Biology subjects

Ruppin, E.

Publications and source records attributed to Ruppin, E..

At least 19 recordsLinked to original sources

bioRxiv2026

Transient Splicing-Associated Isoform Remodeling Defines Early MAPKi Resistance Adaptation in Melanoma

A long-standing question in targeted therapy resistance is how tumor cells rapidly enter drug-tolerant states before stable resistance emerges. In BRAF-mutant melanoma, early adaptation to MAPK inhibitor (MAPKi) therapy is accompanied by profound phenotypic plasticity, yet the molecular mechanisms that enable this transition remain poorly understood. Here, we show that MAPKi adaptation is characterized by a transient transcript isoform remodeling that is largely invisible to conventional gene-expression analysis. We integrated and analyzed multiple different datasets, including bulk and single-cell RNA-seq datasets from melanoma cell lines, murine models, patient-derived xenografts and patient tumors, spanning no-treatment, on-treatment/minimal residual disease, and acquired-resistant states. We find that differential transcript usage peaks during the adaptive on-treatment phase and then is markedly attenuated when resistance was established. These dynamics were conserved across experimental systems and supported by independent exon-usage analyses. Notably, most genes undergoing differential transcript usage showed no corresponding change in total gene expression, and genes with isoform-level changes more strongly highlighted MAPKi resistance-associated pathways, including PI3K/AKT/mTOR and receptor tyrosine kinase signaling. Further, classifiers trained on cell-line transcript-usage features identified the adaptive state more accurately than gene-expression-based models in independent PDX and patient datasets. Interestingly, MAPKi-associated isoform changes recapitulated melanoblast-associated developmental splicing programs and were linked to a few key splicing regulators. Together, these findings identify an underappreciated regulatory layer connecting drug tolerance, developmental plasticity, and acquired resistance. Beyond the current context, our results suggest that comprehensively tracking isoform and splicing alterations may potentially advance our understanding of the emergence of treatment resistance in additional indications and treatment regimes.

cancer biology↗

bioRxiv2026

Qombucha: Reconstructing unobserved progenitor methylation profiles reveals distinct developmental programs in glioblastoma

Glioblastoma (GBM) is a highly aggressive brain cancer characterized by substantial intratumoral heterogeneity. Previous research demonstrates that GBM may have complex cell origins. To elucidate the interplay between brain development and GBM progression, we developed Qombucha (Quadratic prOgraMming Based tUmor deConvolution with cell HierArchy), a computational framework that uses DNA methylation data to infer tumor cell-type composition and profiles of unobserved progenitor cells. Unprecedentedly, Qombucha incorporates a developmental cell hierarchy that models mature brain cell types and their progenitors. Applied to a large TCGA GBM dataset spanning the RTK I, RTK II, and MES TYP subtypes, Qombucha identifies a distinct cell type composition profile for each subtype and recapitulates known biological patterns, including elevated microglia infiltration in MES TYP tumors. It also identifies subtype-specific developmental programs and shows that higher progenitor-cell abundance is associated with poorer survival. Qombucha-imputed cell fractions map methylation profiles of tumor samples to a compact, 11-dimensional latent space; in an independent NCI GBM cohort, this compact representation improves subtype clustering and enables accurate subtype classification, achieving performance comparable to state-of-the-art models based on full methylation profiles with much higher dimensionality. These results suggest that tumor cellular composition captures the core biological axes along which GBM subtypes diverge.

bioinformatics↗

bioRxiv2026

Proteomic Profiling Identifies CLDN3 as a Tumor-Selective Therapeutic Target in Small Cell Lung Cancer

Small cell lung cancer (SCLC) remains a highly lethal disease with limited targetable surface antigens beyond delta-like ligand 3 (DLL3). We sought to systematically identify and validate tumor-selective cell-surface targets in relapsed SCLC. To this end, we developed an integrated proteogenomic pipeline combining single-cell RNA sequencing, combinatorial optimization, mass spectrometry-based proteomics, and immunohistochemistry to systematically map the SCLC surfaceome of 49 tumors across 25 patients with relapsed SCLC. Using this approach we found claudin-3 (CLDN3) to be a consistently expressed and tumor-selective antigen, with broader coverage than DLL3, which is the current clinical benchmark. CLDN3 was highly expressed across various treatment states, while maintaining low expression in most nonmalignant tissues. Functional validation using a novel CLDN3-specific monoclonal antibody (ABN501) demonstrated NK cell-mediated cytotoxicity in vitro and tumor regression in vivo, as well as a favorable safety profile. These findings support clinical development of CLDN3-directed therapies and demonstrate the utility of integrated proteogenomic approaches for antigen discovery in solid tumors.

