (Months)
CCR6
CD1C
CD1E
CD40
EOMES
GBP2
HLAA
HLAB
HLAH
JAK3
NKAP
SIRPA
TLR5 XCL1
Signature
(B)
B cells naive
1
(D)
Disease-free Survival by Signature Expression mRNA Z-score (log RNA Seq V2 RSEM)
B cells memory
Plasma cells
0.8
1.00
4
T cells CD8
T cells CD4 naive
0.6
T cells CD4 memory resting
Survival Probability
0.75
T cells follicular helper
-0.4
T cells regulatory (Tregs)
0.50
Natural killer cells resting
0.2
Natural killer cells activated
Monocytes
0
0.25
Macrophages MO
p < 0.0001
Macrophages M1
-0.2
Macrophages M2
0.00
Dendritic cells resting
-0.4
0
10
20
40
50
Dendritic cells activated
Signature Expression
Time (Months)
30
60
Mast cells resting
-0.6
Number at risk
Mast cells activated
High
30
27
25
17
10
9
9
Eosinophils
-0.8
Low
31
18
11
7
5
4
4
Neutrophils
0
10
20
30
40
50
60
-1
Time (Months)
Cortisol-secreting ACC tumours have been considered the more aggressive phenotype among ACC tumours. Despite the known im- munosuppressive role of supra-physiologic glucocorticoid levels, this is the first human study to characterize the immunogenomic associations of cortisol excess related to ACC TME and correlation to patient prog- nosis. It is understood that glucocorticoids play a key regulatory role in the cell transcription process and homeostasis. Previous studies have demonstrated major alterations in immune cell genome expression under the treatment of exogenous glucocorticoids.42,43 In our study, about 1 in 20 of the genes showed significantly different expressions between CS- and nonCS-ACC. Consistent with previous studies, DEGs were primarily related to cellular and metabolic processes and biolog- ical regulation; however, a small portion was identified to be directly related to immunological processes (DEIGs). This deductive analysis served as a starting point for our study to further define the potential immunosuppressive role of excess cortisol in the ACC TME.
Cortisol is a corticosteroid with both glucocorticoid and min- eralocorticoid activity that is physiologically regulated by the hippocampus-pituitary-adrenal (HPA) axis. CS-ACC tumours escape the HPA negative feedback loop, leading to cortisol concentrations often over threefold the upper limit of normal. Recent studies have characterized a variety of mechanisms by which excess cortisol and synthetic cortisol-like therapeutics (prednisone, betamethasone, etc.) impair the immune response and effects of immunotherapy in various cancer types through immune cell deactivation, dampen immune cell recruitment and maturation as well as the induction of apoptosis in lymphocytes.43-46 Supra-physiologic doses of exogenous glucocorti- coids are associated with poor immune checkpoint inhibitor (ICI) re- sponse, including programmed death (PD-1) and PD-1 ligand-1 (PDL-1) monoclonal antibodies and cytotoxic T lymphocyte-associated anti- gen-4 monoclonal antibodies. 44-46 The mechanistic failure of ICIs in the setting of excess glucocorticoids has been mostly attributable to
multimodal lymphocyte inhibition and deactivation. 42-46 Importantly, however, glucocorticoids have been shown to regulate cytokine se- cretion in T/NK lymphocytes and potentiate the inhibitory capac- ity of programmed cell death 1 by upregulating its expression on T cells.43 In this study, cortisol secretion was associated with decreased CD8+ T and NK cell infiltration in the TME compared to nonCS-ACC. This collage of evidence suggests combating glucocorticoid suppres- sion of lymphocytes may serve as a potential therapeutic target wor- thy of investigation, particularly in CS-ACC tumours.
Excess glucocorticoid signalling has also been shown to inhibit macrophage differentiation towards a proinflammatory phenotype by attenuating the induction of proinflammatory genes that inhibits their differentiation of M1 phenotype. 47-49 In our study, we observed significant M2 macrophage infiltration to be the predominant mac- rophage phenotype in all ACC tumours. Significantly fewer activated M1 macrophages were noted within TME of CS-ACC compared to nonCS-ACC tumours. Increasing macrophage recruitment, matura- tion and activation may be another means of TME optimization and a potential avenue for future therapeutic development in CS-ACCs.
