Biology subjects

Rao, A.

Publications and source records attributed to Rao, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Cancer cell adaptations along a proliferation-robustness axis drive TME pro-tumor phenotypes

Tumors are highly complex dynamic ecosystems with causal relationships between cancer cells and their tumor microenvironment (TME). To disentangle these interactions, we performed long-term co-culture experiments with cancer cells evolving alongside continuously replenished fibroblasts or macrophages. This allowed us to distinguish between adaptive and physiological responses in specific cancer-TME interactions, which we then also recapitulated in patient tumors. We found that co-culture with fibroblasts induced the epithelial-mesenchymal transition in cancer cells and resulted in their increased drug tolerance, while co-culture with macrophages promoted stress, hypoxia responses and enhanced tumorigenicity. Adaptations were recurrent and partially reversible, but could not be explained by clonal selection, suggesting a dynamic non-mutational process. Moreover, adapted cancer cells elicited cell state changes in the fibroblasts and macrophages consistent with an extended phenotype mode of cancer evolution. Our findings reframe cancer-TME interactions by a systematic dissection according to sets of specific adaptations and programmed responses.

cancer biology↗

bioRxiv2026

Cellular proximity and conditional coordination in the colorectal and pancreatic tumor microenvironment: a patient-level analysis of multiplexed imaging

Immune cells in the tumor microenvironment are organized, and in colorectal and pancreatic cancer their arrangement around the tumor epithelium carries prognostic information. Multiplexed tissue imaging records the position and phenotype of every cell in a section, and studies of such images now routinely report that two cell types are spatially associated. However, the summaries behind these reports answer different questions. A proximity statistic asks whether two cell types lie close together more often than expected by chance. A conditional network asks whether their local densities rise and fall together once the other cell types are held fixed. The two can disagree, because every cell type is abundant where tissue is cell-dense and because the images of one patient are repeated measurements of that patient. We asked how epithelial and immune cells are organized in two settings: colorectal cancer with a Crohn's-like reaction (CLR) or diffuse inflammatory infiltration (DII), in 140 CODEX images from 35 patients, and pancreatic ductal adenocarcinoma (PDAC) against its precursor, intraductal papillary mucinous neoplasm (IPMN), in 207 multiplex immunofluorescence images from 141 patients. We developed a patient-level analysis that keeps proximity, recurrent local composition and conditional coordination as separate quantities, tests conditional organization as a function of local epithelial density within each section, checks before every conditional estimate that the two cell types are disjoint sets of cells observed together in enough images, and calibrates each conclusion by patient-label permutation, cellularity adjustment under two definitions, spatial-scale perturbation and fully nested leave-one-patient-out prediction. In colorectal cancer, the conditional association between epithelial and myeloid density turned negative with local epithelial density about four times faster in DII than in CLR (difference $-0.0553$ per quartile; 95% CI, $-0.0833$ to $-0.0273$; patient-label familywise $P=0.006$), with no accompanying difference in proximity. In the pancreas, cytotoxic and helper T cells were closer together in PDAC than in IPMN at every scale examined ($q\leq0.023$), whereas their conditional association differed between the two diseases under only one of the two cellularity definitions. Recurrent neighborhoods carried the most patient-level information in colorectal cancer (AUC 0.794) and combined spatial features in the pancreas (AUC 0.771). Pancreatic epithelial--immune and regulatory T-cell quantities could not be estimated from these cores and are reported as such. The analysis is implemented in the R package spatialMIFtools, available at https://github.com/sagnikbhadury/spatialMIFtools.

bioinformatics↗

bioRxiv2026

TET loss of function selects for aneuploidies that confer a proliferative advantage

