RESEARCH
Adrenal tumors provide insight into the role of cortisol in NK cell activity
Andrew E Greenstein1, Mouhammed Amir Habra2, Subhagya A Wadekar1 and Andreas Grauer1
1Corcept Therapeutics, Menlo Park, California, USA
2Department of Endocrine Neoplasia and Hormonal Disorders, MD Anderson Cancer Center, Houston, Texas, USA
Correspondence should be addressed to A E Greenstein: agreenstein@corcept.com
Abstract
Elevated glucocorticoid (GC) activity may limit tumor immune response and immune checkpoint inhibitor (ICI) efficacy. Adrenocortical carcinoma (ACC) provides a unique test case to assess correlates of GC activity, as approximately half of ACC patients exhibit excess GC production (GC+). ACC multi-omics were analyzed to identify molecular consequences of GC+ and assess the rationale for combining the glucocorticoid receptor (GR) antagonist relacorilant with an ICI. GC status, mRNA expression, and DNA mutation and methylation data from 71 adrenal tumors were accessed via The Cancer Genome Atlas. Expression of 858 genes differed significantly between GC- and GC+ ACC cases. KEGG pathway analysis showed higher gene expression of three pathways involved in steroid synthesis and secretion in GC+ cases. Fifteen pathways, most related to NK cells and other immune activity, showed lower expression. Hypomethylation was primarily observed in the steroid synthesis pathways. Tumor-infiltrating CD4+ memory (P = 0.003), CD8+ memory (P < 0.001), and NKT-cells (P = 0.014) were depleted in GC+ cases; tumor- associated neutrophils were enriched (P < 0.001). Given the pronounced differences between GC+ and GC- ACC, the effects of cortisol on NK cells were assessed in vitro (NK cells from human PBMCs stimulated with IL-2 or IL-12/15). Cortisol suppressed, and relacorilant restored, NK cell activation, proliferation, and direct tumor cell killing. Thus, GR antagonism may increase the abundance and function of NK and other immune cells in the tumor microenvironment, promoting immune response in GC+ ACC and other malignancies with GC+. This hypothesis will be tested in a phase 1 trial of relacorilant + ICI.
Key Words
adrenocortical carcinoma
natural killer cells
glucocorticoids
cortisol
Cushing syndrome
Endocrine-Related Cancer (2021) 28, 583-592
Introduction
Elevated glucocorticoid (GC) activity (hypercortisolism) has been implicated in the pathophysiology of multiple cancer types, including breast, ovarian, squamous cell and cervical cancer, and lymphomas (Mormont & Lévi 1997, Abercrombie et al. 2004, Palesh et al. 2008, Jehn et al. 2010, Sharma et al. 2018). Patients administered synthetic GCs (e.g. prednisone or dexamethasone) prior to immune checkpoint inhibitor (ICI) therapy are reported to experience worse outcomes across multiple oncology
indications (Habra et al. 2019, Head et al. 2019). Cortisol is a potent, abundant, and immunosuppressive endogenous GC and its target, the glucocorticoid receptor (GR), is expressed in nearly every cell in the body (Miller et al. 1998, Nicolaides et al. 2000). Thus, elevated endogenous GC may suppress immune function and ICI efficacy analogous to synthetic GC.
Adrenocortical carcinoma (ACC), a rare endocrine cancer, is often accompanied by excessive secretion of
| Endocrine-Related | A E Greenstein et al. | Glucocorticoids affect ACC and | 28:8 | 584 |
|---|---|---|---|---|
| Cancer | NK cells |
steroid hormones. Transformation of GC-producing cells in the adrenal gland is common and can lead to ACTH- independent constitutive GC production. Approximately half of ACC tumors are hormonally functional, with 43% producing cortisol, either alone or in combination with other hormones (Else et al. 2014). Public ACC multi- omics data include annotations of GC or other hormone production (Assié et al. 2014, Zheng et al. 2016). Thus, ACC provides a unique dataset in which transcriptional profiling, promoter methylation, and tumor mutation burden can be compared between cases with and without excess cortisol.
