Stromal lymphocytes are associated with upgrade of B3 breast lesions

TL;DR

Stromal lymphocytes in breast lesions may help predict which cases will upgrade to cancer, potentially reducing unnecessary surgeries.

Contribution

A predictive model combining stromal lymphocyte counts, age, and lesion type was developed to identify upgrade risk in B3 breast lesions.

Findings

Upgraded B3 lesions showed significantly higher stromal lymphocyte counts than non-upgraded ones (p < 0.01).

A predictive model using lymphocytes, age, and lesion type achieved an area under the curve of 0.82 for upgrade prediction.

The model has high sensitivity but limited specificity for identifying upgrade risk.

Abstract

Various histopathological, clinical and imaging parameters have been evaluated to identify a subset of women diagnosed with lesions with uncertain malignant potential (B3 or BIRADS 3/4A lesions) who could safely be observed rather than being treated with surgical excision, with little impact on clinical practice. The primary reason for surgery is to rule out an upgrade to either ductal carcinoma in situ or invasive breast cancer, which occurs in up to 30% of patients. We hypothesised that the stromal immune microenvironment could indicate the presence of carcinoma associated with a ductal B3 lesion and that this could be detected in biopsies by counting lymphocytes as a predictive biomarker for upgrade. A higher number of lymphocytes in the surrounding specialised stroma was observed in upgraded ductal and papillary B3 lesions than non-upgraded (p < 0.01, negative binomial model, n = 307). We developed a model using lymphocytes combined with age and the type of lesion, which was predictive of upgrade with an area under the curve of 0.82 [95% confidence interval 0.77–0.87]. The model can identify some patients at risk of upgrade with high sensitivity, but with limited specificity. Assessing the tumour microenvironment including stromal lymphocytes may contribute to reducing unnecessary surgeries in the clinic, but additional predictive features are needed.

The online version contains supplementary material available at 10.1186/s13058-024-01857-y.

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