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Biology subjects

Chanfon, A.

Publications and source records attributed to Chanfon, A..

2 recordsLinked to original sources

Spatiotemporal organisation of residual disease in mouse and human BRCA1-deficient mammary tumours and breast cancer

Breast cancer remains one of the prominent causes of death worldwide. Although chemotherapeutic agents often result in substantial reduction of primary or metastatic tumours, remaining drug-tolerant tumour cell populations, known as minimal residual disease (MRD), pose a significant risk of recurrence and therapy resistance. In this study, we describe the spatiotemporal organisation of therapy response and MRD in BRCA1;p53-deficient mouse mammary tumours and human clinical samples using a multimodal approach. By integrating single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), and imaging mass cytometry (IMC) across multiple treatment timepoints, we characterise dynamic interactions between tumour cell subpopulations and their surrounding microenvironment. Our analysis identifies a distinct, drug-tolerant epithelial-mesenchymal transition (EMT) cancer cell population, which exhibits a conserved expression program in human BRCA1-deficient tumours and significantly correlates with adverse clinical outcomes. We further reveal the spatial distribution of residual EMT-like tumour cells within specific anatomical niches, providing a framework for understanding the persistence of MRD and potential therapeutic vulnerabilities. These findings yield a comprehensive molecular roadmap of MRD, opening new avenues for therapeutic strategies targeting EMT-driven drug tolerance and tumour relapse.

cancer biology↗

Deep learning-based 3D spatial transcriptomics with X-Pression

Spatial transcriptomics technologies currently lack scalable and cost-effective options to profile tissues in three dimensions. Technological advances in microcomputed tomography enabled non-destructive volumetric imaging of tissue blocks with sub-micron resolution at a centimetre scale. Here, we present X-Pression, a deep convolutional neural network-based frame-work designed to reconstruct 3D expression signatures of cellular niches from volumetric microcomputed tomography data. By training on a singular 2D section of a paired spatial transcriptomics experiment, X-Pression achieves high accuracy and is capable of generalising to out-of-sample examples. We utilised X-Pression to demonstrate the benefit of 3D examination of tissues on a paired SARS-CoV-2 vaccine efficacy spatial transcriptomics and microcomputed tomography cohort of a recently developed live attenuated SARS-CoV-2 vaccine. By applying X-Pression to the entire mouse lung, we visualised the sites of viral replication at the organ level and the simultaneous collapse of small alveoli in their vicinity. In addition, we assessed the immunological response following vaccination and virus challenge infection. X-Pression offers a valuable and cost-effective addition to infer expression signatures without the need for consecutive 2D sectioning and reconstruction, providing new insights into transcriptomic profiles in three dimensions.

bioinformatics↗