Stimulation of the glucocorticoid receptor impacts NF-KB fam- ily proteins to inhibit their transcriptional activity.50 This results in innate and adaptive immune suppression through the decreased expression of co-stimulatory molecules, cytokines and chemok- ines as well as the upregulation of co-inhibitory molecules.49,5º As observed in this study, several downstream NF-KB product genes showed downregulation in CS-ACC (CCR2, CD40, CD40LG, EOMES, TLR5).48,49,51 Additionally, glucocorticoid-mediated inhibition of NF- KB signalling pathways has been shown to hinder DC maturation and antigen presentation efficiency.51-53
Although the use of the TCGA database empowered this study by providing a sufficiently robust database of clinicogenomic param- eters to derive meaningful associations in characterizing the TME of these ultra-rare tumours, the collaborative is limited to large, academic referral centres which may lead to selection bias towards more ag- gressive, later stage disease with over-representation of CS-ACC and metastatic disease. This potential selection bias may limit the gener- alizability of our conclusions. Furthermore, the collaborative nature of the TCGA database also limits the granularity of clinical data avail- able. For example, the TCGA database only reports on ACC hormone hypersecretion (nonfunctional, cortisol, aldosterone, oestrogen etc.) and does not include the diagnostic test use or laboratory values. Our analysis was limited to utilizing clinical and biochemical evaluation of excess cortisol production as a surrogate for degree of Cushing’s dis- ease which showed a trend towards more severe immune suppressive immunogenomic and TIIC profiles but was limited by the low statistical power of sub-analysis (Figure S1). Furthermore, the treatment of pa- tients with ACC is very heterogeneous across institutions with vari- ations in surgical technique, radiation therapy and mitotane regimen (including dose, frequency and therapeutic level). Altogether, such lim- itations hinder our ability to further characterize and account for many clinical and treatment factors that may impact OS and DFS in CS- and nonCS-ACC patients. Additionally, our analysis is limited to bulk sam- ple mRNA sample deconvolution using the CIBERSORTx algorithm.
Additionally, although the deductive design of this study bene- fits the sensitivity for identifying DEIGs between CS- and nonCS- ACCs, this method, along with a relatively small patient population, may limit the specificity of our analysis, thus increasing the potential for type 2 errors and false positive correlations. Similarly, although there were no statistically significant differences in demographics, treatment and tumour stage/pathology between CS- and nonCS- ACC patient groups, it is plausible that accumulation of factors more prevalent in the CS-ACC group-but not statistically different-may conspire to negatively impact survival, potentially confounding the correlations identified in this study.
The CIBERSORTx algorithm for TIIC estimation is highly correla- tive for certain immune cell populations, including CD8 T cells and B- cell subtypes; however, these methods are less precise at estimating DC populations and DC subtypes.54 Nonetheless, DC estimations were included in our analysis due to their crucial role in potentiating ICI and T-cell activation. Although DC2 infiltration was increased in CS- compared to nonCS-ACC and associated with shortened OS and DFS, we suspect a molecular process of DCs may be influenced by excess cortisol in the ACC TME that we are unable to investigate fur- ther with the available data (such as DC migration, maturation and antigen presentation efficiency). Mature activated DCs are equipped to capture antigens and to produce large numbers of immunogenic MHC-peptide complexes to potentiate T-cell immunity. However, glucocorticoids have been shown to distinctly alter the phenotype of DCs by stunting maturation, hindering migration and inhibiting the expression of MHC proteins.51-53 The overall impact of gluco- corticoids on DCs has been summarized as a partial conversion to a monocyte-macrophage phenotype and impaired capacity to reach maturation resulting in decreased T-cell stimulation.51,52 Altogether the impact of excess cortisol among CS-ACC may result in increased accumulation of inefficient DCa in the ACC TME.
