Biology subjects

Brisken, C.

Publications and source records attributed to Brisken, C..

7 recordsLinked to original sources

bioRxiv2025

A modular lentiviral system for multiplexed gene perturbation and functional analysis reveals interdependence of hormone receptors in breast cancer growth in vivo

Precise and flexible control of gene expression is essential for dissecting gene function in complex biological systems. Although recent developments in genetic engineering and CRISPR/Cas9 technology have expanded tools for gene activation, suppression and editing, their application in physiologically relevant models remains challenging, time consuming, and expensive. Here, we present a modular, doxycycline-inducible vector system that integrates gene overexpression, shRNA-mediated knockdown, and CRISPR/Cas9-mediated regulation within a single, lentivirus-compatible system. The modular design allows rapid exchange of selection markers, epitope tags, and reporters via Gateway cloning, providing broad adaptability across experimental settings. In addition to standard fluorescent and luminescent reporters, the system includes advanced sensors, such as Fucci cell cycle reporters, to enable monitoring of cellular processes. By combining fluorescence barcoding with combinatorial genetic perturbations, the platform supports multiplexed analysis of gene function and genetic interactions through phenotypic characterization by fluorescence imaging or flow cytometry. We demonstrate its utility in vivo with breast cancer intraductal xenografts, which suggest that ER+ breast cancer cells (MCF7) rely on androgen (AR), estrogen (ER) and progesterone receptors (PR) for in vivo growth. This versatile gene perturbation system provides tight temporal control, streamlined implementation, and high-content phenotyping capacity facilitating efficient in vitro and in vivo studies while reducing the use of animals in in vivo validation experiments. It thus expands the experimental repertoire for dynamic, multigene interrogation in complex systems.

molecular biology↗

bioRxiv2025

LOX inhibition disrupts a collagen-integrin-MYC axis as a translatable targeting strategy in invasive lobular carcinoma

Invasive lobular carcinoma (ILC) accounts for 15% of breast cancers yet lacks specific therapy because ILCs are underrepresented in clinical trials and preclinical models are lacking. We established intraductal xenograft models to test whether the clinical pan-lysyl-oxidase PXS-5505, now in phase trials for myelofibrosis can exploit the collagen-rich matrix dependency created by CDH1 loss. PXS-5505 remodels fibrillar collagen, and halts tumor expansion and metastatic seeding across ER+ and triple negative models without systemic toxicity. Genome-wide CRISPR screens reveal ITGAV and ITGB5 as synthetic lethal partners of CDH1 and LOX inhibition downregulates their expression together with MYC, NF-{kappa}B, and AP-1 transcriptional programmes. Collagen fibre density/alignment, and MYC/AP-1 gene signatures serve as pharmacodynamic readouts of drug activity. These data uncover a tractable ECM-integrin-MYC axis in ILC and nominate PXS-5505, alone or with endocrine therapy, for window of opportunity trials in this understudied breast cancer subtype. One Sentence SummaryTargeting matrix remodelling in ILC inhibits ILC progression and alters multiple molecular endpoints, providing a translatable therapeutic strategy for this understudied subtype that requires better treatments.

cancer biology↗

bioRxiv2025

Normal breast tissue classifiers assess large-scale tissue compartments with high accuracy

Cancer research emphasises early detection, yet quantitative methods for normal tissue analysis remain limited. Digitised haematoxylin and eosin (H&E)-stained slides enable computational histopathology, but artificial intelligence (AI)-based analysis of normal breast tissue (NBT) in whole slide images (WSIs) remains scarce. We curated 70 WSIs of NBTs from multiple sources and cohorts with pathologist-guided manual annotations of epithelium, stroma, and adipocytes (https://github.com/cancerbioinformatics/OASIS). We developed robust convolutional neural network (CNN)-based, patch-level classification models, named NBT-Classifiers, to tessellate and classify NBTs at different scales. Across three external cohorts, NBT-Classifiers trained on 128{square}x{square}128{square}{micro}m and 256{square}x{square}256{square}{micro}m patches achieved AUCs of 0.98-1.00. The model learned independent normal features different from those of precancerous and cancerous epithelium, which were further visualised using two explainable AI techniques. When integrated into an end-to-end preprocessing pipeline, NBT-Classifiers facilitate efficient downstream analysis within peri-lobular regions. NBT-Classifiers provide robust compartment-specific analytical tools and enhance our understanding of NBT appearances, which serve as valuable reference points for identifying premalignant changes and guiding early breast cancer prevention strategies.

