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Peer-reviewedCC BY-NC-NDNature

Non-invasive profiling of the tumour microenvironment with spatial ecotypes

  • Wubing Zhang1,2
  • Erin L. Brown1,2
  • Abul Usmani3
  • Noah J. Earland3
  • Minji Kang1,4,2
  • Chibuzor Olelewe5
  • Anushka Viswanathan5
  • Pradeep S. Chauhan5
  • Chloé Beate Steen6,7,8
  • Hyun Soo Jeon4
  • Susanna Avagyan1,2
  • Irfan Alahi9,5
17 more authors
  • Nicholas P. Semenkovich10,11,12
  • Janella C. Schwab13,1,2
  • Chloe M. Sachs3
  • Faridi Qaium5
  • Peter K. W. Harris3
  • Qingyuan Cai14
  • Andrew J. Gentles1,15,16
  • James Knight17
  • Rondell P. Graham18
  • Antonietta Bacchiocchi19
  • Peter C. Lucas18,20
  • Ryan Courtney Fields21
  • Mario Sznol22,23
  • Ruth Halaban19,23
  • Di Chen24
  • Aadel A. Chaudhuri25,26,27,28
  • Aaron M. Newman29,30,31,32
32 affiliations
  1. 1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
  2. 2Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
  3. 3Division of Cancer Biology, Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
  4. 4Department of Computer Science, Stanford University, Stanford, CA, USA
  5. 5Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA
  6. 6Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
  7. 7Institute for Cancer Research, Oslo University Hospital and University of Oslo, Oslo, Norway
  8. 8Precision Immunotherapy Alliance, University of Oslo, Oslo, Norway
  9. 9Department of Computer Science and Engineering, Washington University, St. Louis, MO, USA
  10. 10Data Science Institute, Medical College of Wisconsin, Milwaukee, WI, USA
  11. 11Department of Medicine, Clement J. Zablocki VA Medical Center, Milwaukee, WI, USA
  12. 12Division of Endocrinology and Molecular Medicine, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
  13. 13Department of Bioengineering, Stanford University, Stanford, CA, USA
  14. 14Department of Systems Biology, Columbia University, New York, NY, USA
  15. 15Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
  16. 16Stanford Cancer Institute, Stanford University, Stanford, CA, USA
  17. 17Yale Center for Genome Analysis, Department of Molecular Biophysics and Biochemistry, Institute for Biomolecular Design and Discovery, Yale University, New Haven, CT, USA
  18. 18Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
  19. 19Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA
  20. 20Mayo Clinic Comprehensive Cancer Center, Rochester, MN, USA
  21. 21Department of Surgery, University of Rochester School of Medicine, Rochester, NY, USA
  22. 22Department of Medicine, Division of Medical Oncology, Yale University School of Medicine, New Haven, CT, USA
  23. 23Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA
  24. 24Division of Dermatology, Washington University School of Medicine, St. Louis, MO, USA
  25. 25Department of Computer Science and Engineering, Washington University, St. Louis, MO, USA. [email protected]
  26. 26Department of Immunology, Mayo Clinic, Rochester, MN, USA. [email protected]
  27. 27Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA. [email protected]
  28. 28Mayo Clinic Comprehensive Cancer Center, Rochester, MN, USA. [email protected]
  29. 29Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA. [email protected]
  30. 30Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. [email protected]
  31. 31Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA. [email protected]
  32. 32Stanford Cancer Institute, Stanford University, Stanford, CA, USA. [email protected]
Open at sourceDOICite

Abstract

Multicellular programs in the tumour microenvironment (TME) drive cancer pathogenesis and response to therapy but remain challenging to identify and profile clinically1–3. Here, we present a machine-learning framework for multi-analyte profiling of spatially dependent cell states and multicellular ecosystems, termed spatial ecotypes (SEs). By integrating over 10 million single-cell and spot-level spatial transcriptomes from diverse human carcinomas and melanomas, we identified nine SEs with broad conservation, each of which has unique biology, geospatial features and clinical outcome associations, including several linked to immunotherapy response. Notably, SEs were distinguishable by DNA methylation profiling and were recoverable from plasma cell-free DNA (cfDNA) using deep learning. In cfDNA from nearly 100 patients with melanoma, SE levels exhibited striking associations with immunotherapy response. Our data reveal fundamental units of TME organization and demonstrate a multimodal platform for profiling solid and liquid TMEs, with implications for improved risk stratification and therapy personalization. Multimodal machine learning reveals that tumour microenvironments can be decomposed into spatially organized multicellular ecosystems, termed spatial ecotypes, that can be accessed non-invasively via liquid biopsy and used to profile individual cancers and target treatments.

