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Cancer Discov2025;15(10):2139-2165.October 6, 2025Journal Article

AAnet Resolves a Continuum of Spatially Localized Cell States to Unveil Intratumoral Heterogeneity.

Aarthi Venkat1,2, Scott E Youlten3, Beatriz P San Juan4,5, Carley A Purcell4,5, Shabarni Gupta4,5, Matthew Amodio2,6, Daniel P Neumann7, John G Lock7, Anton E Westacott4,5, Cerys S McCool4,5, Daniel B Burkhardt3,8, Andrew Benz9,10, Annelie Mollbrink11, Joakim Lundeberg11, David van Dijk1,6,12, Jeff Holst7, Leonard D Goldstein4,5, Sarah Kummerfeld4,5, Smita Krishnaswamy1,3,6,13, Christine L Chaffer4,5
1Computational Biology and Bioinformatics Program, Yale University, New Haven, Connecticut.
2Broad Institute at MIT and Harvard, Cambridge, Massachusetts.
3Department of Genetics, Yale School of Medicine, New Haven, Connecticut.
4Kinghorn Cancer Centre, Garvan Institute of Medical Research, Darlinghurst, Australia.
5St Vincent's Clinical School, University of New South Wales Medicine, University of New South Wales, Darlinghurst, Australia.
6Department of Computer Science, Yale University, New Haven, Connecticut.
7University of New South Wales, Kensington, Australia.
8NVIDIA, Toronto, Canada.
9Cellarity, Somerville, Massachusetts.
10Department of Mathematics, Yale University, New Haven, Connecticut.
11SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden.
12Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut.
13Applied Mathematics Program, Yale University, New Haven, Connecticut.
PubMed PMC DOI PDF

Abstract

UNLABELLED: Identifying functionally important cell states and structure within heterogeneous tumors remains a significant biological and computational challenge. Current clustering- or trajectory-based models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. We present Archetypal Analysis network (AAnet), a neural network that learns archetypal states w…

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