Kroenke, J. | Search bioRxiv

bioRxiv2026

Multiscale biological interactions define clinical trajectories in acute myeloid leukemia

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.

cancer biology↗

bioRxiv2026

Translating multi-omics complexity into sparse prognostic biomarkers for multiple myeloma

Multiple myeloma (MM) exhibits profound molecular heterogeneity, yet current risk stratification relies on cytogenetics or single-omics signatures that often fail to capture cross-layer regulatory complexity. We re-analyzed a multi-omics dataset integrating copy-number, transcriptomic, proteomic, and phosphoproteomic data to dissect how common genomic driver alterations propagate through the molecular cascade. Supervised classification demonstrated that downstream layers, particularly the proteome and phosphoproteome, classify genomic events more accurately than primary genomic or transcriptomic data. Intriguingly, trans-acting features alone were sufficient for classification, indicating that while direct dosage effects manifest at the RNA level, downstream network responses dominate the proteomic state. Multi-omics factor analysis (MOFA2) identified a continuous latent axis predicting progression-free and overall survival independent of R-ISS. This factor captured a gain(1q)/del(13q) axis modulated by immune infiltration and NSD2 expression, integrating variance across all four modalities. To enable clinical translation, we derived sparse, single-modality proxies using elastic net regression. An RNA proxy faithfully recapitulated the multi-omic factor and validated independently in published microarray and RNAseq cohorts, demonstrating robust prognostic utility across treatment eras. These findings reveal that multi-omics integration uncovers hidden prognostic axes obscured by single-omics analyses, and that sparse proxies can bridge the gap between complex discovery and clinical implementation.

cancer biology↗