Biology subjects

Higgs, B. W.

Publications and source records attributed to Higgs, B. W..

7 recordsLinked to original sources

bioRxiv2025

Mechanistically explainable AI model for predicting synergistic cancer therapy combinations

This study introduces a Large Language Model (LLM)-based framework that combines drug combination data with a knowledge graph to predict synergistic oncology drug combinations with mechanistic insights. Using a retrieval-augmented generation (RAG) approach, over 50,000 in vitro drug pair assay results and 1,631 human clinical trial or preclinical test entries were integrated to enhance predictive accuracy and explainability. Validation achieved an F1 score of 0.80, demonstrating the frameworks potential to streamline drug discovery and improve translational strategies in cancer treatment.

cancer biology↗

bioRxiv2025

Integrative immune subtyping of HNSCC reveals clinically relevant phenotypes and treatment-associated transitions

Head and neck squamous cell carcinoma (HNSCC) exhibits profound heterogeneity in clinical presentation, treatment response, and immune landscape. While prior classification systems have identified molecular and immune subtypes in this disease, their applicability to real-world clinical settings remains restricted to small, homogeneous cohorts and limited by lack of multimodal data integration and interpretation. We performed integrated multi-omics analysis including transcriptomic, genomic (copy number, single-nucleotide variants) on 1,149 tumors from 1,102 HNSCC patients across treatment settings. Using the Similarity Network Fusion (SNF) algorithm, we defined immune subtype clusters (ISCs) based on the full immune gene landscape. These clusters were characterized using mutational, transcriptional, and immune cell enrichment analyses, and mapped to hypoxia and traditional subtypes. Associations with clinical outcomes, including progression-free survival, were evaluated across first-line and post-metastatic treatment settings. Four distinct immune subtype clusters (ISC1-ISC4) were identified: ISC1: immune-cold and EMT-enriched; ISC2: immune activated; ISC3: mixed immune-regulatory and stromal-enriched phenotype; and ISC4: immunosuppressed. Distinct treatment response patterns were observed across subtypes in subjects treated with checkpoint inhibitors, chemotherapy, and combination regimens. 44 Patients with matched pre/post treatment tumors revealed treatment-associated transitions between immune subtypes: checkpoint inhibitor treatment enriched for immune activation, while chemotherapy treatment enriched for immunosuppressive signaling pathways. This study provides a clinically relevant immune subtyping framework for HNSCC based on real-world, multi-omics data. These subtypes reflect dynamic tumor-immune states and associated with treatment response and survival, supporting their use in guiding immune-based therapy in HNSCC.

immunology↗

bioRxiv2025

Liver transplant rejection features memory cell priming, spatially differentiated networks and novel drug targets.

Acute T-cell-mediated transplant rejection, the earliest contributor to immunological graft failure responds variably to T-cell suppression. Early rejection can co-exist with donor-specific antibodies which also shorten graft survival, without affecting graft histology visibly. Here, during early liver transplant rejection, blood leukocytes manifest primed T-cells with heat shock protein (HSP) signaling and transcriptional programs for memory cell expansion. Corresponding biopsies demonstrate upregulated immune synapse and proteasomal genes in T-cell-infiltrated portal and central regions of the liver lobule. With donor-specific antibodies, the intervening intralobular region demonstrates a germinal-center-like allograft response with upregulated CD40, chemokine, T-follicular and complement signaling, which intensifies along the direction of sinusoidal blood flow from portal to central regions. Proteasomal and HSP90 inhibitors suppress donor-specific alloresponses of T- and B-cells in blood samples from patients with early rejection. The molecular injury spectrum of early rejection is complex, spatially differentiated and reveals novel early immunosuppressive strategies to extend graft survival.

immunology↗

bioRxiv2023

VIBE: An R-package for advanced RNA-seq data exploration, disease stratification and therapeutic targeting

BackgroundDevelopment of therapies e.g. antibody-based treatments, rely on several factors, including the specificity of target expression and characterization of downstream signaling pathways. While existing tools for analyzing and visualizing RNA-seq data offer evaluation of individual gene-level expression, they lack a comprehensive assessment of pathway-guided analysis, relevant for single- and dual-targeting therapeutics. Here, we introduce VIBE (VIsualization of Bulk RNA Expression data), an R package which provides a thorough exploration of both individual and combined gene expression, supplemented by pathway-guided analyses. VIBEs versatility proves pivotal for disease stratification and therapeutic targeting in cancer, immune, metabolic, and other disorders. ResultsVIBE offers a wide array of functions that streamline the visualization and analysis of transcriptomics data for single- and dual-targeting therapies such as antibodies. Its intuitive interface allows users to evaluate the expression of target genes and their associated pathways across various indications, aiding in target and disease prioritization. Metadata, such as specific treatment or number of prior lines of therapy, can be easily incorporated to refine the identification of patient cohorts hypothesized to derive benefit from a given drug. Through real-world scenario representations using simulated data, we demonstrate how VIBE can be used to assist in indication selection for several user cases. VIBE integrates statistics in all graphics, enabling data-informed decision-making. Its enhanced user experience features include boxplot sorting and group genes either individually or averaged based on pathways, ensuring custom visuals for insightful decisions. For a deeper dive into its extensive functionalities, please review the vignettes on the GitHub repository (https://github.com/genmab/VIBE). ConclusionsVIBE facilitates detailed visualization of individual and cohort-level summaries such as concordant or discordant expression of two genes or pathways. Such analyses can help to prioritize disease indications that are amenable to treatment strategies like bispecific antibody therapies or pathway-guided monoclonal antibody therapies. By using this tool, researchers can enhance the indication selection and potentially accelerate the development of novel targeted therapies with the end goal of precision, personalization, and ensuring treatments align perfectly with individual patient needs across a spectrum of medical domains.

bioinformatics↗

bioRxiv2022

Enhanced donor antigen presentation by B-cells predicts acute cellular rejection and late outcomes after transplantation.