This study may offer additional insight into why strong immune infiltration is rarely seen in CS-ACC and why current immunological therapeutic options have been of limited efficacy. Our findings sug- gest that the ACC cortisol hypersecretion impacts TME in favour of immune resistance. Excess cortisol in the ACC TME may potentially facilitate more aggressive tumour biology and poor prognosis. To date, several studies have now highlighted the negative effects of synthetic glucocorticoids on the outcome of immunotherapy.41,42 In line with this, patients with CS-ACC were recently reported to expe- rience decreased response to immunotherapy and experience poor patient outcomes compared to nonCS-ACC patients when treated with anti-PD-L1 agent Pembrolizumab and mitotane.25
This study findings support two previous related but different studies. In 2004, Wolkersdörfer et al.55 suggested the immune es- cape of ACC may be the consequence of altered Fas/Fas-L system expression and loss of MHC class H and HLA expression in an ACC cell line stimulated to secrete cortisol. In 2020, Landwehr et al.24 demonstrated decreased CD8+ T cell infiltration among CS-ACCs compared to nonCS and CD8+ T cell infiltration to be associated with improved prognosis. The decreased expression of HLA-A, B, F, H and CD8+ T cell infiltration among CS-ACC and their associations with
poor patient prognosis observed in this study would further support these findings and suggest a potential role for excess cortisol in im- pacting antigen presentation and lymphocyte activation in CS-ACCs. Furthermore, the mRNA expression levels of several genes coding for cortisol synthesis enzymes (CYP11A1, CYP17A1, CYP21A2) and steroid metabolism transcription factors (PDX1, NR5A1) were upreg- ulated in CS-ACC and associated with decreased CD8+ T-cell infiltra- tion. These gene products and pathways may provide for actionable drug targets to combat immune resistance in CS-ACCs.
In summary, our study characterized a distinct immunogenomic profile with a significant prognostic value associated with CS-ACC compared to nonCS-ACC that may contribute to the poor outcomes associated in patients with CS-ACC. In depth future studies aimed at uncovering the full impact of excess glucocorticoid metabolism and secretion in TME and comprehensive targeting of steroid metabo- lism may provide new immunotherapeutic applications for effective treatment of aggressive and poorly responsive CS-ACC tumours to improve patient survival. Our findings may help guide future studies needed to clarify the potential mechanisms of immune resistance and immunotherapy failure in CS-ACC. Such insight may empower strategies to reduce the potentially harmful effects of excess corti- sol secretion and synthetic glucocorticoids used to control side ef- fects and symptoms associated with many immunotherapies.
CONFLICT OF INTEREST
None.
AUTHOR CONTRIBUTION
Jordan J Baechle: Conceptualization (lead); Data curation (lead); Formal analysis (lead); Methodology (lead); Visualization (lead); Writing-original draft (lead); Writing-review & editing (support- ing). David N Hanna: Formal analysis (supporting); Visualization (supporting); Writing-review & editing (supporting). Sekhar Konjeti: Formal analysis (supporting); Methodology (supporting); Visualization (supporting); Writing-review & editing (equal). Jeffrey C Rathmell: Conceptualization (supporting); Supervision (support- ing); Writing-review & editing (supporting). W Kimryn Rathmell: Conceptualization (supporting); Supervision (supporting); Writing- review & editing (supporting). Naira Baregamian: Conceptualization (supporting); Supervision (lead); Visualization (supporting); Writing- original draft (supporting); Writing-review & editing (lead).
ORCID
Jordan J. Baechle D https://orcid.org/0000-0002-4498-6647
REFERENCES