Mutations in the Ten-Eleven Translocation (TET) family of DNA dioxygenases are among the most common epigenetic alterations in clonal hematopoiesis and hematologic malignancies, yet how TET loss of function reshapes genome evolution during cellular expansion remains to be fully understood. Here we show that loss of Tet2/3 in invariant natural killer T (iNKT) cells drives aggressive lymphoid expansion that is consistently associated with recurrent chromosome 17 trisomy, a dominant aneuploid state observed across more than ten independently derived tumors following transplantation into immunocompetent recipient mice. Single-cell whole-genome sequencing of TET-deficient iNKT cells induced to proliferate in culture with antigen and IL-2 stimulation revealed that rare pre-existing cells with chromosome 17 trisomy appeared soon after Tet gene deletion in the thymus, together with cells harboring other copy number alterations, but only the cells with chromosome 17 trisomy were progressively enriched during further expansion. Similarly, inducibly Tet-deficient mouse embryonic stem cells (mESCs) showed selection for aneuploid states that were present prior to Tet gene deletion, including trisomy of chromosome 6. Reanalysis of independent datasets from Dnmt3a-deficient mouse lymphomas identified recurrent chromosome gains, including chromosome 17 trisomy. Our results establish TET and DNMT3A deficiency as drivers of karyotypic evolution, and suggest that disruption of DNA methylation/ demethylation pathways through either TET or DNMT3A deletion promotes chromosomal instability, in part by facilitating the emergence and selection of cryptic aneuploid subclones during clonal expansion.

genetics↗

bioRxiv2026

A druggable chromatin vulnerability in suppressive neutrophils restores antitumor immunity

Many cancers evade immunity by converting neutrophils into polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs), but the chromatin mechanisms that maintain this pathological state remain unclear. Here we show that tumor-induced suppressive neutrophils depend on mSWI/SNF chromatin remodeling. Two orally bioavailable mSWI/SNF ATPase antagonists, a degrader and a catalytic inhibitor, produced immune-dependent tumor control across syngeneic models, including tumors resistant to PD-1 blockade, and enhanced checkpoint therapy without overt toxicity. Treatment rapidly depleted intratumoral PMN-MDSCs, restored functional CD8+ T cells, and blocked tumor explant-induced expansion and suppressive activity of mouse and human PMN-MDSCs. mSWI/SNF antagonism collapsed chromatin accessibility and PU.1/C/EBP{beta} occupancy at regulatory elements that sustain PMN-MDSC identity. Thus, suppressive neutrophils harbor a druggable chromatin vulnerability that can be exploited to restore cancer immunity.

cancer biology↗

bioRxiv2026

Integrated Analysis of Skeletal Muscle Transcriptional Networks Characterizes Dysregulation in Pathways and Trait-Associated Regulatory Regions in Type 2 Diabetes

Skeletal muscle, a primary site of insulin-mediated glucose uptake, plays a central role in the pathogenesis of type 2 diabetes. It is therefore critical to understand the disease-associated alterations in skeletal muscle and identify the underlying drivers of this dysregulation. Here, we characterize type 2 diabetes associated transcriptional dysregulation using 301 skeletal muscle biopsies from living donors with and without diabetes. Using weighted gene co-expression network analysis, we identify 56 distinct gene modules, which we further characterize using single-nucleus RNA-seq-derived cell type signatures and pathway enrichment analysis. We identify numerous cell type-associated dysregulated pathways in skeletal muscle tissue from individuals with diabetes, including muscle fiber-associated mitochondrial function and mRNA splicing and processing; endothelial vascularization and phospholipase D signaling; and macrophage- and T-cell-associated inflammation. Through analysis of module hub genes and transcription factor regulatory network analysis, we further identify candidate driver genes of this dysregulation including ATP5L, ATF2, SIRT1, and THRAP3 in muscle fibers; JAM2 and CLEC14A in endothelial cells; and F13A1 and IRF8 in immune cells. Finally, we integrate our co-expression networks with single-nucleus ATAC-seq data to identify proximal and distal genomic regulatory elements and identify context-specific enrichment for type 2 diabetes and related trait GWAS signals in muscle fiber and endothelial modules. Together, our results reveal dysregulation in pathways in muscle tissue from individuals with diabetes, identify candidate drivers, and connect the genomic drivers of this dysregulation across type 2 diabetes and related metabolic traits.

bioinformatics↗

bioRxiv2026

NeuroFlow: An Integrated, Cross-Platform Workflow for Mouse Brain Atlas Registration and Quantification