Therapies to reduce tumor burden for patients with ACC are limited and treatment outcomes are generally poor. GC excess (GC+) is associated with decreased overall survival and disease-free survival in patients with ACC (Else et al. 2014). Furthermore, patients with GC+ ACC show poor responses to ICI therapy (Habra et al. 2019, Raj et al. 2020). Akin to the diminished ICI response caused by synthetic GC, we hypothesize that ICI responses in GC+ ACC are diminished by endogenous GC. The underlying biology responsible for diminished ICI responses may be improved by GR antagonism. Further, understanding the molecular effects of GC in ACC may provide insight into the role of endogenous cortisol in other cancer types with elevated GC activity. Here, we present an ACC multi-omics analysis that identified molecular consequences of GC activity. A key insight from the in silico analysis, related to effects of GC excess on natural killer (NK) cells, was directly assessed in vitro: relacorilant (CORT125134, Corcept Therapeutics), an investigational selective GR modulator (SGRM) that antagonizes the GR, can reverse the effects of cortisol and restore NK cell activation, proliferation, and target cell killing. This work provides a rationale for combining relacorilant and ICI in the treatment of ACC.
Materials and methods
The Cancer Genome Atlas (TCGA) analysis
GC or other hormone status (based on clinical signs and symptoms or biochemical evidence), mRNA expression, DNA mutation, and DNA methylation data from distinct adrenal resections (n= 71) were accessed via TCGA (www. cancer.gov/tcga). The diagnosis of GC excess (GC+/-) or other hormone excess (H+/-) was provided by the participating sites from which each specimen originated. The diagnosis of GC excess (GC+/-) or other hormone excess (H+/-) was provided by the participating sites from
which each specimen originated to classify patients as having either pure hypercortisolism or mixed syndromes in the opinion of the investigator (Zheng et al. 2016). The criteria to diagnose cortisol production was not specified but likely incorporated results of late-night salivary cortisol, serum cortisol, dexamethasone suppression test, and/or 24-h urinary free cortisol (Gilbert & Lim 2008). Two sarcomatoid cases were excluded from the analysis. Using mRNA data in TCGA, genes that differed significantly (>two-fold change in expression and adjusted P ≤ 0.05) by general (i.e. estrogen and androgen) hormone or GC status were identified. Four different comparisons based on presence or absence of GC excess, presence or absence of general hormone excess, and presence of non- glucocorticoid hormone excess (NGC+) were performed. To identify the function of the genes affected by GC status, Kyoto Encyclopedia of Genes and Genomes (KEGG, RRID:SCR_012773) (Kanehisa & Goto 2000) pathway analysis was applied to the genes with >two-fold change and adjusted P ≤ 0.05. Unsupervised clustering (by gene expression alone) and supervised clustering (by gene expression and hormone status) clustering of two select KEGG pathways was performed to better understand the effects of GC excess on NK and T cells. Furthermore, 394,036 methylation probes were analyzed, and data were normalized using beta-mixture quantile normalization (BMIQ). To deconvolute immune cell type abundance, xCell was applied to the mRNA data (Aran et al. 2017). Mutations were called as previously described (Zheng et al. 2016).
Different tumor types available in TCGA were scored using a published GR activity signature (West et al. 2018). Random forests were then used to derive a new gene signature predictive of GC+ tumors. Signature genes were identified by bootstrapping random forests on random subsets comprising 80% of the data and comparing the mean bootstrapped importance of genes with a threshold value. We used bootstrapping to derive a probability distribution of gene importances, which we later used to estimate which features are the most predictive of the GC+/- patients. This method is appropriate with relatively small sample sets like this, and it provides more robust estimates of output parameters, such as feature importance. The threshold value was calculated by applying the same procedure to a random forest predicting randomized labels instead of the true GC+/- labels to simulate lack of signal. The 99.9th quantile of gene importance was selected as the threshold (0.0028). Based on the known distribution of GC+/- cases in ACC, a cutoff score of 0.75 was derived to distinguish GC+/-
| Endocrine-Related | A E Greenstein et al. | Glucocorticoids affect ACC and | 28:8 | 585 |
|---|---|---|---|---|
| Cancer | NK cells |
tumors when the gene signature is applied to other tumor types.