cancer biology↗

bioRxiv2026

Hepatobiliary Progenitor-like Reprogramming in Liver Metastases

Metastatic progression requires cancer cells to adapt to the unique constraints of distant organ microenvironments, yet the mechanisms that drive organ-specific adaptations remain poorly understood. Here, we show that the liver actively rewrites metastatic cancer cell identity, driving tumor cells toward a hepatobiliary progenitor-like state. Through integrated transcriptomic, proteomic, metabolomic, and epigenomic analyses of patient-derived rapid-autopsy samples and experimental models, we identify this state as selectively enriched in liver metastases. It is characterized by co-activation of hepatic and biliary/progenitor regulators HNF4A and SOX9 and is observed across multiple epithelial cancers, indicating a conserved response to the hepatic niche. Mechanistically, hepatocyte-derived TGF-{beta} and hypoxia converge to activate a HIF-1-ACLY axis, increasing nuclear acetyl-coenzyme A availability and histone acetylation at hepatic lineage regulatory elements to drive hepatobiliary reprogramming. This coordinated niche-response program can be captured transcriptionally and is associated with inferior overall survival. Disruption of this pathway suppresses hepatic reprogramming and impairs liver metastatic fitness. These findings identify the liver as an active determinant of metastatic cell fate, linking microenvironmental signaling to metabolic and chromatin remodeling programs that enable lineage plasticity. More broadly, they reveal organ-specific reprogramming as a fundamental principle of metastasis and a therapeutic vulnerability in liver metastases.

cancer biology↗

bioRxiv2026

Cell type-centric interaction networks define spatial architecture of intrahepatic cholangiocarcinoma

Tumor spatial organization critically shapes disease progression and therapeutic response, yet remains poorly defined. Intrahepatic cholangiocarcinoma (iCCA), a rare and aggressive liver malignancy with extensive stromal and immune remodeling, provides a compelling model to study tumor architecture. We generated a single-cell spatial atlas of 1 million cells from 131 iCCA patients using 53-plex spatial proteomics. To systemically characterize tumor spatial organization, we developed a graph-based deep learning framework to define cell type-centric interaction networks, identifying 41 distinct multicellular spatial patterns. Integration of these networks revealed higher-order tumor- and immune-enriched microenvironments associated with patient outcomes. Notably, neutrophil-associated tumor-enriched and tumor-desert microenvironments delineated patient groups with opposing clinical outcomes and distinct neutrophil states. These findings were validated by single-cell spatial transcriptomic profiling of 6 million cells from 162 iCCA patients. Together, this study defines the spatial architecture of iCCA and provides a comprehensive resource for exploring tumor spatial organization.

cancer biology↗

bioRxiv2026

Histopathology-inferred spatial transcriptomics characterizes the tumor microenvironment in 1,500 head and neck tumors and predicts clinical outcomes

Head and neck squamous cell carcinoma (HNSC) is a prevalent malignancy associated with poor prognosis despite recent therapeutic advances. We hypothesized that a comprehensive understanding of the spatial heterogeneity and organization of the tumor microenvironment (TME) can substantially improve risk stratification and prediction of treatment response in HNSC. As spatial transcriptomics (ST) remains labor-intensive and costly, we developed HEiST (H&E-Inferred Spatial Transcriptomics), a deep learning framework that predicts spatially resolved gene expression profiles directly from routine hematoxylin and eosin (H&E)-stained histology slides. After rigorous validation across two independent external ST cohorts, we applied HEiST to infer spatial transcriptomes across 1,500 HNSC patient tumors spanning two publicly available datasets and two newly generated cohorts, one treated with concurrent chemoradiotherapy (CCRT) and one with immunotherapy. This large-scale analysis uncovered reproducible spatial clusters characterizing the HNSC TME, defining two distinct prognostic Spatiotypes, Immune-Exhausted and Immune-Activated, with significantly distinct survival outcomes. Critically, spatial cluster composition accurately predicts HPV status and yields treatment response predictors for both CCRT/radiotherapy and immunotherapy that outperform costly gene-expression and direct image-based approaches. Notably, the ST cluster-based predictor of immunotherapy response markedly surpasses the performance of commonly used FDA-approved biomarkers, including CPS, TPS, and their combination. To the best of our knowledge, this represents the first virtual spatial profiling effort and the most comprehensive large-scale spatial TME analysis in HNSC to date. HEiST thus introduces a scalable, low-cost, and spatially grounded biomarker discovery for precision oncology in HNSC.

