Biology subjects

Flowers, C. R.

Publications and source records attributed to Flowers, C. R..

7 recordsLinked to original sources

bioRxiv2026

Microbial valerate is associated with CAR T dysbiosis and its supplementation enhances CAR T function in B-cell lymphoma

Anaerobe-depleting antibiotic exposure is associated with inferior progression-free survival after CD19 CAR T-cell therapy in large B-cell lymphoma, yet the cellular mechanisms linking gut dysbiosis to the CAR T-cell product and whether this imprint is reversible have remained undefined. In two independent CAR-T candidate cohorts, low stool valerate at the time of CAR-T eligibility identified a multi-metabolite-deficient dysbiotic gut microbiome state marked by depletion of fiber-fermenting commensals and loss of carbohydrate-fermentation, SCFA-biosynthesis, and amino-acid metabolism pathways. Reanalysis of single-cell RNA sequencing from 42 lymphoma patients stratified by piperacillin-tazobactam/imipenem/meropenem (PIM) exposure revealed that PIM-exposed CAR T-cell products were CD4-skewed, with significantly elevated AP-1/immediate-early gene (IEG) and cellular activation signatures that together predicted inferior progression-free survival. Ex vivo conditioning of CAR T-cells with valerate produced a chromatin and transcription factor program distinct from butyrate or propionate, characterized by KLF/SP/EGR family engagement, KLF4 promoter opening, and broad induction of AP-1/IEG and MHC class II transcripts, whereas butyrate drove broader chromatin remodeling with TBX21/EOMES/NF-{kappa}B gains and KLF2 promoter closure, and propionate induced an NFY-centered program with preferential commitment to low-mitochondrial-content states. Untargeted metabolomics confirmed valerate uptake and mitochondrial {beta}-oxidation in CAR T-cells, while dietary sodium valerate supplementation in meropenem-treated mice bearing A20 lymphoma significantly reduced tumor burden and extended survival compared with CAR T-cells alone. These findings identify stool valerate as a bedside-deployable biomarker of dysbiosis-imprinted CAR T-cell dysfunction and support ex vivo or dietary valerate supplementation as a clinically tractable strategy to improve CAR-T anti-tumor function in patients with disrupted gut microbiomes.

cancer biology↗

bioRxiv2026

A patient derived xenograft repository capturing clinical and molecular heterogeneity of large B-cell lymphoma

Large B-cell lymphomas (LBCLs) are a clinically and molecularly diverse group of malignancies with a rapidly evolving therapeutic landscape that has introduced new areas of clinical need such as post-CD19 chimeric antigen receptor T (CART19) progression. Patient derived xenograft (PDX) models are an important tool for mechanistic studies and preclinical evaluation of new therapies and can be generated from a variety of clinical contexts that capture tumor-intrinsic resistance mechanisms. We therefore undertook a comprehensive effort to generate PDX models that encompass the molecular landscape of LBCLs and include important clinical scenarios for new drug development. Here we describe the first 48 models within this publicly available repository, capturing the transcriptional and genetic subsets of LBCL. These models also include 23 generated from post-CART progression biopsies which reproduce patterns of progression driven by CD19 mutation or expression loss, as well as tumor cell-intrinsic CART19 resistance that we validate in vivo. STATEMENT OF SIGNIFICANCEYang et al. describe X-LYMPH (Xenografts of Lymphoma), a publicly available and molecularly annotated PDX repository that captures the heterogeneity of large B-cell lymphoma. X-LYMPH includes models of chimeric antigen receptor T cell resistance, providing a shared foundation for mechanistic research and therapeutic development for lymphomas.

cancer biology↗

bioRxiv2025

Signaling induced biophysical disruption of repressed chromatin domains drives immune cell fate

Cell fate transitions require signal-induced chromatin derepression, yet mechanisms governing transitions from repressed to active chromatin states are poorly understood. We discover, at fate-defining genes across immune cell types, a signal-induced histone code, and describe domains of H3 serine 28 phosphorylation (H3S28ph) spanning architectural features, often coincident with repressive H3 lysine 27 trimethylation (H3K27me3). Employing biophysical, single cell, and functional approaches to study signal-induced cell differentiation in the immune system, we uncover epigenomic transitions and cell fate choices precipitated by histone phosphorylation (H3ph). Mechanistically, H3ph overrides Polycomb Repressive Complex 2 (PRC2) chromatin repression, biophysically disrupts polynucleosome compaction, and promotes loss of H3K27me3, while increasing activating H3K27 acetylation and H3K36 dimethylation to drive domain interactivity and stabilize transcription. We demonstrate the activity of H3ph in several cell fate transitions and illuminate biophysical mechanisms enabling rapid signal-activated chromatin derepression, processes with general relevance for cellular differentiation and activation.

immunology↗

bioRxiv2023

MASH: Mediation Analysis of Survival Outcome and High-dimensional Omics Mediators with Application to Complex Diseases

