Search

Male breast cancer (MBC) is a rare disease. Due to its rarity, treatment is still directed by data mainly extrapolated from female breast cancer (FBC) treatment, despite the fact that it has recently become clear that MBC has its own molecular characteristics. DDX3 is a RNA helicase with tumor suppressor and oncogenic potential that was described as a prognosticator in FBC and can be targeted by small molecule inhibitors of DDX3. The aim of this study was to evaluate if DDX3 is a useful prognosticator for MBC patients. Nuclear as well as cytoplasmic DDX3 expression was studied by immunohistochemistry in a Dutch retrospective cohort of 106 MBC patients. Differences in 10-year survival by DDX3 expression were analyzed using log-rank test. The association between clinicopathologic variables, DDX3 expression, and survival was tested in uni- and multivariate Cox-regression analysis. High cytoplasmic DDX3 was associated with high androgen receptor (AR) expression while low nuclear DDX3 was associated with negative lymph node status. Nuclear and cytoplasmic DDX3 were not associated with each other. In a univariate analysis, high cytoplasmic DDX3 (p = 0.045) was significantly associated with better 10-year overall survival. In multivariate analyses, cytoplasmic DDX3 had independent prognostic value (p = 0.017). In conclusion, cytoplasmic DDX3 expression seems to be a useful prognosticator in MBC, as high cytoplasmic DDX3 indicated better 10-year survival., (© 2021. The Author(s).)

Purpose: Preoperative partial breast irradiation (PBI) has the potential to induce tumor regression. We evaluated the differences in the numbers of preirradiation tumor infiltrating lymphocytes (TILs) between responders and nonresponders after preoperative PBI in low-risk patients with breast cancer. Furthermore, we evaluated the change in number of TILs before and after irradiation., Methods and Materials: In the prospective ABLATIVE study, low-risk patients with breast cancer underwent treatment with single-dose preoperative PBI (20 Gy) to the tumor and breast-conserving surgery after 6 or 8 months. In the preirradiation diagnostic biopsy and postirradiation resection specimen, numbers of TILs in 3 square regions of 450 × 450 μm were counted manually. TILs were visualized with CD3, CD4, and CD8 immunohistochemistry. Differences in numbers of preirradiation TILs between responders and nonresponders were tested using Mann-Whitney U test. Responders were defined as pathologic complete or near-complete response, and nonresponders were defined "as all other response." Changes in numbers of TILs after preoperative PBI was evaluated with the Wilcoxon signed rank test., Results: Preirradiation tissue was available from 28 patients, postirradiation tissue from 29 patients, resulting in 22 pairs of preirradiation and postirradiation tissue. In these 35 patients, 15 had pathologic complete response (43%), 11 had a near-complete response (31%), 7 had a partial response (20%), and 2 had stable disease (6%). The median numbers of CD3 + TILs, CD4 + TILs, and CD8 + TILs in the preirradiation tumor tissue were 49 (interquartile range [IQR], 36-80), 45 (IQR, 28-57), and 19 (IQR, 8-35), respectively. The number of preirradiation TILs did not differ significantly between responders and nonresponders. The median numbers of CD3 + TILs, CD4 + TILs, and CD8 + TILs in postirradiation tumor tissue were 17 (IQR, 13-31), 26 (IQR, 16-35), and 7 (IQR, 5-11), respectively., Conclusions: After preoperative PBI in this limited cohort, the number of TILs in tumor tissue decreased. No differences in numbers of preirradiation TILs between responders and nonresponders were observed., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Programmed death-1 (PD-1) is an immune checkpoint that is able to inhibit the immune system by binding to its ligand programmed death-ligand 1 (PD-L1). In many cancer types, among which breast cancer, prognostic and/or predictive values have been suggested for both PD-1 and PD-L1. Previous research has demonstrated discrepancies in PD-L1 expression between primary breast tumors and distant metastases, however data so far have been scarce. We therefore evaluated immunohistochemical expression levels of PD-1 and PD-L1 in primary breast tumors and their paired distant metastases, and evaluated prognostic values. Tissue microarrays from formalin-fixed paraffin-embedded resection specimens of primary breast cancers and their matched distant metastases were immunohistochemically stained for PD-1 and PD-L1. PD-1 was available in both primary tumor and metastasis in 82 patients, and PD-L1 in 49 patients. PD-1 was discrepant between primary tumor and metastasis in half of the patients (50%), PD-L1 on tumor cells was discrepant in 28.5%, and PD-L1 on immune cells in 40.8% of the patients. In primary tumors there was a correlation between PD-1 positivity and a higher tumor grade, and between immune PD-L1 and ER negativity. In survival analyses, a significantly better overall survival was observed for patients with PD-L1 negative primary breast tumors that developed PD-L1 positive distant metastases (HR 3.013, CI 1.201-7.561, p = 0.019). To conclude, PD-1 and tumor and immune PD-L1 seem to be discordantly expressed between primary tumors and their matched distant metastases in about one-third to a half of the breast cancer patients. Further, gained expression of PD-L1 in metastases seems to indicate better survival. This illustrates the need of reassessing PD-1 and PD-L1 expression on biopsies of distant metastases to optimize the usefulness of these biomarkers.

