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Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets

Overview of attention for article published in Genome Medicine, August 2010
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3 CiteULike
Title
Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets
Published in
Genome Medicine, August 2010
DOI 10.1186/gm174
Pubmed ID
Authors

Charles Swanton, James M Larkin, Marco Gerlinger, Aron C Eklund, Michael Howell, Gordon Stamp, Julian Downward, Martin Gore, P Andrew Futreal, Bernard Escudier, Fabrice Andre, Laurence Albiges, Benoit Beuselinck, Stephane Oudard, Jens Hoffmann, Balázs Gyorffy, Chris J Torrance, Karen A Boehme, Hansjuergen Volkmer, Luisella Toschi, Barbara Nicke, Marlene Beck, Zoltan Szallasi

Abstract

The European Union multi-disciplinary Personalised RNA interference to Enhance the Delivery of Individualised Cytotoxic and Targeted therapeutics (PREDICT) consortium has recently initiated a framework to accelerate the development of predictive biomarkers of individual patient response to anti-cancer agents. The consortium focuses on the identification of reliable predictive biomarkers to approved agents with anti-angiogenic activity for which no reliable predictive biomarkers exist: sunitinib, a multi-targeted tyrosine kinase inhibitor and everolimus, a mammalian target of rapamycin (mTOR) pathway inhibitor. Through the analysis of tumor tissue derived from pre-operative renal cell carcinoma (RCC) clinical trials, the PREDICT consortium will use established and novel methods to integrate comprehensive tumor-derived genomic data with personalized tumor-derived small hairpin RNA and high-throughput small interfering RNA screens to identify and validate functionally important genomic or transcriptomic predictive biomarkers of individual drug response in patients. PREDICT's approach to predictive biomarker discovery differs from conventional associative learning approaches, which can be susceptible to the detection of chance associations that lead to overestimation of true clinical accuracy. These methods will identify molecular pathways important for survival and growth of RCC cells and particular targets suitable for therapeutic development. Importantly, our results may enable individualized treatment of RCC, reducing ineffective therapy in drug-resistant disease, leading to improved quality of life and higher cost efficiency, which in turn should broaden patient access to beneficial therapeutics, thereby enhancing clinical outcome and cancer survival. The consortium will also establish and consolidate a European network providing the technological and clinical platform for large-scale functional genomic biomarker discovery. Here we review our current understanding of molecular mechanisms driving resistance to anti-angiogenesis agents, the current limitations of laboratory and clinical trial strategies and how the PREDICT consortium will endeavor to identify a new generation of predictive biomarkers.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Hungary 1 1%
Sweden 1 1%
Switzerland 1 1%
Canada 1 1%
United Kingdom 1 1%
Unknown 78 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 19%
Student > Ph. D. Student 10 12%
Other 8 9%
Professor > Associate Professor 8 9%
Student > Doctoral Student 7 8%
Other 15 18%
Unknown 21 25%
Readers by discipline Count As %
Medicine and Dentistry 21 25%
Agricultural and Biological Sciences 20 24%
Biochemistry, Genetics and Molecular Biology 10 12%
Neuroscience 3 4%
Economics, Econometrics and Finance 2 2%
Other 8 9%
Unknown 21 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 September 2014.
All research outputs
#8,535,472
of 25,374,647 outputs
Outputs from Genome Medicine
#1,248
of 1,585 outputs
Outputs of similar age
#38,218
of 104,281 outputs
Outputs of similar age from Genome Medicine
#8
of 11 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,585 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 104,281 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.