Adrenocortical Carcinoma Steroid Profiles: In Silico Pan-Cancer Analysis of TCGA Data Uncovers Immunotherapy Targets for Potential Improved Outcomes

Transcriptional profiles reveal histologic origin and prognosis across 33 The Cancer Genome Atlas tumor types

Background

In recent years, with the development of transcriptome sequencing, the molecular characteristics of tumors are gradually revealed. Because of the complexity of tumor transcriptome, there is a need to look for the molecular signatures which can be used to evaluate the tissue origin and cell stemness of tumors in order to promote the diagnosis and treatment of tumors.

Methods

Tumor tissue-specific gene sets (TTSGs) consisting of 200 genes were selected using RNA expression data of 9,875 patients from 33 tumor types. t-distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction and visualization of TTSGs in each tumor type. To evaluate oncogenic dedifferentiation and loss of cell stemness, Euclidean distance from each sample to a human embryo single-cell RNA-seq dataset (GSE36552) of TTSGs was calculated as TTSGs index indicating dissimilarity of tumors and embryo. TTSGs index was evaluated for prognosis in each tumor type. Two published signature indexes, the mRNA signature index (mRNAsi) and CIBERSORT, were compared to assess the correlation between the TTSGs index with cell stemness and immune microenvironment. Finally, the difference of prognosis, immune microenvironment and radiotherapy outcomes were compared between patients with high and low TTSGs index.

Results

In this study, all 33 tumor types in The Cancer Genome Atlas (TCGA) were embedded into isolated clusters by t-SNE and confirmed by k-nearest neighbors (kNN) algorithm. Clusters of squamous-cell carcinoma were adjacent to each other revealing similar histologic origin. Basal-like breast cancer was separated from luminal and HER-2-amplified subtypes and closed to squamous-cell carcinoma. TTSGs index was related to overall survival outcomes in cancers derived from liver, thyroid, brain, cervical and kidney. There was a positive correlation between mRNAsi and TTSGs index in pan-kidney and pan-neuronal cancers. Furthermore, cell fractions of M2 macrophages and total leukocytes increased in the group with higher TTSGs index. Patients with higher TTSGs index had longer overall survival time and less radiation therapy resistance compared to patients with lower TTSGs index.

Conclusions

The signature of TTSGs is related to tumor expression features that distinguish tumors of different histologic origin using t-SNE. The signature also relates to prognosis of certain kinds of tumors.

Discovery of a glucocorticoid receptor (GR) activity signature correlates with immune cell infiltration in adrenocortical carcinoma

BACKGROUND

Adrenocortical carcinoma (ACC) is a rare and highly aggressive endocrine malignancy, of which >40% present with glucocorticoid excess. Glucocorticoids and glucocorticoid receptor (GR) signaling have long been thought to suppress immunity and promote tumor progression by acting on immune cells. Here, we provide new insights into the interaction between GR signaling activity and the immune signature of ACC as a potential explanation for immune escape and resistance to immunotherapy.

METHODS

First, GR immunohistochemical staining and immunofluorescence analysis of tumor-infiltrating lymphocyte (CD4 T, CD8 T cells, natural killer (NK) cells, dendritic cells and macrophages) were performed in 78 primary ACC tissue specimens. Quantitative data of immune cell infiltration in ACC were correlated with clinical characteristics. Second, we discovered a GR activity signature (GRsig) using GR-targeted gene networks derived from global gene expression data of primary ACC. Finally, we identified two GRsig-related subtypes based on the GRsig and assessed the differences in immune characteristics and prognostic stratification between the two subtypes.

