ABSTRACT The cell phenotype is not a direct manifestation of the genotype but rather a product of cellular history and the environmental context. However, individual biomolecules cannot change independently and show coordinated behavior. To study this in acute myeloid leukemia (AML), we built a unique multi-omics biomolecular network made from proteins, metabolites and histone posttranslational modifications (hPTMs) sequentially extracted from each cell pellet. Edges between the nodes are measured directly using 400 LC-MSMS runs that cover 18 AML cell lines. We provide a novel conceptual framework to illustrate the different classes of functional entanglement between and within omics layers and present the data in three interactive data browsers to allow full community access. To help navigate the network, we approach it from the perspective of two biomolecular targets, i.e. CD34 and the epigenetic mark Histone H3 lysine 27 trimethylation (H3K27me3). Now, this easily accessible biomolecular network serves as a starting point for building and testing hypotheses and streamlining drug development, in the process positioning biomolecular associations center stage in understanding phenotypic complexity.

Background Weighted Gene Co-expression Network Analysis (WGCNA) is a widely adopted systems biology method to discover gene modules and module-trait associations, mostly from transcriptomics. Designed for a single layer, it cannot jointly analyze multi-omics layers, a consequential limitation in modern biomedical research. WGCNA modules are often hard to interpret, requiring vast follow-up for contextualization. Moreover, no integrated framework exists to visualize condition-specific, cross-omics relationships at module or feature level. Results To address these limitations, we developed WGCNA+, a novel R package extending WGCNA to multi-omics. WGCNA+ offers key innovations: (i) a unified multi-omics pipeline for per-layer network inference and cross-layer module enrichment; (ii) SVD-accelerated topological overlap matrix calculation that greatly reduces computation time; (iii) a consensus framework identifying modules reproducible across independent datasets/conditions; (iv) LASAGNA, a companion R package for phenotype-conditioned, multi-partite graph visualization of cross-omics relationships; (v) AI-powered annotation and infographics offering immediate biological insight. We tested WGCNA+ across public transcriptomics, proteomics, and miRNA datasets. WGCNA+ detects biologically meaningful modules, cross-omics feature and phenotype correlations, and provides AI-powered interpretation that accelerates research. Conclusions WGCNA+ addresses existing gaps with a principled, efficient framework for co-expression network analysis across omics. It detects cross-omics regulatory modules and their phenotype association to support basic research, biomarker discovery and pathway analysis. It uniquely offers AI-assisted interpretation and infographics, aiding hypothesis generation. Complementing WGCNA+, LASAGNA is a phenotype-aware multi-partite visualization framework to explore cross-omics relationships. Altogether, these features make WGCNA+ an innovative, powerful tool for clinical and translational research. Availability and implementation WGCNA+ and LASAGNA are implemented in R language for statistical computing, version≥ 3.5. WGCNA+ and LASAGNA are fully and freely available with no restrictions ( https://github.com/bigomics/WGCNAplus ; https://github.com/bigomics/lasagna ).

Regulatory factor X 7 (RFX7) nonsense mutations have been found in different human B cell malignancies. We therefore set out to study the role of RFX7 in B cell activation and lymphomagenesis. Here we show that RFX7 truncations cause loss-of-function and dominant-negative effects. Moreover, low RFX7 mRNA levels correlate with worse diffuse large B cell lymphoma prognosis. Accordingly, Rfx7 deletion in B cells accelerates pathogenesis in mouse Bcl6- and p53-loss-driven B cell lymphoma models. Rfx7-deficient B cells exhibit increased Myc activity and enhanced germinal center B cell and plasmablast responses. These alterations are reverted by Myc haploinsufficiency, which provides partial protection from nonsymptomatic p53-/-Rfx7-/- B cell lymphoma, but does not prevent detrimental Myc deregulation in aggressive disease. Deletion of Aicda, which favors genomic alterations in activated B cells, limits lymphoma development in the p53-/-Rfx7-/- double-hit mouse model. These results indicate that Rfx7 represses B cell activation, Myc activity, and Myc- and activation-induced cytidine deaminase (AID)-dependent pro-lymphomagenic processes.

Summary In recent years, computational methods have emerged that calculate enrichment of gene signatures within individual samples. These signatures offer critical insights into the coordinated activity of functionally related genes, proteins or metabolites, enabling the identification of unique molecular profiles in individual cells and patients. This strategy is pivotal for patient stratification and advancement of personalized medicine. However, the rise of large-scale datasets, including single-cell profiles and population biobanks, has exposed significant computational inefficiencies in existing methods. Current methods often demand excessive runtime and memory resources, becoming impractical for large datasets. Overcoming these limitations is a focus of current efforts by bioinformatics teams in academia and the pharmaceutical industry, as essential to support basic and clinical biomedical research. To address this critical need, we developed PLAID (Pathway Level Average Intensity Detection), an ultrafast and memory optimized single sample gene set enrichment algorithm that utilizes sparse matrix computation. PLAID delivers highly accurate gene set scoring and surpasses the performance of current methods in single-cell and bulk transcriptomics, and proteomics data. PLAID uniquely integrates the most widely used gene set scoring algorithms, enabling researchers to apply multiple methods for cross-validation with outstanding runtime efficiency and minimal memory requirement. Availability and implementation PLAID is implemented in the R language for statistical computing. PLAID source code and installation instructions are available with no restrictions at https://github.com/bigomics/plaid.