cancer biology↗

bioRxiv2025

AI-based supervised treatment response prediction from tumor transcriptomics: A large-scale pan-cancer study

Precision oncology aims to guide treatment decisions using biomarkers. While DNA-based panels are increasingly applied, RNA transcriptomics remain underused due to limited datasets and the absence of robust models. We assembled the largest transcriptomic resource for drug response prediction to date, spanning 91 cohorts, 5,675 patients, nine cancer types, and six frontline therapies: anti-PD-1/PD-L1 immune-checkpoint inhibitors, trastuzumab, bevacizumab, BRAF inhibitors, paclitaxel, and FAC/FEC (Fluorouracil-Adriamycin-Cyclophosphamide/Fluorouracil-Epirubicin-Cyclophosphamide) chemotherapy. We developed EXPRESSO (EXpression-Profile-RESponSe-Optimizer), a supervised machine-learning framework that predicts treatment response from pre-treatment transcriptomes by integrating drug targets and context-specific biomarkers. EXPRESSO achieves mean ROC-AUCs of 0.62-0.73 and median odds ratios of 2.4-4.6 across therapies, outperforming 20 published transcriptomic signatures and other machine learning methods. Prospective validation on 22 independent cohorts confirms that performance generalizes beyond cross-validation. The EXPRESSO signature additionally stratifies progression-free survival in immune checkpoint blockade-treated cohorts, demonstrating prognostic value beyond binary response prediction. Robustness analysis reveals that predictive performance plateaued for some therapies with increasing training cohorts but continued to improve for others. These findings suggest inherent limits of supervised brute-force learning for certain treatments, but additional data and deeper mechanistic modeling may further enhance transcriptomics-based predictors. SIGNIFICANCEEXPRESSO forms the next step in studying the feasibility of harnessing bulk transcriptomic data to inform therapeutic decision-making, advancing the role of transcriptomics from exploratory biomarker discovery to actionable predictive modeling.

cancer biology↗

bioRxiv2025

Co-targeting MERTK and EGFR with a Bispecific Antibody Overcomes Drug Resistance Across Mutations in Exons 19, 20, and 21

Resistance of lung cancer to EGFR-specific tyrosine kinase inhibitors (TKIs), such as osimertinib, often arises from secondary mutations or the activation of bypass signaling pathways. We noted elevated levels of GAS6 and its receptor, MERTK, in patients acquiring resistance to osimertinib, hence hypothesized that the GAS6-MERTK axis can serve as a therapeutic target. GAS6 promoted cell survival and an anti-GAS6 antibody we generated delayed tumor relapses in a xenograft model. Likewise, MERTK ablation sensitized lung cancer cells to osimertinib due to a newly described EGFR-to-MERTK crosstalk. Hence, we developed Bis3, a bispecific antibody targeting both MERTK and EGFR and promoting their degradation. Bis3 plus TKIs cooperatively inhibited the growth of TKI-resistant lung cancer spheroids and markedly delayed relapses of patient-derived xenografts harboring the clinically challenging exon-20 mutations. These findings establish the GAS6-MERTK axis as a driver of drug resistance and provide a rationale for clinical development of Bis3.

cancer biology↗

bioRxiv2025

Mapping Structural Aging across Human Tissues reveals tissue-specific trajectories, coordinated deterioration and genetic determinants