RESULTS

GR was expressed in 90% of the ACC tumors, and CD8+ cytotoxic T lymphocytes were the most common infiltrating cell type in ACC specimens (88%, 8.6 cells/high power field). GR expression positively correlated with CD8+ T cell (Phi=0.342, p<0.001), CD4+ T cell (Phi=0.280, p<0.001), NK cell (Phi=0.280, p<0.001), macrophage (Phi=0.285, p<0.001), and dendritic cell (Phi=0.397, p<0.001) infiltration. Clustering heatmap analysis also displayed high immune cell infiltration in GR high-expressing tumors and low immune cell infiltration in GR-low tumors. High GR expression and high immune cell infiltration were significantly associated with better survival. Glucocorticoid excess is associated with low immune cell abundance and unfavorable prognosis. A GRsig comprizing n=34 GR-associated genes was derived from Gene Expression Omnibus/The Cancer Genome Atlas (TCGA) data sets and used to define two GRsig-related subtypes in the TCGA cohort. We demonstrated distinct differences in the immune landscape and clinical outcomes between the two subtypes.

CONCLUSION

GR expression positively correlates with tumor-infiltrating immune cells in ACC. The GRsig could serve as a prognostic biomarker and may be helpful for prognosis prediction and response to immunotherapy. Consequently, targeting the GR signaling pathway might be pivotal and should be investigated in clinical studies.

Steroidogenic Factor 1, a Goldilocks Transcription Factor from Adrenocortical Organogenesis to Malignancy

Steroidogenic factor-1 (SF-1, also termed Ad4BP; NR5A1 in the official nomenclature) is a nuclear receptor transcription factor that plays a crucial role in the regulation of adrenal and gonadal development, function and maintenance. In addition to its classical role in regulating the expression of P450 steroid hydroxylases and other steroidogenic genes, involvement in other key processes such as cell survival/proliferation and cytoskeleton dynamics have also been highlighted for SF-1. SF-1 has a restricted pattern of expression, being expressed along the hypothalamic-pituitary axis and in steroidogenic organs since the time of their establishment. Reduced SF-1 expression affects proper gonadal and adrenal organogenesis and function. On the other hand, SF-1 overexpression is found in adrenocortical carcinoma and represents a prognostic marker for patients' survival. This review is focused on the current knowledge about SF-1 and the crucial importance of its dosage for adrenal gland development and function, from its involvement in adrenal cortex formation to tumorigenesis. Overall, data converge towards SF-1 being a key player in the complex network of transcriptional regulation within the adrenal gland in a dosage-dependent manner.

Identifying Immune-Specific Subtypes of Adrenocortical Carcinoma Based on Immunogenomic Profiling

BACKGROUND

The tumor immune microenvironment (TIME) of adrenocortical carcinoma (ACC) is heterogeneous. However, a classification of ACC based on the TIME remains unexplored.

METHODS

We hierarchically clustered ACC based on the enrichment levels of twenty-three immune signatures to identify its immune-specific subtypes. Furthermore, we comprehensively compared the clinical and molecular profiles between the subtypes.

RESULTS

We identified two immune-specific subtypes of ACC: Immunity-H and Immunity-L, which had high and low immune signature scores, respectively. We demonstrated that this subtyping method was stable and reproducible by analyzing five different ACC cohorts. Compared with Immunity-H, Immunity-L had lower levels of immune cell infiltration, worse overall and disease-free survival prognosis, and higher tumor stemness, genomic instability, proliferation potential, and intratumor heterogeneity. Furthermore, the ACC driver gene CTNNB1 was more frequently mutated in Immunity-L than in Immunity-H. Several proteins, such as mTOR, ERCC1, Akt, ACC1, Cyclin_E1, β-catenin, FASN, and GAPDH, were more highly expressed in Immunity-L than in Immunity-H. In contrast, p53, Syk, Lck, PREX1, and MAPK were more highly expressed in Immunity-H. Pathway and gene ontology analysis showed that the immune, stromal, and apoptosis pathways were highly enriched in Immunity-H, while the cell cycle, steroid biosynthesis, and DNA damage repair pathways were highly enriched in Immunity-L.

CONCLUSIONS

ACC can be classified into two stable immune-related subtypes, which have significantly different antitumor responses, molecular characteristics, and clinical outcomes. This subtyping may provide clinical implications for prognostic and immunotherapeutic stratification of ACC.

