Two Main Subtypes of Aldosterone-Producing Adrenocortical Adenomas by Morphological and Expression Phenotype

Background: Aldosteronism is still a considerable diagnostic challenge generally diagnosed in a 3-tiered system (initial screening, a confirmation of the diagnosis, and a determination of the specific subtype). Since the recognition that ¾ of cases are due to bilateral hyperplasia, the spectrum of adenomas needs further characterization to determine the origin of aldosterone secretion. Design: We selected unilateral aldosterone-producing adrenocortical adenomas (AP-ACA, 33) responsible of primary aldosteronism defined by WHO criteria from a consecutive series of 98 ACA. We analyzed the histological features (growth pattern, nuclear characteristics, cytoplasmic staining qualities) of the tumor and the expression profile by quantitative RT-PCR of key molecular players of glomerulosa differentiation (SFRP2, β-catenin, AT1R, CYP21 CYP11B2, NURR1 and NUR77) in both the tumor and the surrounding parenchyma. RNA was extracted, cleaned from normal and neoplastic tissues (RNeasy columns), first-strand cDNA synthesized using T7-(dT24)-oligomer and used as template for cRNA synthesis.. The peritumoral parenchyma was also evaluated for the cytohistological features of the glomurulosa and its extension into deep cortex/medulla and periadrenal soft tissues. Quantitative results were cross-validated (expression factor>2, significance<0.01). Variables were studied regarding morphological appearances of the tumor and the status of the peritumoral glomerulosa. Results: Two main groups of AP-ACA were identified morphologically with a corresponding molecular profile. AP-ACA composed predominantly of clear foamy cells (10) that revealed minimal expression of AT1R, CYP21 and CYP11B2 and AP-ACA composed predominantly of eosinophilic cells (23) expressing significantly high AT1R, CYP21 and CYP11B2. The peritumoral parenchyma revealed functional hyperplastic glomerulosa in 31 cases, more prominent and with extra-adrenal extension in clear cell AP-ACA. Conclusions: The common presence of peritumoral hyperplasia suggests a proliferative response of cells to unidentified paracrine/autocrine factor as main mechanism in AP-ACA, which are not involved in glomerulosa differentiation in the clear cell subtype. Clear cell AP-ACA causes a syndrome of aldosteronism characterized by histologic features intermediate between adrenal adenoma and adrenal hyperplasia. Category: Endocrine PathologyUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Histological Risk Classification Predicts Malignancy and Recurrence in Paragangliomas

Background: Mid-term outcome information in risk stratified patient cohort is needed to inform prognosis in individual patients with paragangliomas (PGL), adjuvant therapy choice and future research. The objective is to define the outcome relevance of a novel risk stratification scheme for PGLs. Design: A classification scheme for PGLs was devised and specimen were assessed for invasion capacity (infiltrative edges with broad fibrous bands, extra-adrenal extension [recording capsular, microscopic periadrenal and gross periadrenal], capsular and peritumoral vascular invasion [recording thin- and thick-walled blood vessels]), tumorigenic expansion (expansile nodules with diffuse areas, hypercellular homogenous areas, necrosis [recording multifocal and confluent subtypes]) and mitogenic activity (MFC/10HPF, presence of atypical mitotic figures). Patients were prospectively stratified as low risk or high risk (presence of at least one feature of invasive capacity and two features of tumorigenic expansion). Patients underwent systematic treatment and follow up for their PGLs in a tertiary referral center. Results: The multilevel analysis based on 78 patients identified statistically significant differences in clinical and biochemical presentation between low risk and high risk patients for gender (p<0.05), noradrenalin (4.6±8.5 vs 11.6±16.9), dopamine (0.6±0.3 vs 1.7±2.4), size of lesion (49.8±19.5 vs 89.2±45.8) and malignancy, 0% vs 21.6% (p<0.01), treatment modalities for MIBG therapy, 0% vs 40.5% (p<.0001), MVR, 0% vs 23.3% (p<.001) and lymph node dissection, 13.5% vs 40.5% (p<0.01) and distant metastases, 0% vs 21.6% (p<0.01). Disease free survival was significantly lower in HR patients 0% vs 78.4% (p=0.004). Histological risk stratification predicts DFS with AUC of 0.8 (95% CI: 0.69-0.90; p<0.01). 7/37 patients with HR had a synchronous diagnosis of malignancy based on other criteria and 4 patients suffered local recurrence. Conclusions: Stratification as low risk excluded a synchronous diagnosis of malignancy and disease recurrence of a follow-up interval of 1-75 months (median 12 months). A high-risk status is associated with high risk of malignancy and disease recurrence.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Contribution of the microvessel network to the clonal and kinetic profiles of adrenal cortical proliferative lesions

