Purpose

To investigate the molecular events associated with the activation of androgen receptor (AR) as a potential therapeutic target in patients with salivary duct carcinoma (SDC).

Experimental design

Comprehensive molecular and expression analysis of the AR gene in 35 tumor specimens (20 males and 15 females) and cell lines derived from SDC using Western blotting and RT-PCR, FISH analysis, and DNA sequencing was conducted. In vitro and in vivo animal studies were also performed.

Results

AR expression was detected in 70% of the tumors and was mainly nuclear and homogenous in both male and female SDCs, although variable cytoplasmic and/or nuclear localization was also found. We report the identification of ligand-independent AR splice variants, mutations, and extra AR gene copy in primary untreated SDC tumors. In contrast to prostate cancer, no AR gene amplification was observed. In vitro knockdown of AR in a female derived SDC cell line revealed marked growth inhibition in culture and in vivo androgen-independent tumor growth.

Conclusions

Our study provides new detailed information on the molecular and structural alterations associated with AR gene activation in SDC and sheds more light on the putative functional role of AR in SDC cells. On the basis of these data, we propose that patients with SDC (male and female) can be stratified for hormone-based therapy in future clinical trials.

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.