Histological variation is a prognostic feature of metastatic urothelial cancer1-3, but its evolutionary trajectory remains poorly defined. We developed a metastatic bladder cancer rapid autopsy programme enriched in histological subtypes4 to profile individuals with terminal disease. Here by reconstructing the evolutionary histories of patient tumours, we show that metastasis-to-metastasis seeding is the dominant pattern of cancer spread and that increased polyclonal migration predicts poor prognosis. The burden, heterogeneity and timing of genomic alterations differ markedly among histological subtypes. Plasmacytoid and neuroendocrine variants develop early driver alterations associated with shorter survival. Mutational signature analyses and experimental models demonstrated that plasmacytoid tumours uniquely use the Fanconi anaemia pathway to mitigate chemotherapy-induced genomic scarring. Single-nucleus profiling revealed mixed cell states in histological subtypes and an association between transcriptional heterogeneity and patient survival. Characterization of the tumour microenvironment uncovered distinct immune states across subtypes, with plasmacytoid tumours exhibiting immune-inflamed profiles, whereas squamous tumours are predominantly immunosuppressive. Last, we demonstrate that post-mortem cell-free DNA captures genomic and transcriptional heterogeneity of the subtypes, which provides a potential strategy for noninvasive assessment of tumour identity and aggressiveness. Our results provide new insights into how tumour heterogeneity shapes the evolutionary history of disease progression in bladder cancer histological subtypes.
P. Itagi, Samantha L. Schuster, Sonali Arora et al.· Nature· 0 citations
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.
M. Bootsma, M. Sharifi, J. Sperger et al.· Clinical Cancer Research· 0 citations
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