Characterizing steroid hormone receptor chromatin binding landscapes in male and female breast cancer

Male breast cancer (MBC) is rare and largely hormonally driven. Here, the authors examine the action of steroid hormone receptors in male and female breast cancers and find gender selective hormone receptor action that associates with the survival of MBC patients

Tumor-Infiltrating Lymphocytes in Low-Risk Patients With Breast Cancer Treated With Single-Dose Preoperative Partial Breast Irradiation

Contains fulltext : 232805.pdf (Publisher’s version ) (Open Access)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 x 450 mum 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

From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

Contains fulltext : 201136.pdf (Publisher’s version ) (Closed access

1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset

Contains fulltext : 193420.pdf (publisher's version ) (Open Access)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