Registration of histological sections to a reference atlas is essential for anatomical localization and region-based quantitative analysis. Although established workflows are powerful, image preparation, registration, quantification, and visualization often rely on multiple software packages, some of which require platform-specific installation or locally configured programming environments. Here, we present NeuroFlow, a browser-based workflow for quantitative analysis of mouse brain histology. NeuroFlow integrates image registration, signal detection, quantification, and visualization within a single interface and operates across major operating systems without additional software installation. It supports affine and nonlinear alignment, as well as real-time oblique reslicing of the reference atlas. All processing is performed locally in a desktop browser, without requiring a local Python environment, MATLAB installation, or associated packages and toolboxes, and without uploading images to a remote server. This design preserves user control over data and keeps intermediate results accessible for inspection and review. NeuroFlow is available at https://guangweizhang.com/tool-neuroflow.html.

neuroscience↗

bioRxiv2026

The atypical IκB factor IκBδ enhances CD8 T cell accumulation and effector functions in solid tumors

Two prominent mechanisms by which tumors fend off immune control are by constraining the ability of T cells and CAR T cells to survive and expand in the tumor, and by restraining their ability to sustain full cytotoxic capacity. We identified I{kappa}B{delta}, encoded by Nfkbid, a poorly characterized I{kappa}B family member, as a molecular lever that overcomes both of these constraints on anti-tumor CD8+ tumor-infiltrating lymphocytes (TILs). Nfkbid is an NFAT target gene that is expressed in CD8+ effector T cells and, at modest levels, in CD8+ TILs. We found that Nfkbid depletion impaired TIL accumulation, exacerbating the growth of solid tumors. On the other hand, ectopic I{kappa}B{delta} overexpression enhanced TIL expansion, reduced the expression of exhaustion-associated transcription factors and inhibitory receptors, and elevated cytotoxic molecule production, leading to enhanced tumor control. I{kappa}B{delta} has a shorter protein isoform that is identical in a core region spanning the ankyrin-repeat domain known to interact with NF{kappa}B proteins, but that lacks the [~]150-residue N-terminal region. We showed that the shared core region is sufficient to drive T cell accumulation, whereas the N-terminal peptide region is required for robust effector function and to counter exhaustion, underscoring that tumor-infiltrating CD8+ T cell accumulation and effector differentiation are separable programs. Our current study provides evidence that I{kappa}B{delta}, an atypical member of the NF{kappa}B family, is a lever to overcome two cardinal deficits that limit CD8+ TIL anti-tumor efficacy: impaired accumulation in the tumor and diminished effector function.

immunology↗

bioRxiv2026

Optimised haemoglobin depletion improves clinical proteomics from dried blood spots

Equitable access to large sample cohorts for robust, high-throughput proteomics for biomarker discovery is a major barrier to widescale clinical implementation. Dried blood spots (DBS) offer a minimally invasive alternative to venous blood draws, enabling at-home microsampling (<50 {micro}L) for centralised analysis, thus enhancing research participation. This approach is particularly relevant for under-represented groups, including children, the elderly, minority backgrounds and those with long-term health conditions such as chronic kidney disease (CKD), where disease fluctuations may occur outside the clinic, and vein preservation is critical. Proteomic analysis has demonstrated great utility in monitoring disease progression, and for biomarker/therapeutic target discovery. However, liquid chromatography-tandem mass spectrometry (LC-MS/MS) of whole blood is hindered by the wide dynamic range and the relatively high abundance of proteins such as haemoglobin, compromising biomarker discovery. Here, we establish an optimised workflow for protein extraction and haemoglobin depletion from microsamples obtained using DBS, enabling sensitive and high-throughput proteomic analysis. We demonstrate that haemoglobin depletion increases protein identifications by [~]50%, mitigating ion suppression and dynamic range effects, enabling the identification of putative biomarkers from patients with stage 5 CKD on dialysis. We also evaluated a commercial cell-free DBS device which yielded a sample more representative of plasma compared to traditional DBS and enabled greater depletion of haemoglobin compared to traditional DBS with haemoglobin depletion methods. Our findings offer a scalable approach for biomarker discovery, facilitating remote, longitudinal clinical studies.

