A 3D morphogenetic blueprint for metastatic outgrowth in breast cancer

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Authors: Robin Caire, Roberta Bordo, Francesca Zanconato, Tito Panciera, Estelle Audoux, Paolo Contessotto, Michaela Fakiola, Ramona Bason, Oriana Romano, Ambela Suli, Giusy Battilana, Matteo Marchionni
+21 more Mattia Forcato, Sara Donzelli, M. Dieci, Gaia Griguolo, Mariantonia Carosi, Matteo Fassan, Vincenza Guzzardo, Angelo Paolo Dei Tos, Silvia Marsoni, Peng Wu, Denis Wirtz, Shanshan He, Cecilia Casali, Francesco Volpin, Giovanni Blandino, Claudio Tripodo, Silvio Bicciato, Valentina Guarneri, Massimiliano Pagani, Michelangelo Cordenonsi, Stefano Piccolo
Cited by: 5
References: 68

Abstract

The tissue-level processes underpinning metastatic outgrowth remain unclear. We combined single-cell RNA sequencing, spatial transcriptomics, and AI-supported 3D imaging in human breast cancer with functional investigations in mice to uncover a 3D morphogenetic process essential for macrometastatic expansion. Macrometastases pervasively activate a metastatic trabecular morphogenesis (MTM) gene-expression program that redeploys developmental branching morphogenesis to build macrometastases as a 3D trabecular lattice of epithelial cords. MTM HIGH cells pre-exist in primary tumors destined to metastasize, whereas MTM LOW primaries are non-metastatic and display a compact, expansile growth architecture. Chromatin immunoprecipitation sequencing (ChIP-seq) on metastatic organoids identifies ETV1/4/5 as master regulators of MTM and branching cancer morphogenesis, required for metastatic outgrowth but dispensable for primary tumor take, bulk growth, and initial metastatic dissemination. Spatial and functional analyses reveal stromal fibroblast growth factor (FGF)→fibroblast growth factor receptor (FGFR) signaling as an actionable MTM dependency. Thus, we link metastatic outgrowth to a 3D developmental morphogenetic process, exposing therapeutic vulnerabilities specific to the lethal macrometastatic stage.

Fields of Study & Subjects

Cancer Cells and Metastasis Breast Cancer Treatment Studies AI in cancer detection

Publication details

Journal / source
Cell
Publisher
Elsevier BV
Volume
189
Issue
12
Pages
3701–3718.e30
Publication date
31 March 2026
Language
EN