Biology subjects

Kwee, I.

Publications and source records attributed to Kwee, I..

9 recordsLinked to original sources

bioRxiv2026

Focus on the edges: a biomolecular network of histone PTMs, metabolites and proteins unveils functional entanglement in AML

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.

cancer biology↗

bioRxiv2025

PLAID: ultrafast single-sample gene set enrichment scoring

MotivationIn recent years, computational methods have emerged that calculate enrichment of gene signatures within individual samples, rather than across pooled samples. These signatures offer critical insights into the coordinated activity of functionally related genes, proteins or metabolites, enabling identification of unique molecular profiles based on gene set and pathway activity 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. ResultsTo 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. A distinctive feature of PLAID is its integration of 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 ImplementationPLAID 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

bioinformatics↗

bioRxiv2024

NPmatch: Latent Batch Effects Correction of Omics data by Nearest-Pair Matching

MotivationBatch 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 NPmatch, a nearest-neighbor matching-based method that adjusts BEs satisfactorily and outperforms current methods in a wide range of datasets. ResultsWe assessed distinct metrics and graphical readouts, and compared our method to commonly used BE correction methods. NPmatch demonstrates overall superior performance in correcting for BEs while preserving biological differences than existing methods. Altogether, our method 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. Data availability and implementationNPmatch is freely available on Github (https://github.com/bigomics/NPmatch) and on Omics Playground (https://bigomics.ch/omics-playground). The datasets underlying this article are the following: GSE120099, GSE82177, GSE162760, GSE171343, GSE153380, GSE163214, GSE182440, GSE163857, GSE117970, GSE173078, GSE10846. All these datasets are publicly available and can be freely accessed on the Gene Expression Omnibus (GEO) repository.

bioinformatics↗

bioRxiv2023

PI3Kδ activation, IL6 over-expression, and CD37 loss cause resistance to the targeting of CD37-positive lymphomas with the antibody-drug conjugate naratuximab emtansine

