Harnessing citizen science through mobile phone technology to screen for immunohistochemical biomarkers in bladder cancer View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-07

AUTHORS

Peter Smittenaar, Alexandra K. Walker, Shaun McGill, Christiana Kartsonaki, Rupesh J. Robinson-Vyas, Janette P. McQuillan, Sarah Christie, Leslie Harris, Jonathan Lawson, Elizabeth Henderson, Will Howat, Andrew Hanby, Gareth J. Thomas, Selina Bhattarai, Lisa Browning, Anne E. Kiltie

ABSTRACT

BACKGROUND: Immunohistochemistry (IHC) is often used in personalisation of cancer treatments. Analysis of large data sets to uncover predictive biomarkers by specialists can be enormously time-consuming. Here we investigated crowdsourcing as a means of reliably analysing immunostained cancer samples to discover biomarkers predictive of cancer survival. METHODS: We crowdsourced the analysis of bladder cancer TMA core samples through the smartphone app 'Reverse the Odds'. Scores from members of the public were pooled and compared to a gold standard set scored by appropriate specialists. We also used crowdsourced scores to assess associations with disease-specific survival. RESULTS: Data were collected over 721 days, with 4,744,339 classifications performed. The average time per classification was approximately 15 s, with approximately 20,000 h total non-gaming time contributed. The correlation between crowdsourced and expert H-scores (staining intensity × proportion) varied from 0.65 to 0.92 across the markers tested, with six of 10 correlation coefficients at least 0.80. At least two markers (MRE11 and CK20) were significantly associated with survival in patients with bladder cancer, and a further three markers showed results warranting expert follow-up. CONCLUSIONS: Crowdsourcing through a smartphone app has the potential to accurately screen IHC data and greatly increase the speed of biomarker discovery. More... »

PAGES

220-229

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41416-018-0156-0

DOI

http://dx.doi.org/10.1038/s41416-018-0156-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1105107286

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29991697


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34 schema:description BACKGROUND: Immunohistochemistry (IHC) is often used in personalisation of cancer treatments. Analysis of large data sets to uncover predictive biomarkers by specialists can be enormously time-consuming. Here we investigated crowdsourcing as a means of reliably analysing immunostained cancer samples to discover biomarkers predictive of cancer survival. METHODS: We crowdsourced the analysis of bladder cancer TMA core samples through the smartphone app 'Reverse the Odds'. Scores from members of the public were pooled and compared to a gold standard set scored by appropriate specialists. We also used crowdsourced scores to assess associations with disease-specific survival. RESULTS: Data were collected over 721 days, with 4,744,339 classifications performed. The average time per classification was approximately 15 s, with approximately 20,000 h total non-gaming time contributed. The correlation between crowdsourced and expert H-scores (staining intensity × proportion) varied from 0.65 to 0.92 across the markers tested, with six of 10 correlation coefficients at least 0.80. At least two markers (MRE11 and CK20) were significantly associated with survival in patients with bladder cancer, and a further three markers showed results warranting expert follow-up. CONCLUSIONS: Crowdsourcing through a smartphone app has the potential to accurately screen IHC data and greatly increase the speed of biomarker discovery.
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