Biology subjects

Gentles, A. J.

Publications and source records attributed to Gentles, A. J..

16 recordsLinked to original sources

bioRxiv2026

Deep-learning-driven tumor microenvironment profiling improves immunotherapy response prediction

Immunotherapy has transformed the management of many cancers, such as clear cell renal cell carcinoma (ccRCC), yet durable clinical benefit is achieved in only a subset of patients. Immunotherapy outcomes are governed by the multicellular tumor microenvironment (TME), through the structured and directional interplay of regulatory programs and ligand-receptor communications. Yet most existing methods for immunotherapy response prediction rely on independent signatures or cell types and fail to model the structured and directional signaling network within the TME. To address this gap, we introduce EcoNet, a computational framework that first models ecosystems consisting of co-occurring cell states, then constructs intercellular and intracellular signaling networks based on the ecosystems, and applies a graph attention network to derive embeddings from the signaling network for immunotherapy response prediction. Pretrained on 2,532 pan-cancer immunotherapy samples across multiple cohorts, EcoNet outperformed existing prediction methods by 7.5-19.9% and marker-based signatures by 4.4-21.2% in AUC, and its high-attention genes were enriched among those required for T-cell-mediated tumor killing in an independent CRISPR screen; applied to ccRCC with disease-specific fine-tuning, it stratified an independent ccRCC cohort by clinical outcome (P=0.024). Biologically, high-attention genes capture an interferon-responsive program that enhances antigen presentation and immune infiltration. To validate these findings, we curated a ccRCC clinical cohort profiled with CosMx single-cell spatial transcriptomics (46 samples, ~450,000 cells) and found the response-associated TME configuration identified by EcoNet localized to immune-enriched niches, supporting the response stratification. These results establish EcoNet as a generalizable framework for TME-informed therapeutic response prediction, readily extensible to other cancer types and therapeutic contexts.

bioinformatics↗

bioRxiv2026

Mouse lymph node colonization models predict human tumor metastasis in melanoma

Aggressive tumor progression and distant metastases of cancers such as cutaneous melanoma are thought to be preceded by regional lymph node (LN) infiltration. Previous mouse studies have selectively enriched for tumor cells with enhanced LN migratory capacity but the mechanistic biology underpinning this proclivity has not been directly linked to human outcomes. Likewise, the exact role LN colonization plays in distant metastasis in humans remains unclear. To translate and conserve findings from mouse models to humans, we used a computational framework termed Translatable Components Regression (TransComp-R) to integrate mouse LN tumor samples with human primary and metastatic tumors. We identified transcriptional programs of early- and late-stage mouse tumor generations that stratified primary and metastatic tumors from patients with melanoma. These transcriptional programs associated with immune function, cell cycle checkpoints, and DNA-to-protein biosynthesis pathways. We then employed EcoTyper and identified six distinct melanoma-specific cellular communities (ecotypes) comprising co-occurring cell states. Of these six melanoma ecotypes, we identified a community strongly associated with primary tumor cells, while the other five represented a spectrum of metastatic tumor microenvironments with increased adverse outcomes. These ecotypes and their significantly expressed genes may contribute to the tumor-immune microenvironment that facilitates LN colonization in melanoma. We further validated the physical co-localization of these melanoma ecotypes using spatial transcriptomics. Our integration of pre-clinical models with human data reveals potential biological pathways and transcriptomic signatures for future investigation and supports the utility of mouse models in studying cancer metastasis.

systems biology↗

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

Spatially Resolved Epithelial States Delineate Immunosuppressive Niches of Impaired Myeloid Antigen Presentation in PDAC