In vitro human NK-cell activation assays
Human NK cells were isolated from peripheral blood mononuclear cells (PBMCs) from three donors using NK cell isolation kits (Miltenyi Biotec, Bergisch Gladbach, Germany) per the manufacturer’s instructions. Ethically approved consent was received from the Queen Square Research Ethics Committee, UK (REC approval number 17/LO/0221) to use the human NK cells in this study. Specifically, the consent includes the use of leukocyte cones, under- or overweight whole blood packs, and buffy coats from anonymized blood donations, given to the National Health Service Blood and Transplant Services (NHSBT) and was obtained after a full explanation of the purpose and nature of the studies for which they were used. NK cells were stimulated for 5 days with 500 U/ml IL-2 ± cortisol and relacorilant. Activation and proliferation were assessed by fluorescence-activated cell sorting (FACS). Supernatants harvested on day 5 were analyzed for levels of IFNy, Granzyme A, and TNFa using a Luminex® kit (MilliporeSigma). RNA was isolated from the cells at day 5 using an RNeasy® RNA kit (Qiagen) according to the manufacturer’s instructions. Gene expression in cells harvested on day 5 was analyzed using an IO360™ panel (NanoString Technologies, Seattle, WA, USA). Significant changes in gene expression by each treatment condition were calculated in nSolver™ 4.0 (NanoString Technologies). After a list of significant genes was compiled, the list was filtered for genes with at least a two-fold change between stimulated without cortisol vs stimulated with cortisol. Data were visualized in GraphPad Prism (RRID:SCR_002798).
In vitro tumor cell killing assays with human NK cells
Human PBMCs were stimulated with 50 ng/ml IL-12+1 ng/ml IL-15 for 24 h ± cortisol and relacorilant. K562 tumor cells were added for a defined ratio, and tumor cell killing was assessed 4 h later by FACS with the eF780 Fixable Viability Dye (ThermoFisher).
Results
Significant differences in gene expression between GC+ and GC- ACC
An analysis of TCGA ACC mRNA data identified the absence vs presence of GC excess as affecting the largest number of
genes (858 genes) among the four comparisons performed (comparison 1, GC- vs GC+, in Fig. 1A and colored regions in Fig. 1B). Absence vs presence of any hormone led to a significant difference in 439 genes (comparison 2, H- vs H+). There was no significant difference between tumors without hormone excess and those with excess in non-GC hormones (comparison 3, H- vs NGC+). A comparison of non-GC vs GC excess tumors revealed 185 significantly different genes (comparison 4, NGC+ vs GC+).
Steroid synthesis pathways are elevated while immune pathways are suppressed in GC+ ACC
The function of the 858 genes that differed by GC status were examined using KEGG pathway analysis. KEGG pathways that were upregulated in GC+ cases included several steroid synthesis pathways (Fig. 1C). This confirmed that GC+ tumors have higher expression of GC-producing genes. The majority of pathways affected by GC excess, surprisingly, represented immune processes suppressed in the GC+ cases. Genes involved in NK cell activity, among other immune activities, were lower in GC+ tumors.
Lymphocyte gene suppression is associated with GC production
To better understand the effects of GC excess on NK and T cells, genes from two KEGG pathways (‘T-cell receptor signaling pathway’ and ‘natural killer cell-mediated cytotoxicity’) were assessed by GC status. Unsupervised clustering of normalized gene expression for these two KEGG pathways showed lower gene expression in GC+ cases (Fig. 2A). When clustering GC+ and GC- tumors separately, GC+ cases trended toward lower expression in these immune-related pathways (Fig. 2B). These visualizations confirm the impact of excess cortisol on the abundance of transcripts in each pathway.
Methylation and cellular infiltration contribute to gene expression differences
Differences in tumor gene expression can be caused by promoter methylation, altered cellular infiltrate, or other mechanisms. To understand the cause of these differences in ACC, first, differences in promoter methylation were analyzed. In GC+ ACC cases, many genes were significantly hypomethylated (Supplementary Fig. 1, light blue, see section on supplementary materials given at the end of this article), while fewer genes were hypermethylated (Supplementary Fig. 1, red). The hypomethylated genes
A Hormone status in TCGA ACC tumors (N=71)
Glucocorticoid absent (GC-, n=40)
Glucocorticoid present (GC+, n=31)
Glucocorticoid status Any hormone status
Any hormone absent (H-, n=25)
Any hormone present (H+, n=46)
Non-GC hormones present (NGC+, n=15)
| Comparison 1: | |
| GC- vs GC+ | 858 significantly different genes |
| Comparison 2: | |
| H- vs H+ | 439 significantly different genes |
| Comparison 3: | |
| H- vs NGC+ | O significantly different genes |
| Comparison 4: | |
| NGC+ vs GC+ | 185 significantly different genes |
B
-log10(Adjusted P Value)
C
Terpenoid backbone biosynthesis
Steroid biosynthesis
Upregulated
2-fold change
Aldosterone synthesis and secretion
in GC+ ACC
Primary immunodeficiency
Downregulated in GC+ ACC
Allograft rejection
4
Graft-versus-host disease
Type I diabetes mellitus
Autoimmune thyroid disease
Viral protein interaction with cytokine and cytokine receptor
Viral myocarditis
T cell receptor signaling pathway
2
Antigen processing and presentation
Th17 cell differentiation
. .
