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Quantification of the Heterogeneity of Prognostic Cellular Biomarkers in Ewing Sarcoma Using Automated Image and Random Survival Forest Analysis

Author

Listed:
  • Claudia Bühnemann
  • Simon Li
  • Haiyue Yu
  • Harriet Branford White
  • Karl L Schäfer
  • Antonio Llombart-Bosch
  • Isidro Machado
  • Piero Picci
  • Pancras C W Hogendoorn
  • Nicholas A Athanasou
  • J Alison Noble
  • A Bassim Hassan

Abstract

Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF) machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK) localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases). Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%). The distribution of image features for biomarkers in the remaining high quality material (118 cases, 104 features per case) were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36) was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality controlled tumour material could be achieved. Such an automated and integrated methodology has potential application in the identification of prognostic classifiers based on tumour cell heterogeneity.

Suggested Citation

  • Claudia Bühnemann & Simon Li & Haiyue Yu & Harriet Branford White & Karl L Schäfer & Antonio Llombart-Bosch & Isidro Machado & Piero Picci & Pancras C W Hogendoorn & Nicholas A Athanasou & J Alison No, 2014. "Quantification of the Heterogeneity of Prognostic Cellular Biomarkers in Ewing Sarcoma Using Automated Image and Random Survival Forest Analysis," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-14, September.
  • Handle: RePEc:plo:pone00:0107105
    DOI: 10.1371/journal.pone.0107105
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    1. David B. Seligson & Steve Horvath & Tao Shi & Hong Yu & Sheila Tze & Michael Grunstein & Siavash K. Kurdistani, 2005. "Global histone modification patterns predict risk of prostate cancer recurrence," Nature, Nature, vol. 435(7046), pages 1262-1266, June.
    2. Mike Tyers & Matthias Mann, 2003. "From genomics to proteomics," Nature, Nature, vol. 422(6928), pages 193-197, March.
    3. Ishwaran, Hemant & Kogalur, Udaya B., 2010. "Consistency of random survival forests," Statistics & Probability Letters, Elsevier, vol. 80(13-14), pages 1056-1064, July.
    4. Emrys A Jones & Alexandra van Remoortere & René J M van Zeijl & Pancras C W Hogendoorn & Judith V M G Bovée & André M Deelder & Liam A McDonnell, 2011. "Multiple Statistical Analysis Techniques Corroborate Intratumor Heterogeneity in Imaging Mass Spectrometry Datasets of Myxofibrosarcoma," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-14, September.
    5. Corbin E. Meacham & Sean J. Morrison, 2013. "Tumour heterogeneity and cancer cell plasticity," Nature, Nature, vol. 501(7467), pages 328-337, September.
    6. Nicholas Navin & Jude Kendall & Jennifer Troge & Peter Andrews & Linda Rodgers & Jeanne McIndoo & Kerry Cook & Asya Stepansky & Dan Levy & Diane Esposito & Lakshmi Muthuswamy & Alex Krasnitz & W. Rich, 2011. "Tumour evolution inferred by single-cell sequencing," Nature, Nature, vol. 472(7341), pages 90-94, April.
    7. Jun Kong & Lee A D Cooper & Fusheng Wang & Jingjing Gao & George Teodoro & Lisa Scarpace & Tom Mikkelsen & Matthew J Schniederjan & Carlos S Moreno & Joel H Saltz & Daniel J Brat, 2013. "Machine-Based Morphologic Analysis of Glioblastoma Using Whole-Slide Pathology Images Uncovers Clinically Relevant Molecular Correlates," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-1, November.
    8. Jan Budczies & Frederick Klauschen & Bruno V Sinn & Balázs Győrffy & Wolfgang D Schmitt & Silvia Darb-Esfahani & Carsten Denkert, 2012. "Cutoff Finder: A Comprehensive and Straightforward Web Application Enabling Rapid Biomarker Cutoff Optimization," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-7, December.
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