Abstract B049: Deep Learning Enables Identification of Cell Types and Clusters (iCTC) in Immune Tumor Ecosystems for Prognostic Assessment in Cancer

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Clinical Cancer Research. 2025 Jul 10;31:B049-B049. doi: 10.1158/1557-3265.AIMACHINE-B049

Abstract B049: Deep Learning Enables Identification of Cell Types and Clusters (iCTC) in Immune Tumor Ecosystems for Prognostic Assessment in Cancer Stringer Lab

Squires JR, Sun Y, Hoffmann AD, Zhang Y, Minor AC, Singh A, Scholten D, Ding H, Mao C, Platanias LC, Luo Y, Fang D, Gradishar WJ, Cristofanilli M, Stringer C, Liu H

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Abstract

Spatial multiomic profiling has been transforming the understanding of local tumor ecosystems. Yet, the spatial analyses of tumor-immune interactions at systemic levels, such as in liquid biopsies, are challenging. Within the last 10 years, we have longitudinally collected nearly 3,000 patient blood samples for multiplexing imaging of circulating tumor cells (CTCs) and their interactions with white blood cells (WBCs). Multicellular CTC clusters exhibit enhanced metastatic potential. The detection of CTCs and characterization of tumor immune ecosystems are constrained by (1) low frequency of CTCs in blood samples; (2) specific lineages of immune cells are not recognized by limited channels of current imaging methods, (3) reliance on labor-intensive manual analysis slows down the discovery of biomarkers for predicting therapy response and survival in cancer patients. We hypothesize that an AI-powered platform will accelerate the lineage and spatial characterization of tumor immune ecosystems for prognostic evaluations.

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Abstract B049: Deep Learning Enables Identification of Cell Types and Clusters (iCTC) in Immune Tumor Ecosystems for Prognostic Assessment in Cancer

Abstract

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