Bulk transcriptomic classifiers stratify pancreatic ductal adenocarcinoma (PDAC) into classical and basal-like subtypes with prognostic and therapeutic relevance, yet increasing evidence indicates that these epithelial programs frequently coexist within individual tumors. How these intermediate states affect the local tumor microenvironment remains poorly defined. Here, we integrate multiplexed ion beam imaging (MIBI) with bulk RNA sequencing to resolve epithelial subtype identity at the level of spatially contiguous cancer nests and quantify their associated microenvironments. Across 47 primary tumor samples from 34 patients, we identified classical, intermediate, and basal cancer cell states at single-cell resolution and delineated discrete cancer nests with mixed or dominant subtype compositions. Distance-resolved spatial analysis reveals that basal-rich cancer nests are surrounded by locally immunosuppressive microenvironments characterized by reduced expression of MHC class II and co-stimulatory molecules in proximal myeloid cells, independent of myeloid abundance. These regions are enriched in fibroblast-dominated neighborhoods and distinct cell-cell interaction architectures. Using EcoTyper analysis of two independent bulk RNA-seq cohorts, including an OHSU discovery cohort (N = 277 patients) and TCGA as a validation cohort (N = 147 patients), we identified poor-prognosis tumor ecotypes enriched for basal epithelial states that similarly exhibited depleted myeloid antigen presentation signatures, linking spatial niche phenotypes to transcriptional ecotypes and patient outcomes. Together, these findings demonstrate that epithelial subtype programs in PDAC are organized at the level of spatially defined cancer nests and that basal cancer programs reside within localized niches of myeloid antigen presentation dysfunction, linking intratumoral architecture to immune suppression and clinical prognosis.

cancer biology↗

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↗

bioRxiv2026

DeSpotX: Identifiability-Based Decontamination for Spatial Transcriptomics

Spatial transcriptomics (ST) at single-cell resolution profiles gene expression in its native spatial context, but a substantial fraction of transcripts contaminate neighboring cells, compromising downstream biological analyses. Existing decontamination methods rely on heuristic priors and either ignore the spatial structure of contamination or aggregate over neighbors without separating contamination from native expression, leaving the decomposition ambiguous. To resolve this ambiguity, we introduce DeSpotX, a deep generative model that uses anchor genes, defined as genes not natively expressed in a given cell cluster, to constrain the contamination decomposition and make it identifiable. DeSpotX further uses spatial information to estimate contamination locally through a cluster-masked, distance-weighted average over neighboring cells, and prevents over-correction of low-expression signal through a learned diffusion prior. On spike-in simulations across five datasets and four ST platforms, DeSpotX achieves AUROC > 0.94 on every dataset, with gains of 0.02 to 0.12 over the best baseline, and remains robust to inaccuracies in the cell-cluster annotation and in anchor gene construction. On real tissues, we show that the decontaminated counts produce improved marker-gene specificity, more spatially coherent expression, and cell-cell communication networks consistent with known biology. We further show that iterating decontamination and cell-cluster annotation refines these outcomes, reassigning ligand-receptor signaling to the expected source cells in mouse brain and breast cancer tissues.

bioinformatics↗

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↗

bioRxiv2025

MONTAGE: A Computation Framework to Identify Spatially Resolved Functional Enrichment Gradients in the Tissue Microenvironment via Spatial Communities

Single-cell spatial-omics has advanced our understanding of how tumor microenvironment contributes to cancer progression. However, most single-cell spatial-omics studies focus on cell types and neighborhoods, offering limited information about functional and clinical relevance of cellular organization. We introduce MONTAGE, a computational framework to reconstruct, functionally analyze, and identify clinically relevant cellular spatial communities (SCs). MONTAGE generates a gene signature matrix of SCs from integrated atlases that offer single-cell resolution with transcriptomics, and reconstructs tissue maps ("montages"), reflecting sequential SC compositional changes along spatial gradients of biological function enrichments. The MONTAGE signature matrix can also be used to deconvolve bulk and spot-based spatial transcriptomic data, and combining with clinical metadata, allows assessing SC clinical relevance. In a head and neck cancer study, MONTAGE reveals SCs enriched with malignant cells and granulocytes shifting to SCs enriched with macrophages and fibroblasts along gradient of epithelial-mesenchymal transition, and clinical prognostic significance of the SCs.

systems biology↗

bioRxiv2024

A single cell framework identifies functionally and molecularly distinct multipotent progenitors in adult human hematopoiesis

