Targeted next-generation sequencing of cancer genes in poorly differentiated thyroid cancer

Poorly differentiated thyroid carcinoma (PDTC) is a rare malignancy with higher mortality than well-differentiated thyroid carcinoma. The histological diagnosis can be difficult as well as the therapy. Improved diagnosis and new targeted therapies require knowledge of DNA sequence changes in cancer-relevant genes. The TruSeq Amplicon Cancer Panel was used to screen cancer genomes from 25 PDTC patients for somatic single-nucleotide variants in 48 genes known to represent mutational hotspots. A total of 4490 variants were found in 23 tissue samples of PDTC. Ninety-eight percent (4392) of these variants did not meet the inclusion criteria, while 98 potentially pathogenic or pathogenic variants remained after filtering. These variants were distributed over 33 genes and were all present in a heterozygous state. Five tissue samples harboured not a single variant. Predominantly, variants in P53 (43% of tissue samples) were identified, while less frequently, variants in APC, ERBB4, FLT3, KIT, SMAD4 and BRAF (each in 17% of tissue samples) as well as ATM, EGFR and FBXW7 (each in 13% of tissue samples) were observed. This study identified new potential genetic targets for further research in PDTC. Of particular interest are four observed ERBB4 (alias HER4) variants, which have not been connected to this type of thyroid carcinoma so far. In addition, APC and SMAD4 mutations have not been reported in this subtype of cancer either. In contrast to other reports, we did not find CTNNB1 variants

Expression and Secretion of Endostatin in Thyroid Cancer

Background: In thyroid cancer (TC) endostatin was identified as a powerful negative regulator of tumor angiogenesis in vitro. It is currently being evaluated in phase I trials for antiangiogenic therapy in various solid tumors. The aim of this study was to evaluate endostatin expression in archival TC specimens and its secretion following stimulation with thyrotropin (TSH) and epidermal growth factor (EGF) in TC cell lines. Methods: Tissue microarrays of 44 differentiated and 7 anaplastic TC and their metastasis were immunostained for endostatin protein expression and compared with corresponding non-neoplastic thyroid tissue (NT). In vitro, six differentiated (FTC133, FTC236, HTC, HTCTSHr, XTC, and TPC1) and three anaplastic (C643, Hth74, Kat4.0) TC cell lines were evaluated for basal as well as TSH (1-100 mU/ml) and EGF stimulated (1-100 ng/ml) endostatin. Results: Endostatin was detected in all TC and more than half of the NT. Endostatin expression was more frequent and intense in differentiated as compared to anaplastic TC. In vitro, basal endostatin secretion varied between 33 ± 5 pg/ml (FTC236) and 549 ± 65 pg/ml (TPC1) and was doubled in FTC, when the ''primary'' (FTC133) was compared with the metastasis (FTC236). Some cell lines showed TSH-induced (e.g., 60% in XTC) or EGFinduced (e.g., 120% in TPC1) upregulation of endostatin secretion, while others did not, despite documented receptor expression. Conclusion: This study demonstrates endostatin expression in TC, metastasis and-less frequently and intensely-in NT, suggesting a possible association to tumor progression. In vitro, endostatin secretion of some cell lines is regulated by TSH and EGF, however the individual differences deserve further functional studies. These results support rather tumorspecific than histotype-specific expression and regulation of endostatin in TC

Laparoscopy versus open adrenalectomy in patients with solid tumor metastases: results of a multicenter European study

Background: The outcome of adrenalectomy carried out by laparoscopy or open surgery for solid tumor metastases was assessed. Methods: A total of 317 patients with histologically confirmed adrenal metastatic disease collected from 30 centres in Europe underwent adrenalectomy by laparoscopy (n=146) or open laparotomy (n=171). Differences between laparoscopic and open adrenalectomy were assessed by a single Cox analysis for both procedures. Results: The median overall survival was 24.0 [95% confidence interval (CI): 21.4-26.6] months for open adrenalectomy and 45.0 (95% CI: 22.6-67.4) for laparoscopic adrenalectomy (P=0.008). Survival rates were 68%, 49%, 35% and 29% at 1, 2, 3 and 5 years for open surgery vs. 88%, 62%, 52% and 46% for laparoscopy, respectively. In the subgroup of R0 resections, the difference in survival in favor of laparoscopy (median 46 vs. 27 months) was marginally significant (P=0.073). Renal cancer [hazard ratio (HR) 0.42; 95% CI: 0.23-0.76, P=0.005], surgery of the primary tumor (HR 0.33; 95% CI: 0.19-0.54), and use of chemotherapy (HR 0.62; 95% CI: 0.43-0.88) were associated with a better survival, whereas type of resection (R1/R2 vs. R0) was associated with a worse prognosis (HR 2.29; 95% CI: 1.52-3.44, P<0.001). Conclusions: Laparoscopic adrenalectomy patients showed a longer survival than open adrenalectomy individuals, as minimally invasive approach was attempted more common in less advanced disease which led to higher number of R0 resections