P =: 05
Th1 and Th2 cell differentiation
Natural killer cell mediated cytotoxicity
Hematopoietic cell lineage
Fold Change in Expression Between GC-and GC+
Cytokine-cytokine receptor interaction
Cell adhesion molecules (CAMs)
-4
0
4
T
0
10
20
30
Lower Expression in GC+
Higher Expression in GC+
Number of Genes in Pathway Affected by GC Excess
were primarily associated with aldosterone, GC, and bile synthesis/secretion, similar to the pathways found to be upregulated in GC+ ACC (Fig. 1C). In contrast, the immune pathways with downregulated gene expression identified by mRNA analysis were not enriched in either the hypo- or hypermethylated sets. Thus, differences in methylation may explain the upregulation of steroidogenesis pathways but not the downregulation of immune pathways.
Since promoter hypermethylation could not account for the reduced abundance of immune-related transcripts in ACC tumors, the abundance of various cell types was determined in the ACC tumors. Cell abundance was deconvoluted from the mRNA data using xCell (Aran et al. 2017). GC+ ACC tumors showed lower lymphocyte abundance with higher myeloid and mesenchymal stem cell abundance as compared to GC- tumors. T cells
(P < 0.005) and natural killer T cells (NKT cells, P = 0.014) were less abundant in GC+ cases compared to GC- (Fig. 3). In contrast, mesenchymal stem cells and neutrophils were more abundant in GC+ cases (P < 0.001, Fig. 3). Total missense and nonsense mutation were separately assessed to determine tumor mutation burden (TMB). Higher TMB was observed in the GC+ cases (P = 0.029, Supplementary Fig. 2).
Gene signature can predict tumors resembling GC+ ACC cases
GR activity was assessed in different tumor types using a published GR-driven gene signature (West et al. 2018). These data confirmed that GR activity is high in ACC compared to other tumor types in TCGA (Fig. 4).
A Unsupervised Clustering
Glucocorticoid
Hormone Status
Hormone
RAETIE
MAVZ
HLA-DQA2
GZMB
SINI4
ITGAL
CO247
Genes in Pathways
HLA-OQA1
HLA-CPB1
HLA-ORA
PDCDT
SH2DIA
GD3D CORE
CORA
CORA
PRKCA JAR’S
HLA-A HLA-F
HLA-B
HLALE
CD25
TCGA.OR.A5JO TOGA.OR.A5JK TCGA OR ASLK
TCGA.OR.A5LN
TCGA.OR.A5LA
IL2RA
TOGA.OR.A5L5
TOGA.PK ASHA
TOGA OR ASI 9
TOGA OB AS IM TOGA. OR.ASJV
TCGA. OR.ASJC
TCGA.OR.ASJI
TCGA.OR A5JD
TOGA.OR.A5LP
TCGA.OR.A5JR
TCGA.OR.A5KT
TCGA.OR.ASL.
YOGA OR ASL
TOGA.ORA5JJ
TCGA.OR.ASLG
TCGA.OR.A5KZ
TCGA.OR.A5J2
TCGA.PK.A5H9
TCGA.OR.A5LH
TCGA OR ASK1
TEGA ARKI
TCGA. CH. ADJE
TCGA.OR.ASJL
TOGA.OR.A5L6
TCGA.OR.ASLR
TOGA.OR.A5JT
TCGA.OR.A5K3 TCGA.OR.A5JC
ICGA OR ASIX
TOGA. OH.ASJX
TCGA.OR.ADIM
TCGA.OR ASK8 TCGA.OR.A5J5
TCGA.OR.A5J9 TCGA.OU.ASPI
TCGA.OR.ASJA
TOGA.OR.A5L4
TCGA.OR A5KA
TOGA OR ASKU
TOGA.OD ABY
TOGA.OR.ASLI
TOGA.OR.A5JP
TCGA.OR.A5J1
TCGA.OR.ASKY
TCGAOR A5KB TOGA OR ASJE
TCGA.OR ASK2
TCGA. OH.ADJG
TOGA. OH.ASLL
TCGA.P6.ASOF
