The intra-tumoral stroma in patients with breast cancer increases with age

Purpose The tumor microenvironment in older patients is subject to changes. The tumor-stroma ratio (TSR) was evaluated in order to estimate the amount of intra-tumoral stroma and to evaluate the prognostic value of the TSR in older patients with breast cancer (≥70 years). Methods Two retrospective cohorts, the FOCUS study (N = 619) and the Nottingham Breast Cancer series (N = 1793), were used for assessment of the TSR on hematoxylin and eosin stained tissue slides. Results The intra-tumoral stroma increases with age in the FOCUS study and the Nottingham Breast Cancer series (B 0.031, 95% CI 0.006-0.057, P = 0.016 and B 0.034, 95% CI 0.015-0.054, P ≤ 0.001, respectively). Fifty-one percent of the patients from the Nottingham Breast Cancer series ≤40 years had a stroma-high tumor compared to 73% of the patients of ≥90 years from the FOCUS study. The TSR did not validate as an independent prognostic parameter in patients ≥70 years. Conclusions The intra-tumoral stroma increases with age. This might be the result of an activated tumor microenvironment. The TSR did not validate as an independent prognostic parameter in patients ≥70 years in contrast to young women with breast cancer as published previously

Standardization of the tumor-stroma ratio scoring method for breast cancer research

Purpose: The tumor-stroma ratio (TSR) has repeatedly proven to be correlated with patient outcomes in breast cancer using large retrospective cohorts. However, studies validating the TSR often show variability in methodology, thereby hampering comparisons and uniform outcomes. Method: This paper provides a detailed description of a simple and uniform TSR scoring method using Hematoxylin and Eosin (H&E)-stained core biopsies and resection tissue, specifically focused on breast cancer. Possible histological challenges that can be encountered during scoring including suggestions to overcome them are reported. Moreover, the procedure for TSR estimation in lymph nodes, scoring on digital images and the automatic assessment of the TSR using artificial intelligence are described. Conclusion: Digitized scoring of tumor biopsies and resection material offers interesting future perspectives to determine patient prognosis and response to therapy. The fact that the TSR method is relatively easy, quick, and cheap, offers great potential for its implementation in routine diagnostics, but this requires high quality validation studies