Low fertility and population replacement in Scotland

It has been argued that Scotland faces population ageing and decline that will have potentially serious economic and social consequences, and that the origin of these processes lie in its low and declining fertility rates. After considering alternatives to the total period rate measure of fertility, empirical evidence and theoretical argument about low fertility and its consequences is briefly reviewed. The paper argues that low fertility in general may not be the problem it is often purported to be, that Scotland has relatively high fertility, and that pro-natalist policies are neither desirable nor necessary. It suggests that low fertility and population ageing may be viewed as positive developments, and that within Europe, Scotland is distinguished more by its excess of early deaths than by any shortage of births.Peer reviewe

PD-L1 Expression and Immune Cell Infiltration in Gastroenteropancreatic (GEP) and Non-GEP Neuroendocrine Neoplasms With High Proliferative Activity

The potential of neuroendocrine neoplasms (NEN) to respond to checkpoint inhibitors is largely unknown and full of great expectations. Immunohistochemical (IHC) studies of programmed cell death ligand 1 (PD-L1) expression in the tumor microenvironment and its implications in predicting the response to checkpoint inhibition is a very active subject. Currently, the combined analysis of PD-L1 expression and tumor-associated immune cell (TAIC) infiltration is considered the best predictive marker of therapeutic response. Here we investigated the expression of PD-L1 on tumor cells (TC) and tumor-infiltrating immune cells (IC) by IHC in 68 NEN samples with a high proliferation rate (Ki-67 &gt;20%) from 57 patients and in 22 samples we correlated it with TAIC density by assessing intratumoral infiltration of CD3+, CD8+, and CD68+ cells. Furthermore, the tumor microenvironment was evaluated according to the classification of Teng et al. We detected PD-L1 expression in 31.6% of NEN G3. Its expression usually was weak and more IC than TC expressed PD-L1. The proportion of tumors positive for PD-L1 was comparable in NEN from different sites of origin but varied depending on tumor differentiation and disease extension. No positive IHC staining was found in 3 well-differentiated neuroendocrine tumors (NETs) with a proliferation rate above 20% (NET G3). When analyzing TAIC, we rarely (18.2%) detected intratumoral CD8+ cells, whereas infiltration by CD3+ and CD68+ cells was more common (45.5 and 59.1%, respectively). By combining CD3+ cells and PD-L1 status, we identified the immune ignorant phenotype of tumor microenvironment as being the most common phenotype, supporting the concept of a preferably combined immunotherapeutic approach in neuroendocrine carcinoma (NEC)

Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments

Predicting and elucidating the etiology of fatty liver disease : A machine learning modeling and validation study in the IMI DIRECT cohorts

Background Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n= 795) or at high risk of developing the disease (n= 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (= 5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86;p = 5%) rather than a continuous one. Conclusions In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see:) and made it available to the community.Peer reviewe

Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments

Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study

Objective We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D). Research Design and Methods 732 recently diagnosed T2D patients from the IMI-DIRECT study were extensively phenotyped over three years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS) and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression. Results Faster HbA1c progression was independently associated with faster deterioration of OGIS and GS, and increasing CLIm; visceral or liver fat, HDL-cholesterol and triglycerides had further independent, though weaker, roles (R2=0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from AUROC=0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS and CLIm was relatively stable (odds ratios 0.07 to 0.09). T2D polygenic risk score and baseline pancreatic fat, GLP-1, glucagon, diet, and physical activity did not show an independent role. Conclusions Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of T2D patients in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression

Whole blood co-expression modules associate with metabolic traits and type 2 diabetes : an IMI-DIRECT study

Background The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D. Methods Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts. Results We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling. Conclusions Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.Peer reviewe