Monoclonal adrenocortical lesions have been characterized by an inverse correlation between proliferation and apoptosis, and polyclonal lesions show a direct correlation. Their relationship with the vascular pattern remains unknown in adrenocortical nodular hyperplasias (ACNHs), adenomas (ACAs), and carcinomas (ACCs). We studied 20 ACNHs, 25 ACAs, and 10 ACCs (World Health Organization classification criteria) from 55 women. The analysis included X-chromosome inactivation assay (on microdissected samples), slide and flow cytometry, and in situ end labeling. Endothelial cells were stained with anti-CD31, and the blood vessel area and density were quantified by image analysis in the same areas. Appropriate tissue controls were run in every case. Regression analyses between kinetic and vascular features were performed in both polyclonal and monoclonal lesions. Polyclonal patterns were observed in 14 of 18 informative ACNHs and 3 of 22 informative ACAs, and monoclonal patterns were seen in 4 of 18 ACNHs, 19 of 22 ACAs, and 9 of 9 ACCs. A progressive increase in microvessel area was observed in the ACNH–ACA–ACC transition but was statistically significant between benign and malignant lesions only (191.36 ± 168.32 v 958.07 ± 1279.86 μm2; P 186 μm2 (P =.0000008). Monoclonal lesions showed parallel trends (but with opposite signs) for microvessel area and density in comparison with proliferation and apoptosis, whereas polyclonal lesions showed inverse trends. In conclusion, the kinetic advantage of monoclonal adrenal cortical lesions (increased proliferation, decreased apoptosis) is maintained by parallel increases in microvessel area and density. HUM PATHOL 32:1232-1239. Copyright © 2001 by W.B. Saunders Compan

Urinary Steroid Profiling for the Preoperative Identification of Adrenocortical Adenomas with Regression and Myelolipomatous Changes

Background: Adrenocortical neoplasms are classically divided into adenomas (ACA) and carcinomas (ACC). Heterogeneous appearance and greater size are criteria to suggest malignancy, along with the urinary steroid profile (USP). The presence of regression and myelolipomatous changes in adenomas (ACA-RML) can contribute to confusion with ACC and its USP remains unknown. Objective: To evaluate the features of ACA-RML in comparison with other adrenocortical neoplasms. Design: We selected consecutive ACA (11), ACA-RML (7) and ACC (13) cases for which USP analysis was performed before surgery and tissue was available for histological evaluation (King's College Hospital, 2005-2012). Cases were classified according to WHO and Armed Forces Institute of Pathology criteria. USPs were obtained by gas chromatography/mass spectrometry. Total excretion of individual steroids and indices (sums and ratios chosen to reflect steroid metabolic activity) were compared between ACA-RML, ACA, and ACC. Steroids that have proved to be useful markers of ACC were also compared empirically between groups, including tetrahydro-11-deoxycortisol, pregnene3,16,20-triols, 16a- and 21-hydroxypregnenolone and tetrahydro-11-deoxycorticosterone. Results: In comparison with ACA, tumors in ACA-RML were significantly larger (8.5±2.4 vs. 3.5±1.0, P=0.002), presented in older patients and showed relatively higher incidence in males. Mitotic figure counts were significantly lower (0.39±0.04 vs. 0.93±0.11 in ACA, p=0.001) and revealed higher frequency of apoptotic cells (100% vs. 9% in ACA, p= 0.001). The USP of ACA-RML showed no diagnostic features of ACC, along with lower levels of DHA and DHA metabolites. Conclusions: ACA-RML reveals distinctive histological features, and lack of USP markers of malignancy. It is important to recognize ACA-RML because its size and heterogeneous appearance raise the possibility of ACC; in this context, USP is an important tool for a correct preoperative diagnosis. Category: Endocrine PathologyUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Ectopic hyperprolactinaemia due to a malignant Uterine Tumor Resembling Ovarian Sex Cord Tumors (UTROCST)

Purpose Moderate hyperprolactinaemia (2–5 times upper limit of normal) occurring in a patient with a normal pituitary MRI is generally considered to be due to a lesion below the level of detection of the MRI scanner assuming macroprolactin and stress have been excluded. Most patients with mild-to-moderate hyperprolactinaemia and a normal MRI respond to dopamine agonist therapy. We present the rare case of a patient who had prolactin elevation typical of a prolactin-secreting pituitary macroadenoma,with a normal cranial MRI, and in whom the prolactin rose further with dopamine agonist treatment. Subsequent investigations revealed ectopic hyperprolactinaemia to a uterine tumor resembling ovarian sex cord tumor (UTROSCT) which resolved following tumor resection. Although mostly considered to be benign, the UTROSCT recurred with recurrent hyperprolactinaemia and intraabdominal metastases. Methods We have systematically and critically reviewed existing literature relating to ectopic hyperprolactinaemia in general and UTROCST specifically. Results Fewer than 80 cases of UTROSCTs have been reported globally of which about 23% have shown malignant behaviour. There are fewer than 10 cases of paraneoplastic hyperprolactinaemia originating from uterine neoplasms including one other case of ectopic hyperprolactinaemia to a UTROSCT. Conclusions Our case demonstrates the importance of screening for extracranial hyperprolactinaemia in the context of: (1) substantially raised prolactin (10× ULN) and (2) normal cranial MRI assuming macroprolactin has been excluded. The majority of extracranial ectopic prolactin-secreting tumors occur in the reproductive organs.Methods: We have systematically and critically reviewed existing literature relating to ectopic hyperprolactinaemia in general and UTROCST specifically. Results: Fewer than 80 cases of UTROSCTs have been reported globally of which about 23% have shown malignant behaviour. There are fewer than 10 cases of paraneoplastic hyperprolactinaemia originating from uterine neoplasms including one other case of ectopic hyperprolactinaemia to a UTROSCT. Conclusions: Our case demonstrates the importance of screening for extracranial hyperprolactinaemia in the context of: (1) substantially raised prolactin (10xULN) and (2) normal cranial MRI assuming macroprolactin has been excluded. The majority of extracranial ectopic prolactin-secreting tumors occur in the reproductive organs

