1092 Artificial intelligence framework to identify spatial patterns of cancer immunotherapy outcomes from multiplexed proteomics imaging
Year: 2025
Type: conference-abstract
Abstract: Background Immunotherapy has led to tremendous advancements in cancer treatment. However, about two-thirds of patients with solid tumors do not respond to current immunotherapies, such as immune-checkpoint blockade or cellular therapies. The recent progress in multiplexed protein imaging has advanced our ability to identify topological structures in tissue microenvironments associated with distinct clinical characteristics. A significant challenge with high-dimensional imaging is how to systematically infer spatial features underlying different patient groups, like immunotherapy response vs resistance groups.Methods We develop an artificial intelligence framework for identifying differential protein patterns between distinct groups from multiplexed proteomics images. Our image region-based framework does not require any prior manual/semi-automatic steps like cell segmentation and cell type annotation as required in existing spatial data analysis workflows and therefore can capture essential features not defined by humans. The framework is also suitable for use with a low number of labeled samples, as is the case for early-phase exploratory spatial studies.Results We applied the framework to identify differential protein patterns between different outcome or treatment groups in humans and mice. On a pre-treatment multiplexed imaging dataset of triple-negative breast cancer patients treated with anti-PD-L1 therapy, 1 the framework shows that a higher relative abundance of a signature region enriched in Ki-67, TCF7, CD8, CD45, GATA3, MHCI, and PD-L1 is significantly associated with patient pathological complete response.Conclusions We expect that our proposed AI framework will be a useful tool for generating novel hypotheses regarding biomarkers or regulators of cancer immunotherapy outcomes.Reference Wang XQ, Danenberg E, Huang CS, Egle D, Callari M, Bermejo B, Dugo M, Zamagni C, Thill M, Anton A, Zambelli S, Russo S, Ciruelos EM, Greil R, Győrffy B, Semiglazov V, Colleoni M, Kelly CM, Mariani G, Del Mastro L, Biasi O, Seitz RS, Valagussa P, Viale G, Gianni L, Bianchini G, Ali HR. Spatial predictors of immunotherapy response in triple-negative breast cancer. Nature. 2023;621:868–876.
Authors: Gourab Ghosh Roy, Peng Jiang
Institutions: National Institutes of Health, National Cancer Institute
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4416075486
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