Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

Automated Quantification of sTIL Density with H&E-Based Digital Image Analysis Has Prognostic Potential in Triple-Negative Breast Cancers

Triple-negative breast cancer (TNBC) is an aggressive and difficult-to-treat cancer type that represents approximately 15% of all breast cancers. Recently, stromal tumor-infiltrating lymphocytes (sTIL) resurfaced as a strong prognostic biomarker for overall survival (OS) for TNBC patients. Manual as...

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Bibliographic Details
Published in:Cancers Vol. 13; no. 12; p. 3050
Main Authors: Thagaard, Jeppe, Stovgaard, Elisabeth Specht, Vognsen, Line Grove, Hauberg, Søren, Dahl, Anders, Ebstrup, Thomas, Doré, Johan, Vincentz, Rikke Egede, Jepsen, Rikke Karlin, Roslind, Anne, Kümler, Iben, Nielsen, Dorte, Balslev, Eva
Format: Journal Article
Language:English
Published: Basel MDPI AG 18-06-2021
MDPI
Subjects:
Alcohol
Automation
Biomarkers
Breast cancer
Cancer immunotherapy
Cancer therapies
Chemotherapy
Computer applications
Image processing
Immune system
Lymphocytes
Mathematical models
Pathology
Patients
Prognosis
Tumor-infiltrating lymphocytes
Tumors
Denmark
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Summary:Triple-negative breast cancer (TNBC) is an aggressive and difficult-to-treat cancer type that represents approximately 15% of all breast cancers. Recently, stromal tumor-infiltrating lymphocytes (sTIL) resurfaced as a strong prognostic biomarker for overall survival (OS) for TNBC patients. Manual assessment has innate limitations that hinder clinical adoption, and the International Immuno-Oncology Biomarker Working Group (TIL-WG) has therefore envisioned that computational assessment of sTIL could overcome these limitations and recommended that any algorithm should follow the manual guidelines where appropriate. However, no existing studies capture all the concepts of the guideline or have shown the same prognostic evidence as manual assessment. In this study, we present a fully automated digital image analysis pipeline and demonstrate that our hematoxylin and eosin (H&E)-based pipeline can provide a quantitative and interpretable score that correlates with the manual pathologist-derived sTIL status, and importantly, can stratify a retrospective cohort into two significant distinct prognostic groups. We found our score to be prognostic for OS (HR: 0.81 CI: 0.72–0.92 p = 0.001) independent of age, tumor size, nodal status, and tumor type in statistical modeling. While prior studies have followed fragments of the TIL-WG guideline, our approach is the first to follow all complex aspects, where appropriate, supporting the TIL-WG vision of computational assessment of sTIL in the future clinical setting.
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Equal contributors.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers13123050