biochemistry↗

bioRxiv2026

Rewiring Oncogenic Transcriptional Complexes with Domain-ALTeration Chimeras (DALTACs) in Prostate Cancer

Transcriptional addiction to the androgen receptor (AR) underlies metastatic castration-resistant prostate cancer (mCRPC), where AR maintains oncogenic programs through domain-specific interactions with the acetyltransferases p300/CBP. Here, we describe a distinct therapeutic modality, Domain-ALTerAtion Chimeras (DALTACs), that rewire endogenous protein complexes by enforcing non-native domain-domain interactions. Our first-in-class AR-p300/CBP DALTAC-1 induces synthetic proximity between the AR ligand-binding domain and p300/CBP-bromodomain, misconfiguring their native interface and locking the complex in a non-productive state. DALTAC-1 triggers a super-inhibitory effect, suppressing AR-driven transcription and proliferation more potently than combined AR and p300/CBP inhibition. Mechanistically, DALTAC-1 reprograms p300/CBP substrate engagement, abolishing the histone mark H2B N-terminal acetylation (H2BNTac) while inducing neomorphic acetylation of AR and coactivators, ultimately collapsing the AR neo-enhanceosome. Chromatin profiling revealed redistribution of AR and p300/CBP toward palindromic AREs, accompanied by disruption of cofactor and transcriptional machinery recruitment at oncogenic AR/ERG neo-enhancers. DALTAC-1 exhibits exquisite lineage selectivity, displaying potent activity in AR-p300/CBP dual-dependent cells while sparing AR-negative cells. In multiple in vivo models, DALTAC-1 induces deep and durable tumor regressions. Together, these findings establish DALTACs as a broadly applicable strategy to rewire disease-defining complexes through domain-topology alteration, expanding induced-proximity therapeutics, while DALTAC-1's precise lineage-selectivity supports its translational potential in AR-driven prostate cancer.

cancer biology↗

bioRxiv2026

ISPAT-3D: Spatially Varying Conditional Volumetric Network Estimation for 3D Tumor Imaging

The spatial organization of the tumor microenvironment shapes immune function and disease progression, yet existing methods for cell-type interaction networks from multiplexed tissue images operate in two dimensions and ignore spatial auto-correlation. We introduce ISPat-3D (Informed Spatially Aware Patterns in 3D), a hierarchical Bayesian framework that recovers spatially varying, zone-specific interaction networks from 3D multiplexed cancer imaging data. The method partitions the tissue volume into tumor intensity zones, fits an anisotropic Gaussian process per cell type and zone with separate lengthscales for the tissue plane and axial direction, decomposes the residuals via multi-study factor analysis, and extracts partial correlation networks from the resulting precision matrices. Simulations demonstrate accurate recovery of shared and zone-specific structure with high power and controlled FDR. We apply ISPat-3D to two 3D datasets: the colorectal cancer atlas (CRC1) 3D CyCIF specimen and a HER2-positive ductal breast carcinoma (BC) specimen from a 3D IMC. In CRC1, zone-specific networks reveal a T cell module intensifying with tumor burden, with the dominant regulatory association shifting from CD4+{leftrightarrow}Treg at intermediate density to CD8+{leftrightarrow}Treg at maximal density, consistent with cytotoxic suppression at the tumor core. In BC, the shared network shows near-perfect conditional coupling between cancer-associated fibroblasts and the myoepithelial layer, while zone-specific networks reveal CAF{leftrightarrow}endothelial co-localisation at intermediate and high burden, consistent with angiogenic remodeling, and a B cell{leftrightarrow}CAF association confined to high-density zones, consistent with tertiary lymphoid structure formation. Across both tumors, ISPat-3D identifies volumetric spatial conditional interactions not recoverable from 2D sections.

bioinformatics↗

bioRxiv2026

Flanking DNA sequences determine DNA methylation maintenance in proliferation, cancer and aging

