Biology subjects

Mittendorf, E. A.

Publications and source records attributed to Mittendorf, E. A..

3 recordsLinked to original sources

bioRxiv2025

APOE Tumor-Associated Macrophages and CD4-DOCK4 T Cells Reveal Distinct Microenvironmental Features in HER2-Low and HER2-0 Hormone Receptor-Positive Breast Cancer

Novel anti-HER2 antibody-drug conjugates (ADCs), such as trastuzumab deruxtecan (T-DXd), have shown efficacy in tumors with varying HER2 expression, including HER2-low and even tumors with minimal HER2 presence. This has sparked interest in the biology underlying the HER2 expression spectrum. Using molecular and multiplexed imaging, we revealed distinct immune and stromal features in treatment-naive, hormone receptor-positive (HR+) HER2-low versus HER2-0 tumors. HER2-0 tumors exhibit inflammatory and tissue remodeling gene signatures, with enrichment of APOE tumor-associated macrophages (TAMs) and DOCK4 CD4 T cells. In contrast, HER2-low tumors are more immunosuppressed, with elevated cell cycle, metabolic, and estrogen signaling pathways, suggesting increased proliferative activity. These findings underscore key biological differences between HR+ HER2-low and HER2-0 breast cancers, and may inform more tailored therapeutic strategies. Statement of significanceThis study revealed the distinct biological profiles of HR+ HER2-low and HER2-0 breast tumors. HER2-0 tumors exhibit inflammatory and tissue remodeling signatures, whereas HER2-low tumors have elevated cell cycle, metabolic, and estrogen signaling. These insights may help refine therapeutic approaches to improve outcomes for breast cancer patients.

cancer biology↗

bioRxiv2024

An estrogen receptor signaling transcriptional program linked to immune evasion in human hormone receptor-positive breast cancer

T cells are generally sparse in hormone receptor-positive (HR+) breast cancer, potentially due to limited antigen presentation, but the driving mechanisms of low T cell abundance remains unclear. Therefore, we defined and investigated programs ( gene modules), related to estrogen receptor signaling (ERS) and immune signaling using bulk and single-cell transcriptome and multiplexed immunofluorescence of breast cancer tissues from multiple clinical sources and human cell lines. The ERS gene module, dominantly expressed in cancer cells, was negatively associated with immune-related gene modules TNF/NF-{kappa}B signaling and type-I interferon (IFN-I) response, which were expressed in distinct stromal and immune cell types, but also, in part, expressed and preserved as a cancer cell-intrinsic mechanisms. Spatial analysis revealed that ERS strongly correlated with reduced T cell infiltration, potentially due to its association with suppression of TNF/NF-{kappa}B-induced angiogenesis and IFN-I-induced HLA expression in macrophages. Preoperative endocrine therapy in ER+/HER2-breast cancer patients produced better responses in ERS-high patients, with TNF/NF-{kappa}B expression associated with reduced ERS. Targeting these pathways may enhance T cell infiltration in HR+ breast cancer patients. Statement of SignificanceThis study elucidates the immunosuppressive role of ER signaling in breast cancer, highlighting a complex interplay between cancer, stromal, and immune cells and reveals potential approaches to enhance immunogenicity in HR+ breast cancer. These findings offer crucial insights into immune evasion in breast cancer and identify strategies to enhance T cell abundance.

cancer biology↗

bioRxiv2023

Quality Control for Single Cell Analysis of High-plex Tissue Profiles using CyLinter

Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artefacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.

pathology↗