Modeling Initiation of Ewing Sarcoma in Human Neural Crest Cells

Ewing sarcoma family tumors (ESFT) are aggressive bone and soft tissue tumors that express EWS-ETS fusion genes as driver mutations. Although the histogenesis of ESFT is controversial, mesenchymal (MSC) and/or neural crest (NCSC) stem cells have been implicated as cells of origin. For the current study we evaluated the consequences of EWS-FLI1 expression in human embryonic stem cell-derived NCSC (hNCSC). Ectopic expression of EWS-FLI1 in undifferentiated hNCSC and their neuro-mesenchymal stem cell (hNC-MSC) progeny was readily tolerated and led to altered expression of both well established as well as novel EWS-FLI1 target genes. Importantly, whole genome expression profiling studies revealed that the molecular signature of established ESFT is more similar to hNCSC than any other normal tissue, including MSC, indicating that maintenance or reactivation of the NCSC program is a feature of ESFT pathogenesis. Consistent with this hypothesis, EWS-FLI1 induced hNCSC genes as well as the polycomb proteins BMI-1 and EZH2 in hNC-MSC. In addition, up-regulation of BMI-1 was associated with avoidance of cellular senescence and reversible silencing of p16. Together these studies confirm that, unlike terminally differentiated cells but consistent with bone marrow-derived MSC, NCSC tolerate expression of EWS-FLI1 and ectopic expression of the oncogene initiates transition to an ESFT-like state. In addition, to our knowledge this is the first demonstration that EWS-FLI1-mediated induction of BMI-1 and epigenetic silencing of p16 might be critical early initiating events in ESFT tumorigenesis

Understanding tumor heterogeneity as functional compartments - superorganisms revisited

Compelling evidence broadens our understanding of tumors as highly heterogeneous populations derived from one common progenitor. In this review we portray various stages of tumorigenesis, tumor progression, self-seeding and metastasis in analogy to the superorganisms of insect societies to exemplify the highly complex architecture of a neoplasm as a system of functional "castes.

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research