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Machine learning-based characterization of the breast cancer tumor microenvironment for assessment of neoadjuvant-treatment response

SABCS 2022

Study Background

Neoadjuvant treatment of breast cancer has been shown to potentially reduce the extent and morbidity of subsequent surgery. Response to neoadjuvant therapy may also be prognostic; complete pathologic response (pCR) following neoadjuvant treatment is associated with improved long-term outcomes1. pCR, defined as the absence of residual invasive cancer, is determined by evaluation of H&E-stained breast resections and regional lymph nodes following neoadjuvant treatment; however, pathologist assessment is subject to intra- and inter-reader variability.

Here we report machine learning (ML)-based models to identify tissue regions and cell types in the tumor microenvironment (TME) of H&E-stained breast cancer specimens. Model predictions were used to derive tumor bed area and a residual cancer burden score (RCB)-like score to assess residual disease after neoadjuvant therapy2,3.

1. Spring, LM et al. Clin Cancer Res. 2020; 26:2838-2848.
2. Hamy, A-S et al. PLoS One. 2020; 15(6): e0234191.
3. Yau, C et al. Lancet Oncol. 2022; 23:149-160.
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Kirkup et al.