@article {69864,
	title = {Abstract 2420: Deep learning enables automated detection of circulating tumor cell-immune cell interactions with prognostic insights in cancer},
	journal = {Cancer Research},
	volume = {85},
	year = {2025},
	month = {2025 Apr 21},
	pages = {2420-2420},
	abstract = {<p>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.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).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.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.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.</p>
},
	issn = {0008-5472},
	doi = {10.1158/1538-7445.AM2025-2420},
	url = {https://doi.org/10.1158/1538-7445.AM2025-2420},
	author = {Sun, Yuanfei and Squires, Joshua R. and Hoffmann, Andrew and Zhang, Youbin and Minor, Allegra and Singh, Anmol and Scholten, David and Mao, Chengsheng and Luo, Yuan and Fang, Deyu and Gradishar, William J. and Cristofanilli, Massimo and Stringer, Carsen and Liu, Huiping}
}
@article {69974,
	title = {Analysis of deep learning for automated recognition of immune cells interacting with CTCs for prognostic assessment in cancer.},
	journal = {Journal of Clinical Oncology},
	volume = {43},
	year = {2025},
	month = {2025 May 28},
	pages = {e13028-e13028},
	abstract = {<p>e13028Background: Liquid biopsy has emerged as a powerful, minimally invasive tool for predicting treatment response and survival in breast and other advanced cancers. However, the detection and characterization of circulating tumor cells (CTCs) {\textemdash} a key factor in metastatic progression{\textemdash}remain challenging due to their low frequency and reliance on manual, time-intensive validation using only a couple of established methods for immunofluorescence staining, such as CellSearch. Harnessing deep learning for automated CTC detection and characterization of the blood cells interacting with CTCs holds the potential to advance prognostic evaluations and guide more effective therapies significantly. Methods: Leveraging FDA-approved CellSearch technology and sequencing approaches, we analyzed 2,853 blood specimens, longitudinally collected from 1358 patients with advanced cancer (breast, prostate, etc) and additional diseases. We built a novel deep learning platform, CTCpose, which integrates machine learning and AI-driven image analysis to automate the detection and categorization of CTCs, white blood cells (WBCs), and their clustering interactions. We extracted cellular and nuclear features to enable precise evaluation of individual CTCs, WBCs, homotypic CTC clusters, heterotypic CTC{\textendash}WBC clusters, and immune cell aggregates. Results: By employing the CTCpose platform, we achieved fully automated identification of CTCs and immune cells, unraveling the spatial organization and functional characteristics of both homotypic and heterotypic clusters. These highly granular assessments revealed clinically significant correlations with patient survival, disease progression, and therapeutic outcomes. Our data underscore the critical role of CTC{\textendash}immune cell interactions and the dynamic shifts in CTC phenotypes{\textemdash}both as single cells and clusters{\textemdash}in stratifying patients by risk and informing treatment strategies. Conclusions: This work illustrates the transformative power of deep learning in the analysis of liquid biopsy samples. By overcoming the limitations of traditional CTC detection, we have established a robust framework that integrates imaging data with large-scale patient cohorts to deliver predictive models of high clinical relevance. The CTCpose platform not only refines our understanding of CTC{\textendash}immune cell biology but also paves the way for personalized oncology approaches, highlighting the impactful convergence of artificial intelligence and precision medicine.</p>
},
	doi = {10.1200/JCO.2025.43.16_suppl.e13028},
	url = {https://ascopubs.org/doi/abs/10.1200/JCO.2025.43.16_suppl.e13028},
	author = {Liu, Huiping and Squires, Joshua and Sun, Yuanfei and Hoffmann, Andrew Daniel and Zhang, Youbin and Platanias, Leonidas C. and Gradishar, William John and Cristofanilli, Massimo and Stringer, Carsen}
}
