title: Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling.
abstract: Solid tumors are heterogeneous tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Furthermore, tissue architecture is generally not reflected in molecular assays, rendering this rich information underused. To address these challenges, we developed a computational approach based on standard hematoxylin and eosin-stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively. To deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples. We next devised a predictor for survival in estrogen receptor-negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures. Image processing also allowed us to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples.
authors.affiliation: Cancer Research UK Cambridge Research Institute, Cambridge CB2 0RE, UK. yinyin.yuan@icr.ac.uk
authors.forename: Yinyin
authors.initials: Y
authors.lastname: Yuan
authors.forename: Henrik
authors.initials: H
authors.lastname: Failmezger
authors.forename: Oscar M
authors.initials: OM
authors.lastname: Rueda
authors.forename: H Raza
authors.initials: HR
authors.lastname: Ali
authors.forename: Stefan
authors.initials: S
authors.lastname: Gräf
authors.forename: Suet-Feung
authors.initials: SF
authors.lastname: Chin
authors.forename: Roland F
authors.initials: RF
authors.lastname: Schwarz
authors.forename: Christina
authors.initials: C
authors.lastname: Curtis
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Dunning
authors.forename: Helen
authors.initials: H
authors.lastname: Bardwell
authors.forename: Nicola
authors.initials: N
authors.lastname: Johnson
authors.forename: Sarah
authors.initials: S
authors.lastname: Doyle
authors.forename: Gulisa
authors.initials: G
authors.lastname: Turashvili
authors.forename: Elena
authors.initials: E
authors.lastname: Provenzano
authors.forename: Sam
authors.initials: S
authors.lastname: Aparicio
authors.forename: Carlos
authors.initials: C
authors.lastname: Caldas
authors.forename: Florian
authors.initials: F
authors.lastname: Markowetz
mesh_terms: D001331:Automation; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000473:pathology*; D005260:Female; D018628:Gene Dosage / Q000235:genetics; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D023281:Genomics; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016246:Lymphocytes, Tumor-Infiltrating / Q000473:pathology; D011379:Prognosis; D011960:Receptors, Estrogen / Q000378:metabolism; D017154:Stromal Cells / Q000378:metabolism / Q000473:pathology; D016019:Survival Analysis
chemical_list: D011960:Receptors, Estrogen