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

• Published in: NPJ breast cancer Vol. 9; no. 1; pp. 40 - 10
• Main Authors: Chen, Yuli, Li, Haojia, Janowczyk, Andrew, Toro, Paula, Corredor, Germán, Whitney, Jon, Lu, Cheng, Koyuncu, Can F., Mokhtari, Mojgan, Buzzy, Christina, Ganesan, Shridar, Feldman, Michael D., Fu, Pingfu, Corbin, Haley, Harbhajanka, Aparna, Gilmore, Hannah, Goldstein, Lori J., Davidson, Nancy E., Desai, Sangeeta, Parmar, Vani, Madabhushi, Anant
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 17.05.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1347; 692/53/2422; Biomedical and Life Sciences; Biomedicine; Breast cancer; Cancer Research; Cell Biology; Human Genetics; Oncology
• ISSN: 2374-4677; 2374-4677
• DOI: 10.1038/s41523-023-00545-y

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.