"Stem cell" origin of the hematopoietic defect in dyskeratosis congenita

We have used the long-term bone marrow culture (LTBMC) system to analyze hematopoiesis in three patients with dyskeratosis congenita (DC), two of whom had aplastic anemia, and the third had a normal blood count (apart from mild macrocytosis) and normal BM cellularity. Hematopoiesis was severely defective in all three patients, as measured by a low incidence of colony-forming cells and a low level of hematopoiesis in LTBMC. The function of the marrow stroma was normal in its ability to support the growth of hematopoietic progenitors from normal marrows seeded onto them in all three cases, but the generation of hematopoietic progenitors from patients marrow cells inoculated onto normal stromas was reduced, thus suggesting the defect to be of stem cell origin. The parents and unaffected brother of one of the families have also been studied in LTBMC and all showed normal hematopoietic and stromal cell function. From this study we speculate that there are some similarities between DC and the defect in the W/Wv mouse.     

Comparing methods for clinical investigator site inspection selection: a comparison of site selection methods of investigators in clinical trials

ABSTRACT

Background During the past two decades, the number and complexity of clinical trials have risen dramatically increasing the difficulty of choosing sites for inspection. FDA’s resources are limited and so sites should be chosen with care.

Purpose To determine if data mining techniques and/or unsupervised statistical monitoring can assist with the process of identifying potential clinical sites for inspection.

Methods Five summary-level clinical site datasets from four new drug applications (NDA) and one biologics license application (BLA), where the FDA had performed or had planned site inspections, were used. The number of sites inspected and the results of the inspections were blinded to the researchers. Five supervised learning models from the previous two years (2016–2017) of an on-going research project were used to predict site inspections results, i.e., No Action Indicated (NAI), Voluntary Action Indicated (VAI), or Official Action Indicated (OAI). Statistical Monitoring Applied to Research Trials (SMART^TM) software for unsupervised statistical monitoring software developed by CluePoints (Mont-Saint-Guibert, Belgium) was utilized to identify atypical centers (via a p-value approach) within a study.Finally, Clinical Investigator Site Selection Tool (CISST), developed by the Center for Drug Evaluation and Research (CDER), was used to calculate the total risk of each site thereby providing a framework for site selection. The agreement between the predictions of these methods was compared. The overall accuracy and sensitivity of the methods were graphically compared.

Results Spearman’s rank order correlation was used to examine the agreement between the SMART^TM analysis (CluePoints’ software) and the CISST analysis. The average aggregated correlation between the p-values (SMART^TM) and total risk scores (CISST) for all five studies was 0.21, and range from ?0.41 to 0.50. The Random Forest models for 2016 and 2017 showed the highest aggregated mean agreement (65.1%) amongst outcomes (NAI, VAI, OAI) for the three available studies. While there does not appear to be a single most accurate approach, the performance of methods under certain circumstances is discussed later in this paper.

Limitations Classifier models based on data mining techniques require historical data (i.e., training data) to develop the model. There is a possibility that sites in the five-summary level datasets were included in the training datasets for the models from the previous year’s research which could result in spurious confirmation of predictive ability. Additionally, the CISST was utilized in three of the five site selection processes, possibly biasing the data.

Conclusion The agreement between methods was lower than expected and no single method emerged as the most accurate.     

The use of 7-amino actinomycin D in identifying apoptosis: simplicity of use and broad spectrum of application compared with other techniques

The detection and quantitation of apoptotic cells is becoming increasingly important in the investigation of the role of apoptosis in cellular proliferation and differentiation. The pathogenesis of hematologic disorders such as aplastic anemia and the development of neoplasia are believed to involve dysregulation of apoptosis. To quantitate accurately the proportion of apoptosis cells within different cell types of a heterogeneous cell population such as blood or bone marrow, a method is required that combines the analysis of large numbers of cells with concurrent immunophenotyping of cell surface antigens. In this study, we have evaluated such a method using the fluorescent DNA binding agent, 7-amino actinomycin D (7AAD), to stain three diverse human cell lines, induced to undergo apoptosis by three different stimuli. Flow cytometric analysis defines three populations on the basis of 7AAD fluorescence and forward light scatter. We have shown by cell sorting and subsequent morphological assessment and terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick end labeling that the populations defined by 7AAD represent live, apoptotic, and late-apoptotic/dead cells. This method is quick, simple, reproducible, and cheap and will be a valuable tool in the investigation of the role of apoptosis in normal physiology and in disease states.