High specificity of BCL11B and GLG1 for EWSR1-FLI1 and EWSR1-ERG positive Ewing sarcoma

Ewing sarcoma (EwS) is an aggressive cancer displaying an undifferentiated small-round-cell histomorphology that can be easily confused with a broad spectrum of differential diagnoses. Using comparative transcriptomics and immunohistochemistry (IHC), we previously identified BCL11B and GLG1 as potential specific auxiliary IHC markers for EWSR1-FLI1-positive EwS. Herein, we aimed at validating the specificity of both markers in a far larger and independent cohort of EwS (including EWSR1-ERG-positive cases) and differential diagnoses. Furthermore, we evaluated their intra-tumoral expression heterogeneity. Thus, we stained tissue microarrays from 133 molecularly confirmed EwS cases and 320 samples from morphological mimics, as well as a series of patient-derived xenograft (PDX) models for BCL11B, GLG1, and CD99, and systematically assessed the immunoreactivity and optimal cut-offs for each marker. These analyses demonstrated that high BCL11B and/or GLG1 immunoreactivity in CD99-positive cases had a specificity of 97.5% and an accuracy of 87.4% for diagnosing EwS solely by IHC, and that the markers were expressed by EWSR1-ERG-positive EwS. Only little intra-tumoral heterogeneity in immunoreactivity was observed for differential diagnoses. These results indicate that BCL11B and GLG1 may help as specific auxiliary IHC markers in diagnosing EwS in conjunction with CD99, especially if confirmatory molecular diagnostics are not available

Automatic identification of variables in epidemiological datasets using logic regression

textabstractBackground: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. Methods: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. Results: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies

Effects of dietary calcium and phosphorus regimen on growth performance, bone strength and carcass quality and yield of large white tom turkeys

An experiment was conducted to estimate the calcium (Ca) and nonphytate phosphorus (npP) levels needed for toms in the starter (ST) (3-9 wk of age) and the grower/finisher (G/F) (9-15/15-17 wk of age) periods to support growth performance, bone breaking strength and carcass parameters. After 3 wk of group brooding, poults (B.U.T.) were divided into treatment (trt) pens and fed pellets containing Ca and npP at approximately NRC requirements (3 wk interval basis) or at typical industry (IND) levels (breeder recommendations). At 9 wk of age, birds from each ST trt were fed either a low npP (75% of NRC requirement) diet, the NRC recommended level, or an IND level of npP (Ca:npP=2:1 for all trts) until marketed at 17 wk of age. The birds were weighed every 3 wks and at 17 wk of age. Feed intake was estimated by feed disappearance to calculate feed efficiency. There were 15 pens of 31 birds/pen for each trt in the ST period and 5 pens for each of the 6 trt combinations during the G/F period. Three toms/pen were selected at 15 and 17 wk for bone and component yield measurements. All birds from 3 pens/trt were judged for a walking score (range 1-5, 5 best) during the 17th wk. There was no difference in body weight or feed intake in the ST period. Body weight was decreased when the NRC ST-low npP G/F trt was fed relative to the products in this experiment, especially, for FTM. There was an average moisture reduction of 2.4% for both PBMs and FTMs used in this experiment

A comparative view on the expression patterns of PD-L1 and PD-1 in soft tissue sarcomas.

