Visual Intratumor Heterogeneity and Breast Tumor Progression

Author:

Li Yao1ORCID,Van Alsten Sarah C.2ORCID,Lee Dong Neuck3ORCID,Kim Taebin1ORCID,Calhoun Benjamin C.45ORCID,Perou Charles M.456ORCID,Wobker Sara E.45ORCID,Marron J. S.135ORCID,Hoadley Katherine A.56ORCID,Troester Melissa A.245

Affiliation:

1. Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

2. Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

3. Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

4. Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

5. UNC Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

6. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

Abstract

High intratumoral heterogeneity is thought to be a poor prognostic indicator. However, the source of heterogeneity may also be important, as genomic heterogeneity is not always reflected in histologic or ‘visual’ heterogeneity. We aimed to develop a predictor of histologic heterogeneity and evaluate its association with outcomes and molecular heterogeneity. We used VGG16 to train an image classifier to identify unique, patient-specific visual features in 1655 breast tumors (5907 core images) from the Carolina Breast Cancer Study (CBCS). Extracted features for images, as well as the epithelial and stromal image components, were hierarchically clustered, and visual heterogeneity was defined as a greater distance between images from the same patient. We assessed the association between visual heterogeneity, clinical features, and DNA-based molecular heterogeneity using generalized linear models, and we used Cox models to estimate the association between visual heterogeneity and tumor recurrence. Basal-like and ER-negative tumors were more likely to have low visual heterogeneity, as were the tumors from younger and Black women. Less heterogeneous tumors had a higher risk of recurrence (hazard ratio = 1.62, 95% confidence interval = 1.22–2.16), and were more likely to come from patients whose tumors were comprised of only one subclone or had a TP53 mutation. Associations were similar regardless of whether the image was based on stroma, epithelium, or both. Histologic heterogeneity adds complementary information to commonly used molecular indicators, with low heterogeneity predicting worse outcomes. Future work integrating multiple sources of heterogeneity may provide a more comprehensive understanding of tumor progression.

Funder

University Cancer Research Fund of North Carolina, the Susan G Komen Foundation

National Cancer Institute

National Cancer Institute Specialized Program of Research Excellence

US Department of Defense

National Science Foundation

Publisher

MDPI AG

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