Hematopoietic multipotent progenitors (MPPs) regulate blood cell production to appropriately meet the biological demands of the human body. Human MPPs remain ill-defined whereas mouse MPPs have been well characterized with distinct immunophenotypes and lineage potencies. Using multiomic single cell analyses and complementary functional assays, we identified new human MPPs and oligopotent progenitor populations within Lin-CD34+CD38dim/lo adult bone marrow with distinct biomolecular and functional properties. These populations were prospectively isolated based on expression of CD69, CLL1, and CD2 in addition to classical markers like CD90 and CD45RA. We show that within the canonical Lin-CD34+CD38dim/loCD90CD45RA-MPP population, there is a CD69+ MPP with long-term engraftment and multilineage differentiation potential, a CLL1+ myeloid-biased MPP, and a CLL1-CD69-erythroid-biased MPP. We also show that the canonical Lin-CD34+CD38dim/loCD90-CD45RA+ LMPP population can be separated into a CD2+ LMPP with lymphoid and myeloid potential, a CD2-LMPP with high lymphoid potential, and a CLL1+ GMP with minimal lymphoid potential. We used these new HSPC profiles to study human and mouse bone marrow cells and observe limited cell type specific homology between humans and mice and cell type specific changes associated with aging. By identifying and functionally characterizing new adult MPP sub-populations, we provide an updated reference and framework for future studies in human hematopoiesis.

cancer biology↗

bioRxiv2023

AML/T cell interactomics uncover correlates of patient outcomes and the key role of ICAM1 in T cell killing of AML

T cells are important for the control of acute myeloid leukemia (AML), a common and often deadly malignancy. We observed that some AML patient samples are resistant to killing by human engineered cytotoxic CD4+ T cells. Single-cell RNA-seq of primary AML samples and CD4+ T cells before and after their interaction uncovered transcriptional programs that correlate with AML sensitivity or resistance to CD4+ T cell killing. Resistance-associated AML programs were enriched in AML patients with poor survival, and killing-resistant AML cells did not engage T cells in vitro. Killing-sensitive AML potently activated T cells before being killed, and upregulated ICAM1, a key component of the immune synapse with T cells. Without ICAM1, killing-sensitive AML became resistant to killing to primary ex vivo-isolated CD8+ T cells in vitro, and engineered CD4+ T cells in vitro and in vivo. Thus, ICAM1 on AML acts as an immune trigger, allowing T cell killing, and could affect AML patient survival in vivo. SIGNIFICANCEAML is a common leukemia with sub-optimal outcomes. We show that AML transcriptional programs correlate with susceptibility to T cell killing. Killing resistance-associated AML programs are enriched in patients with poor survival. Killing-sensitive, but not resistant AML activate T cells and upregulate ICAM1 that binds to LFA-1 on T cells, allowing immune synapse formation which is critical for AML elimination. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/558911v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@84e2e1org.highwire.dtl.DTLVardef@4b2a40org.highwire.dtl.DTLVardef@c2d1f3org.highwire.dtl.DTLVardef@65db2c_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2022

Community assessment of methods to deconvolve cellular composition from bulk gene expression

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

bioinformatics↗

bioRxiv2022

Robust alignment of single-cell and spatial transcriptomes with CytoSPACE

Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, a method for aligning single-cell and spatial transcriptomes via convex linear optimization. Across diverse platforms and tissue types, we show that CytoSPACE outperforms previous methods with respect to noise-tolerance, accuracy, and efficiency, enabling improved analysis of spatial transcriptomics data at single-cell resolution.

genomics↗

bioRxiv2022

Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA reveals novel spatial biology

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We propose a novel machine learning algorithm, CELESTA, which uses both cells protein expression and spatial information to identify cell type of individual cells. We demonstrate the performance of CELESTA on multiplexed immunofluorescence in situ images of colorectal cancer and head and neck cancer. Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, which we validate in an independent cohort. By coupling our in situ spatial analysis with single-cell RNA-sequencing data on proximal sections of the same tissue specimens, we identify and validate cell-cell crosstalk associated with lymph node metastasis, demonstrating the power of spatial biology to reveal clinically-relevant cellular interactions.