pathology↗

bioRxiv2022

The Breast Cancer Epigenomics Track Hub

BackgroundPioneering research has shown that high-throughput epigenomics assays such as ChlP-seq and ATAC-seq are applicable to patient-derived breast tumor samples. A host of public data has been accumulated since then, which are potentially of high value for basic research as well as personalized medicine. Such data sets constitute encyclopedias of biological knowledge. However, their impact has so far been limited by access obstacles, especially with regard to extraction and visualization of small portions of data that could potentially answer specific questions arising in a research context. ResultsWe developed the breast cancer epigenomics track hub (BC hub), a resource intended to make it easy for occasional users to find, access and view data of their interest. The BC hub harbors ChIP-seq, ATAC-seq and copy number data from breast tumors, normal breast cells, patient-derived xenografts and breast cancer cell lines in a genome browsable track format. The tracks can be accessed via hyperlinks that automatically configure customized views for different interest groups. Here, we present a detailed description of the resource and informative use cases illustrating its potential in answering specific biological questions. ConclusionsWe show that track hubs constitute a powerful way of bringing epigenomics data to the user who could benefit from them. The examples presented highlight the added-value of joint visualization of breast cancer data from different sources. The proof-of-concept provided here exemplifies and underscores the importance of efforts to make biological data FAIR (findable, accessible, interoperable and reusable), and may serve as an encouragement of similar bottom-up initiatives in other research fields. The BC hub is freely accessible at https://bchub.epfl.ch.

cancer biology↗

bioRxiv2021

Epithelial-mesenchymal plasticity determines estrogen receptor positive (ER+) breast cancer dormancy and reacquisition of an epithelial state drives awakening

Estrogen receptor -positive (ER+) breast cancers (BCs) represent more than 70% of all breast cancers and pose a particular clinical challenge because they recur up to decades after initial diagnosis and treatment. The mechanisms governing tumor cell dormancy and latent disease remain elusive due to a lack of adequate models. Here, we compare tumor progression of ER+ and triple-negative (TN) BC subtypes with a clinically relevant mouse intraductal xenografting approach (MIND). Both ER+ and TN BC cells disseminate already during the in situstage. However, TN disseminated tumor cells (DTCs) proliferate at the same rate as cells at the primary site and give rise to macro-metastases. ER+ DTCs have low proliferative indices, form only micro-metastases and lose epithelial characteristics. Expression of CDH1 is decreased whereas the mesenchymal marker VIM and the transcription factors, ZEB1/ZEB2, which control epithelial-mesenchymal plasticity (EMP) are increased. EMP is not detected earlier during ER+ BC development and not required for invasion or metastasis. In vivo, forced transition to the epithelial state through ectopic E-cadherin expression overcomes dormancy with increased growth of lung metastases. We conclude that EMP is essential for the generation of a dormant cell state and the development of latent disease. Targeting exit from EMP is of therapeutic potential.

cancer biology↗

bioRxiv2020

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cells contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier. Author summaryBreast cancer is the most commonly diagnosed tumor in women worldwide and its incidence in the population is increasing over time. Because our understanding of such disease has been hampered by the lack of adequate human preclinical model, efforts have been made in order to develop better approaches to model the human complexity. Recent advances in this regard were achieved with Patient-Derived Xenografts (PDXs), which entail the implantation of human-derived specimens to recipient immunosuppressed mice and are, thus far, the preclinical system best recapitulating the heterogeneity of both normal and malignant human tissues. However, histological analyses of the resulting tissues are usually confounded by the presence of cells of different species. To circumvent this hurdle and to facilitate the discrimination of human and murine cells in xenografted samples, we developed Single Cell Classifier (SCC), a deep learning-based open-source software, available as a plugin in ImageJ/Fiji, performing automated species classification of individual cells in H&E stained sections. We show that SCC can reach up to 96% classification accuracy to classify cells of different species mainly leveraging on their contextual features in both normal and tumor PDXs. SCC will improve and automate histological analyses of human-in-mouse xenografts and is open to new in-house built models for further classification tasks and applications in image analysis.

systems biology↗

bioRxiv2018

Intraductal patient derived xenografts of estrogen receptor positive breast cancer recapitulate the histopathological spectrum and metastatic potential of human lesions

Estrogen receptor positive (ER+) or \"luminal\" breast cancers were notoriously difficult to establish as patient-derived xenografts (PDXs). We and others recently demonstrated that the microenvironment is critical for ER+ tumor cells; by grafting them into milk ducts >90% take rates are achieved and many features of the human disease are recapitulated. This intra-ductal (ID) approach holds promise for personalized medicine, yet human and murine stroma are organized differently and this and other species specificities may limit the value of this model. Here, we analyzed 21 ER+ ID-PDXs histopathologically. We find that ID-PDXs vary in extent and define four histopathological patterns: flat, lobular, in situ, and invasive, which occur in pure and combined forms. The ID-PDXs replicate earlier stages of tumor development than their clinical counterparts. Micrometastases are already detected when lesions appear in situ. Tumor extent, histopathological patterns, and metastatic load correlate with biological properties of their tumors of origin. Our findings add evidence to the validity of the intraductal model for in vivo studies of ER+ breast cancer and raise the intriguing possibility that tumor cell dissemination may occur earlier than currently thought.\n\nConflict of interest statementThe authors declare no conflict of interest.

cancer biology↗