1. Bilimoria KY, Shen WT, Elaraj D, et al. Adrenocortical carcinoma in the United States. Cancer. 2008;113(11):3130-3136. 10.1002/ cancer.23886
2. Golden SH, Robinson KA, Saldanha I, Anton B, Ladenson PW. Prevalence and incidence of endocrine and metabolic disorders in the United States: a comprehensive review. J Clin Endocrinol Metab. 2009;94(6):1853-1878. 10.1210/jc.2008-2291
3. Fassnacht M, Johanssen S, Quinkler M, et al. Limited prognostic value of the 2004 International Union Against Cancer staging clas- sification for adrenocortical carcinoma. Cancer. 2008;115(2):243- 250. 10.1002/cncr.24030
4. Kebebew E, Reiff E, Duh Q-Y, Clark OH, McMillan A. Extent of disease at presentation and outcome for adrenocortical carci- noma: have we made progress? World J Surg. 2006;30(5):872-878. 10.1007/s00268-005-0329-x
5. Lughezzani G, Sun M, Perrotte P, et al. The European network for the study of adrenal tumors staging system is prognostically su- perior to the international union against cancer-staging system: a north American validation. Eur J Cancer. 2010;46(4):713-719. 10.1016/j.ejca.2009.12.007
6. Baechle JJ, Marincola Smith P, Solórzano CC, et al. Cumulative GRAS score as a predictor of survival after resection for adrenocor- tical carcinoma: analysis from the U.S. Adrenocortical carcinoma. Ann Surg Oncol. 2021. 10.1245/s10434-020-09562-8
7. Prendergast KM, Smith PM, Tran TB, et al. Features of synchro- nous versus metachronous metastasectomy in adrenal cortical car- cinoma: analysis from the US adrenocortical carcinoma database. Surgery. 2020;167(2):352-357. 10.1016/j.surg.2019.05.024
8. Ayala-Ramirez M, Jasim S, Feng L, et al. Adrenocortical carci- noma: clinical outcomes and prognosis of 330 patients at a ter- tiary care center. Eur J Endocrinol. 2013;169(6):891-899. 10.1530/ EJE-13-0519
9. Assié G, Letouzé E, Fassnacht M, et al. Integrated genomic charac- terization of adrenocortical carcinoma. Nat Genet. 2014;46(6):607- 612. 10.1038/ng.2953
10. Zheng S, Cherniack AD, Dewal N, et al. Comprehensive pan- genomic characterization of adrenocortical carcinoma. Cancer Cell. 2016;29(5):723-736. 10.1016/j.ccell.2016.04.002
11. Waszut U, Szyszka P, Dworakowska D. Understanding mitotane mode of action. J Physiol Pharmacol. 2017;68(1):13-26.
12. Kerkhofs TM, Ettaieb MH, Hermsen IG, Haak HR. Developing treatment for adrenocortical carcinoma. Endocr Relat Cancer. 2015;22(6):R325-R338. 10.1530/ERC-15-0318
13. Habra MA, Stephen B, Campbell M, et al. Phase II clinical trial of pem- brolizumab efficacy and safety in advanced adrenocortical carcinoma. J Immunother Cancer. 2019;7(1):253. 10.1186/s40425-019-0722-x
14. Le Tourneau C, Hoimes C, Zarwan C, et al. Avelumab in patients with previously treated metastatic adrenocortical carcinoma: phase 1b results from the JAVELIN solid tumor trial. J Immunother Cancer. 2018;6(1):111. 10.1186/s40425-018-0424-9
15. Fiorentini C, Grisanti S, Cosentini D, et al. Molecular Drivers of Potential Immunotherapy Failure in Adrenocortical Carcinoma. J Oncol. 2019;2019:1-7. 10.1155/2019/6072863
6. Brabo EP, Moraes AB, Neto LV. The role of immune checkpoint inhibitor therapy in advanced adrenocortical carcinoma revisited: review of literature. J Endocrinol Invest. 2020;43(11):1531-1542. 10.1007/s40618-020-01306-5
17. Berruti A, Fassnacht M, Haak H, et al. Prognostic role of overt hy- percortisolism in completely operated patients with adrenocortical cancer. Eur Urol. 2014;65(4):832-838. 10.1016/j.eururo.2013.11.006
18. Cain DW, Cidlowski JA. Immune regulation by glucocorticoids. Nat Rev Immunol. 2017;17(4):233-247. 10.1038/nri.2017.1
.9. Taves MD, Ashwell JD. Glucocorticoids in T cell development, dif- ferentiation and function. Nat Rev Immunol. 2021;21(4):233-243. 10.1038/s41577-020-00464-0
20. Mosca M, Tani C, Carli L, Bombardieri S. Glucocorticoids in sys- temic lupus erythematosus. Clin Exp Rheumatol. 2011;29(5 Suppl 68):S126-S129.
