Ribosome dysfunction underlies the pathogenesis of many cancers and heritable ribosomopathies. Here, we investigate how mutations in either ribosomal protein large (RPL) or ribosomal protein small (RPS) subunit genes selectively affect erythroid progenitor development and clinical phenotypes in Diamond-Blackfan anemia (DBA), a rare ribosomopathy with limited therapeutic options. Using single-cell assays of patient-derived bone marrow, we delineated two distinct cellular trajectories segregating with ribosomal protein genotypes. Almost complete loss of erythroid specification was observed in RPS-DBA. In contrast, we observed relative preservation of qualitatively abnormal erythroid progenitors and precursors in RPL-DBA. Although both DBA genotypes exhibited a proinflammatory bone marrow milieu, RPS-DBA was characterized by erythroid differentiation arrest, whereas RPL-DBA was characterized by preserved GATA1 expression and activity. Compensatory stress erythropoiesis in RPL-DBA exhibited disordered differentiation underpinned by an altered glucocorticoid molecular signature, including reduced ZFP36L2 expression, leading to milder anemia and improved corticosteroid response. This integrative analysis approach identified distinct pathways of erythroid failure and defined genotype-phenotype correlations in DBA. These findings may help facilitate therapeutic target discovery.

Understanding the biological and clinical impact of copy number aberrations (CNA) for the development of precision therapies in cancer remains an unmet challenge. Genetic amplification of chromosome 1q (chr1q-amp) is a major CNA conferring adverse prognosis in several types of cancer, including in the blood cancer multiple myeloma (MM). Although several genes across chr1q portend high-risk MM disease, the underpinning molecular aetiology remains elusive. Here, with reference to the 3D chromatin structure, we integrate MM patient multi-omics datasets with genetic variables to obtain an associated clinical risk map across chr1q and to identify 103 adverse prognosis genes in chr1q-amp MM. Prominent amongst these genes, the transcription factor PBX1 is ectopically expressed by genetic amplification and epigenetic activation of its own preserved 3D regulatory domain. By binding to reprogrammed super-enhancers, PBX1 directly regulates critical oncogenic pathways and a FOXM1-dependent transcriptional programme. Together, PBX1 and FOXM1 activate a proliferative gene signature which predicts adverse prognosis across multiple types of cancer. Notably, pharmacological disruption of the PBX1-FOXM1 axis with existing agents (thiostrepton) and a novel PBX1 small-molecule inhibitor (T417) is selectively toxic against chr1q-amplified myeloma and solid tumour cells. Overall, our systems medicine approach successfully identifies CNA-driven oncogenic circuitries, links them to clinical phenotypes and proposes novel CNA-targeted therapy strategies in multiple myeloma and other types of cancer.

Gene regulatory networks (GRNs) are key determinants of cell function and identity and are dynamically rewired during development and disease. Despite decades of advancement, challenges remain in GRN inference: dynamic rewiring, causal inference, feedback-loop modeling, and context specificity. To address them, we develop Dictys, a dynamic GRN inference and analysis method which leverages multi-omic single-cell assays of chromatin accessibility and gene expression, context specific transcription factor (TF) footprinting, stochastic process network, and efficient probabilistic modeling of scRNA-seq read counts. Dictys improves GRN reconstruction accuracy and reproducibility and enables the inference and comparative analysis of context specific and dynamic GRNs across developmental contexts. Dictys' network analyses recover unique insights in human blood and mouse skin development with cell-type specific and dynamic GRNs. Its dynamic network visualizations enable time-resolved discovery and investigation of developmental driver TFs and their regulated targets. Dictys is available as a free, open source, and user-friendly Python package.

Multiple myeloma is a genetically heterogeneous cancer of the bone marrow plasma cells (PC). Distinct myeloma transcriptome profiles are primarily driven by myeloma initiating events (MIE) and converge into a mutually exclusive overexpression of the CCND1 and CCND2 oncogenes. Here, with reference to their normal counterparts, we find that myeloma PC enhanced chromatin accessibility combined with paired transcriptome profiling can classify MIE-defined genetic subgroups. Across and within different MM genetic subgroups, we ascribe regulation of genes and pathways critical for myeloma biology to unique or shared, developmentally activated or de novo formed candidate enhancers. Such enhancers co-opt recruitment of existing transcription factors, which although not transcriptionally deregulated per se, organise aberrant gene regulatory networks that help identify myeloma cell dependencies with prognostic impact. Finally, we identify and validate the critical super-enhancer that regulates ectopic expression of CCND2 in a subset of patients with MM and in chronic lymphocytic leukemia.