Integrin CD11b activation drives anti-tumor innate immunity

Myeloid cells are recruited to damaged tissues where they can resolve infections and tumor growth or stimulate wound healing and tumor progression. Recruitment of these cells is regulated by integrins, a family of adhesion receptors that includes integrin CD11b. Here we report that, unexpectedly, integrin CD11b does not regulate myeloid cell recruitment to tumors but instead controls myeloid cell polarization and tumor growth. CD11b activation promotes pro-inflammatory macrophage polarization by stimulating expression of microRNA Let7a. In contrast, inhibition of CD11b prevents Let7a expression and induces cMyc expression, leading to immune suppressive macrophage polarization, vascular maturation, and accelerated tumor growth. Pharmacological activation of CD11b with a small molecule agonist, Leukadherin 1 (LA1), promotes pro-inflammatory macrophage polarization and suppresses tumor growth in animal models of murine and human cancer. These studies identify CD11b as negative regulator of immune suppression and a target for cancer immune therapy

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.Peer reviewe