Identifying high-risk multiple myeloma patients: A novel approach using a clonal gene signature.

Multiple myeloma (MM) is a heterogeneous disease with a small subset of high-risk patients having poor prognoses. Identifying these patients is crucial for treatment management and strategic decisions. In this study, we developed a novel computational framework to define prognostic gene signatures by selecting genes with expression driven by clonal copy number alterations. We applied this framework to MM and developed a clonal gene signature (CGS) consisting of 22 genes and evaluated in five independent datasets. The CGS provided significant prognostic values after adjusting for well-established factors including cytogenetic abnormalities, International Staging System (ISS), and Revised ISS (R-ISS). Importantly, CGS demonstrated higher performance in identifying high-risk patients compared to the GEP70 and SKY92 signatures recommended for prognostic stratification of MM. CGS can further stratify patients into subgroups with significantly differential prognoses when applied to the high- and low-risk groups identified by GEP70 and SKY92. Additionally, CGS scores are significantly associated with patient response to dexamethasone, a commonly used treatment for MM. In summary, we proposed a computational framework that requires only gene expression data to identify CGSs for prognosis prediction. CGS provides a useful biomarker for improving prognostic stratification in MM, especially for identifying the highest-risk patients.

nSEA: n-Node Subnetwork Enumeration Algorithm Identifies Lower Grade Glioma Subtypes with Altered Subnetworks and Distinct Prognostics.

Advances in molecular characterization have reshaped our understanding of low-grade glioma (LGG) subtypes, emphasizing the need for comprehensive classification beyond histology. Lever-aging this, we present a novel approach, network-based Subnetwork Enumeration, and Analysis (nSEA), to identify distinct LGG patient groups based on dysregulated molecular pathways. Using gene expression profiles from 516 patients and a protein-protein interaction network we generated 25 million sub-networks. Through our unsupervised bottom-up approach, we selected 92 subnetworks that categorized LGG patients into five groups. Notably, a new LGG patient group with a lack of mutations in EGFR, NF1, and PTEN emerged as a previously unidentified patient subgroup with unique clinical features and subnetwork states. Validation of the patient groups on an independent dataset demonstrated the robustness of our approach and revealed consistent survival traits across different patient populations. This study offers a comprehensive molecular classification of LGG, providing insights beyond traditional genetic markers. By integrating network analysis with patient clustering, we unveil a previously overlooked patient subgroup with potential implications for prognosis and treatment strategies. Our approach sheds light on the synergistic nature of driver genes and highlights the biological relevance of the identified subnetworks. With broad implications for glioma research, our findings pave the way for further investigations into the mechanistic underpinnings of LGG subtypes and their clinical relevance.Availability: Source code and supplementary data are available at https://github.com/bebeklab/nSEA.

SWI/SNF Blockade Disrupts PU.1-Directed Enhancer Programs in Normal Hematopoietic Cells and Acute Myeloid Leukemia.

In acute myeloid leukemia (AML), SWI/SNF chromatin remodeling complexes sustain leukemic identity by driving high levels of MYC. Previous studies have implicated the hematopoietic transcription factor PU.1 (SPI1) as an important target of SWI/SNF inhibition, but PU.1 is widely regarded to have pioneer-like activity. As a result, many questions have remained regarding the interplay between PU.1 and SWI/SNF in AML as well as normal hematopoiesis. Here we found that PU.1 binds to most of its targets in a SWI/SNF-independent manner and recruits SWI/SNF to promote accessibility for other AML core regulatory factors, including RUNX1, LMO2, and MEIS1. SWI/SNF inhibition in AML cells reduced DNA accessibility and binding of these factors at PU.1 sites and redistributed PU.1 to promoters. Analysis of nontumor hematopoietic cells revealed that similar effects also impair PU.1-dependent B-cell and monocyte populations. Nevertheless, SWI/SNF inhibition induced profound therapeutic response in an immunocompetent AML mouse model as well as in primary human AML samples. In vivo, SWI/SNF inhibition promoted leukemic differentiation and reduced the leukemic stem cell burden in bone marrow but also induced leukopenia. These results reveal a variable therapeutic window for SWI/SNF blockade in AML and highlight important off-tumor effects of such therapies in immunocompetent settings. SIGNIFICANCE: Disruption of PU.1-directed enhancer programs upon SWI/SNF inhibition causes differentiation of AML cells and induces leukopenia of PU.1-dependent B cells and monocytes, revealing the on- and off-tumor effects of SWI/SNF blockade.