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

Mimics and Pitfalls of Primary Ovarian Malignancy Imaging

The complex anatomy and similarity of imaging features of various pathologies in the pelvis can make accurate radiology interpretation difficult. While prompt recognition of ovarian cancer remains essential, awareness of processes that mimic ovarian tumors can avoid potential misdiagnosis and unnecessary surgery. This article details the female pelvic anatomy and highlights relevant imaging features that mimic extra-ovarian tumors, to help the radiologists accurately build a differential diagnosis of a lesion occupying the adnexa

Dual Src and MEK Inhibition Decreases Ovarian Cancer Growth and Targets Tumor Initiating Stem-Like Cells

Rational targeted therapies are needed for treatment of ovarian cancers. Signaling kinases Src and MAPK are activated in high-grade serous ovarian cancer (HGSOC). Here, we tested the frequency of activation of both kinases in HGSOC and the therapeutic potential of dual kinase inhibition. MEK and Src activation was assayed in primary HGSOC from The Cancer Genome Atlas (TGGA). Effects of dual kinase inhibition were assayed on cell-cycle, apoptosis, gene, and proteomic analysis; cancer stem cells; and xenografts. Both Src and MAPK are coactivated in 31% of HGSOC, and this associates with worse overall survival on multivariate analysis. Frequent dual kinase activation in HGSOC led us to assay the efficacy of combined Src and MEK inhibition. Treatment of established lines and primary ovarian cancer cultures with Src and MEK inhibitors saracatinib and selumetinib, respectively, showed target kinase inhibition and synergistic induction of apoptosis and cell-cycle arrest and tumor inhibition in xenografts. Gene expression and proteomic analysis confirmed cell-cycle inhibition and autophagy. Dual therapy also potently inhibited tumor-initiating cells. Src and MAPK were both activated in tumor-initiating populations. Combination treatment followed by drug washout decreased sphere formation and ALDH1 cells. tumors dissociated after dual therapy showed a marked decrease in ALDH1 staining, sphere formation, and loss of tumor-initiating cells upon serial xenografting. Selumetinib added to saracatinib overcomes EGFR/HER2/ERBB2-mediated bypass activation of MEK/MAPK observed with saracatinib alone and targets tumor-initiating ovarian cancer populations, supporting further evaluation of combined Src-MEK inhibition in clinical trials.

MAPK Activation Predicts Poor Outcome and the MEK Inhibitor, Selumetinib, Reverses Antiestrogen Resistance in ER-Positive High-Grade Serous Ovarian Cancer

OBJECTIVE: While 67% of high grade serous ovarian cancers (HGSOC) express the estrogen receptor (ER), most fail antiestrogen therapy. Since mitogen-activated protein kinases (MAPK) activation is frequent in ovarian cancer, we investigated if estrogen regulates MAPK and if MEK inhibition (MEKi) reverses anti-estrogen resistance. METHODS: Effects of MEKi (selumetinib), anti-estrogen (fulvestrant), or both were assayed in ER+ HGSOC in vitro and in xenografts. Response biomarkers were investigated by gene expression microarray and reverse phase protein array (RPPA). Genes differentially expressed in two independent primary HGSOCs datasets with high vs low pMAPK by RPPA were used to generate a “MAPK-activated gene signature”. Gene signature components reversed by MEKi were then identified. RESULTS: High intratumor pMAPK independently predicts decreased survival (HR = 1.7, CI>95% 1.3–2.2, p=0.0009) in 408 TCGA HGSOC. A differentially expressed “MAPK-activated” gene subset was also prognostic. “MAPK-activated genes” in HGSOC differ from those in breast cancer. Combined MEK and ER blockade showed greater anti-tumor effects in xenografts than monotherapy. Gene set enrichment analysis and RPPA showed dual therapy downregulated DNA replication and cell cycle drivers, and upregulated lysosomal gene sets. Selumetinib reversed expression of a subset of “MAPK-activated genes” in vitro and/or in xenografts. Three of these genes were prognostic for poor survival (p=0.000265) and warrant testing as a signature predictive of MEKi response. CONCLUSION: High pMAPK is independently prognostic and may underlie antiestrogen failure. Data support further evaluation of fulvestrant and selumetinib in ER+ HGSOC. The MAPK-activated HGSOC signature may help identify MEK inhibitor responsive tumors

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