Alex H. Chang

All supplementary Figures and tables.

Purpose: Circulating tumor cells (CTC) 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 multichannel fluorescence microscopy data. Experimental Design: 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 morphologic 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 artificial intelligence 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.

Table S1. Clinical characteristics of the 177LuPSMA-treated cohort

Figure S10. Sample cDNA concentration and tumor fraction by CTC phenotype in the survival analysis subset.

Figure S1. Calibration of ESTIMATE tumor content prediction.

Figure S5. Tumor fraction and immune content across CTC phenotypes.

Figure S3. Reproducibility of tumor/immune content assessment, gene expression and pathway scores.

Figure S13. CTC FOLH1 expression and transcriptional phenotype and PSMA-PET characteristics.

Figure S8. MKI67 expression in each CTC phenotype cluster for all samples.

Figure S16. Cell surface target expression across CTC phenotype clusters for all samples.