PurposeEnhanced B-cell presentation of donor alloantigen relative to presentation of HLA-mismatched reference alloantigen is associated with acute cellular rejection (ACR), when expressed as a ratio called the antigen presenting index (API) in an exploratory cohort of liver and intestine transplant (LT, IT) recipients. MethodsTo test clinical performance, we measured the API using the previously described 6-hour assay in 84 LT and 54 IT with median age 3.3 years (0.05-23.96). Recipients experiencing ACR within 60 days after testing were termed rejectors. ResultsWe first confirmed that B-cell uptake and presentation of alloantigen induced and thus reflected the alloresponse of T-helper cells, which were incubated without and with cytochalasin and primaquine to inhibit antigen uptake and presentation, respectively. Transplant recipients included 76 males and 62 females. Rejectors were tested at median 3.6 days before diagnosis. The API was higher among rejectors compared with non-rejectors (2.2 {+/-} 0.2 vs 0.6 {+/-} 0.04, p-value=1.7E-09). In logistic regression and ROC analysis, API [≥] 1.1 achieved sensitivity, specificity, positive and negative predictive values for predicting ACR in 99 training set samples. Corresponding metrics ranged from 80-88% in 32 independent post-transplant samples, and 73-100% in 20 independent pre-transplant samples. In time-to-event analysis, API [≥] 1,1 predicted higher incidence of late DSA after API measurements in LT (p=0.011) and graft loss in IT recipients (p=0.008), compared with recipients with API<1.1, respectively. ConclusionEnhanced donor antigen presentation by circulating B-cells predicts rejection after liver or intestine transplantation as well as higher incidence of DSA and graft loss late after transplantation

immunology↗

bioRxiv2021

Impaired T-cell and antibody immunity after COVID-19 infection in chronically immunosuppressed transplant recipients

Assessment of T-cell immunity to the COVID-19 coronavirus requires reliable assays and is of great interest, given the uncertain longevity of the antibody response. Some recent reports have used immunodominant spike (S) antigenic peptides and anti-CD28 co-stimulation in varying combinations to assess T-cell immunity to SARS-CoV-2. These assays may cause T-cell hyperstimulation and could overestimate antiviral immunity in chronically immunosuppressed transplant recipients, who are predisposed to infections and vaccination failures. Here, we evaluate CD154-expressing T-cells induced by unselected S antigenic peptides in 204 subjects-103 COVID-19 patients and 101 healthy unexposed subjects. Subjects included 72 transplanted and 130 non-transplanted subjects. S-reactive CD154+T-cells co-express and can thus substitute for IFN{gamma} (n=3). Assay reproducibility in a variety of conditions was acceptable with coefficient of variation of 2-10.6%. S-reactive CD154+T-cell frequencies were a) higher in 42 healthy unexposed transplant recipients who were sampled pre-pandemic, compared with 59 healthy non-transplanted subjects (p=0.02), b) lower in Tr COVID-19 patients compared with healthy transplant patients (p<0.0001), c) lower in Tr patients with severe COVID-19 (p<0.0001), or COVID-19 requiring hospitalization (p<0.05), compared with healthy Tr recipients. S-reactive T-cells were not significantly different between the various COVID-19 disease categories in NT recipients. Among transplant recipients with COVID-19, cytomegalovirus co-infection occurred in 34%; further, CMV-specific T-cells (p<0.001) and incidence of anti-receptor-binding-domain IgG (p=0.011) were lower compared with non-transplanted COVID-19 patients. Healthy unexposed transplant recipients exhibit pre-existing T-cell immunity to SARS-CoV-2. COVID-19 infection leads to impaired T-cell and antibody responses to SARS-CoV-2 and increased risk of CMV co-infection in transplant recipients.

immunology↗

bioRxiv2019

Novel mediation analysis of human plasma proteome and metabolome reveals mediators of improved glycemia after gastric bypass surgery

Molecular mechanisms by which Roux-en-Y gastric bypass (RYGB) improves glycemic control and metabolism in type 2 diabetes (T2D) remain incompletely understood. In the SLIMM-T2D trial, participants with T2D were randomized to RYGB or nonsurgical management and their fasting plasma proteome and metabolome were analyzed for up to 3 years. To identify analytes that mediate improvement in outcomes, we developed a high-throughput mediation analysis method (Hitman), which is significantly more powerful than existing methods. Top-ranking analyte mediators of glycemia improvement were growth hormone receptor and prolylhydroxyproline, which were more significant than any clinical mediator, including BMI. Beta-alanine and Histidine Metabolism (both including CNDP1) were top differentially regulated pathways, and Valine, Leucine and Isoleucine Degradation was also a top differentially-regulated pathway and a top mediator of improvement in insulin resistance. The identified analytes may serve as novel targets for T2D therapy. More broadly, Hitman can identify analyte mediators of outcomes in randomized trials for which high-throughput data are available.

systems biology↗