DNA methylation is a stable epigenetic modification essential for promoter silencing, retrotransposon silencing, genomic imprinting, and X-chromosome inactivation. Symmetrical DNA methylation at CpG dinucleotides is maintained after every round of cell division by the DNMT1-UHRF1 maintenance methyltransferase complex. Here we define a conserved rank order of DNA hexanucleotide sequences surrounding CpG sites that determines baseline DNA methylation levels in cells and the probability that DNA methylation is retained across cell divisions. This rank order is conserved in vertebrates and does not depend on TET enzymatic activity. CpG sites in hexanucleotide sequences less favored by DNMT1 are more susceptible to replication-dependent loss of DNA methylation over time; consequently, the methylation status of these motifs serves as a marker of cumulative cell divisions, biological age and cancer progression. Thus, the intrinsic vulnerability stemming from the sequence preference of the DNMT1-UHRF1 complex compromises the long-term stability of DNA methylation, especially at heterochromatic sites in proliferating cells, and contributes to the epigenetic dysregulation observed in cancer and aging.

genomics↗

bioRxiv2026

Spatially Varying Graphical Models for Cell-Cell Interaction Networks in Multiplexed Tissue Imaging

Multiplexed tissue imaging platforms resolve dozens of cell types at single-cell spatial resolution, enabling characterization of conditional interaction networks governing tumor immune microenvironments. Existing methods rely on marginal pairwise co-occurrence statistics without conditioning on third cell types, or estimate a global interaction coefficient per cell type pair that ignores spatial heterogeneity across tissue compartments. We present GP-GHS, a Bayesian nodewise regression framework for inferring spatially varying cell-cell interaction networks from multi-plexed imaging data. Each regression coefficient is modeled as a Gaussian process over the tissue domain, approximated via a Hilbert Space Gaussian Process (HSGP) expansion for scalability. A group horseshoe prior assigns a single local shrinkage parameter across all spectral basis coefficients for each candidate edge, enforcing edge inclusion as a group decision rather than independent coefficient level decisions. This separation of roles, where group shrinkage governs edge existence and the spectral prior governs spatial smoothness conditional on existence, enables recovery of spatially structured graphs with high sensitivity. Posterior inference uses a closed form block Gibbs sampler with nodewise regressions parallelized across cores. In simulation studies, GP-GHS dominates all competitors on F1 and MCC across sparsity levels and problem sizes, with ablations isolating group shrinkage as the critical modeling ingredient. Applied to a 140 image CODEX dataset from advanced colorectal cancer patients stratified by pathology subtype, GP-GHS identifies 13 differentially active edges at FDR < 0.05, forming a Treg-centered immunosuppressive network amplified in the diffuse inflammatory subtype, consistent with known mechanisms of Treg recruitment and macrophage-mediated immunosuppression in colorectal cancer.

bioinformatics↗

bioRxiv2026

Quantum Hamiltonian Learning using Time-Resolved Measurement Data and its Application to Gene Regulatory Network Inference

We present a new Hamiltonian-learning framework based on time-resolved measurement data from a fixed local IC-POVM and its application to inferring gene regulatory networks. We introduce the quantum Hamiltonian-based gene-expression model (QHGM), in which gene interactions are encoded as a parameterized Hamiltonian that governs gene expression evolution over pseudotime. We derive finite-sample recovery guarantees and establish upper bounds on the number of time and measurement samples required for accurate parameter estimation with high probability, scaling polynomially with system size. To recover the QHGM parameters, we develop a scalable variational learning algorithm based on empirical risk minimization. Our method recovers network structure efficiently on synthetic benchmarks and reveals novel, biologically plausible regulatory connections in Glioblastoma single-cell RNA sequencing data, highlighting its potential in cancer research. This framework opens new directions for applying quantum-like modeling to biological systems beyond the limits of classical inference.

bioinformatics↗

bioRxiv2026

Intestinal intraepithelial lymphocyte migration and tissue homeostasis are dictated by Gα13

