Supplementary Materials1. pre-B-cell receptor signaling, to be associated with relapse. This model, termed Developmentally Dependent Predictor of Relapse (DDPR), significantly improves currently established risk stratification methods. DDPR features exist at diagnosis and persist at relapse. Leveraging a data-driven approach, we demonstrate the predictive value of single-cell omics for patient stratification in a translational setting and GDC-0973 (Cobimetinib) provide a framework for application in human cancers. Introduction Despite high rates of initial response to frontline treatment, cancer mortality largely results from relapse or metastasis. Although there is debate as to whether resistant cancer cells are present at the time of initial diagnosis or whether they emerge GDC-0973 (Cobimetinib) under the pressure of therapy, many studies have suggested that it is the former1C4. Such cells can be rare and are not accurately represented in animal models or patient-derived xenografts5,6. Hence, the identification and study of the cellular species underlying cancer persistence will require high-throughput single-cell analyses of primary human tissues and new analytical tools to align these rare populations with clinical outcomes. B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is a common childhood malignancy. Despite dramatic improvements in survival using current treatment regimens, relapse is the most frequent cause of cancer-related death among children GDC-0973 (Cobimetinib) with BCP-ALL7. BCP-ALL is characterized by the clonal proliferation of blast cells in the bone marrow and/or peripheral blood that bear the hallmarks of immature B cells. Known molecular alterations stall the development of B lymphocytes (B lymphopoiesis) in BCP-ALL8C12. Healthy B lymphopoiesis occurs through sequential developmental stages marked by losses and appearances of surface proteins, intracellular mediators of DNA rearrangement, and activation of signaling pathways that regulate decisions of cell fate13,14. We previously applied single-cell cytometry by time-of-flight (CyTOF; mass cytometry) to align developing B cells into a unified trajectory, which enabled us to better define human pre-pro-B, pro-B, and pre-B cells and their regulatory signaling during early developmental checkpoints14. Currently, for children with BCP-ALL, risk prediction strategies integrate clinical, genetic, and treatment response features gathered during the first months of treatment15. As in most risk-prediction scenarios, prediction is imperfect. We reasoned that performing deep phenotypic single-cell studies of diagnostic leukemic samples could identify cell populations predictive of relapse and discover novel aspects of resistance to treatment in this disease. Building on our study of normal early B lymphopoiesis, we performed a mass cytometry analysis of primary diagnostic BCP-ALL samples. Aligning individual BCP-ALL cells with developmental states along the normal B-cell trajectory demonstrated expansion across the pre-pro-B to pre-BI transition. Applying machine learning to proteomic features extracted from these expanded cell populations, we constructed a predictive model of relapse that was validated in an independent patient cohort. This model exposed six cellular features that implicated a developmental phenotype and behavioral identity of two cell populations in portending relapse. Analysis of matched diagnosis-relapse pairs confirmed the persistence of these predictive features at relapse. Therefore, BCP-ALL samples viewed through a lens of high-resolution developmental maturity indicated that a unique and reproduced cellular behavior across individuals is a main driver of relapse. Results Deep phenotyping reveals developmental heterogeneity in BCP-ALL To understand the degree to which child years BCP-ALL mimics the differentiation of its cells of source, we profiled 60 main diagnostic bone marrow aspirates with varied medical genetics by single-cell mass cytometry in comparison to normal bone marrow from Nafarelin Acetate five healthy donors (Fig. 1a and Supplementary Furniture 1C3). Examining manifestation of proteins regularly used in diagnostic circulation cytometry on leukemic blasts exposed expected patterns of manifestation, with overexpression of CD10 and CD34 as compared to healthy bone marrow (Fig. 1b). To visualize similarity to normal developing B cells, we compared BCP-ALL cells to their healthy bone marrow counterparts using principal component GDC-0973 (Cobimetinib) analysis (PCA) (Fig. 1c and Supplementary Fig. 1). Healthy developing B cells occupied a remarkably clear path with this representation space (Fig. 1c, remaining). Once projected into the same space, BCP-ALL cells from individual patients fell into areas with similarity to healthy populations, with a heavy skewing towards early stages of B lymphopoiesis (Fig. 1c, right), as expected8. We therefore reasoned that aligning individual leukemic cells to their GDC-0973 (Cobimetinib) closest developmental state would enable us to view each BCP-ALL sample as a set of aberrant developing B-cell populations, potentially uncovering novel aspects of BCP-ALL biology. Open in a separate window Number 1 Mass cytometry analysis of BCP-ALL reveals phenotypic heterogeneity of leukemic cells(a) Summary of main BCP-ALL sample processing for mass cytometry analysis (observe Supplementary Furniture 1C3 for patient information, antibody panel, and perturbation conditions, respectively). 60 main BCP-ALL samples and 5 healthy control bone marrow aspirates were included. Prognostic cytogenetic translocations recognized at analysis, as.