Longitudinal Phenotyping of Circulating Tumor Cells using a Scalable Deep Learning Framework

Abstract Purpose: Circulating tumor cells (CTCs) provide a minimally invasive window into metastatic disease and treatment response, but their clinical utility has been constrained by manual, subjective, low-throughput identification in multi-channel fluorescence microscopy data. Methods: To address this limitation, we developed and clinically validated the System for Enhanced Evaluation of Tumor Cells (SEE-TC), a deep learning approach for scalable, reproducible phenotyping of individual circulating cells. SEE-TC was developed and evaluated on more than 8.5 million cells from 3,386 blood samples spanning six cancer types, enabling generalization across heterogeneous imaging conditions, staining panels, and acquisition platforms. Results: SEE-TC achieved single-cell segmentation accuracy on par with humans, learned biologically relevant latent cellular representations that correlate with established morphological and immunofluorescent biomarkers, and reliably distinguished CTCs from background populations without reliance on arbitrary thresholds. When applied longitudinally, SEE-TC provides a quantitative, patient-level readout of CTC burden over time which was significantly associated with worse overall survival across multiple cancer types. Conclusions: To our knowledge, this is the first fully automated AI approach to single-cell segmentation and CTC phenotyping. By transforming CTC analysis from a human-dependent task into a scalable and reproducible digital assay, SEE-TC enables high-fidelity longitudinal monitoring of tumor burden and supports broader clinical deployment of CTC-based liquid biopsies in precision oncology. It is currently being deployed to identify CTCs and quantify target expression for both prognostic and predictive biomarker evaluation in multiple prospective clinical trials on a commercial platform.

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Publication Details

Journal
Clinical Cancer Research
Published
2026-08-18
DOI
https://doi.org/10.1158/1078-0432.ccr-26-0754
Primary Topic
Cancer Cells and Metastasis
Type
article
Field-Weighted Citation Impact
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article

Longitudinal Phenotyping of Circulating Tumor Cells using a Scalable Deep Learning Framework

David F. Jarrard, William A. Hall, Scott T. Tagawa, Joshua M. Lang et al.
Clinical Cancer Research
Cancer Cells and Metastasis
article

Longitudinal Phenotyping of Circulating Tumor Cells using a Scalable Deep Learning Framework

David F. Jarrard, William A. Hall, Scott T. Tagawa, Joshua M. Lang, Ticiana A.B. Leal, Hamza Bakhtiar, Hannah M. Krause, Hamid Emamekhoo, Petros D. Grivas, Alex H. Chang, Kari B. Wisinski, Xiao X. Wei, Vincent The-Luc Ma, Marina N. Sharifi, Pedro Coelho Barata, Nan Sethakorn, Jamie M. Sperger, Rana R. McKay, Christos E. Kyriakopoulos, Scott M. Dehm, Amy K. Taylor, Matthew L. Bootsma, Shuang George Zhao, Ruth O’Regan, Jennifer L. Schehr, Charlotte N. Stahlfeld, Emily Abella, V. Carreño, Cristina I. Truica, Muhammad Dar, Kaitlin Durnen, Matthew Mannino, Will Stump, David Gallo, Anthony Serritella
article en

Abstract

Abstract Purpose: Circulating tumor cells (CTCs) provide a minimally invasive window into metastatic disease and treatment response, but their clinical utility has been constrained by manual, subjective, low-throughput identification in multi-channel fluorescence microscopy data. Methods: To address this limitation, we developed and clinically validated the System for Enhanced Evaluation of Tumor Cells (SEE-TC), a deep learning approach for scalable, reproducible phenotyping of individual circulating cells. SEE-TC was developed and evaluated on more than 8.5 million cells from 3,386 blood samples spanning six cancer types, enabling generalization across heterogeneous imaging conditions, staining panels, and acquisition platforms. Results: SEE-TC achieved single-cell segmentation accuracy on par with humans, learned biologically relevant latent cellular representations that correlate with established morphological and immunofluorescent biomarkers, and reliably distinguished CTCs from background populations without reliance on arbitrary thresholds. When applied longitudinally, SEE-TC provides a quantitative, patient-level readout of CTC burden over time which was significantly associated with worse overall survival across multiple cancer types. Conclusions: To our knowledge, this is the first fully automated AI approach to single-cell segmentation and CTC phenotyping. By transforming CTC analysis from a human-dependent task into a scalable and reproducible digital assay, SEE-TC enables high-fidelity longitudinal monitoring of tumor burden and supports broader clinical deployment of CTC-based liquid biopsies in precision oncology. It is currently being deployed to identify CTCs and quantify target expression for both prognostic and predictive biomarker evaluation in multiple prospective clinical trials on a commercial platform.

Clinical Cancer Research
University of Minnesota (US), University of Wisconsin System (US), University of Wisconsin–Madison (US), University of Washington (US), Medical College of Wisconsin (US), Cornell University (US), University of Minnesota System (US), University of California San Diego (US), Madison Group (United States) (US), University of Wisconsin Carbone Cancer Center (US), Dana-Farber Cancer Institute (US), University Hospitals Seidman Cancer Center (US), University of Rochester (US), Penn State Milton S. Hershey Medical Center (US)
Openalex Percentile: Top 12%
Cancer Cells and Metastasis
DOISourceOpenAlex
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