Integrating Spatially Adjusted Protein Summaries for Survival Prediction in Spatial Proteomics
- 1Icahn School of Medicine at Mount Sinai
Abstract
Recent advances in spatial proteomics, particularly imaging mass cytometry, enable the measurement of protein expression at the single-cell level while preserving a spatial context. Conventional survival analyses, however, typically rely on patient-level averages of protein intensities and therefore overlook spatial heterogeneity and tissue architecture. To address this limitation, we introduce a framework that incorporates spatial information into survival modeling by generating spatially adjusted protein summaries (SAPS). In this approach, cell-level protein intensities within each patient are modeled using spatial spline regression to capture spatial trends. From these models, we extract two complementary features: a spatially adjusted mean expression and a residual variance that reflects cell-to-cell variability unexplained by spatial effects. These summaries are then incorporated into Cox proportional hazards models in combination with clinical covariates. We further show that our estimator is asymptotically equivalent to an oracle estimator under mild regularity conditions. In simulation studies, our proposed framework achieved improved predictive performance compared to other alternative methods. The application of the method to breast cancer imaging mass cytometry data indicate that spatially adjusted summaries may enhance survival prediction and reveal biologically interpretable spatial protein patterns, suggesting high translational potential. This methodology offers an efficient means of translating complex spatial proteomics data into patient-level features, providing both improved survival prediction and new insights into the role of spatial heterogeneity in cancer outcomes. R package is available on the Comprehensive R Archive Network repository at https://cran.r-project.org/web/packages/SurvSPro/index.html
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BibTeX
@misc{ahn2026integrating,
title = {{Integrating Spatially Adjusted Protein Summaries for Survival Prediction in Spatial Proteomics}},
author = {S. Ahn and E. J. Oh and D. Prada and A. Shojaie},
howpublished = {bioRxiv},
year = {2026},
month = jun,
doi = {10.64898/2026.06.08.730964},
url = {https://www.biorxiv.org/content/10.64898/2026.06.08.730964v2},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Integrating Spatially Adjusted Protein Summaries for Survival Prediction in Spatial Proteomics
AU - S. Ahn
AU - E. J. Oh
AU - D. Prada
AU - A. Shojaie
PY - 2026
DA - 2026/06/08
PB - bioRxiv
DO - 10.64898/2026.06.08.730964
UR - https://www.biorxiv.org/content/10.64898/2026.06.08.730964v2
AB - Recent advances in spatial proteomics, particularly imaging mass cytometry, enable the measurement of protein expression at the single-cell level while preserving a spatial context. Conventional survival analyses, however, typically rely on patient-level averages of protein intensities and therefore overlook spatial heterogeneity and tissue architecture. To address this limitation, we introduce a framework that incorporates spatial information into survival modeling by generating spatially adjusted protein summaries (SAPS). In this approach, cell-level protein intensities within each patient are modeled using spatial spline regression to capture spatial trends. From these models, we extract two complementary features: a spatially adjusted mean expression and a residual variance that reflects cell-to-cell variability unexplained by spatial effects. These summaries are then incorporated into Cox proportional hazards models in combination with clinical covariates. We further show that our estimator is asymptotically equivalent to an oracle estimator under mild regularity conditions. In simulation studies, our proposed framework achieved improved predictive performance compared to other alternative methods. The application of the method to breast cancer imaging mass cytometry data indicate that spatially adjusted summaries may enhance survival prediction and reveal biologically interpretable spatial protein patterns, suggesting high translational potential. This methodology offers an efficient means of translating complex spatial proteomics data into patient-level features, providing both improved survival prediction and new insights into the role of spatial heterogeneity in cancer outcomes. R package is available on the Comprehensive R Archive Network repository at https://cran.r-project.org/web/packages/SurvSPro/index.html
ER -