PurposeThe transmembrane protein CD37 is expressed almost exclusively in lymphoid tissues, with the highest abundance in mature B cells. CD37-directed antibody- and, more recently, cellular-based approaches have shown preclinical and promising early clinical activity. Naratuximab emtansine (Debio 1562, IMGN529) is an antibodydrug conjugate (ADC) that incorporates an anti-CD37 monoclonal antibody conjugated to the maytansinoid DM1 as payload. Naratuximab emtansine has shown activity as a single agent and in combination with the anti-CD20 monoclonal antibody rituximab in B cell lymphoma patients. Experimental DesignWe assessed the activity of naratuximab emtansine using in vitro models of lymphomas, correlated its activity with CD37 expression levels, characterized two resistance mechanisms to the ADC, and identified combination partners providing synergy. ResultsThe anti-tumor activity of naratuximab emtansine was tested in 54 lymphoma cell lines alongside its free payload. The median IC50 of naratuximab emtansine was 780 pM, and the activity, primarily cytotoxic, was more potent in B than in T cell lymphoma cell lines. In the subgroup of cell lines derived from B cell lymphoma, there was some correlation between sensitivity to DM1 and sensitivity to naratuximab emtansine (r=0.28, P = 0.06). After prolonged exposure to the ADC, one diffuse large B cell lymphoma (DLBCL) cell line developed resistance to the ADC due to the biallelic loss of the CD37 gene. After CD37 loss, we also observed upregulation of IL6 (IL-6) and other transcripts from MYD88/IL6-signaling. Recombinant IL6 led to resistance to naratuximab emtansine, while the anti-IL6 antibody tocilizumab improved the cytotoxic activity of the ADC in CD37-positive cells. In a second model, resistance was sustained by an activating mutation in the PIK3CD gene, associated with increased sensitivity to PI3K{delta} inhibition and a switch from functional dependence on the anti-apoptotic protein MCL1 to reliance on BCL2. The addition of idelalisib or venetoclax to naratuximab emtansine overcame resistance to the ADC in the resistant derivative while also improving the cytotoxic activity of the ADC in the parental cells. ConclusionsTargeting B cell lymphoma with the CD37 targeting ADC naratuximab emtansine showed vigorous anti-tumor activity as a single agent, which was also observed in models bearing genetic lesions associated with inferior outcomes, such as MYC translocations and TP53 inactivation or resistance to R-CHOP. Resistance DLBCL models identified active combinations of naratuximab emtansine with drugs targeting IL6, PI3K{delta}, and BCL2. Despite notable progress in recent decades, we still face challenges in achieving a cure for a substantial number of lymphoma patients (1,2). A pertinent example is diffuse large B cell lymphoma (DLBCL), the most prevalent type of lymphoma (3). More than half of DLBCL patients can achieve remission, but around 40% of them experience refractory disease or relapse following an initial positive response (3). Regrettably, the prognosis for many of these cases remains unsatisfactory despite introducing the most recent antibody-based or cellular therapies (3,4), underscoring the importance of innovating new therapeutic strategies and gaining insights into the mechanisms of therapy resistance. CD37 is a transmembrane glycoprotein belonging to the tetraspanin family, primarily expressed on the surface of immune cells, principally in mature B cells but also, at lower levels, in T cells, macrophages/monocytes, granulocytes and dendritic cells (5) (6-8). CD37 plays a crucial role in various immune functions, including B cell activation, proliferation, and signaling, although its precise role still needs to be fully elucidated. CD37 interacts with multiple molecules, including SYK, LYN, CD19, CD22, PI3K{delta}, PI3K{gamma}, and different integrins, among others (6-8). In mice, the lack of CD37 is paired with reduced T cell-dependent antibody-secreting cells and memory B cells, apparently due to the loss of CD37-mediated clustering of 4{beta}1 integrins (VLA-4) on germinal center B cells and decreased downstream activation of PI3K/AKT signaling and cell survival (5). Reflecting the expression pattern observed in normal lymphocytes, CD37 exhibits elevated expression in all mature B-cell lymphoid neoplasms, including most lymphoma subtypes, and absence in early progenitor cells or terminally differentiated plasma cells (6,8-14). In DLBCL, CD37 expression has been reported between 40% and 90% of cases across multiple studies performed using different antibodies (10,14-16). CD37-directed antibody- and, more recently, cellular-based approaches have shown preclinical (7,10-14,17-23) and early promising clinical activity (24-32). Among the CD37-targeting agents, naratuximab emtansine (Debio 1562, IMGN529) is an antibody-drug conjugate (ADC) that incorporates the anti-CD37 humanized IgG1 monoclonal antibody K7153A conjugated to the maytansinoid DM1, as payload, via the thioether linker, N-succinimidyl-4-(N-maleimidomethyl)cyclohexane-1-carboxylate (SMCC) (10). Based on the initial in vitro and in vivo evidence of anti-tumor activity in lymphoma and chronic lymphocytic leukemia (CLL) (7,10), naratuximab emtansine entered the clinical evaluation as a single agent. The phase 1 study exploring naratuximab emtansine enrolled 39 patients with relapsed/refractory B cell lymphoma (27). The overall response rate (ORR) was 13% across all patients and 22% in DLBCL patients, including the only observed complete remission (CR) (27). In preliminary results of a phase 2 trial exploring the combination of naratuximab emtansine with the anti-CD20 monoclonal antibody rituximab (18), based on positive preclinical data (18), the ORR was 45% in 76 patients with DLBCL with 24 CRs (32%), 57% in 14 patients with follicular lymphoma (five CR), 50% in four MCL patients (2 CR) (31). Here, we studied the pattern of activity of naratuximab emtansine across a large panel of cell lines derived from DLBCL and other lymphoma subtypes and characterized two resistance mechanisms to the ADC.

cancer biology↗

bioRxiv2022

Myc controls IL-15-driven expansion and translational machinery of NK cells

Myc is a pleiotropic transcription factor involved in cancer, cell proliferation, and metabolism. Its regulation and function in Natural Killer (NK) cells, which are innate cytotoxic lymphocytes important to control viral infections and cancer, remain poorly defined. Here we show that mice deficient for Myc in NK cells presented a severe reduction in these lymphocytes. Myc was required for NK cell development and expansion in response to the key cytokine interleukin (IL)-15, which induced Myc through transcriptional and posttranslational mechanisms. Mechanistically, Myc ablation in vivo largely impacted NK cells ribosomagenesis, reducing their translation and expansion capacities. Similar results were obtained by inhibiting MYC in human NK cells. Impairing translation by pharmacological intervention phenocopied the consequences of deleting or blocking MYC in vitro. Notably, mice lacking Myc in NK cells exhibited defective anticancer immunity, which reflected their decreased numbers of mature NK cells exerting suboptimal cytotoxic functions. These results indicate that MYC is a central node in NK cells, connecting IL-15 to translational fitness, expansion, and anticancer immunity.

immunology↗

bioRxiv2021

IL-1α secreted by subcapsular sinus macrophages promotes melanoma metastasis in the sentinel lymph node by upregulating STAT3 signaling in the tumor.

