Abstract P6-04-08: Machine learning-based characterization of the breast cancer tumor microenvironment for assessment of neoadjuvant-treatment response

Abstract 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 outcomes. 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, a key component of the residual cancer burden score (RCB) used to assess neoadjuvant-treatment pathological response. Methods: Convolutional neural network (CNN) models were trained using digitized H&E-stained whole slide images (WSIs) of 2700 neoadjuvant-treated breast cancer specimens (resections and biopsies) from 4 sources, and an additional 1100 breast cancer primary resections from TCGA. 229,901 pathologist annotations were used to train CNN models to segment tissue regions (cancer epithelium, stroma, diffuse inflammatory infiltrate, ductal carcinoma in situ, lymph nodes and necrosis) and cell types (cancer epithelial cells, fibroblasts, lymphocytes, macrophages, foamy macrophages and plasma cells) at single-pixel resolution. These tissue region segmentations were then used to derive tumor bed area using a convex hull algorithm. Each model was evaluated by board certified pathologists for performance. Model predictions of tumor bed area were evaluated in comparison to mean measurements from 3 pathologists for each of 22 held-out test slides. To further assess cell model performance, 5 pathologists exhaustively annotated 120 frames (300 x 300 pixels) on test samples from a dataset not used in model development (N=536; resections and biopsies) to produce consensus ground truth cell labels. Model predictions were compared with pathologist annotations in these frames using Pearson correlation, precision, recall, and F1 metrics. Only those classes with greater than 50 consensus cells identified were evaluated. Results: CNN predictions of tissue and cell classes within H&E breast cancer WSIs showed concordance with manual pathologist consensus labels. The weighted average Pearson correlation (across the relevant cell types) between the model and consensus was 0.75, comparable to the correlation of 0.81 between pathologists and consensus. Classification metrics for each cell class are reported in Table 1. Reduced performance of the model relative to the average pathologist performance may be due to heterogeneous slide characteristics and infrequency of some cell types in the data. For prediction of tumor bed area, CNN model predictions showed moderate correlation with pathologist consensus (Pearson r=0.65, 95% CI: 0.38-0.81). Conclusions: CNN model classification of cell types and tissue regions across entire H&E breast cancer WSIs shows concordance with pathologist consensus. Model predictions of tumor bed area also show concordance with pathologist assessment and can be used to derive the RCB score. These models can be reproducibly applied to quantify diverse histological features in large datasets, potentially enabling improved standardization and efficiency of pathologist evaluation of the breast cancer TME and neoadjuvant response. Classification Metrics for Individual Cell Classes Citation Format: Christian Kirkup, Sanjana Vasudevan, Filip Kos, Benjamin Trotter, Murray Resnick, Andrew H. Beck, Michael Montalto, Ilan Wapinski, Ben Glass, Mary Lin, Stephanie Hennek, Archit Khosla, Michael G. Drage, Laura Chambre. Machine learning-based characterization of the breast cancer tumor microenvironment for assessment of neoadjuvant-treatment response [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-04-08.