J. Barinka, S. Graessle, M. Pajonk, R. Allesoee, S. Bamopoulos, M. Moennig, C. Roethemeier, L. Jopp-Saile, D. Vonficht, E. Besiridou, J. Ihlow, J. Braune, E. P. Vlachou, S. Beneyto-Calabuig, M. Kardorff, S. Neunhaeuser, V. Fregona, S. Feurstein, A. Halik, H. George, J. B. Zaugg, D. Huebschmann, M. Hundemer, C. Lutz, A. Trumpp, L. Bullinger, U. Keller, C. Mueller-Tidow, J. Westermann, F. Damm, J. Kroenke, T. Sauer, L. Velten, S. Raffel, S. Haas
Cancer is characterized by complex interactions across genetic, cellular, and microenvironmental scales. However, a quantitative understanding of how these interactions shape clinical trajectories remains limited. Here, we present a multi-scale single-cell dataset from 184 treatment-naive acute myeloid leukemia (AML) patients spanning all major genetic subtypes, together with an analytical framework to dissect interactions across biological scales. We show that distinct clinical outcomes are encoded by specific cross-scale, cross-compartment interactions present at diagnosis: response to induction therapy is governed by interactions between genetic alterations and leukemic differentiation state; relapse following chemotherapy is associated with non-genetic programs linked to metabolism; and relapse after allogeneic stem cell transplantation is driven by interactions between the immune microenvironment and residual healthy hematopoiesis. Together, our study provides a framework to resolve intra- and inter-patient heterogeneity in cancer and supports a model in which clinical trajectories in AML emerge from defined interactions across biological scales.