TCGA.OR.ASJW
TOGA.OR.A5JF
TCGA OR A5 13
TCGA.OR.ASLC
TOGA OR ASLC
TOGA. OR.ASJS
TOGA OR ASKS TCGA.OR ASKO
TCGA.OR.ASKV
TCGA.OR.A5KW
TCGA OR ASLO
Tech op Ane
ICGA.OCH ROLE
TCGA. Un.ASLO
TOGA. OR. ASLO
TCGA.OR.A5J7
TCGA OR ASKO
TCGA.OR ASK9
TOGA.OR.A5JM
Lower normalized gene expression
2
0
Higher normalized 2 gene expression
ACC Tumors
B Supervised Clustering by GC Status
Glucocorticoid
Hormone Status
Hormone
RAETIE
PRACE
HLA-DQA2
GZMB
STATA
PIPRC
GRAP2
LCK
ITGAL
CD347
ITK
Genes in Pathways
HLA-DQA1
HLA-DPB1
HLA-DRA
PDCDI
GOOG SH2DIA
COD COSE
CORR
PRKCA
MAV3 HLA-A
A
HLA-B
HLA-E
CD28 AL2RA
TCGA.OR.A5JO
TCGA.OR.A5JK TOGA.OR ASLK TCGA.OR.A5LN
TOGA.OR ASLA
TCGA.OR.ASLS
TCGA.PK.ASHA
ICGA OR. ASLS
TCGA.OR.Pod
TOGA OR A5JO
TOGA OR A5LP
TOGA. OR ASJR
TCGA.PK.A5H9
TCGA.OR.A5LH
TOGA OR ASK1
ICGA. OR.ASL6
TOGA ORASUL
TCGA.OR.AS.IZ
TCGA. OR.ASLA
TOGA.OR A5K3
TOGA.OR A5JC
TCGA.OR.A5JX
TCGA.OR.A5JJ
TOGA OR A5J2
TOGA OR A5J1
TOGA OR A5J5
TOGA OR A5J9 TCGA.OU.ASPI
TCGA.OR.A5LT
TCGA OR AS IP
TCGA.OR.ASJA
TOGA. OR ASK4
TOGA. OR.ASKU
TCGA. OR.ASIS
TCGA.OR.A5LO
TOGA.OR A5LB
TCGA.OR.ASLD
TCGA.OR.ASKT
TOGA OD ARO TOGA OR ASL.
TCGA.OR.A5LG
TCGA.OR.ASKZ
ICA OR AS IT
LEGA OR ASLA
TCGA.OR.ASJY
TEGA OR ASJY
TOGRA CIL HOLS
TCGAOR ASKW
TCGA.OR.ASKY
TCGA.OR.A5JG
TOGA OR ASKE
TCGA.OR.MOL
TCGA.PB.ASOF
TOGA.OR.A.W
TCGA.OR.A5JF
TOGA OR A5J3
TCGA.OR.ASL3 TOGA OR A5LC
TOGAOR MER TOGA.OR HET
TOGA. OR.ASKO
TCGA.OR.A5L8
TOGA.OR ASK9 TOGA.OR.A5JM
Lower normalized gene expression
Higher normalized gene expression
-2
0
ACC Tumors
There was no difference between ACC cases with different hormone and GC status (insert in Fig. 4). The published GR-driven gene signature (West et al. 2018) was derived primarily from ER+ breast cancer cells and tumors and thus may not be ideal for the assessment of ACC-specific transcriptional profiles.
As the published GR activity signature scores were consistent between ACC cases, we defined a new gene signature capable of identifying GC+ ACC cases. Random forest methods were used to train a cross-validated model that distinguishes GC+/- ACC cases with a receiver- operator characteristic curve area under the curve (ROC AUC) of 0.87 ± 0.09 (Fig. 5A). The sensor component of the inflammasome (NLRP1) and a mediator of NK activation by IL-15 (ZNF683) were identified as important parts of this signature (insert in Fig. 5A), confirming the relevance of NK
cells in GC biology. The gene signature was then applied to other tumors types in TCGA to identify those with GC+-like transcriptional profiles (Fig. 5B). According to this score, uveal (UVM) and skin cutaneous melanomas (SKCM) may have the highest frequency of cases similar to GC+ ACC of the 13 tumor types assessed (Fig. 5C). This gene signature may be useful in a tissue-based diagnostic to find GC+ ACC cases or similar cases among other tumor types.