MOTIVATION:Batch effects (BEs) are a predominant source of noise in omics data and often mask real biological signals. BEs remain common in existing datasets. Current methods for BE correction mostly rely on specific assumptions or complex models, and may not detect and adjust BEs adequately, impacting downstream analysis and discovery power. To address these challenges we developed NPM, a nearest-neighbor matching-based method that adjusts BEs and may outperform other methods in a wide range of datasets. RESULTS:We assessed distinct metrics and graphical readouts, and compared our method to commonly used BE correction methods. NPM demonstrates the ability in correcting for BEs, while preserving biological differences. It may outperform other methods based on multiple metrics. Altogether, NPM proves to be a valuable BE correction approach to maximize discovery in biomedical research, with applicability in clinical research where latent BEs are often dominant. AVAILABILITY AND IMPLEMENTATION:NPM is freely available on GitHub (https://github.com/bigomics/NPM) and on Omics Playground (https://bigomics.ch/omics-playground). Computer codes for analyses are available at (https://github.com/bigomics/NPM). The datasets underlying this article are the following: GSE120099, GSE82177, GSE162760, GSE171343, GSE153380, GSE163214, GSE182440, GSE163857, GSE117970, GSE173078, and GSE10846. All these datasets are publicly available and can be freely accessed on the Gene Expression Omnibus repository.

Objectives Ankylosing spondylitis (AS) is a chronic inflammatory rheumatic disease affecting mainly the axial skeleton. Peripheral involvement (arthritis, enthesitis and dactylitis) and extra-musculoskeletal manifestations, including uveitis, psoriasis and bowel inflammation, occur in a relevant proportion of patients. AS is responsible for chronic and severe back pain caused by local inflammation that can lead to osteoproliferation and ultimately spinal fusion. The association of AS with the human leucocyte antigen-B27 gene, together with elevated levels of chemokines, CCL17 and CCL22, in the sera of patients with AS, led us to study the role of CCR4+ T cells in the disease pathogenesis.Methods CD8+CCR4+ T cells isolated from the blood of patients with AS (n=76) or healthy donors were analysed by multiparameter flow cytometry, and gene expression was evaluated by RNA sequencing. Patients with AS were stratified according to the therapeutic regimen and current disease score.Results CD8+CCR4+ T cells display a distinct effector phenotype and upregulate the inflammatory chemokine receptors CCR1, CCR5, CX3CR1 and L-selectin CD62L, indicating an altered migration ability. CD8+CCR4+ T cells expressing CX3CR1 present an enhanced cytotoxic profile, expressing both perforin and granzyme B. RNA-sequencing pathway analysis revealed that CD8+CCR4+ T cells from patients with active disease significantly upregulate genes promoting osteogenesis, a core process in AS pathogenesis.Conclusions Our results shed light on a new molecular mechanism by which T cells may selectively migrate to inflammatory loci, promote new bone formation and contribute to the pathological ossification process observed in AS.

Abstract CD37-directed antibody and cellular-based approaches have shown preclinical and promising early clinical activity. Naratuximab emtansine (Debio 1562; IMGN529) is an antibody-drug conjugate (ADC) incorporating an anti-CD37 monoclonal antibody conjugated to the maytansinoid DM1 as payload, with activity as a single agent and in combination with rituximab in patients with lymphoma. We studied naratuximab emtansine and its free payload in 54 lymphoma models, correlated its activity with CD37 expression, characterized two resistance mechanisms, and identified combination partners providing synergy. The activity, primarily cytotoxic, was more potent in B- than T-cell lymphoma cell lines. After prolonged exposure to the ADC, one diffuse large B-cell lymphoma (DLBCL) cell line developed resistance to the ADC due to the CD37 gene biallelic loss. After CD37 loss, we also observed upregulation of interleukin-6 (IL-6) and related transcripts. Recombinant IL-6 led to resistance. Anti-IL-6 antibody tocilizumab improved the ADC’s cytotoxic activity in CD37+ cells. In a second model, resistance was sustained by a PIK3CD activating mutation, with increased sensitivity to PI3Kδ inhibition and a functional dependence switch from MCL1 to BCL2. Adding idelalisib or venetoclax overcame resistance in the resistant derivative and improved cytotoxic activity in the parental cells. In conclusion, targeting B-cell lymphoma with the naratuximab emtansine showed vigorous antitumor activity as a single agent, which was also observed in models bearing genetic lesions associated with inferior outcomes, such as Myc Proto-Oncogene (MYC) translocations and TP53 inactivation or R-CHOP (rituximab, cyclophosphamide, doxorubicin, Oncovin [vincristine], and prednisone) resistance. Resistant DLBCL models identified active combinations of naratuximab emtansine with drugs targeting IL-6, PI3Kδ, and BCL2.

Supplementary Figure from Subcapsular Sinus Macrophages Promote Melanoma Metastasis to the Sentinel Lymph Nodes via an IL1α–STAT3 Axis

Supplemental Table S1: Cell lines and growth medium.