Background: Male breast cancer is rare, as it represents less than 1% of all breast cancer cases. In addition, male breast cancer appears to have a different biology than female breast cancer. Programmed death-1 (PD-1) and its ligand, programmed death-ligand 1 (PD-L1), seem to have prognostic and predictive values in a variety of cancers, including female breast cancer. However, the role of PD-1 and PD-L1 expression in male breast cancer has not yet been studied., Objectives: To compare PD-1 and PD-L1 expression in male breast cancer to female breast cancer and to evaluate prognostic values in both groups., Patients and Methods: Tissue microarrays from formalin-fixed paraffin-embedded resection material of 247 female and 164 male breast cancer patients were stained for PD-1 and PD-L1 by immunohistochemistry., Results: PD-1 expression on tumor-infiltrating lymphocytes was significantly less frequent in male than in female cancers (48.9 vs. 65.3%, p = 0.002). In contrast, PD-L1 expression on tumor and immune cells did not differ between the two groups. In male breast cancer, PD-1 and tumor PD-L1 were associated with grade 3 tumors. In female breast cancer, PD-1 and PD-L1 were associated with comparably worse clinicopathological variables. In a survival analysis, no prognostic value was observed for PD-1 and PD-L1 in either male and female breast cancer. In a subgroup analysis, female patients with grade 3/tumor PD-L1-negative or ER-negative/immune PD-L1-negative tumors had worse overall survival., Conclusions: PD-1 seems to be less often expressed in male breast cancer compared to female breast cancer. Although PD-1 and PD-L1 are not definite indicators for good or bad responses, male breast cancer patients may therefore respond differently to checkpoint immunotherapy with PD-1 inhibitors than female patients.

Background: The presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine-learning algorithm, can be used to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine-learning models, large, well-curated datasets are needed., Results: We released a dataset of 1,399 annotated whole-slide images (WSIs) of lymph nodes, both with and without metastases, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand Challenges. Slides were collected from five medical centers to cover a broad range of image appearance and staining variations. Each WSI has a slide-level label indicating whether it contains no metastases, macro-metastases, micro-metastases, or isolated tumor cells. Furthermore, for 209 WSIs, detailed hand-drawn contours for all metastases are provided. Last, open-source software tools to visualize and interact with the data have been made available., Conclusions: A unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use.

Male breast cancer (MBC) is rare and poorly characterized. Like the female counterpart, most MBCs are hormonally driven, but relapse after hormonal treatment is also noted. The pan-hormonal action of steroid hormonal receptors, including estrogen receptor alpha (ERα), androgen receptor (AR), progesterone receptor (PR), and glucocorticoid receptor (GR) in this understudied tumor type remains wholly unexamined. This study reveals genomic cross-talk of steroid hormone receptor action and interplay in human tumors, here in the context of MBC, in relation to the female disease and patient outcome. Here we report the characterization of human breast tumors of both genders for cistromic make-up of hormonal regulation in human tumors, revealing genome-wide chromatin binding landscapes of ERα, AR, PR, GR, FOXA1, and GATA3 and enhancer-enriched histone mark H3K4me1. We integrate these data with transcriptomics to reveal gender-selective and genomic location-specific hormone receptor actions, which associate with survival in MBC patients.

Importance: Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency., Objective: Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin-stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists' diagnoses in a diagnostic setting., Design, Setting, and Participants: Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC)., Exposures: Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation., Main Outcomes and Measures: The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor., Results: The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC)., Conclusions and Relevance: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.

Metastatic breast cancer remains one of the leading causes of death in women and identification of novel treatment targets is therefore warranted. Functional studies showed that the RNA helicase DDX3 promotes metastasis, but DDX3 expression was never studied in patient samples of metastatic cancer. In order to validate previous functional studies and to evaluate DDX3 as a potential therapeutic target, we investigated DDX3 expression in paired samples of primary and metastatic breast cancer. Samples from 79 breast cancer patients with distant metastases at various anatomical sites were immunohistochemically stained for DDX3. Both cytoplasmic and nuclear DDX3 expression were compared between primary and metastatic tumors. In addition, the correlation between DDX3 expression and overall survival was assessed. Upregulation of cytoplasmic (28%; OR 3.7; p = 0.002) was common in breast cancer metastases, especially in triple negative (TN) and high grade cases. High cytoplasmic DDX3 levels were most frequent in brain lesions (65%) and significantly correlated with high mitotic activity and triple negative subtype. In addition, worse overall survival was observed for patients with high DDX3 expression in the metastasis (HR 1.79, p = 0.039). Overall, we conclude that DDX3 expression is upregulated in distant breast cancer metastases, especially in the brain and in TN cases. In addition, high metastatic DDX3 expression correlates with worse survival, implying that DDX3 is a potential therapeutic target in metastatic breast cancer, in particular in the clinically important group of TN patients., Competing Interests: Compliance with ethical standards Conflict interests Paul van Diest and Venu Raman have applied for a patent for the use of DDX3 as a cancer biomarker and are on the advisory board of Natsar Pharmaceuticals. Ethics approval and consent to participate For this study only anonymous archival leftover pathology material was used. Therefore no formal consent is required according to Dutch legislation [23], as this use of redundant tissue for research purposes is part of the standard treatment agreement with patients in our hospitals [24]. The medical research ethics committee of the UMC Utrecht confirmed that official approval of this study is not required (reference number WAG/mb/16/029330).