Effects of cortisol and relacorilant on NK cell function in vitro
Given the prominent suppression of NK-related genes in GC+ cases, the direct effects of GR modulation of human NK cells were assessed. Human NK cell activation (abundance of CD25+CD69+ cells) was increased by stimulation with
Less abundant in GC+
CD4+ naive T cells
CD8+ T cells
CD4+ memory T cells
0.12
P =. 003
P =. 002
0.3
P =. 004
0.6
XCell score
0.08
XCell score
XCell score
0.2
0.4
0.04
0.2
0.1
0.00
0.0
0.0
GC-
GC+
GC-
GC+
GC-
GC+
CD8+ Tcm cells
NKT cells
0.6
P <. 001
1.00
P =. 014
0.75
0.4
XCell score
XCell score
0.50
0.2
0.25
0.0
0.00
GC-
GC+
GC-
GC+
More abundant in GC+
Mesenchymal stem cells
Neutrophils
2.0
P <. 001
P <. 001
0.09
1.5
XCell score
XCell score
0.06
1.0
0.03
0.5
0.00
GC-
GC+
GC-
GC+
IL-2, suppressed by cortisol, and restored by relacorilant (Mann-Whitney P = 0.0039, Fig. 6A). Proliferation of NK cells was also increased by stimulation, suppressed by cortisol, and restored by relacorilant (Mann-Whitney P = 0.0099, Fig. 6B). Cytokine production (both transcript and secreted protein) was also increased by stimulation, suppressed by cortisol, and restored by relacorilant (Fig. 6C, D, E and F). Genes that were significantly induced by stimulation, suppressed by cortisol, and restored by relacorilant included key NK-activation genes, including the IL-2 receptor (IL2RA) and the activator LAG3 (Fig. 6F). These data provide experimental confirmation of
the observed effects of GC on NK cell populations in ACC tumors.
Activation, proliferation, and cytokine secretion are all indicative of a functional change in NK cells mediated by cortisol and relacorilant. To determine if this functional change also affected target cell killing, PBMCs stimulated with IL-12+IL-15 were incubated with K562 tumor cells. At various effector:tumor cell ratios, cortisol suppressed tumor cell killing and relacorilant restored it (Fig. 6G). There was a significant improvement in tumor killing when relacorilant was added at the 5:1 effector:tumor ratio (Mann-Whitney P = 0.004) (Fig. 6H). These findings
5
Kruskal-Wallis, P <. 001
5-
4
4-
GR Summarized Activity
3
!
NGC+ GC+ H- H+
0
0
3
0
0
·
2
. 6
0
0
3
·
¢
9
10
0
1
:
·
ACC CHOL LUSC PAAD LIHC PRAD OV LUAD BRCA CESC LGG UVM BLCA SKCM Cancer Type
confirm that GCs suppress tumor cell killing by human NK cells in vitro.
Discussion
ACC is a grievous disease in which patients face challenges both in tumor and hormone management. ACC patients with GC excess experience Cushing syndrome, which by itself can increase the comorbidities associated with ACC, including thromboembolic, musculoskeletal, cardiovascular, infectious, and metabolic complications (Yaneva et al. 2013). In addition, GCs are potent transcriptional regulators and mediators of immune- cell function. The TCGA ACC data provides a unique dataset in which rich multi-omics data are paired with clinical assessment of GC excess. This exaggerated cortisol physiology was investigated to better understand ACC, select biomarkers for a phase 1 trial with a GR antagonist, and to glean insights into possible subclinical and/or local manifestations of GC activity in other tumor types.
Analysis of the TCGA ACC dataset showed a significant impact of presence vs absence of GC excess. Significant differences in 858 genes were observed between ACC cases with or without GC excess, while fewer genes showed significant differences across our other comparisons, such as cases with or without excess in any steroid hormone. Genes involved in steroid synthesis, including the KEGG pathways ‘steroid biosynthesis’, ‘aldosterone synthesis and secretion’, and ‘terpenoid backbone biosynthesis’, were, higher in cases with GC excess. Increased promoter hypomethylation was observed for steroid synthesis genes in the GC+ cases, consistent with reports of epigenetic regulation of cortisol synthesis (Liu et al. 2004). In contrast, no difference in methylation between GC+ and GC- cases was observed for immune genes in the ‘T-cell receptor signaling pathway’ and ‘natural killer cell- mediated cytotoxicity’ KEGG pathways. Genes in those two pathways, indicative of T-cell and NK cells presence and activation, were higher in the GC- cases. Furthermore, fewer infiltrating immune cells (T cells and NKT cells) were found in GC+ tumors. These findings, consistent with those reported with immunohistochemistry methods (Landwehr et al. 2020), suggest that the observed immune effects in GC+ cases were likely a consequence of poor infiltration of immune cells into GC+ tumors.