Clinical implications of intratumor heterogeneity : challenges and opportunities

In this review, we highlight the role of intratumoral heterogeneity, focusing on the clinical and biological ramifications this phenomenon poses. Intratumoral heterogeneity arises through complex genetic, epigenetic, and protein modifications that drive phenotypic selection in response to environmental pressures. Functionally, heterogeneity provides tumors with significant adaptability. This ranges from mutual beneficial cooperation between cells, which nurture features such as growth and metastasis, to the narrow escape and survival of clonal cell populations that have adapted to thrive under specific conditions such as hypoxia or chemotherapy. These dynamic intercellular interplays are guided by a Darwinian selection landscape between clonal tumor cell populations and the tumor microenvironment. Understanding the involved drivers and functional consequences of such tumor heterogeneity is challenging but also promises to provide novel insight needed to confront the problem of therapeutic resistance in tumors

Tumor Heterogeneity: Mechanisms and Bases for a Reliable Application of Molecular Marker Design

Tumor heterogeneity is a confusing finding in the assessment of neoplasms, potentially resulting in inaccurate diagnostic, prognostic and predictive tests. This tumor heterogeneity is not always a random and unpredictable phenomenon, whose knowledge helps designing better tests. The biologic reasons for this intratumoral heterogeneity would then be important to understand both the natural history of neoplasms and the selection of test samples for reliable analysis. The main factors contributing to intratumoral heterogeneity inducing gene abnormalities or modifying its expression include: the gradient ischemic level within neoplasms, the action of tumor microenvironment (bidirectional interaction between tumor cells and stroma), mechanisms of intercellular transference of genetic information (exosomes), and differential mechanisms of sequence-independent modifications of genetic material and proteins. The intratumoral heterogeneity is at the origin of tumor progression and it is also the byproduct of the selection process during progression. Any analysis of heterogeneity mechanisms must be integrated within the process of segregation of genetic changes in tumor cells during the clonal expansion and progression of neoplasms. The evaluation of these mechanisms must also consider the redundancy and pleiotropism of molecular pathways, for which appropriate surrogate markers would support the presence or not of heterogeneous genetics and the main mechanisms responsible. This knowledge would constitute a solid scientific background for future therapeutic planning

Gut microbiome in BALB/c and C57BL/6J mice undergoing experimental thyroid autoimmunity associate with differences in immunological responses and thyroid function

Experimental models of hyperthyroid Graves’ disease (GD) and Graves’ orbitopathy (GO) are efficiently developed by genetic immunisation by electroporation with human thyrotropin hormone receptor (hTSHR) A-subunit plasmid in female BALB/c (H-2d) mice. We investigated susceptibility in C57BL/6 J (H-2b) animals to allow studies on disease mechanisms in transgenic and immune response gene knock-out mice. Higher numbers of female C57BL/6 J were positive for pathogenic thyroid stimulating antibodies, but induced hyperthyroidism remained at a low frequency compared to BALB/c animals. Assessment of hTSHR specific T cells showed reduced proliferation in C57BL/6 J animals accompanied with anti-inflammatory IL-10, with less pro-inflammatory IFN-γ compared to BALB/c. Whilst the orbital tissue from immune BALB/c mice showed inflammation and adipogenesis, in contrast C57BL/6 J animals showed normal pathology. We characterised the gut microbiota using 16 S ribosomal RNA gene sequencing to explore its possible pathogenic role in the model. Despite being housed under identical conditions, we observed significantly different organisation of the microbiota (beta-diversity) in the two strains. Taxonomic differences were also noted, with C57BL/6 J showing an enrichment of Operational Taxonomic Units (OTUs) belonging to the Paludibacter and Allobaculum, followed by Limibacter, Anaerophaga and Ureaplasma genera. A higher number of genera significantly correlating with clinical features was observed in C57BL/6 J compared to BALB/c; for example, Limibacter OTUs correlated negatively with thyroid-stimulating antibodies in C57BL/6 J mice. Thus, our data suggest gut microbiota may play a pivotal immunomodulatory role that differentiates the thyroid function and orbital pathology outcome in these two inbred strains undergoing experimental GO

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p