Crowdsourcing for translational research: Analysis of biomarker expression using cancer microarrays

Jonathan Lawson, Rupesh J. Robinson-Vyas, Janette P. McQuillan, Andy Paterson, Sarah Christie, Matthew Kidza-Griffiths, Leigh Anne McDuffus, Karwan A. Moutasim, Emily C. Shaw, Anne E. Kiltie, William J. Howat, Andrew M. Hanby, Gareth J. Thomas, Peter Smittenaar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Background:Academic pathology suffers from an acute and growing lack of workforce resource. This especially impacts on translational elements of clinical trials, which can require detailed analysis of thousands of tissue samples. We tested whether crowdsourcing-enlisting help from the public-is a sufficiently accurate method to score such samples.Methods:We developed a novel online interface to train and test lay participants on cancer detection and immunohistochemistry scoring in tissue microarrays. Lay participants initially performed cancer detection on lung cancer images stained for CD8, and we measured how extending a basic tutorial by annotated example images and feedback-based training affected cancer detection accuracy. We then applied this tutorial to additional cancer types and immunohistochemistry markers-bladder/ki67, lung/EGFR, and oesophageal/CD8-to establish accuracy compared with experts. Using this optimised tutorial, we then tested lay participants' accuracy on immunohistochemistry scoring of lung/EGFR and bladder/p53 samples.Results:We observed that for cancer detection, annotated example images and feedback-based training both improved accuracy compared with a basic tutorial only. Using this optimised tutorial, we demonstrate highly accurate (>0.90 area under curve) detection of cancer in samples stained with nuclear, cytoplasmic and membrane cell markers. We also observed high Spearman correlations between lay participants and experts for immunohistochemistry scoring (0.91 (0.78, 0.96) and 0.97 (0.91, 0.99) for lung/EGFR and bladder/p53 samples, respectively).Conclusions:These results establish crowdsourcing as a promising method to screen large data sets for biomarkers in cancer pathology research across a range of cancers and immunohistochemical stains.

Original languageEnglish
Pages (from-to)237-245
Number of pages9
JournalBritish Journal of Cancer
Issue number2
Publication statusPublished - 17 Jan 2017

Bibliographical note

Publisher Copyright:
© The Author(s) named above.


  • biomarker
  • cancer
  • crowdsourcing
  • immunohistochemistry
  • pathology
  • tissue microarray


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