NAGIOS: RODERIC FUNCIONANDO

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

Repositori DSpace/Manakin

IMPORTANT: Aquest repositori està en una versió antiga des del 3/12/2023. La nova instal.lació está en https://roderic.uv.es/

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

dc.contributor.author	Bühnemann, Claudia	es_ES
dc.contributor.author	Li, Simon	es_ES
dc.contributor.author	Yu, Haiyue	es_ES
dc.contributor.author	Branford White, Harriet	es_ES
dc.contributor.author	Schäfer, Karl L.	es_ES
dc.contributor.author	Llombart Bosch, Antonio	es_ES
dc.contributor.author	Machado, Isidro	es_ES
dc.contributor.author	Picci, Piero	es_ES
dc.contributor.author	Hogendoorn, Pancras C. W.	es_ES
dc.contributor.author	Athanasou, Nicholas A.	es_ES
dc.contributor.author	Noble, J. Alison	es_ES
dc.contributor.author	Hassan, A. Bassim	es_ES
dc.date.accessioned	2015-06-10T08:38:57Z
dc.date.available	2015-06-10T08:38:57Z
dc.date.issued	2014
dc.identifier.citation	PLoS ONE Vol. 9 Issue 9:	es_ES
dc.identifier.uri	http://hdl.handle.net/10550/44283
dc.description.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.	es_ES
dc.title	Quantification of the Heterogeneity of Prognostic Cellular Biomarkers in Ewing Sarcoma Using Automated Image and Random Survival Forest Analysis	es_ES
dc.type	journal article	es_ES
dc.identifier.doi	10.1371/journal.pone.0107105	es_ES
dc.identifier.idgrec	099689	es_ES