Assessment of GR activity via a published gene signature (West et al. 2018) suggested that intratumor GR activity is similar in ACC cases with or without GC excess. This may be driven by high local cortisol levels within the adrenal gland independent of systemic cortisol levels. Thus, the differences in immune infiltration may be due to the systemic effects of GC, including effects on primary and secondary lymphoid organs throughout the body. Effects of GC on lymphoid organs may also be related to the increased TMB observed in GC+ ACC cases, as high GC may increase tolerance toward neo-antigens. These specific effects of GC excess on lymphocyte tumor infiltration may be reversed by a GR antagonist.
Aberrant cortisol production or activity of ACC has been reported (Cirillo & Prime 2011, Cirillo et al. 2017, Sharma et al. 2018), but its assessment is not commonly part of solid tumor care outside of ACC. Since systemic GC excess had such a pronounced effect on transcription, we next looked for other tumors types that resembled GC+ cases. We reasoned that similarities to GC+ ACC could underly local or systemic cortisol activity. To achieve this, we developed a gene signature that can predict GC+- like tumor cases. We note that the accuracy of this gene signature (and other analyses of the TCGA data reported here) hinges upon the correct classification of GC status
A Random Forest: Importance of Found Genes
B Gene Signature Applied to TCGA Tumor Types
ADGRL3
SPTAN1
1.5
GC-
GC+
Unknown
AGFG1
ZNF683
8
.
TYW5
C3orf80
GC-
MYBPHL
CPNE4
GC+
1.0
RIMKLB
LAMC3
4
ANGPTL2
NDRG4
Expression TMM, log2
Summarized Score
COLCA2
QSOX2
0.5
EML2
NRXN3
0
:
AADAT
GTPBP4
CBWD2
0.0
MAGIX
RTN4R
B9D1
-4
PCOLCE2
ARAP2
EIF2S1
ACC
UVM
SKCM
LIHC
CHOL
OV
BLCA CESC LUSC
PAAD
LUAD B
BRCA
RAD
LGG
PAPSS2
:
Cancer type
IKZF3
·
RPLPO
DENND2C
NLRP1
ZNF683
C Predicted Frequency of GC+ Cases
KIAA0040
ILDR2
CYP3A4
ACC
GC+
GC-
WDYHV1
COMMD6
PIN1
UVM
NR5A1
CPN2
SKCM
SETBP1
LRWD1
LIHC
CHD7
ELOVL7
OV
DLG3
NLRP1
PRAD
FAM166B
Cancer type
LRIG1
PAAD
SERTAD4
PLS3
LUSC
T
T
0.00
0.01
0.02
0.03
LUAD
Gene Importance (a.u.)
LGG
CHOL
CESC
BRCA
BLCA
0.00
0.25
0.50
Relative frequency
0.75
1.00
in the underlying dataset. This diagnosis is not based on a single test with a well-defined threshold; it is polyfactorial and ultimately requires judgment of the diagnosing physician. The derived gene signature could be useful in future efforts to diagnose GC excess from a single test using a tumor biopsy or resected tumor. When non-ACC tumors were scored with our signature, uveal and skin cutaneous melanomas exhibited the highest frequency of cases that resembled the transcriptional signature of GC+ ACC, albeit these cases are still rare. This supports previous reports of local cortisol production in the skin (Vukelic et al. 2011). Based on our findings, it would be reasonable to assess such tumors for immune effects of GR antagonism.
Suppression of NK cells was prominent in the GC+ ACC multi-omics data. NK activation genes were significantly
lower in GC+ cases, and the NK activation gene ZNF683 was among the most important genes distinguishing GC+ from GC- cases. Based on these findings, functional studies assessing the direct effects of GR modulation on human NK cells were conducted. We used the SGRM relacorilant, which antagonizes GR, to counteract the effects of the GR agonist cortisol in human NK cells. These studies confirmed that GR is a key regulator of NK function. Cortisol suppressed NK proliferation, upregulation of cell surface markers of activation, tumor cell killing, IFNy secretion, and IFNy transcription. It also suppressed the secretion of other effector cytokines and expression of the IL-2 receptor (IL2RA). These observations corroborate the decrease in NK activation genes observed in GC+ ACC. Cortisol suppressed, and relacorilant promoted the
A
NK Cell Activation
B
NK Cell Proliferation
C
INFY
CD25 + CD69 + NK Cells (% Stimulated Control)
125-
125-
15
P =. 0039
NK Proliferation (% Stimulated Control)
P =. 0099
LLOQ = 0.5
ULOQ = 9.82
100-
100-
IFNy (ng/ml)
10
75-
75-
50-
50-
5.
