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

TriMix and tumor antigen mRNA electroporated dendritic cell vaccination plus ipilimumab: link between T-cell activation and clinical responses in advanced melanoma

BackgroundWe previously reported that dendritic cell-based mRNA vaccination plus ipilimumab (TriMixDC-MEL IPI) results in an encouraging rate of tumor responses in patients with pretreated advanced melanoma. Here, we report the TriMixDC-MEL IPI-induced T-cell responses detected in the peripheral blood.MethodsMonocyte-derived dendritic cells electroporated with mRNA encoding CD70, CD40 ligand, and constitutively active TLR4 (TriMix) as well as the tumor-associated antigens tyrosinase, gp100, MAGE-A3, or MAGE-C2 were administered together with IPI for four cycles. For 18/39 patients, an additional vaccine was administered before the first IPI administration. We evaluated tumor-associated antigen specific T-cell responses in previously collected peripheral blood mononuclear cells, available from 15 patients.ResultsVaccine-induced enzyme-linked immunospot assay responses detected after in vitro T-cell stimulation were shown in 12/15 patients. Immune responses detected in patients with a complete or partial response were significantly stronger and broader, and exhibited a higher degree of multifunctionality compared with responses in patients with stable or progressive disease. CD8+ T-cell responses from patients with an ongoing clinical response, either elicited by TriMixDC-MEL IPI or on subsequent pembrolizumab treatment, exhibited the highest degree of multifunctionality.ConclusionsTriMixDC-MEL IPI treatment results in robust CD8+ T-cell responses in a meaningful portion of stage III or IV melanoma patients, and obviously in patients with a clinical response. The levels of polyfunctional and multiantigen T-cell responses measured in patients with a complete response, particularly in patients evidently cured after 5+ years of follow-up, may provide a benchmark for the level of immune stimulation needed to achieve a durable clinical remission.Trial registration numberNCT01302496

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 science. © The Author(s) 2019. Published by Oxford University Press