Erin Brown

All Publications

• Non-invasive profiling of the tumour microenvironment with spatial ecotypes. Nature Zhang, W., Brown, E. L., Usmani, A., Earland, N., Kang, M., Olelewe, C., Viswanathan, A., Chauhan, P. S., Steen, C. B., Jeon, H. S., Avagyan, S., Alahi, I., Semenkovich, N. P., Schwab, J. C., Sachs, C. M., Qaium, F., Harris, P. K., Cai, Q., Gentles, A. J., Knight, J., Graham, R. P., Bacchiocchi, A., Lucas, P. C., Fields, R. C., Sznol, M., Halaban, R., Chen, D. Y., Chaudhuri, A. A., Newman, A. M. 2026

  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.

  View details for DOI 10.1038/s41586-026-10452-4

  View details for PubMedID 42092150

  View details for PubMedCentralID 7612730

• Liquid biopsy profiling of the tumor microenvironment to determine response to immunotherapy regimens across solid tumors. Brown, E. L., Zhang, W., Usmani, A., Earland, N., Hashmi, A., Olelewe, C., Viswanathan, A., Chauhan, P. S., Kang, M., Steen, C. B., Jeon, H., Avagyan, S., Alahi, I., Semenkovich, N. P., Bergsagel, M., Schwab, J. C., Sachs, C. M., Qaium, F., Harris, P. K., Bacchiocchi, A., Cai, Q., Gentles, A. J., Graham, R. P., Lucas, P. C., Fields, R. C., Orme, J. J., Mansfield, A. S., Sznol, M., Halaban, R., Chen, D. Y., Newman, A. M., Chaudhuri, A. A. AMER ASSOC CANCER RESEARCH. 2026: 94

  View details for DOI 10.1158/1538-7445.AM2026-94

  View details for Web of Science ID 001734381000045

• Improved reconstruction of single-cell developmental potential with CytoTRACE 2. Nature methods Kang, M., Gulati, G. S., Brown, E. L., Qi, Z., Avagyan, S., Armenteros, J. J., Gleyzer, R., Zhang, W., Steen, C. B., D'Silva, J. P., Schwab, J., Clarke, M. F., Chaudhuri, A. A., Newman, A. M. 2025

  Abstract

  While single-cell RNA sequencing has advanced our understanding of cell fate, identifying molecular hallmarks of potency-a cell's ability to differentiate into other cell types-remains a challenge. Here we introduce CytoTRACE 2, an interpretable deep learning framework for predicting absolute developmental potential from single-cell RNA sequencing data. Across diverse platforms and tissues, CytoTRACE 2 outperformed previous methods in predicting developmental hierarchies, enabling detailed mapping of single-cell differentiation landscapes and expanding insights into cell potency.

  View details for DOI 10.1038/s41592-025-02857-2

  View details for PubMedID 41145665

  View details for PubMedCentralID 6390367

• Charting spatially resolved cell states with CytoSPACE. Nature reviews. Cancer Brown, E. L. 2024

  View details for DOI 10.1038/s41568-024-00713-7

  View details for PubMedID 38858510

• High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE. Nature biotechnology Vahid, M. R., Brown, E. L., Steen, C. B., Zhang, W., Jeon, H. S., Kang, M., Gentles, A. J., Newman, A. M. 2023

  Abstract

  Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, an optimization method for mapping individual cells from a single-cell RNA sequencing atlas to spatial expression profiles. Across diverse platforms and tissue types, we show that CytoSPACE outperforms previous methods with respect to noise tolerance and accuracy, enabling tissue cartography at single-cell resolution.

  View details for DOI 10.1038/s41587-023-01697-9

  View details for PubMedID 36879008

  View details for PubMedCentralID 6132072