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Cite

BibTeXRIS
BibTeX
@article{zhang2026noninvasive,
  title = {{Non-invasive profiling of the tumour microenvironment with spatial ecotypes}},
  author = {Wubing Zhang and Erin L. Brown and Abul Usmani and Noah J. Earland and Minji Kang and Chibuzor Olelewe and Anushka Viswanathan and Pradeep S. Chauhan and Chloé Beate Steen and Hyun Soo Jeon and Susanna Avagyan and Irfan Alahi and Nicholas P. Semenkovich and Janella C. Schwab and Chloe M. Sachs and Faridi Qaium and Peter K. W. Harris and Qingyuan Cai and Andrew J. Gentles and James Knight and Rondell P. Graham and Antonietta Bacchiocchi and Peter C. Lucas and Ryan Courtney Fields and Mario Sznol and Ruth Halaban and Di Chen and Aadel A. Chaudhuri and Aaron M. Newman},
  journal = {Nature},
  year = {2026},
  month = may,
  doi = {10.1038/s41586-026-10452-4},
  url = {https://doi.org/10.1038/s41586-026-10452-4},
  note = {CC BY-NC-ND},
}
RIS
TY  - JOUR
TI  - Non-invasive profiling of the tumour microenvironment with spatial ecotypes
AU  - Wubing Zhang
AU  - Erin L. Brown
AU  - Abul Usmani
AU  - Noah J. Earland
AU  - Minji Kang
AU  - Chibuzor Olelewe
AU  - Anushka Viswanathan
AU  - Pradeep S. Chauhan
AU  - Chloé Beate Steen
AU  - Hyun Soo Jeon
AU  - Susanna Avagyan
AU  - Irfan Alahi
AU  - Nicholas P. Semenkovich
AU  - Janella C. Schwab
AU  - Chloe M. Sachs
AU  - Faridi Qaium
AU  - Peter K. W. Harris
AU  - Qingyuan Cai
AU  - Andrew J. Gentles
AU  - James Knight
AU  - Rondell P. Graham
AU  - Antonietta Bacchiocchi
AU  - Peter C. Lucas
AU  - Ryan Courtney Fields
AU  - Mario Sznol
AU  - Ruth Halaban
AU  - Di Chen
AU  - Aadel A. Chaudhuri
AU  - Aaron M. Newman
PY  - 2026
DA  - 2026/05/06
JO  - Nature
DO  - 10.1038/s41586-026-10452-4
UR  - https://doi.org/10.1038/s41586-026-10452-4
AB  - Multicellular programs in the tumour microenvironment (TME) drive cancer pathogenesis and response to therapy but remain challenging to identify and profile clinically1–3. Here, we present a machine-learning framework for multi-analyte profiling of spatially dependent cell states and multicellular ecosystems, termed spatial ecotypes (SEs). By integrating over 10 million single-cell and spot-level spatial transcriptomes from diverse human carcinomas and melanomas, we identified nine SEs with broad conservation, each of which has unique biology, geospatial features and clinical outcome associations, including several linked to immunotherapy response. Notably, SEs were distinguishable by DNA methylation profiling and were recoverable from plasma cell-free DNA (cfDNA) using deep learning. In cfDNA from nearly 100 patients with melanoma, SE levels exhibited striking associations with immunotherapy response. Our data reveal fundamental units of TME organization and demonstrate a multimodal platform for profiling solid and liquid TMEs, with implications for improved risk stratification and therapy personalization. Multimodal machine learning reveals that tumour microenvironments can be decomposed into spatially organized multicellular ecosystems, termed spatial ecotypes, that can be accessed non-invasively via liquid biopsy and used to profile individual cancers and target treatments.
SN  - 0028-0836
SN  - 1476-4687
ER  -