Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival

被引:515
作者
Beck, Andrew H. [2 ,3 ]
Sangoi, Ankur R. [2 ,4 ]
Leung, Samuel [5 ]
Marinelli, Robert J. [6 ]
Nielsen, Torsten O. [5 ]
van de Vijver, Marc J. [7 ]
West, Robert B. [2 ]
van de Rijn, Matt [2 ]
Koller, Daphne [1 ]
机构
[1] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA
[2] Stanford Univ, Dept Pathol, Sch Med, Stanford, CA 94305 USA
[3] Stanford Univ, Biomed Informat Training Program, Sch Med, Stanford, CA 94305 USA
[4] El Camino Hosp, Dept Pathol, Mountain View, CA 94040 USA
[5] Univ British Columbia, Genet Pathol Evaluat Ctr, Vancouver, BC V6H 3Z6, Canada
[6] Stanford Univ, Dept Biochem, Stanford, CA 94305 USA
[7] Univ Amsterdam, Acad Med Ctr, Dept Pathol, NL-1105 AZ Amsterdam, Netherlands
关键词
HISTOLOGIC GRADE; PROGNOSTIC VALUE; SIGNATURE; PREDICTION; METASTASIS; PATHOLOGY; HYPOXIA; CELLS;
D O I
10.1126/scitranslmed.3002564
中图分类号
Q2 [细胞生物学];
学科分类号
071009 ; 090102 ;
摘要
The morphological interpretation of histologic sections forms the basis of diagnosis and prognostication for cancer. In the diagnosis of carcinomas, pathologists perform a semiquantitative analysis of a small set of morphological features to determine the cancer's histologic grade. Physicians use histologic grade to inform their assessment of a carcinoma's aggressiveness and a patient's prognosis. Nevertheless, the determination of grade in breast cancer examines only a small set of morphological features of breast cancer epithelial cells, which has been largely unchanged since the 1920s. A comprehensive analysis of automatically quantitated morphological features could identify characteristics of prognostic relevance and provide an accurate and reproducible means for assessing prognosis from microscopic image data. We developed the C-Path (Computational Pathologist) system to measure a rich quantitative feature set from the breast cancer epithelium and stroma (6642 features), including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. These measurements were used to construct a prognostic model. We applied the C-Path system to microscopic images from two independent cohorts of breast cancer patients [from the Netherlands Cancer Institute (NKI) cohort, n = 248, and the Vancouver General Hospital (VGH) cohort, n = 328]. The prognostic model score generated by our system was strongly associated with overall survival in both the NKI and the VGH cohorts (both log-rank P <= 0.001). This association was independent of clinical, pathological, and molecular factors. Three stromal features were significantly associated with survival, and this association was stronger than the association of survival with epithelial characteristics in the model. These findings implicate stromal morphologic structure as a previously unrecognized prognostic determinant for breast cancer.
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页数:11
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