1.Tissue structure, the organization of cells, vasculature and extracellular matrix, underpins organ function. Yet how it deteriorates with age remains largely uncharacterized. Current aging research focuses primarily on molecular changes, missing this structural dimension. Here we present PathStAR (Pathology-based Structural Aging Rate), a computational framework that quantifies tissue structural aging from histopathology images without training on chronological age. Applying PathStAR to 25,306 postmortem biopsies spanning 40 tissue types from 970 individuals aged 21-70, we show that structural aging unfolds through non-linear phases rather than gradual decline. We identify three distinct temporal programs: early-aging tissues (vascular system, peaking in the 30s), late-aging tissues (uterus and vagina, peaking around menopause) and biphasic-aging tissues (digestive and male reproductive organs, with two acceleration periods). During accelerated phases, tissues exhibit common molecular signature of increased inflammation, decreased energy production, regeneration and quality control alongside tissue-specific pathway disruptions related to its function: Artery-specific decline of peroxisomal function, responsible for fatty acid breakdown and testis-specific decline of spermatogenesis. Cross-organ analysis reveals coordinated deterioration within individuals, not only within expected organ systems but also unexpectedly between digestive and reproductive tissues, traced to shared sex hormone signaling. This reveals a role for sex hormones in maintaining structural integrity of non-reproductive organs during aging. Genome-wide association analysis identifies 123 germline variants associated with organ-specific accelerated structural aging, including variants in the longevity regulator SIRT6 linked specifically to vascular structural decline. As proof of concept, PathStAR captures the established non-linear functional decline of the ovary, fertility loss in the 30s and menopause in the 50s, that bulk transcriptomic and methylation profiles from matched samples fail to detect. PathStAR provides a systematic map of structural aging across the human body: when tissues structure decline, what molecular programs define each phase, and how organs deteriorate in coordination.

bioinformatics↗

bioRxiv2025

Pathologist-interpretable breast cancer subtyping and stratification from AI-inferred nuclear features

Artificial intelligence (AI) is making notable advances in digital pathology but faces challenges in human interpretability. Here we introduce EXPAND (EXplainable Pathologist Aligned Nuclear Discriminator), the first pathologist-interpretable AI model to predict breast cancer tumor subtypes and patient survival. EXPAND focuses on a core set of 12 nuclear pathologist-interpretable features (NPIFs), composing the Nottingham grading criteria used by the pathologists. It is a fully automated, end-to-end diagnostic workflow, which automatically extracts NPIFs given a patient tumor slide and uses them to predict tumor subtype and survival. EXPANDs performance is comparable to that of existing deep learning non-interpretable black box AI models. It achieves areas under the ROC curve (AUC) values of 0.73, 0.79 and 0.75 for predicting HER2+, HR+ and TNBC tumor subtypes, respectively, matching the performance of proprietary models that rely on substantially larger and more complex interpretable feature sets. The 12 NPIFs demonstrate strong and independent prognostic value for patient survival, underscoring their potential as biologically grounded, interpretable biomarkers for survival stratification in BC. These results lay the basis for building interpretable AI diagnostic models in other cancer indications. The complete end-to-end pipeline is made publicly available via GitHub (https://github.com/ruppinlab/EXPAND) to support community use and reproducibility.

bioinformatics↗

bioRxiv2025

Pan-cancer prediction of tumor immune activation and response to immune checkpoint blockade from tumor transcriptomics and histopathology

Accurately predicting which patients will respond to immune checkpoint blockade (ICB) remains a major challenge. Here, we present TIME_ACT, an unsupervised 66-gene transcriptomic signature of tumor immune activation derived from TCGA (The Cancer Genome Atlas) melanoma data. First, we demonstrate that TIME_ACT scores accurately identify tumors with activated immune microenvironments across different cancer types. Further, analysis of spatial features reveals that tumor microenvironment regions with dense lymphocyte infiltration near tumor cells have high TIME_ACT scores, successfully marking localized immune activation. Second, across 25 transcriptomic ICB cohorts encompassing nine cancer types, TIME_ACT achieves a mean AUC of 0.76 and a mean odds ratio of 5.77, significantly outperforming 30 established transcriptomic signatures and prediction methods for ICB response, including a recently developed foundation model for immunotherapy response prediction. Third, we show that TIME_ACT scores can be accurately inferred from routine tumor histopathology slides and that slide-inferred TIME_ACT scores predict ICB response across nine new independent patient cohorts spanning eight cancer types, achieving a mean AUC of 0.72 and a mean odds ratio of 4.99. These findings establish TIME_ACT as a robust, pan-cancer biomarker that enables accurate, low-cost, and clinically scalable prediction of ICB response from routine histopathology.

