Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer

• Type of Material: Article
• Authors: Yuli Chen, Shaanxi Normal University; Haojia Li, Case Western Reserve University; Andrew Janowczyk, Case Western Reserve University; Paula Toro, Case Western Reserve University; Germán Corredor, Case Western Reserve University; Jon Whitney, Case Western Reserve University; Cheng Lu, Shaanxi Normal University; Can F Koyuncu, Case Western Reserve University; Mojgan Mokhtari, Case Western Reserve University; Christina Buzzy, Case Western Reserve University; Shridar Ganesanh, Rutgers Cancer Institute of New Jersey; Michael D Feldman, University of Pennsylvania Perelman School of Medicine; Pingfu Fu, CASE School of Medicine; Haley Corbin, University Hospitals Case Medical Center; Aparna Harbhajanka, University Hospitals Case Medical Center; Hannah Gilmore, University Hospitals Case Medical Center; Lori J Goldstein, Fox Chase Cancer Center; Nancy E Davidson, University of Washington; Sangeeta Desai, Homi Bhabha National Institute; Vani Parmar, Homi Bhabha National Institute; Anant Madabhushi, Emory University
• Language: English
• Date: 2023-12-01
• Publisher: Springer Nature Limited
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2023
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41523-023-00545-y
• Title of Journal or Parent Work: npj Breast Cancer
• Volume: 9
• Issue: 1
• Start Page: 40
• End Page: 40
• Research Categories: Engineering, Biomedical

Abstract

Prognostic markers currently utilized in clinical practice for estrogen receptor-positive (ER+) and lymph node-negative (LN−) invasive breast cancer (IBC) patients include the Nottingham grading system and Oncotype Dx (ODx). However, these biomarkers are not always optimal and remain subject to inter-/intra-observer variability and high cost. In this study, we evaluated the association between computationally derived image features from H&E images and disease-free survival (DFS) in ER+ and LN− IBC. H&E images from a total of n = 321 patients with ER+ and LN− IBC from three cohorts were employed for this study (Training set: D1 (n = 116), Validation sets: D2 (n = 121) and D3 (n = 84)). A total of 343 features relating to nuclear morphology, mitotic activity, and tubule formation were computationally extracted from each slide image. A Cox regression model (IbRiS) was trained to identify significant predictors of DFS and predict a high/low-risk category using D1 and was validated on independent testing sets D2 and D3 as well as within each ODx risk category. IbRiS was significantly prognostic of DFS with a hazard ratio (HR) of 2.33 (95% confidence interval (95% CI) = 1.02–5.32, p = 0.045) on D2 and a HR of 2.94 (95% CI = 1.18–7.35, p = 0.0208) on D3. In addition, IbRiS yielded significant risk stratification within high ODx risk categories (D1 + D2: HR = 10.35, 95% CI = 1.20–89.18, p = 0.0106; D1: p = 0.0238; D2: p = 0.0389), potentially providing more granular risk stratification than offered by ODx alone.