1. Straub RH, Cutolo M. Glucocorticoids and chronic inflammation. Rheumatology. 2016;55(suppl 2):ii6-ii14. 10.1093/rheumatology/ kew348
WILEY
22. Cutolo M, Seriolo B, Pizzorni C, et al. Use of glucocorticoids and risk of infections. Autoimmun Rev. 2008;8(2):153-155. 10.1016/j. autrev.2008.07.010
23. Vegiopoulos A, Herzig S. Glucocorticoids, metabolism and meta- bolic diseases. Mol Cell Endocrinol. 2007;275(1-2):43-61. 10.1016/j. mce.2007.05.015
24. Landwehr LS, Altieri B, Schreiner J, et al. Interplay between gluco- corticoids and tumor-infiltrating lymphocytes on the prognosis of adrenocortical carcinoma. J Immunother Cancer. 2020;8(1):e000469. 10.1136/jitc-2019-000469
25. Head L, Kiseljak-Vassiliades K, Clark TJ, et al. Response to immu- notherapy in combination with mitotane in patients with meta- static adrenocortical cancer. J Endocr Soc. 2019;3(12):2295-2304. 10.1210/js.2019-00305
26. Bedrose S, Miller KC, Altameemi L, et al. Combined lenvatinib and pembrolizumab as salvage therapy in advanced adrenal cortical carcinoma. J Immunother Cancer. 2020;8(2):e001009. 10.1136/ jitc-2020-001009
27. Cosentini D, Grisanti S, Dalla Volta A, et al. Immunotherapy failure in adrenocortical cancer: where next? Endocr Connect. 2018;7(12):E5- E8. 10.1530/EC-18-0398
28. Zheng S, Cherniack AD, Dewal N, et al. Comprehensive pan- genomic characterization of adrenocortical carcinoma. Cancer Cell. 2016;29(5):723-736. 10.1016/j.ccell.2016.04.002
29. Steen CB, Liu CL, Alizadeh AA, Newman AM. Profiling cell type abun- dance and expression in bulk tissues with CIBERSORTx. Methods Mol Biol. 2020;2117:135-157. 10.1007/978-1-0716-0301-7_7
30. Mi H, Muruganujan A, Huang X, et al. Protocol update for large- scale genome and gene function analysis with the PANTHER clas- sification system (v.14.0). Nat Protoc. 2019;14(3):703-721. 10.1038/ s41596-019-0128
31. Weiss LM. Comparative histologic study of 43 metastasizing and nonmetastasizing adrenocortical tumors. Am J Surg Pathol. 1984;8(3):163-169. 10.1097/00000478-198403000-00001
32. Reyes N, Benedetti I, Rebollo J, Correa O, Geliebter J. Atypical chemokine receptor CCRL2 is overexpressed in prostate cancer cells. J Biomed Res. 2017;33(1):17-23. 10.7555/JBR.32.20170057
33. Schioppa T, Sozio F, Barbazza I, et al. Molecular basis for CCRL2 regulation of leukocyte migration. Front Cell Dev Biol. 2020;10(8):615031. 10.3389/fcell.2020.615031
4. He Q, Jiang X, Zhou X, Weng J. Targeting cancers through TCR- peptide/MHC interactions. J Hematol Oncol. 2019;12(1):139. Published 2019 Dec 18. 10.1186/s13045-019-0812-8
35. Korbecki J, Grochans S, Gutowska I, Barczak K, Baranowska- Bosiacka I. CC Chemokines in a tumor: a review of pro-cancer and anti-cancer properties of receptors CCR5, CCR6, CCR7, CCR8, CCR9, and CCR10 Ligands. Int J Mol Sci. 2020;21(20):7619. 10.3390/ ijms21207619