Soft tissue sarcomas (STSs) are heterogeneous cancers associated with poor prognosis due to high rates of local recurrence and metastasis. The programmed death receptor ligand 1 (PD-L1) is expressed in several cancers. PD-L1 interacts with its receptor, PD-1, on the surface of tumor-infiltrating lymphocytes (TILs), thereby attenuating anti-cancer immune response. Immune checkpoint inhibitors targeting this interaction have been established as effective anti-cancer drugs. However, studies on the PD-L1 and PD-1 expression status in STS are commonly limited by small sample size, analysis of single STS subtypes, or lack of combinatorial marker assessment. To overcome these limitations, we evaluated the expression patterns of intratumoral PD-L1, the number of TILs, their PD-1 expression, and associations with clinicopathological parameters in a large and comprehensive cohort of 225 samples comprising six STS subtypes. We found that nearly all STS subtypes showed PD-L1 expression on the tumor cells, albeit with a broad range of positivity across subtypes (50% angiosarcomas to 3% synovial sarcomas). Co-expression and correlation analyses uncovered that PD-L1 expression was associated with more PD-1-positive TILs (P < 0.001), higher tumor grading (P = 0.016), and worse patients’ 5-year overall survival (P = 0.028). The results were in line with several publications on single STS subtypes, especially when comparing findings for STS with low and high mutational burden. In sum, the substantial portion of PD-L1 positivity, the co-occurrence of PD-1-positive TILs, and the association of PD-L1 with unfavorable clinical outcome provide rationales for immune checkpoint inhibition in patients with PD-L1-positive STS

Integrative clinical transcriptome analysis reveals TMPRSS2-ERG dependency of prognostic biomarkers in prostate adenocarcinoma.

In prostate adenocarcinoma (PCa), distinction between indolent and aggressive disease is challenging. Around 50% of PCa are characterized by TMPRSS2-ERG (T2E)-fusion oncoproteins defining two molecular subtypes (T2E-positive/negative). However, current prognostic tests do not differ between both molecular subtypes, which might affect outcome prediction. To investigate gene-signatures associated with metastasis in T2E-positive and T2E-negative PCa independently, we integrated tumor transcriptomes and clinicopathological data of two cohorts (total n = 783), and analyzed metastasis-associated gene-signatures regarding the T2E-status. Here, we show that the prognostic value of biomarkers in PCa critically depends on the T2E-status. Using gene-set enrichment analyses, we uncovered that metastatic T2E-positive and T2E-negative PCa are characterized by distinct gene-signatures. In addition, by testing genes shared by several functional gene-signatures for their association with event-free survival in a validation cohort (n = 272), we identified five genes (ASPN, BGN, COL1A1, RRM2 and TYMS)—three of which are included in commercially available prognostic tests—whose high expression was significantly associated with worse outcome exclusively in T2E-negative PCa. Among these genes, RRM2 and TYMS were validated by immunohistochemistry in another validation cohort (n = 135), and several of them proved to add prognostic information to current clinicopathological predictors, such as Gleason score, exclusively for T2E-negative patients. No prognostic biomarkers were identified exclusively for T2E-positive tumors. Collectively, our study discovers that the T2E-status, which is per se not a strong prognostic biomarker, crucially determines the prognostic value of other biomarkers. Our data suggest that the molecular subtype needs to be considered when applying prognostic biomarkers for outcome prediction in PCa

Systematic multi-omics cell line profiling uncovers principles of Ewing sarcoma fusion oncogene-mediated gene regulation.

Ewing sarcoma (EwS) is characterized by EWSR1-ETS fusion transcription factors converting polymorphic GGAA microsatellites (mSats) into potent neo-enhancers. Although the paucity of additional mutations makes EwS a genuine model to study principles of cooperation between dominant fusion oncogenes and neo-enhancers, this is impeded by the limited number of well-characterized models. Here we present the Ewing Sarcoma Cell Line Atlas (ESCLA), comprising whole-genome, DNA methylation, transcriptome, proteome, and chromatin immunoprecipitation sequencing (ChIP-seq) data of 18 cell lines with inducible EWSR1-ETS knockdown. The ESCLA shows hundreds of EWSR1-ETS-targets, the nature of EWSR1-ETS-preferred GGAA mSats, and putative indirect modes of EWSR1-ETS-mediated gene regulation, converging in the duality of a specific but plastic EwS signature. We identify heterogeneously regulated EWSR1-ETS-targets as potential prognostic EwS biomarkers. Our freely available ESCLA (http://r2platform.com/escla/) is a rich resource for EwS research and highlights the power of comprehensive datasets to unravel principles of heterogeneous gene regulation by chimeric transcription factors