Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling

被引:281
作者
Yuan, Yinyin [1 ,2 ]
Failmezger, Henrik [3 ,4 ,5 ]
Rueda, Oscar M. [1 ,2 ]
Ali, H. Raza [1 ,2 ]
Graef, Stefan [1 ,2 ]
Chin, Suet-Feung [1 ,2 ]
Schwarz, Roland F. [1 ,2 ]
Curtis, Christina [6 ]
Dunning, Mark J. [1 ]
Bardwell, Helen [1 ]
Johnson, Nicola [7 ]
Doyle, Sarah [7 ]
Turashvili, Gulisa [8 ,9 ]
Provenzano, Elena [7 ,10 ,11 ]
Aparicio, Sam [8 ,9 ]
Caldas, Carlos [1 ,2 ,10 ,11 ,12 ]
Markowetz, Florian [1 ,2 ]
机构
[1] Canc Res UK Cambridge Res Inst, Cambridge CB2 0RE, England
[2] Univ Cambridge, Dept Oncol, Cambridge CB2 2XZ, England
[3] Max Planck Plant Breeding Res, D-50829 Cologne, Germany
[4] Univ Munich, Ctr Integrated Prot Sci Munich, Gene Ctr, D-81377 Munich, Germany
[5] Univ Munich, Ctr Integrated Prot Sci Munich, Dept Biochem, D-81377 Munich, Germany
[6] Univ So Calif, Keck Sch Med, Dept Prevent Med, Los Angeles, CA 90033 USA
[7] Cambridge Univ Hosp NHS Fdn Trust, Addenbrookes Hosp, Dept Histopathol, Cambridge CB2 0QQ, England
[8] Univ British Columbia, Dept Pathol & Lab Med, Vancouver, BC V6T 2B5, Canada
[9] British Columbia Canc Res Ctr, Vancouver, BC V5Z 1L3, Canada
[10] Cambridge Univ Hosp NHS Fdn Trust, Addenbrookes Hosp, Cambridge Breast Unit, Cambridge CB2 2QQ, England
[11] NIHR Cambridge Biomed Res Ctr, Cambridge CB2 2QQ, England
[12] Cambridge Expt Canc Med Ctr, Cambridge CB2 0RE, England
关键词
CANCER; EXPRESSION; TISSUE; MICROENVIRONMENT; SEGMENTATION; CHEMOTHERAPY; CARCINOMA; SUBTYPE;
D O I
10.1126/scitranslmed.3004330
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
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.
引用
收藏
页数:10
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