cancer biology↗

bioRxiv2025

CRISPR-Based Gene Dependency Screens reveal Mechanism of BRAF Inhibitor Resistance in Anaplastic Thyroid Cancer

Anaplastic thyroid cancer (ATC) is the most aggressive form of thyroid cancer. Despite recent advances in treating BRAFV600E-driven ATC, therapy resistance remains a significant challenge, often resulting in disease progression and death. Leveraging a focused CRISPR/KO screen in parallel with a CRISPR/activation screen, both tailored on response to BRAFV600E inhibitor treatment, we identified TAZ (encoded by the WWTR1 gene) deficiency as synthetically lethal with BRAF inhibitor in ATC. TAZ is overexpressed in ATC compared to well-differentiated thyroid tumors. We demonstrate that TAZ-deficient ATC cells display heightened sensitivity to BRAF inhibitors both in vitro and in vivo. Using gene essentiality score across a large panel of cancer cell lines, we found that BRAFV600E-driven cancers are highly sensitive to TAZ loss, unlike their counterparts with wild-type BRAF and non-BRAFV600E. Mechanistically, we demonstrate that dabrafenib triggers the Unfolded Protein Response (UPR) under ER stress and suppresses protein synthesis. TAZ loss represses the UPR, reverses the inhibition of protein synthesis, and triggers increased cell death by ferroptosis in dabrafenib-treated ATC. Collectively, our findings unveil TAZ as a new target to overcome resistance to BRAF inhibitors in undifferentiated thyroid cancer.

cancer biology↗

bioRxiv2025

Deep learning inference of cell type-specific gene expression from breast tumor histopathology

Cell type-specific gene expression from single-cell RNA sequencing (RNA-seq) is valuable for breast cancer precision oncology but available cohorts are still limited due to its high cost. Deconvolution methods infer cell type-specific expression from bulk RNA-seq at a lower cost, yet expenses and processing time of bulk RNA-seq are also non-negligible and limit their application too. To address these limitations, we developed SLIDE-EX (SLide-based Inference of DEconvolved gene EXpression), a deep-learning tool that predicts cell type-specific gene expression and abundances directly from routine breast cancer histopathology whole slide images (WSIs), using deconvolved bulk RNA-seq data as training labels. Trained on the TCGA-breast cohort and tested in cross validation and on an independent cohort of 160 cases, SLIDE-EX robustly infers the expression of thousands of genes across 9 distinct cell types, performing best for cancer associated fibroblasts and cancer cells. The abundance of these two cell types could also be robustly predicted, together with that of myeloid cells. The robustly predicted genes reflect key biological functions of their respective cell types. From a translational perspective, the inferred cell type specific expression profiles predict chemotherapy response more accurately than models based on direct prediction from the slides or from the inferred bulk expression in two independent cohorts. Going forward, SLIDE-EX is a generic approach that opens up possibilities for rapid, cost-effective cell type-specific gene expression inference in potentially any cancer type, further democratizing the characterization of the tumor microenvironment.

cancer biology↗

bioRxiv2025

Single-cell-guided identification of logic-gated antigen combinations for designing effective and safe CAR therapy

