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.
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Yuan et al. showed that morphological information on TILs combined with gene expression data can significantly improve prognosis prediction for ER-negative breast cancers.
“For instance, Yuan et al. [251] showed that morphological information on TILs combined with gene expression data can significantly improve prognosis prediction for ER-negative breast cancers while the distribution patterns for TILs and the related genomics information are characterized for multiple cancers in [152].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [251]
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- Authors
- Yuan, Y., Failmezger, H., Rueda, O. M., Ali, H. R., Gräf, S., Chin, S.-F., Schwarz, R. F., Curtis, C., Dunning, M. J., Bardwell, H., Johnson, N., Doyle, S., Turashvili, G., Provenzano, E., Aparicio, S., Caldas, C., & Markowetz, F.
- Affiliations
- University of Cambridge (GB)
- Center for Integrated Protein Science Munich (DE)
- University of Southern California (US)
- Cancer Research UK (GB)
- Cambridge University Hospitals NHS Foundation Trust (GB)
- University of British Columbia (CA)
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia V6T 2B5, Canada.
- Corresponding author
- Yuan, Yinyin
- Failmezger, Henrik
- Rueda, Oscar M.
- Ali, H. Raza
- Gräf, Stefan
- Markowetz, Florian
- ORCID
- Venue
- Science Translational Medicine
- Published
- 2012-10-24
- Volume
- 4
- Issue
- 157
- Pages
- 157ra143-157ra143
- ISSN
- 1946-6234 (ISSN-L)
- 1946-6242
- OpenAlex
- W2150461375
- Cited by
- 426
- References
- 2
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 18 · 2023: 40 · 2022: 34 · 2021: 46 · 2020: 38 · 2019: 35 · 2018: 36 · 2017: 44 · 2016: 37 · 2015: 27 · 2014: 25 · 2013: 16 · 2012: 3
- FWCI
- 22.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science Translational Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Cancer Research UK
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- breast cancer, H&E staining, tumor cellularity, copy number profiles, estrogen receptor-negative breast cancer, survival prediction, prognostic factors, stromal cells, spatial patterns, image analysis, genomic profiling
- MeSH terms
- Automation, Breast Neoplasms, Female, Humans, Image Processing, Computer-Assisted, Prognosis, Receptors, Estrogen, Gene Expression Regulation, Neoplastic, Survival Analysis, Lymphocytes, Tumor-Infiltrating, Stromal Cells, Gene Dosage, Gene Expression Profiling, Genomics
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