title: Quantification of the heterogeneity of prognostic cellular biomarkers in ewing sarcoma using automated image and random survival forest analysis.
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.
authors.affiliation: CR-UK, Tumour Growth Group, Oxford Molecular Pathology Institute, Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom.
authors.forename: Claudia
authors.initials: C
authors.lastname: Bühnemann
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, Oxford, United Kingdom.
authors.forename: Simon
authors.initials: S
authors.lastname: Li
authors.affiliation: CR-UK, Tumour Growth Group, Oxford Molecular Pathology Institute, Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom; Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, Oxford, United Kingdom.
authors.forename: Haiyue
authors.initials: H
authors.lastname: Yu
authors.affiliation: CR-UK, Tumour Growth Group, Oxford Molecular Pathology Institute, Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom.
authors.forename: Harriet
authors.initials: H
authors.lastname: Branford White
authors.affiliation: Institute of Pathology, Heinrich-Heine University, Medical Faculty, Düsseldorf, Germany.
authors.forename: Karl L
authors.initials: KL
authors.lastname: Schäfer
authors.affiliation: Pathology Department, University of Valencia, Valencia, Spain.
authors.forename: Antonio
authors.initials: A
authors.lastname: Llombart-Bosch
authors.affiliation: Pathology Department, University of Valencia, Valencia, Spain.
authors.forename: Isidro
authors.initials: I
authors.lastname: Machado
authors.affiliation: Research, The Rizzoli Institute, Bologna, Italy.
authors.forename: Piero
authors.initials: P
authors.lastname: Picci
authors.affiliation: Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.
authors.forename: Pancras C W
authors.initials: PC
authors.lastname: Hogendoorn
authors.affiliation: Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Nuffield Orthopaedic Centre, University of Oxford, Oxford, United Kingdom.
authors.forename: Nicholas A
authors.initials: NA
authors.lastname: Athanasou
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, Oxford, United Kingdom.
authors.forename: J Alison
authors.initials: JA
authors.lastname: Noble
authors.affiliation: CR-UK, Tumour Growth Group, Oxford Molecular Pathology Institute, Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom.
authors.forename: A Bassim
authors.initials: AB
authors.lastname: Hassan
mesh_terms: D000071443:12E7 Antigen; D000465:Algorithms; D015703:Antigens, CD / Q000378:metabolism; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000378:metabolism*; D001859:Bone Neoplasms / Q000378:metabolism* / Q000473:pathology; D015815:Cell Adhesion Molecules / Q000378:metabolism; D045744:Cell Line, Tumor; D002467:Cell Nucleus / Q000378:metabolism*; D003593:Cytoplasm / Q000378:metabolism; D006801:Humans; D011379:Prognosis; D012512:Sarcoma, Ewing / Q000378:metabolism* / Q000473:pathology
chemical_list: D000071443:12E7 Antigen; D015703:Antigens, CD; D014408:Biomarkers, Tumor; C061828:CD99 protein, human; D015815:Cell Adhesion Molecules