25
25-
*
*
0
0
0
…
*
*
Unstim
IL-2
IL-2 +
IL-2 +
IL-2 +
CORT
RELA CORT +
Unstim
IL-2
IL-2 + CORT
IL-2 +
IL-2 +
RELA CORT +
Unstim
IL-2
IL-2 +
IL-2 +
IL-2 +
RELA
RELA
CORT RELA CORT +
RELA
D
TNFa
E
Granzyme A
F
NK Cells
2000
LLOQ = 80
250
ULOQ = 4000
LLOQ = 0.036
TNFa (pg/ml)
Granzyme A (ng/ml)
ULOQ = 200.07
12-
CCL3/L1
1500
200
RNA Abundance (Log2 Counts)
TNFRSF9
10-
IL2RA
150
DUSP5
1000
8-
100
IFNG
6
LAG3
500
50
HLA-DQA1
4.
*
0
0
2
Unstim
IL-2
IL-2 +
IL-2 +
IL-2 +
CORT RELA CORT +
Unstim
IL-2
IL-2 +
IL-2 + II
IL-2 +
CORT RELA CORT +
Unstim.
IL-2
IL-2
IL-2
RELA
RELA
+ CORT
+ CORT
+ RELA
G
K562 Cell Killing at Various Effector: Tumor Ratios
H
K562 Cell Killing (5:1 ratio)
60-
200-
Tumor Cell Death (%)
Unstim
IL-2
K562 Killing (% of Positive Control)
175-
40-
IL-2 + CORT
150
IL-2 + RELA
125
P =. 004
IL-2 + CORT
+ RELA
100
20
75
50
25
0
0
20:1
10:1
5:1
2:1
0.5:1
Unstim
IL-12 +
IL-15
IL-12 +
IL-15 +
IL L-12
+
IL-12 +
NK: Tumour Cell Ratio
IL-15 +
IL-15 +
CORT RELA CORT + RELA
expression of LAG3 (CD223, LAG3) and 4-1BB (CD137, TNFRSF9), both targets of experimental agonists intended to improve the anti-tumor immune response. Expression of chemokine ligand 3-like 1 (CCL3L1), a chemokine that attracts lymphocytes, was also suppressed by cortisol in stimulated NK cells, which could also explain the reduced T-cell infiltrate into the GC+ ACC. The observed reduced abundance of immune-related transcripts in GC+ ACC provides insight into the mechanisms by which GC may limit response to ICI therapy.
The data presented here suggest that selective GR antagonism with the investigational SGRM relacorilant may be able to counteract the immune suppression caused by systemic cortisol. This does not exclude a potential benefit of GR antagonism in all ACC cases, particularly as
the local GR activity signature was high in all ACC subsets (Fig. 4, inset) and NK function was suppressed at normal (200 nM) cortisol concentrations (Fig. 6). Selective GR antagonism could both promote anti-tumor efficacy of other immune modulators, such as immune checkpoint inhibitors or more experimental NK-targeting agents and reduce the dangerous sequalae of cortisol excess. This hypothesis is being tested directly in a phase 1 study of relacorilant +pembrolizumab in patients with ACC and GC excess (NCT04373265).
Supplementary materials
This is linked to the online version of the paper at https://doi.org/10.1530/ ERC-21-0048.
Declaration of interest
AEG, SW, and A Gare employees and stockholders of Corcept Therapeutics; M A H is a consultant/advisor for Corcept Therapeutics, HRA Pharma, and Calico, as well as an investigator/researcher for Exelixis.
Funding
The studies presented here were solely funded by Corcept Therapeutics.
Acknowledgements
The results shown here are based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. Editorial support was provided by Tina K Schlafly, PhD, of Corcept Therapeutics. The authors thank Stacie Shepherd for helpful conversations and Charles River Labs (Portishead, UK) and Ardigen (Krakow, Poland) for management and guidance of these experiments. The authors also thank the healthy donors for their willingness to donate the critical components, the NK cells, to this project.
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Received in final form 2 June 2021 Accepted 4 June 2021 Accepted Manuscript published online 4 June 2021
@ 2021 Society for Endocrinology Published by Bioscientifica Ltd. Printed in Great Britain