Biology subjects

Benard, B. A.

Publications and source records attributed to Benard, B. A..

4 recordsLinked to original sources

bioRxiv2026

PhenoMapR: scalable mapping of sample phenotypes to single-cell, spatial, and bulk transcriptomics data

Single-cell and spatial transcriptomic studies often lack sufficient sample size to compute robust statistical associations between a sample-level phenotype and cell types or spatial locations. In contrast, lower resolution methods such as bulk gene expression profiling have been applied at scale in large, annotated datasets, providing reliable signatures for phenotype associations. We introduce PhenoMapR, a semi-supervised method designed to integrate the phenotypic rigor of large-scale bulk expression studies with the cellular and spatial granularity of single-cell and spatial transcriptomics. PhenoMapR achieves this by deriving and mapping bulk gene expression signatures onto cells and spatial locations in a computationally efficient and scalable manner. The framework is broadly applicable across biological contexts, supporting the mapping of binary, continuous, and survival phenotypes derived from bulk expression studies across transcriptomic data modalities. This enables the identification of biologically-relevant cellular populations and spatial niches for experimental validation and therapeutic intervention.

bioinformatics↗

bioRxiv2026

Single cell multi-omics enables high-resolution identification and functionalpurification of human acute myeloid leukemia stem cells

In human acute myeloid leukemia (AML), a sub-population of leukemia stem cells (LSCs) drive disease initiation, therapeutic resistance, and relapse. However, the lack of reliable markers to distinguish LSCs from bulk leukemia cells has impeded progress in studying LSC pathogenesis and developing meaningful LSC-specific diagnostics and therapeutics. Existing LSC gene signatures, derived from bulk populations, cannot definitively identify LSCs at single-cell resolution. To address this, we analyzed large patient cohorts with bulk gene expression data and single-cell multi-omic assays to identify a prognostic gene signature that is specifically enriched in a clinically adverse AML sub-population. Using this signature, we defined and prospectively isolated CD34+CD90-CLL1-CD69+CD53- immunophenotypic LSCs that are significantly enriched for LSC content based on limiting dilution xenotransplantation assays. Our findings demonstrate the power of single-cell multi-omics to precisely identify a clinically relevant LSC population and establish a clear framework for future translational research in AML. Key PointsO_LISingle cell multi-omics identifies human AML LSCs at high resolution. C_LIO_LIHOPX and SOCS2 co-expression (hrLSC2) defines a prognostic gene signature in de novo acute myeloid leukemia. C_LIO_LIhrLSC2 marks an AML subpopulation (iLSCs) with a distinct immunophenotype. C_LIO_LIiLSCs can be purified using flow cytometry and are significantly enriched for LSCs. C_LI

cancer biology↗

bioRxiv2025

PRECOG update: An augmented resource of clinical outcome associations with gene expression for pediatric and immunotherapy cohorts

Gene expression can be used to define prognostic and predictive biomarkers across cancers and treatment modalities. PRECOG (https://precog.stanford.edu) is a compendium of datasets with gene expression and clinical outcomes that facilitates visualization of associations between genomic profiles and patient survival. Here, we augment the existing PRECOG with new datasets in previously poorly represented adult cancer types, as well as adding annotated pediatric and immunotherapy treated cohorts. Pediatric PRECOG comprises [~]4,000 patients across 12 cancers; while the immunotherapy cohort (ICI PRECOG) contains [~]4,500 patients across 20 cancer subtypes from 80 distinct datasets across 52 studies. We compute and visualize associations of gene expression with survival outcomes using Cox regression for time-to-event, or logistic regression for responder vs non-responder, across all datasets. We also estimate cell type fractions in samples via computational deconvolution using CIBERSORTx, to identify survival associations at the level of cell types. All expression data, clinical annotations, and gene and cell type survival z-scores and meta z-scores for adult, pediatric, and ICI PRECOG, are available for interactive analysis and download, along with Kaplan-Meier and boxplot visualizations. This updated resource will provide new insights into biomarkers for specific therapies, populations, and cancer types. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/671849v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@13446bdorg.highwire.dtl.DTLVardef@11025fcorg.highwire.dtl.DTLVardef@12e017corg.highwire.dtl.DTLVardef@1637dba_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Integrative multiomic approaches reveal ZMAT3 and p21 as conserved hubs in the p53 tumor suppression network

TP53, the most frequently mutated gene in human cancer, encodes a transcriptional activator that induces myriad downstream target genes. Despite the importance of p53 in tumor suppression, the specific p53 target genes important for tumor suppression remain unclear. Recent studies have identified the p53-inducible gene Zmat3 as a critical effector of tumor suppression, but many questions remain regarding its p53-dependence, activity across contexts, and mechanism of tumor suppression alone and in cooperation with other p53-inducible genes. To address these questions, we used Tuba-seqUltra somatic genome editing and tumor barcoding in a mouse lung adenocarcinoma model, combinatorial in vivo CRISPR/Cas9 screens, meta-analyses of gene expression and Cancer Dependency Map data, and integrative RNA-sequencing and shotgun proteomic analyses. We established Zmat3 as a core component of p53-mediated tumor suppression and identified Cdkn1a as the most potent cooperating p53-induced gene in tumor suppression. We discovered that ZMAT3/CDKN1A serve as near-universal effectors of p53-mediated tumor suppression that regulate cell division, migration, and extracellular matrix organization. Accordingly, combined Zmat3-Cdkn1a inactivation dramatically enhanced cell proliferation and migration compared to controls, akin to p53 inactivation. Together, our findings place ZMAT3 and CDKN1A as hubs of a p53-induced gene program that opposes tumorigenesis across various cellular and genetic contexts.

cancer biology↗