Abstract 2420: Deep learning enables automated detection of circulating tumor cell-immune cell interactions with prognostic insights in cancer
Authors: Yuanfei Sun, Joshua R. Squires, Andrew Hoffmann, Youbin Zhang, Allegra Minor, Anmol Singh, David Scholten, Chengsheng Mao, Yuan Luo, Deyu Fang, William J. Gradishar, Massimo Cristofanilli, Carsen Stringer, Huiping Liu
Journal: Cancer Research (2025)

Abstract Background: Circulating tumor cells (CTCs) are critical biomarkers for predicting therapy response and survival in breast cancer patients. Multicellular CTC clusters exhibit enhanced metastatic potential, yet their detection and characterization are constrained by low frequency in blood samples and reliance on labor-intensive manual analysis. Advancing these methods could significantly improve prognostic evaluation and therapeutic strategies. Methods: Leveraging FDA-approved CellSearch technology and single-cell sequencing, we analyzed 2, 853 blood specimens, longitudinally collected from 1358 patients with advanced cancer (breast, prostate, etc) and other diseases. Integrating machine learning and deep learning tools, we developed a novel CTCpose platform to automate detection and analysis of CTCs, immune cells, and their interactions. Using artificial intelligence (AI)-driven image analysis, we extracted over 270 cellular and nuclear features including intensity, morphometry, fourier shape, gradient/edge, and haralick of cytokeratin, CD45, and DAPI expression patterns, enabling precise characterization of CTCs, white blood cells (WBCs), CTC clusters, and their interactions with immune cells (WBCs). Results: The CTCpose platform enabled automated identification of CTCs, WBCs, homotypic CTC clusters, heterogenous CTC-WBC clusters, and immune cell clusters, providing comprehensive insights into cell morphology, biomarker expression, and spatial organization. These features correlated with patient survival, disease progression, and treatment response. Our findings highlight the clinical significance of CTC-immune cell interactions and dynamic alterations of CTCs (singles and clusters) and underscore their potential in stratifying patients into distinct risk categories. Conclusions: This study demonstrates the transformative potential of deep learning in overcoming limitations of traditional CTC detection methods and integrating imaging data with large cohorts of patient data. By automating and enhancing the analysis of CTC-immune cell interactions, we present a robust framework for developing predictive models with direct clinical relevance. This work opens avenues for personalized treatment strategies, underscoring the impact of AI in advancing precision oncology. Citation Format: Yuanfei Sun, Joshua R. Squires, Andrew Hoffmann, Youbin Zhang, Allegra Minor, Anmol Singh, David Scholten, Chengsheng Mao, Yuan Luo, Deyu Fang, William J. Gradishar, Massimo Cristofanilli, Carsen Stringer, Huiping Liu. Deep learning enables automated detection of circulating tumor cell-immune cell interactions with prognostic insights in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2420.