Abstract Liquid biopsy offers a minimally invasive means to interrogate tumor biology; however, the extreme rarity and phenotypic diversity of circulating tumor cells (CTCs) and related cellular events remain major obstacles to sensitive detection and meaningful analysis. Conventional workflows frequently rely on biophysical enrichment or predefined biomarker panels, both of which can bias cell recovery and constrain discovery. There is therefore a critical need for scalable computational approaches capable of analyzing millions of single-cell observations directly and extracting biological structure without dependence on prior labels. We developed deep learning–based pipelines to analyze nucleated cells isolated from peripheral blood. For each patient, approximately five million cells are obtained via buffy coat preparation, stained with a five-marker fluorescence panel, and imaged by whole-slide microscopy without any enrichment steps. The first pipeline is an unsupervised rare-event detector built on a denoising autoencoder. Applied to samples from 11 breast cancer patients, the method recovered 91 additional events—including CTCs, endothelial cells, cancer-associated fibroblasts (CAFs), and extracellular vesicles—representing a greater than 50% increase with minimal manual tuning. This form of label-free outlier detection is broadly generalizable to high-content imaging studies in which unbiased identification of infrequent or unexpected populations is essential. The second pipeline uses representation learning to derive stable single-cell embeddings. These embeddings support phenotype classification with 92.64% accuracy and also enable unsupervised clustering that reflects intrinsic variation in morphology and marker expression. Notably, the learned features are robust to imaging artifacts, ensuring consistent phenotyping across heterogeneous datasets. Collectively, these deep learning frameworks establish an integrated strategy for enrichment-free rare-event detection, clustering, and cell-type characterization in liquid biopsy, providing a scalable foundation for biomarker discovery Citation Format: Dean Tessone, Amin Naghdloo, Javier Murgoitio-Esandi, Jeremy Mason, Assad Oberai, James B. Hicks, Peter Kuhn. Scalable, unsupervised deep learning frameworks for rare event detection and single cell phenotyping in enrichment free liquid biopsies abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1434.
Tessone et al. (Fri,) studied this question.