Computational portraits of the tumoral microenvironment in human breast cancer

Breast cancer is the most diagnosed cancer in humans. In recent years, myxoid and proportionated stroma have been described as clinically significant in many cancer subtypes. Here computational portraits of tumor-associated stromata were created from a machine learning (ML) classifier using QuPath t...

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Bibliographic Details
Published in:Virchows Archiv : an international journal of pathology 2022-09, Vol.481 (3), p.367-385
Main Authors: Wu, Dongling, Hacking, Sean M., Chavarria, Hector, Abdelwahed, Mohammed, Nasim, Mansoor
Format: Article
Language:English
Subjects:
Biomarkers
Breast cancer
Classifiers
Computer applications
Decision making
ErbB protein
ErbB-2 protein
Estrogen receptors
Estrogens
HER protein
Kinases
Lymph nodes
Machine learning
Medicine
Medicine & Public Health
Microenvironments
Original Article
Pathology
Progesterone
Protein kinase
Receptors
Stroma
Tumors
Tyrosine
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Summary:Breast cancer is the most diagnosed cancer in humans. In recent years, myxoid and proportionated stroma have been described as clinically significant in many cancer subtypes. Here computational portraits of tumor-associated stromata were created from a machine learning (ML) classifier using QuPath to evaluate proportionated stromal area (PSA), myxoid stromal ratio (MSR), and immune stroma proportion (ISP) from whole slide images (WSI). The ML classifier was validated in independent training ( n  = 40) and validation ( n  = 109) cohorts finding MSR, PSA, and ISP to be associated with tumor stage, lymph node status, Nottingham grade, stromal differentiation (SD), tumor size, estrogen receptor (ER), progesterone receptor (PR), and receptor tyrosine-protein kinase erbB-2 (HER-2). Overall, MSR correlated better with the clinicopathologic profile than PSA and ISP. High MSR was found to be associated with high tumor stage, low ISP, and high Nottingham histologic score. As a computational biomarker, high MSR was more likely to be associated with luminal B like, Her-2 enriched, and triple-negative biomarker status when compared to luminal A like. The supervised ML superpixel approach demonstrated here can be performed by a trained pathologist to provide a faster and more uniformed approach to the analysis to the tumoral microenvironment (TME). The TME may be relevant for clinical decision-making, determining chemotherapeutic efficacy, and guiding a more overall precision-based breast cancer care.
ISSN:0945-6317
1432-2307
DOI:10.1007/s00428-022-03376-7