Comprehensive Characterization of the Regulatory Landscape of Adrenocortical Carcinoma: Novel Transcription Factors and Targets Associated with Prognosis

We reconstructed a transcriptional regulatory network for adrenocortical carcinoma (ACC) using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA)-ACC cohort. We investigated the association of transcriptional regulatory units (regulons) with overall survival, molecular phenotypes, and immune signatures. We annotated the ACC regulons with cancer hallmarks and assessed single sample regulon activities in the European Network for the Study of Adrenal Tumors (ENSAT) cohort. We found 369 regulons associated with overall survival and subdivided them into four clusters: RC1 and RC2, associated with good prognosis, and RC3 and RC4, associated with worse outcomes. The RC1 and RC3 regulons were highly correlated with the 'Steroid Phenotype,' while the RC2 and RC4 regulons were highly correlated with a molecular proliferation signature. We selected two regulons, NR5A1 (steroidogenic factor 1, SF-1) and CENPA (Centromeric Protein A), that were consistently associated with overall survival for further downstream analyses. The CENPA regulon was the primary regulator of MKI-67 (a marker of proliferation KI-67), while the NR5A1 regulon is a well-described transcription factor (TF) in ACC tumorigenesis. We also found that the ZBTB4 (Zinc finger and BTB domain-containing protein 4) regulon, which is negatively associated with CENPA in our transcriptional regulatory network, is also a druggable anti-tumorigenic TF. We anticipate that the ACC regulons may be used as a reference for further investigations concerning the complex molecular interactions in ACC tumors.

Targeted Therapy for Adrenocortical Carcinoma: A Genomic-Based Search for Available and Emerging Options

In rare diseases such as adrenocortical carcinoma (ACC), in silico analysis can help select promising therapy options. We screened all drugs approved by the FDA and those in current clinical studies to identify drugs that target genomic alterations, also known to be present in patients with ACC. We identified FDA-approved drugs in the My Cancer Genome and National Cancer Institute databases and identified genetic alterations that could predict drug response. In total, 155 FDA-approved drugs and 905 drugs in clinical trials were identified and linked to 375 genes of 89 TCGA patients. The most frequent potentially targetable genetic alterations included TP53 (20%), BRD9 (13%), TERT (13%), CTNNB1 (13%), CDK4 (7%), FLT4 (7%), and MDM2 (7%). We identified TP53-modulating drugs to be possibly effective in 20-26% of patients, followed by the Wnt signaling pathway inhibitors (15%), Telomelysin and INO5401 (13%), FHD-609 (13%), etc. According to our data, 67% of ACC patients exhibited genomic alterations that might be targeted by FDA-approved drugs or drugs being tested in current clinical trials. Although there are not many current therapy options directly targeting reported ACC alterations, this study identifies emerging options that could be tested in clinical trials.

Expression of glucocorticoid receptor (GR) and clinical significance in adrenocortical carcinoma

Adrenocortical carcinoma (ACC) is a rare endocrine tumor, and most cases present with hormone excess with poor prognosis. Our research aims to determine the clinical and biological significance of glucocorticoid receptor (GR) expression using large cohorts of ACC patients. Immunohistochemistry was used to assess the expression of GR in 78 ACC cases from the West China Hospital (WCH) cohort. RNA-seq data were retrieved from The Cancer Genome Atlas database (TCGA, n=79). Clinicopathological and follow-up data were obtained from two cohorts. The correlation between the GR gene and tumor immune status was estimated using TIMER and GEPIA2. Kaplan-Meier analysis was performed to identify the prognostic value of GR in ACC. In the WCH cohort, positive nuclear GR staining was identified in 90% of the primary ACC cases. Cortisol-secreting ACCs demonstrated significantly lower GR protein expression than did nonfunctioning tumors (P<0.001). This finding was validated by the mRNA data analysis of the TCGA cohort (P = 0.030). GR expression was found to be positively correlated with the immune cell infiltration level and immune-checkpoint-related gene expression in ACC. Survival comparison and multivariate analysis showed that GR expression is an independent prognostic predictor of disease-free survival and overall survival in ACC patients in both cohorts. Our findings suggest that low GR expression is significantly correlated with excess cortisol, immune signatures and poor survival in ACC patients. We propose that GR signaling may play an important role in ACC behavior and thus may be a therapeutic target, which deserves further research.