Environmental exposures such as cigarette smoking influence health out-comes through intermediate molecular phenotypes, such as the methylome, transcriptome, and metabolome. Mediation analysis is a useful tool for in-vestigating the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposures and health outcomes. However, little work has been done on mediation analysis when the mediators are high-dimensional and the outcome is a survival endpoint, and none of it has provided a robust measure of total mediation effect. To this end, we propose an estimation procedure for Mediation Analysis of Survival outcome and High-dimensional omics mediators (MASH) based on sure independence screening for putative mediator variable selection and a second-moment-based measure of total mediation effect for survival data analogous to the R2 measure in a linear model. Extensive simulations showed good performance of MASH in estimating the total mediation effect and identifying true mediators. By applying MASH to the metabolomics data of 1919 subjects in the Framingham Heart Study, we identified five metabolites as mediators of the effect of cigarette smoking on coronary heart disease risk (total mediation effect, 51.1%) and two metabolites as mediators between smoking and risk of cancer (total mediation effect, 50.7%). Application of MASH to a diffuse large B-cell lymphoma genomics data set identified copy-number variations for eight genes as mediators between the baseline International Prognostic Index score and overall survival.

genomics↗

bioRxiv2023

CASi: A multi-timepoint scRNAseq data analysis framework

Single-cell RNA sequencing (scRNA-seq) technology has been widely used to study the differences in gene expression at the single cell level, providing insights into the research of cell development, differentiation, and functional heterogeneity. Various pipelines and workflows of scRNA-seq analysis have been developed but few considered multi-timepoint data specifically. In this study, we develop CASi, a comprehensive framework for analyzing multiple timepoints scRNA-seq data, which provides users with: (1) cross-timepoint cell annotation, (2) detection of potentially novel cell types emerged over time, (3) visualization of cell population evolution, and (4) identification of temporal differentially expressed genes (tDEGs). Through comprehensive simulation studies and applications to a real multi-timepoint single cell dataset, we demonstrate the robust and favorable performance of the proposal versus existing methods serving similar purposes.

bioinformatics↗

bioRxiv2022

A conditional survival distribution-based method for censored data imputation: overcoming the hurdle in machine learning-based survival analysis

Data analyses by machine learning (ML) algorithms are gaining popularity in biomedical research. When time-to-event data are of interest, censoring is common and needs to be properly addressed. Most ML methods cannot conveniently and appropriately take the censoring information into consideration, potentially leading to inaccurate or biased results. We aim to develop a general-purpose method for imputing censored survival data, facilitating downstream ML analysis. In this study, we propose a novel method of imputing the survival times for censored observations. The proposal is based on their conditional survival distributions (CondiS) derived from Kaplan-Meier estimators. CondiS can replace censored observations with their best approximations from the statistical model, allowing for direct application of ML methods. When covariates are available, we extend CondiS by incorporating the covariate information through ML modeling (CondiS-X), which further improves the accuracy of the imputed survival time. Compared with existing methods with similar purposes, the proposed methods achieved smaller prediction errors and higher concordance with the underlying true survival times in extensive simulation studies. We also demonstrated the usage and advantages of the proposed methods through two real-world cancer datasets. The major advantage of CondiS is that it allows for the direct application of standard ML techniques for analysis once the censored survival times are imputed. We present a user-friendly R package to implement our method, which is a useful tool for ML-based biomedical research in this era of big data.

bioinformatics↗

bioRxiv2021

Clonal hematopoiesis is associated with increased toxicity in large B-cell lymphoma patients treated with chimeric antigen receptor T cell therapy

To explore the role of clonal hematopoiesis (CH) on chimeric antigen receptor (CAR) T-cell therapy outcomes, we performed targeted deep-sequencing on 114 large B-cell lymphoma patients treated with anti-CD19 CAR T-cells. We detected CH in 42 (36.8%) pre-treatment patient samples, most frequently in PPM1D (19/114) and TP53 (13/114) genes. The incidence of grade [≥]3 immune-effector cell-associated neurotoxicity syndrome (ICANS) was higher in CH-positive patients compared to CH-negative patients (45.2% vs. 25.0%, p=0.038). Higher toxicities with CH were primarily driven by three CH genes, DNMT3A, TET2 and ASXL1 (DTA mutations). The incidence of grade [≥]3 ICANS [58.9% vs. 25%, p=0.02] and grade [≥]3 cytokine release syndrome [17.7% vs. 4.2%, p=0.08] were higher in patients with DTA mutations than those without CH. The estimated 24-month cumulative incidence of therapy-related myeloid neoplasms after CAR-T therapy was higher in patients with CH than those without CH (19% [95%CI: 5.5-38.7] vs. 4.2% [95%CI: 0.3-18.4], p=0.028). Statement of SignificanceOur study reveals that clonal hematopoiesis mutations, especially those associated with inflammation (DNMT3A, TET2, ASXL1), are associated with severe grade toxicities in lymphoma patients receiving anti-CD19 chimeric antigen receptor therapy. Further studies to investigate the mechanisms and interventions to improve toxicities in the context of CH are warranted.

genetics↗