Intestinal intraepithelial lymphocytes (IEL), including conventional CD8{beta} T resident memory (Trm) cells and unconventional CD8 T cells, promote tissue integrity. Here, we studied the G-protein coupled receptor signals regulating IEL positioning, homeostasis, and function. Deficiency in heterotrimeric G-protein subunit G13 or its effector Arhgef1 caused an intestine-specific loss of all types of CD8+ TCR{beta} and TCR{gamma}{delta} IEL. G13-deficient IEL exhibited restricted intraepithelial movement and impaired maturation. Induction of intestinal CD8{beta}+ Trm upon infection was intact in the absence of G13-signaling, but the cells had poor access to the villous niche and defective survival that could be rescued by increasing TGF{beta}+ or interleukin (IL)15. In vivo CRISPR/Cas9 screening identified GPR132 as a G13-coupled receptor that regulates CD8{beta}+ IEL homeostasis and migration to lysophosphatidylcholine. Mice bearing G13-deficient T cells suffered more severe colitis and increased colorectal tumor growth. The selective requirement for G13-signaling for IEL positioning and survival in the villous niche has implications for therapeutic intervention.

immunology↗

bioRxiv2026

Spatial multi-omics identify an immunosuppressive lipid-laden macrophage niche in primary CNS lymphoma

Primary central nervous system lymphoma (PCNSL) is histologically a subtype of diffuse large B-cell lymphoma (DLBCL), sharing genetic and transcriptomic similarity, but with distinct clinical features, particularly its confinement to the CNS and higher relapse risk. The microenvironmental basis for its divergence from systemic DLBCL remains unclear. Using spatial transcriptomic approaches (Xenium/GeoMx digital spatial profiling) in a comparative study of PCNSL (n=17) and DLBCL (n=76), we found that PCNSL, unlike systemic DLBCL, is dominated by an immunosuppressive macrophage compartment enriched for cholesterol-metabolism programs. In independent cohorts of PCNSL profiled by single-cell RNA sequencing, we validated the presence of a recurrent population of lipid-laden macrophages (LLMs): TREM2/GPNMB-expressing, lipid-remodeled cells transcriptionally distinct from resident microglia and consistent with an infiltrating monocyte origin, not previously characterized in CNS lymphoma. LLMs formed immunosuppressive niches with regulatory T cells, and using Cellscape hyperplex proteomic imaging we demonstrate that LLM-Treg spatial interactions are associated with chemotherapy response. To test whether LLMs are lymphoma-driven and functionally important, we developed an immunocompetent syngeneic PCNSL mouse model, driven by Myd88L252P and Cd79b mutations with Bcl2 overexpression. Monocyte-derived macrophages in lymphoma-bearing brain regions acquired an LLM-like state, not seen in lymphoma-free brain regions or in splenic tumors driven by the same oncogenic lesions. TREM2-SYK signaling sustained this state, and SYK inhibition reversed its tumor-supportive activity ex vivo. These findings identify the LLM program as a targetable immunosuppressive myeloid state in CNS lymphoma.

cancer biology↗

bioRxiv2026

Obscuration to Clarity: Bone Suppression for Enhanced Localization in Pneumothorax Segmentation of Chest Radiographs

Chest radiography (CXR) is a primary modality for assessing cardiopulmonary conditions, but its effectiveness is limited by anatomical obstructions (e.g., ribs, clavicles) that hinder accurate pneumothorax segmentation, boundary delineation, and severity estimation. While deep learning-based bone suppression improves soft-tissue visibility, its utility for precise pixel-wise localization remains underexplored. This study investigates the downstream application of bone suppression for pneumothorax segmentation, integrating it as a preprocessing step to mitigate bony obscuration. We evaluate its impact across CNN and Vision Transformer models on two public datasets, where models trained on bone-suppressed CXRs significantly outperform (p < 0.05) non-suppressed counterparts, achieving up to 17% improvement in Mean Average Surface Distance (MASD), 4.9% in Dice Similarity Coefficient (DSC), and 5.9% in Normalized Surface Dice (NSD), alongside a 9.5% gain in Matthews Correlation Coefficient (MCC). These results demonstrate bone suppression as an architecture-independent enhancement for pneumothorax localization, improving the reliability of automated CXR interpretation.