36. Rollins BJ. Chemokines. Blood. 1997;90(3):909-928.
37. Yu P, Li Y, Li Y, Miao Z, Peppelenbosch MP, Pan Q. Guanylate- binding protein 2 orchestrates innate immune responses against murine norovirus and is antagonized by the viral protein NS7. J Biol Chem. 2020;295(23):8036-8047. 10.1074/jbc.RA120.013544
38. Akira S, Takeda K. Toll-like receptor signalling. Nat Rev Immunol. 2004;4(7):499-511. 10.1038/nri1391
39. Washio K, Kotani T, Saito Y, et al. Dendritic cell SIRPa regulates homeostasis of dendritic cells in lymphoid organs. Genes Cells. 2015;20(6):451-463. 10.1111/gtc.12238
40. O’Shea JJ, Husa M, Li D, et al. Jak3 and the pathogenesis of severe combined immunodeficiency. Mol Immunol. 2004;41(6-7):727-737. 10.1016/j.molimm.2004.04.014
41. Nasir I, Kedees MH, Ehrlich ME, Teitelman G. The role of pregnancy hormones in the regulation of Pdx-1 expression. Mol Cell Endocrinol. 2005;233(1-2):1-13. 10.1016/j.mce.2005.02.001
42. Surjit M, Ganti KP, Mukherji A, et al. Widespread negative re- sponse elements mediate direct repression by agonist-liganded
glucocorticoid receptor. Cell. 2011;145(2):224-241. 10.1016/j. cell.2011.03.027
43. Okoye IS, Xu L, Walker J, Elahi S. The glucocorticoids prednisone and dexamethasone differentially modulate T cell function in re- sponse to anti-PD-1 and anti-CTLA-4 immune checkpoint block- ade. Cancer Immunol Immunother. 2020;69(8):1423-1436. 10.1007/ s00262-020-02555-2
44. Drakaki A, Dhillon PK, Wakelee H, et al. Association of baseline systemic corticosteroid use with overall survival and time to next treatment in patients receiving immune checkpoint inhibitor ther- apy in real-world US oncology practice for advanced non-small cell lung cancer, melanoma, or urothelial carcinoma. Oncoimmunology. 2020;9(1):1824645. 10.1080/2162402X.2020.1824645
45. Maeda N, Maruhashi T, Sugiura D, Shimizu K, Okazaki IM, Okazaki T. Glucocorticoids potentiate the inhibitory capacity of programmed cell death 1 by up-regulating its expression on T cells. J Biol Chem. 2019;294(52):19896-19906. 10.1074/jbc.RA119.010379
46. Xing K, Gu B, Zhang P, Wu X. Dexamethasone enhances programmed cell death 1 (PD-1) expression during T cell activation: an insight into the optimum application of glucocorticoids in anti-cancer therapy. BMC Immunol. 2015;16(1):39. 10.1186/s12865-015-0103-2
47. Ehrchen JM, Roth J, Barczyk-Kahlert K. More than suppression: glu- cocorticoid action on monocytes and macrophages. Front Immunol. 2019;10(10):2028. 10.3389/fimmu.2019.02028
48. Xie Y, Tolmeijer S, Oskam JM, Tonkens T, Meijer AH, Schaaf MJM. Glucocorticoids inhibit macrophage differentiation towards a pro- inflammatory phenotype upon wounding without affecting their migration. Dis Model Mech. 2019;12(5):dmm037887. 10.1242/ dmm.037887
49. Barnes PJ. Anti-inflammatory actions of glucocorticoids: molecular mechanisms. Clin Sci (Lond). 1998;94(6):557-572. 10.1042/cs094055
50. Liu T, Zhang L, Joo D, Sun SC. NF-KB signaling in inflammation. Sig Transduct Target Ther. 2017;2:17023. 10.1038/sigtrans.2017.2
51. Cao Y, Bender IK, Konstantinidis AK, et al. Glucocorticoid receptor translational isoforms underlie maturational stage-specific gluco- corticoid sensitivities of dendritic cells in mice and humans. Blood. 2013;121(9):1553-1562. 10.1182/blood-2012-05-432336
52. Piemonti L, Monti P, Allavena P, et al. Glucocorticoids affect human dendritic cell differentiation and maturation. J Immunol. 1999;162(11):6473-6481.
53. Moser M, De Smedt T, Sornasse T, et al. Glucocorticoids down- regulate dendritic cell function in vitro and in vivo. Eur J Immunol. 1995;25(10):2818-2824.
54. Sturm G, Finotello F, Petitprez F, et al. Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno- oncology. Bioinformatics. 2019;35(14):i436-i445. 10.1093/bioin formatics/btz363
55. Wolkersdorfer GW, Marx C, Brown J, et al. Prevalence of HLA- DRB1 genotype and altered Fas/Fas ligand expression in adreno- cortical carcinoma. J Clin Endocrinol Metab. 2005;90(3):1768-1774. 10.1210/jc.2004-1406
SUPPORTING INFORMATION
Additional supporting information may be found in the online ver- sion of the article at the publisher’s website.
How to cite this article: Baechle JJ, Hanna DN, Sekhar KR, Rathmell JC, Rathmell WK, Baregamian N. Integrative computational immunogenomic profiling of cortisol-secreting adrenocortical carcinoma. J Cell Mol Med. 2021;25:10061- 10072. https://doi.org/10.1111/jcmm.16936