Elasticity Values as a Predictive Modality for Response to Neoadjuvant Chemotherapy in Breast Cancer

Shear-wave elastography (SWE) is an effective tool in discriminating malignant lesions of breast and axillary lymph node metastasis in patients with breast cancer. However, the association between the baseline elasticity value of breast cancer and the treatment response of neoadjuvant chemotherapy is yet to be elucidated. Baseline SWE measured mean stiffness (E-mean) and maximum stiffness (E-max) in 830 patients who underwent neoadjuvant chemotherapy and surgery from January 2012 to December 2022. Association of elasticity values with breast pCR (defined as ypTis/T0), pCR (defined as ypTis/T0, N0), and tumor-infiltrating lymphocytes (TILs) was analyzed. Of 830 patients, 356 (42.9%) achieved breast pCR, and 324 (39.0%) achieved pCR. The patients with low elasticity values had higher breast pCR and pCR rates than those with high elasticity values. A low E-mean (adjusted odds ratio (OR): 0.620; 95% confidence interval (CI): 0.437 to 0.878; p = 0.007) and low E-max (adjusted OR: 0.701; 95% CI: 0.494 to 0.996; p = 0.047) were independent predictive factors for breast pCR. Low elasticity values were significantly correlated with high TILs. Pretreatment elasticity values measured using SWE were significantly associated with treatment response and inversely correlated with TILs, particularly in HR+HER2- breast cancer and TNBC.

Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic potential and poor prognosis, and has limited treatment options. The current standard of care in nonmetastatic settings is neoadjuvant chemotherapy (NACT), but treatment efficacy varies substantially across patients. This heterogeneity is still poorly understood, partly due to the paucity of curated TNBC data. Here we investigate the use of machine learning (ML) leveraging whole-slide images and clinical information to predict, at diagnosis, the histological response to NACT for early TNBC women patients. To overcome the biases of small-scale studies while respecting data privacy, we conducted a multicentric TNBC study using federated learning, in which patient data remain secured behind hospitals' firewalls. We show that local ML models relying on whole-slide images can predict response to NACT but that collaborative training of ML models further improves performance, on par with the best current approaches in which ML models are trained using time-consuming expert annotations. Our ML model is interpretable and is sensitive to specific histological patterns. This proof of concept study, in which federated learning is applied to real-world datasets, paves the way for future biomarker discovery using unprecedentedly large datasets.

Correlation of Yes-Associated Protein 1 with Stroma Type and Tumor Stiffness in Hormone-Receptor Positive Breast Cancer

Simple Summary

YAP1 is an oncogene that can be activated by matrix stiffness, as it can act as a mechanotransducer. So far, only in vitro studies regarding YAP1 activation and matrix stiffness are present. We confirmed the activation of YAP1 in breast cancer using human breast cancer tissue and immunohistochemistry. Tumor stiffness was quantified by shear-wave elastography. Nuclear localization of YAP1 showed correlation with tumor stiffness in hormone-receptor positive (HR+) breast cancer. Also, tumors with non-collagen-type stroma showed an association between YAP1 expression and tumor stiffness. YAP1 expression, along with tumor stiffness, may serve as a prognostic candidate in HR+ breast cancer.

Abstract

(1) Background: Yes-associated protein 1 (YAP1) is an oncogene activated under the dysregulated Hippo pathway. YAP1 is also a mechanotransducer that is activated by matrix stiffness. So far, there are no in vivo studies on YAP1 expression related to stiffness. We aimed to investigate the association between YAP1 activation and tumor stiffness in human breast cancer samples, using immunohistochemistry and shear-wave elastography (SWE). (2) Methods: We included 488 patients with treatment-naïve breast cancer. Tumor stiffness was measured and the mean, maximal, and minimal elasticity values and elasticity ratios were recorded. Nuclear YAP1 expression was evaluated by immunohistochemistry and tumor-infiltrating lymphocytes (TILs); tumor-stroma ratio (TSR) and stroma type of tumors were also evaluated. (3) Results: Tumor stiffness was higher in tumors with YAP1 positivity, low TILs, and high TSR and was correlated with nuclear YAP1 expression; this correlation was observed in hormone receptor positive (HR+) tumors, as well as in tumors with non-collagen-type stroma. (4) Conclusions: We confirmed the correlation between nuclear YAP1 expression and tumor stiffness, and nuclear YAP1 expression was deemed a prognostic candidate in HR+ tumors combined with SWE-measured tumor stiffness.

Multiplexed immunofluorescence identifies high stromal CD68+PD-L1+ macrophages as a predictor of improved survival in triple negative breast cancer

Triple negative breast cancer (TNBC) comprises 10-15% of all breast cancers and has a poor prognosis with a high risk of recurrence within 5 years. PD-L1 is an important biomarker for patient selection for immunotherapy but its cellular expression and co-localization within the tumour immune microenvironment and associated prognostic value is not well defined. We aimed to characterise the phenotypes of immune cells expressing PD-L1 and determine their association with overall survival (OS) and breast cancer-specific survival (BCSS). Using tissue microarrays from a retrospective cohort of TNBC patients from St George Hospital, Sydney (n = 244), multiplexed immunofluorescence (mIF) was used to assess staining for CD3, CD8, CD20, CD68, PD-1, PD-L1, FOXP3 and pan-cytokeratin on the Vectra Polaris™ platform and analysed using QuPath. Cox multivariate analyses showed high CD68+PD-L1+ stromal cell counts were associated with improved prognosis for OS (HR 0.56, 95% CI 0.33-0.95, p = 0.030) and BCSS (HR 0.47, 95% CI 0.25-0.88, p = 0.018) in the whole cohort and in patients receiving chemotherapy, improving incrementally upon the predictive value of PD-L1+ alone for BCSS. These data suggest that CD68+PD-L1+ status can provide clinically useful prognostic information to identify sub-groups of patients with good or poor prognosis and guide treatment decisions in TNBC.