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Biology subjects

Chrenkova, E.

Publications and source records attributed to Chrenkova, E..

2 recordsLinked to original sources

Tumor Heterogeneity Induces Pro- and Anti-metastatic Myeloid Cell Phenotypes in Breast Cancer Lung Metastasis

Metastasis is a major cause of cancer-related mortality, yet targeting metastatic cells directly has been largely unsuccessful due to their plasticity and heterogeneity. Myeloid cells play diverse pro- and anti-metastatic functions and are an attractive alternative target for treating metastasis, but how tumor heterogeneity influences myeloid cell phenotypes during metastasis remains poorly understood. Here, we profiled myeloid cells from primary tumors and matched metastatic lungs of 12 heterogeneous and differentially metastatic patient-derived xenograft models of breast cancer. Comparative analysis of cell type abundances revealed distinct myeloid remodeling specific to primary tumors or metastatic lungs. Beyond compositional differences, we identified gene expression programs that were associated with metastatic burden, such as number or size of metastatic nodules, indicating distinct microenvironmental requirements for metastatic seeding and outgrowth. Examining these metastasis-associated programs using time-course datasets, we discovered an evolution from anti- to pro-metastatic monocyte phenotypes during metastatic progression. We further showed that this phenotypic shift was driven by an increase in two distinct myeloid-derived suppressor cell signatures, and a transcriptionally regulated impairment of monocyte differentiation leading to the depletion of non-classical monocytes. Our results comprehensively dissect the heterogeneity of myeloid cell phenotypes across primary tumor and metastatic sites, opening novel avenues for myeloid-targeting therapies specific to metastasis.

cancer biology↗

LazySlide: accessible and interoperable whole slide image analysis

Histopathological data are foundational in both biological research and clinical diagnostics but remain siloed from modern multimodal and single-cell frameworks. We introduce LazySlide, an open-source Python package built on the scverse ecosystem for efficient whole-slide image (WSI) analysis and multimodal integration. By leveraging vision-language foundation models and adhering to scverse data standards, LazySlide bridges histopathology with omics workflows. It supports tissue and cell segmentation, feature extraction, cross-modal querying, and zero-shot classification, with minimal setup. Its modular design empowers both novice and expert users, lowering the barrier to advanced histopathology analysis and accelerating AI-driven discovery in tissue biology and pathology.

bioinformatics↗