During melanoma metastasization, tumor cells originated in the skin migrate via lymphatic vessels to the sentinel lymph node (sLN) in a process that facilitates their spread across the body. Here, we characterized the innate inflammatory responses to melanoma metastasis in the sLN. For this purpose, we confirmed the migration of fluorescent metastatic melanoma cells to the sLN and we characterized the inflammatory response in the metastatic microenvironment. We found that macrophages located in the subcapsular sinus (SSM), produce pro-tumoral IL-1 after recognition of tumor antigens. Moreover, we confirmed that the administration of an anti-IL-1 depleting antibody reduced metastasis. Conversely, the administration of recombinant IL-1 accelerated the lymphatic spreading of the tumor. Additionally, the elimination of the macrophages significantly reduced the progression of the metastatic spread. To understand the mechanism of action of IL-1 in the context of the lymph node microenvironment, we applied single-cell RNA sequencing to dissected metastases obtained from animals treated with an anti-IL-1 blocking antibody. Amongst the different pathways affected, we identified STAT3 as one of the main targets of IL-1 signaling in metastatic cells. Moreover, we found that the anti-IL-1 anti-tumoral effect was not mediated by lymphocytes, as IL-1R1 KO mice did not show any improvement in metastasis growth. Finally, we found a synergistic anti-metastatic effect of the combination of IL-1 blocking and the STAT3 inhibitor (STAT3i) stattic. In summary, we described a new mechanism by which SSM support melanoma metastasis, highlighting a new target for immunotherapy.

immunology↗

bioRxiv2020

Reanalysis of MERS, SARS and COVID-19 Infection Datasets using VirOmics Playground Reveals Common Patterns in Gene and Protein Expression

Three lethal lower respiratory tract coronavirus epidemics have occurred over the past 20 years. This coincided with major developments in genome-wide gene and protein expression analysis, resulting in a wealth of datasets in the public domain. Seven such in vitro studies were selected for comparative bioinformatic analysis through the VirOmics Playground, a visualisation and exploration platform we developed. Despite the heterogeneous nature of the data sets, several commonalities could be observed across studies and species. Differences, on the other hand, reflected not only variations between species, but also other experimental variables, such as cell lines used for the experiments, infection protocols and viral strains. The datasets analysed here are available online through our platform (https://public.bigomics.ch/app/omicsplayground_viromics).

genomics↗

bioRxiv2018

A prize-collecting Steiner tree application for signature selection to stratify diffuse large B-cell lymphoma subtypes

BackgroundWith the explosion of high-throughput data available in biology, the bottleneck is shifted to effective data interpretation. By taking advantage of the available data, it is possible to identify the biomarkers and signatures to distinguish subtypes of a specific cancer in the context of clinical trials. This requires sophisticated methods to retrieve the information out of the data, and various algorithms have been recently devised.\n\nResultsHere, we applied the prize-collecting Steiner tree (PCST) approach to obtain a gene expression signature for the classification of diffuse large B-cell lymphoma (DLBCL). The PCST is a network-based approach to capture new insights about genomic data by incorporating an interaction network landscape. Moreover, we adopted the ElasticNet incorporating PCA as a classification method. We used seven public gene expression profiling datasets (three for training, and four for testing) available in the literature, and obtained 10 genes as signature. We tested these genes by employing ElasticNet, and compared the performance with the DAC algorithm as current golden standard. The performance of the PCST signature with ElasticNet outperformed the DAC in distinguishing the subtypes. In addition, the gene expression signature was able to accurately stratify DLBCL patients on survival data.\n\nConclusionsWe developed a network-based optimization technique that performs unbiased signature selection by integrating genomic data with biological networks. Our classifier trained with the obtained signature outperformed the state-of-the-art method in subtype distinction and survival data stratification in DLBCL. The proposed method is a general approach that can be applied on other classification problems.

bioinformatics↗

bioRxiv2017

OmicsNet: Integration of Multi-Omics Data using Path Analysis in Multilayer Networks

Integrative analysis of heterogeneous omics data is essential to obtain a comprehensive overview of otherwise fragmented information and to better understand dysregulated biological pathways leading to a specific condition. One of the major challenges in systems biology is to develop computational methods for proper integration of multi-omics datasets. We propose OmicsNet that uses a multilayer network for the integration and analysis of multi-omics data of heterogeneous types. Each layer of the multilayer network represents a certain data type: input layers correspond to genotype features and nodes in the output layer correspond to phenotypes, while intermediate layers may represent genesets or biological concepts to facilitate functional interpretation of the data. OmicsNet then calculates the highest coefficient paths in multilayer network from each genomic feature to the phenotype by computing an integrated score along the paths. These paths may indicate the most plausible signalling cascade caused by perturbed genotype features leading to a particular phenotype response. With example applications, we illustrate the potential power of OmicsNet in the functional analysis, biomarker discovery and drug response prediction in personalized medicine using multi-omics data.

bioinformatics↗