ABSTRACT
BACKGROUND: Clinical trials have been criticized on various counts. Any attempt to improve how trials are conducted or reported requires--amongst other things--an understanding of the number, the nature and the location of those that sponsor them or collaborate on them. Here we sought to identify the nature and location of each sponsor/collaborator. METHODS AND FINDINGS: We examined the 'sponsor/collaborator' field for the 69,160 drug trials that were registered with ClinicalTrials.gov over a 9-year period (2005-2014). Of the 12,823 unique sponsors, 56% had sponsored only one and 27% had sponsored 2-5 trials each. Just 18% were involved with six or more trials each, and we have (arbitrarily) labeled these organizations as 'more experienced' in sponsoring/collaborating on trials. These 18% (2,266 sponsors/collaborators) were analyzed further: (a) 951 were corporate organizations and (b) 1,145 were non-corporates (including 31 individuals) with (c) 170 unclassified. Further, we identified the location of each organization in (a) and (b). CONCLUSIONS: Clinical trials are an important part of a nation's research endeavors, and ultimately contribute to the health of its people. Thus, understanding the clinical trial landscape--including the number and nature of sponsors, and how active they are--is important for every country. We believe that policy makers in particular should be interested in this study to understand the current situation, and to use the numbers as a baseline for the evolving landscape, to assess the impact of their strategies in future.
Subject(s)
Clinical Trials as Topic , Cooperative Behavior , Drug Industry , Registries , HumansABSTRACT
BACKGROUND: Second generation RNA sequencing technology (RNA-seq) offers the potential to interrogate genome-wide differential RNA splicing in cancer. However, since short RNA reads spanning spliced junctions cannot be mapped contiguously onto to the chromosomes, there is a need for methods to profile splicing from RNA-seq data. Before the invent of RNA-seq technologies, microarrays containing probe sequences representing exon-exon junctions of known genes have been used to hybridize cellular RNAs for measuring context-specific differential splicing. Here, we extend this approach to detect tumor-specific splicing in prostate cancer from a RNA-seq dataset. METHOD: A database, SPEventH, representing probe sequences of under a million non-redundant splice events in human is created with exon-exon junctions of optimized length for use as virtual microarray. SPEventH is used to map tens of millions of reads from matched tumor-normal samples from ten individuals with prostate cancer. Differential counts of reads mapped to each event from tumor and matched normal is used to identify statistically significant tumor-specific splice events in prostate. RESULTS: We find sixty-one (61) splice events that are differentially expressed with a p-value of less than 0.0001 and a fold change of greater than 1.5 in prostate tumor compared to the respective matched normal samples. Interestingly, the only evidence, EST (BF372485), in the public database for one of the tumor-specific splice event joining one of the intron in KLK3 gene to an intron in KLK2, is also derived from prostate tumor-tissue. Also, the 765 events with a p-value of less than 0.001 is shown to cluster all twenty samples in a context-specific fashion with few exceptions stemming from low coverage of samples. CONCLUSIONS: We demonstrate that virtual microarray experiments using a non-redundant database of splice events in human is both efficient and sensitive way to profile genome-wide splicing in biological samples and to detect tumor-specific splicing signatures in datasets from RNA-seq technologies. The signature from the large number of splice events that could cluster tumor and matched-normal samples into two tight separate clusters, suggests that differential splicing is yet another RNA phenotype, alongside gene expression and SNPs, that can be exploited for tumor stratification.