bioinformatics↗

bioRxiv2020

Transient "rest" induces functional reinvigoration and epigenetic remodeling in exhausted CAR-T cells

T cell exhaustion limits immune responses against cancer and is a major cause of resistance to CAR-T cell therapeutics. Using a model wherein tonic CAR signaling induces hallmark features of exhaustion, we employed a drug-regulatable CAR to test the impact of transient cessation of receptor signaling (i.e. "rest") on the development and maintenance of exhaustion. Induction of rest in exhausting or already-exhausted CAR-T cells resulted in acquisition of a memory-like phenotype, improved anti-tumor functionality, and wholescale transcriptional and epigenetic reprogramming. Similar results were achieved with the Src kinase inhibitor dasatinib, which reversibly suppresses CAR signaling. The degree of functional reinvigoration was proportional to the duration of rest and was associated with expression of transcription factors TCF1 and LEF1. This work demonstrates that transient cessation of CAR-T cell signaling can enhance anti-tumor potency by preventing or reversing exhaustion and challenges the paradigm that exhaustion is an epigenetically fixed state.

immunology↗

bioRxiv2019

Clinically-relevant cell type cross-talk identified from a human lung tumor microenvironment interactome

Tumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway. To develop a deeper understanding of the interactions between cells within human lung tumors we performed RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We mapped the cell-specific differential expression of prognostically-associated secreted factors and cell surface genes, and computationally reconstructed cross-talk between these cell types to generate a novel resource we call the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identified and validated a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also found a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior. These results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance.\n\nSummaryRNA-seq profiling of sorted populations from primary lung cancer samples identifies prognostically relevant cross-talk between cell types in the tumor microenvironment.

cancer biology↗

bioRxiv2017

Module analysis captures pancancer (epi)genetically deregulated cancer driver genes for smoking and antiviral response

The availability of increasing volumes of multi-omics profiles across many cancers promises to improve our understanding of the regulatory mechanisms underlying cancer. The main challenge is to integrate these multiple levels of omics profiles and especially to analyze them across many cancers. Here we present AMARETTO, an algorithm that addresses both challenges in three steps. First, AMARETTO identifies potential cancer driver genes through integration of copy number, DNA methylation and gene expression data. Then AMARETTO connects these driver genes with co-expressed target genes that they control, defined as regulatory modules. Thirdly, we connect AMARETTO modules identified from different cancer sites into a pancancer network to identify cancer driver genes. Here we applied AMARETTO in a pancancer study comprising eleven cancer sites and confirmed that AMARETTO captures hallmarks of cancer. We also demonstrated that AMARETTO enables the identification of novel pancancer driver genes. In particular, our analysis led to the identification of pancancer driver genes of smoking-induced cancers and antiviral interferon-modulated innate immune response.\n\nSoftware availabilityAMARETTO is available as an R package at https://bitbucket.org/gevaertlab/pancanceramaretto\n\nHighlightsO_LIWe present an algorithm for pancancer identification of cancer driver genes based on multiomics data fusion\nC_LIO_LIGPX2 is a novel driver gene in smoking induced cancers and validated using knockdown of GPX2 in the A549 cell line.\nC_LIO_LIOAS2 is a novel driver gene defining cancers with an antiviral signature supported by increased infiltration of tumor-associated macrophages.\nC_LI\n\nResearch in contextWe present an algorithm that combines multiple sources of molecular data to identify novel genes that are involved in cancer development. We applied this algorithm on multiple cancers in a combined fashion and identified a network of pancancer driver genes. We highlighted two genes in detail GPX2 and OAS2. We showed that GPX2 is an important cancer gene in smoking induced cancers, and validated our predictions using experimental data where GPX2 was inactivated in a lung cancer cell line. Similarly we showed that OAS2 is an important cancer driver gene in cancers that show an antiviral signature.

bioinformatics↗