Phosphoprotein changes induced by PQR309 in B cell lymphoma cell lines revealed using Reverse Phase Protein Array (RPPA) analysis.

Supplementary figures, table legends, Table S1, S9 Figure S1. In vitro antitumor activity of the dual PI3K/mTOR inhibitor apitolisib and its correlation with PQR309. Figure S2. Antitumor In vivo activity of PQR309 in the treatment of RI-1 and SU-DHL-6 xenograft model. Figure S3. Apoptosis is induced by co-treatment of PQR309 with venetoclax or panobinostat in primary cells and in the DLBCL SU-DHL-6 cell line. Figure S4. Specific baseline gene expression signatures are associated with higher or lower sensitivity to PQR309. Figure S5. In vitro antitumor activity of the PI3Kï¤ inhibitor idelalisib and its correlation with PQR309. Figure S6. PQR309 induced decrease in phosphorylation of AKT-Ser473 and p70S6K-Thr389 in DLBCL cell lines. Figure S7. PQR309 reduces AKT-Ser473 phosphorylation in lymphoma cell lines. Figure S8. Transcriptional expression signature of ABC DLBCL cell lines induced by PQR309. Figure S9. ABC and GCB DLBCL PQR309-treated signatures were highly overlapping. Figure S10. Transcript expression levels of PQR309-treated samples. Figure S11. Changes in protein phosphorylation and RNA expression differently contribute to PQR309 affected biologic pathways in ABC DLBCL. Figure S12. PQR309 can largely regulate the same genes affected by the BTK inhibitor ibrutinib, the PI3Kï¤ idelalisib, the dual PI3Kï§/ï¤ inhibitor duvelisib (A), the dual PI3Kï¡/ï¤ inhibitor AZD8835 or the AKT inhibitor AZD5363 (B). Figure S13. BCR pathway signature is similarly affected after ibrutinib, idelalisib and duvelisib treatments in ABC DLBCL cell lines. Figure S14. The dual PI3K/mTOR inhibitor PQR309 and the PIM inhibitor AZD1208 synergize in DLBCL cell lines. Supplementary Table 1. List of phosphoresidues investigated by Carna Bioscience to perform RPPA analysis. Supplementary Table 9. Transcripts differentially expressed in ABC DLBCL cell lines treated with ibrutinib (A), idelalisib (B) or duvelisib (C) versus DMSO.

Supplemental Figures S6. Supplemental Figures S6: OTX015 effects on the production of IL-4 and IL-10 in DLBCL cell lines.

Baseline gene expression analysis of B cell lymphoma cell lines sensitive to PQR309 and idelalisib (dual sensitive) or to PQR309 only (discordant).

Supplemental Figures S1-2. Supplemental Figures S1: effects of OTX015 on cell cycle and cell growth in DLBCL cell lines. Supplemental Figures S2: effects of OTX015 on apoptosis in DLBCL cell lines.

Phosphoproteins changes induced by PQR309 in B cell lymphoma cell lines revealed using Pathscan Akt Signaling Antibody Array Kit.

Fabry disease is a rare disorder caused by variations in the alpha-galactosidase gene. To a degree, Fabry disease is manageable via enzyme replacement therapy (ERT). By understanding the molecular basis of Fabry nephropathy (FN) and ERT's long-term impact, here we aimed to provide a framework for selection of potential disease biomarkers and drug targets. We obtained biopsies from eight control individuals and two independent FN cohorts comprising 16 individuals taken prior to and after up to ten years of ERT, and performed RNAseq analysis. Combining pathway-centered analyses with network-science allowed computation of transcriptional landscapes from four nephron compartments and their integration with existing proteome and drug-target interactome data. Comparing these transcriptional landscapes revealed high inter-cohort heterogeneity. Kidney compartment transcriptional landscapes comprehensively reflected differences in FN cohort characteristics. With exception of a few aspects, in particular arteries, early ERT in patients with classical Fabry could lastingly revert FN gene expression patterns to closely match that of control individuals. Pathways nonetheless consistently altered in both FN cohorts pre-ERT were mostly in glomeruli and arteries and related to the same biological themes. While keratinization-related processes in glomeruli were sensitive to ERT, a majority of alterations, such as transporter activity and responses to stimuli, remained dysregulated or reemerged despite ERT. Inferring an ERT-resistant genetic module of expressed genes identified 69 drugs for potential repurposing matching the proteins encoded by 12 genes. Thus, we identified and cross-validated ERT-resistant gene product modules that, when leveraged with external data, allowed estimating their suitability as biomarkers to potentially track disease course or treatment efficacy and potential targets for adjunct pharmaceutical treatment.

PQR309 activity, combinations of PQR309 with additional drugs, BCL2, MYC and TP53 status in lymphoma cell lines.

Supplementary materials (figures and table legends)

Gene expression data after TK-216 treatment (8h) in U2932. A) Supervised analysis of transcriptome after treatment B) Gene-sets significantly enriched after treatment. C) IRF4/SPIB and lenalidomide related gene-sets significantly enriched after treatment.