Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer.

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples.

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based).

Use of Web-based training for quality improvement between a field immunohistochemistry laboratory in Nigeria and its United States-based partner institution.

The importance of hormone receptor status in assigning treatment and the potential use of human epidermal growth factor receptor 2 (HER2)-targeted therapy have made it beneficial for laboratories to improve detection techniques. Because interlaboratory variability in immunohistochemistry (IHC) tests may also affect studies of breast cancer subtypes in different countries, we undertook a Web-based quality improvement training and a comparative study of accuracy of immunohistochemical tests of breast cancer biomarkers between a well-established laboratory in the United States (University of Chicago) and a field laboratory in Ibadan, Nigeria. Two hundred and thirty-two breast tumor blocks were evaluated for estrogen receptors (ERs), progesterone receptors (PRs), and HER2 status at both laboratories using tissue microarray technique.

Expression of polycomb targets predicts breast cancer prognosis.

Global changes in the epigenome are increasingly being appreciated as key events in cancer progression. The pathogenic role of enhancer of zeste homolog 2 (EZH2) has been connected to its histone 3 lysine 27 (H3K27) methyltransferase activity and gene repression; however, little is known about relationship of changes in expression of EZH2 target genes to cancer characteristics and patient prognosis. Here we show that through expression analysis of genomic regions with H3K27 trimethylation (H3K27me3) and EZH2 binding, breast cancer patients can be stratified into good and poor prognostic groups independent of known cancer gene signatures.

Apc mutation enhances PyMT-induced mammary tumorigenesis.

The Adenomatous Polyposis Coli (APC) tumor suppressor gene is silenced by hypermethylation or mutated in up to 70% of human breast cancers. In mouse models, Apc mutation disrupts normal mammary development and predisposes to mammary tumor formation; however, the cooperation between APC and other mutations in breast tumorigenesis has not been studied. To test the hypothesis that loss of one copy of APC promotes oncogene-mediated mammary tumorigenesis, Apc(Min/+) mice were crossed with the mouse mammary tumor virus (MMTV)-Polyoma virus middle T antigen (PyMT) or MMTV-c-Neu transgenic mice.

Proliferating macrophages associated with high grade, hormone receptor negative breast cancer and poor clinical outcome.

Macrophages, a key cell in the inflammatory cascade, have been associated with poor prognosis in cancers, including breast cancer. In this study, we investigated the relationship of a subset of macrophages-proliferating macrophages (promacs)-with clinico-pathologic characteristics of breast cancer, including tumor size, grade, stage, lymph node metastases, hormone receptor status, subtype, as well as early recurrence, and survival. This study included a discovery and validation set that was conducted at two institutions and laboratories (University of California, San Francisco and University of Chicago) using two independent cohorts of patients with breast cancer.

Wnt/beta-catenin pathway activation is enriched in basal-like breast cancers and predicts poor outcome.

Although Wnt/beta-catenin pathway activation has been implicated in mouse models of breast cancer, there is contradictory evidence regarding its importance in human breast cancer. In this study, invasive and in situ breast cancer tissue microarrays containing luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)(+)/ER(-) and basal-like breast cancers were analyzed for beta-catenin subcellular localization. We demonstrate that nuclear and cytosolic accumulation of beta-catenin, a read-out of Wnt pathway activation, was enriched in basal-like breast cancers.

Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors.

Background: Although numerous mouse models of breast carcinomas have been developed, we do not know the extent to which any faithfully represent clinically significant human phenotypes. To address this need, we characterized mammary tumor gene expression profiles from 13 different murine models using DNA microarrays and compared the resulting data to those from human breast tumors.

Results: Unsupervised hierarchical clustering analysis showed that six models (TgWAP-Myc, TgMMTV-Neu, TgMMTV-PyMT, TgWAP-Int3, TgWAP-Tag, and TgC3(1)-Tag) yielded tumors with distinctive and homogeneous expression patterns within each strain.