bioengineering↗

bioRxiv2026

Reversible control of T cell exhaustion by NR4A transcription factors revealed through targeted protein degradation

Tumor-infiltrating CD8+ T cells (TILs) show progressive loss of effector function and upregulation of inhibitory receptors. NR4A transcription factors have emerged as key regulators of this dysfunctional state. Here we developed degron-based systems enabling rapid degradation of endogenous NR4A proteins in both mouse and primary human T cells. We demonstrate that the continuous presence of each NR4A protein is required to maintain suppression of effector cytokines and expression of co-inhibitory receptors; degradation of individual NR4A proteins rapidly restored these functional features, with each NR4A protein exerting prominent effects on distinct as well as overlapping subsets of genes and surface markers associated with effector, memory and exhaustion programs. Transcriptional profiling of phenotypically defined populations revealed both shared and unique gene programs across NR4A family members. Through CRISPR-mediated endogenous gene editing in primary human CD8+ T cells, we show that targeted degradation of NR4A proteins with a small molecule degrader can maintain cytokine expression and suppress inhibitory receptor expression in cells subjected to chronic stimulation, providing a framework for a powerful strategy for therapeutic intervention. One Sentence SummaryTargeted degradation of endogenous NR4A proteins reveals that individual family members maintain features of T cell dysfunction through overlapping as well as non-redundant mechanisms, providing a therapeutic strategy to restore anti-tumor function.

immunology↗

bioRxiv2026

AADAT-Driven Metabolic Control of Malate and CoQ10 Shapes Immune Evasion in Triple-Negative Breast Cancer

Compared to other subtypes of breast cancer, triple-negative breast cancers (TNBC) have fewer treatment options and exhibit a worse prognosis. Through integrated transcriptomic, metabolomic, immunohistochemical, spatial, and clinical analyses, we identify the mitochondrial enzyme, -aminoadipate aminotransferase (AADAT) as a previously unrecognized metabolic immune checkpoint in TNBC. AADAT mRNA and protein were significantly upregulated in human TNBC, and high AADAT expression was associated with reduced intra-tumoral CD8 T-cell density and inferior survival. Genetic silencing of AADAT in orthotopic murine TNBC models curtailed primary tumor growth and distant metastasis in a CD8 T-cell-dependent manner, enhanced effector T-cell activation, and sensitized tumors to dual PD-1/CTLA-4 blockade. Mechanistically, unbiased metabolomics showed increased malate levels after AADAT knockdown. Additionally, 4-hydroxyphenylpyruvate, an essential precursor for coenzyme Q10(CoQ10) biosynthesis, decreased following AADAT knockdown, suggesting an impaired mitochondrial electron transport chain. CoQ10 supplementation restored metabolic balance and reversed malate accumulation caused by AADAT knockdown, indicating that AADAT helps maintain CoQ10-supported redox homeostasis, thereby preventing malate buildup and export. Notably, malate addition directly boosted CD8 T-cell oxidative metabolism, increased the NAD/NADH ratio and reactive oxygen species, and augmented TNF- and IFN-{gamma} production. In vivo, malate supplementation in drinking water phenocopied AADAT knockdown, restored the response to paclitaxel plus anti-PD-1 therapy in multiple independent syngeneic TNBC models with de novo or acquired resistance to immunotherapy, reduced tumor burden, and prolonged survival. In patient cohorts, higher spatially clustered intra-tumoral malate is associated with co-localization of functional CD8 T cells, decreased exhausted T-cell neighborhoods, and superior post-chemotherapy outcomes. These data position AADAT as a central metabolic orchestrator of immune escape in TNBC and nominate oral malate as a readily translatable adjuvant to reverse chemo-immunotherapy resistance in TNBC. Statement of SignificanceAADAT defines a metabolic-immune axis driving immune evasion and therapy resistance in triple-negative breast cancer. Blocking AADAT or administering oral malate reactivates CD8 T-cell immunity and sensitizes chemo-immunotherapy-resistant tumors to these agents. These findings uncover a readily translatable metabolic vulnerability with potential to improve outcomes for patients with aggressive breast cancer subtypes.

cancer biology↗