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment of hematological malignancies. However, its application in solid tumors remains limited because single targets are unlikely to suffice due to tumor antigen heterogeneity and off-tumor toxicities. To overcome these obstacles, we developed LogiCAR designer, a computational approach that utilizes single-cell transcriptomics data from patient tumors to systematically identify the cancer-specific antigen circuits with logic gates ("AND," "OR," and "NOT") that target the majority of cancer cells in a tumor while sparing normal cells and tissues as much as possible. LogiCAR designer efficiently scales to higher-order antigen combinations involving up to five genes. Applied to a large-scale dataset encompassing approximately 2 million cells (including > 620k tumor cells) from 342 clinical patient samples across all major breast cancer subtypes, LogiCAR designer identified antigen circuits with enhanced tumor-targeting efficacy and improved safety profiles compared to both previously reported circuits and single-target therapies in clinical trials. However, even these optimized shared circuits still proved insufficient for some patients. We hence systematically studied LogiCAR designers ability to identify highly effective CAR circuits that are individualized to each patient. Remarkably, such personalized CAR circuits provide estimated tumor-targeting efficacy tantamount to complete response in 76% of patients and partial response for all patients. Taken together, this analysis is the first systematic quantification of the efficacy and safety of all possible CAR circuits, showing that: (a) the quality of existing solutions leaves much to be desired; (b) the ability of shared circuits optimized across many patients is moderate, and finally, (c) individually tailored circuits offer significantly higher tumor-targeting efficacies for patients. LogiCAR designer offers a rigorous, data-driven way to facilitate the rational design of safe and effective CAR-based immunotherapies for cancer. Statement of Significance Development of a computational approach that efficiently identifies logic-gated CAR target combinations, called circuits, from single-cell transcriptomics, addressing a critical unmet clinical need. Application to the largest ensemble of breast cancer datasets to date, comprising [~]2 million cells (> 620k tumor cells) from 17 clinical cohorts, to identify CAR circuits predicted to be effective. Comprehensive safety profiling of candidate circuits spanning major tissues at both RNA and protein levels. Logic-gated CAR circuits generated by our pipeline address tumor heterogeneity and achieve efficacy and safety scores that surpass clinical trial and previously computationally identified circuits. Individualized rational CAR design offers a transformative approach to deliver precision-engineered CAR therapies with unprecedented efficacy.

bioinformatics↗

bioRxiv2025

Path2Omics: Enhanced transcriptomic and methylation prediction accuracy from tumor histopathology

Precision oncology is becoming increasingly integral to clinical practice, demonstrating notable improvements in treatment outcomes. While molecular data provide comprehensive insights, obtaining such data remains costly and time-consuming. To address this challenge, we developed Path2Omics, a deep learning model that predicts gene expression and methylation from histopathology for 23 cancer types. Path2Omics was trained on 20,497 slides (9,456 formalin-fixed and paraffin-embedded (FFPE) and 11,041 fresh frozen (FF)) from 8,007 patients across 23 The Cancer Genome Atlas cohorts. When tested on FFPE slides, the most readily available format in clinical pathology practice, the integrated model outperformed its individual FF and FFPE components, robustly predicting nearly 5,000 genes on average, approximately five times more than our recently published DeepPT model. Externally evaluated on seven independent cohorts, Path2Omics robustly predicted the expression of approximately 4,400 genes, yielding a 30% increase over the FFPE model alone. Finally, we demonstrate that the inferred gene expression is nearly as effective as the actual values in predicting patient survival and treatment response. These results lay the basis for using Path2Omics to advance precision oncology from histopathology slides in a speedy and cost-effective manner. Statement of significancePath2Omics is a deep learning model that accurately predicts gene expression and methylation from histopathology slides across 23 cancer types. Unlike existing approaches that rely solely on FFPE slides for training, Path2Omics leverages both FFPE and FF slides by constructing two separate models and integrating them. Downstream analyses show that the inferred values from Path2Omics are nearly as effective as actual values in predicting patient survival and treatment response.

bioinformatics↗

bioRxiv2025

Inducible re-epithelialization of cancer cells increases autophagy and DNA damage: implications for breast cancer dormancy

Epithelial lineage differentiation is pivotal to mammary gland development and it can pause metastasis of breast cancer (BC) by inducing tumor dormancy. To simulate this, we expressed epithelial genes in mesenchymal BC cells. Inducible expression of the epithelial OVOL genes in metastatic BC cells suppressed proliferation and migration. We found that C1ORF116, an OVOLs target, is susceptible to genetic and epigenetic aberrations in BC. It is regulated by steroids and functions as a putative autophagy receptor that inhibits antioxidants like thioredoxin. Accordingly, boosting epithelialization lowered glutathione, elevated reactive oxygen species and increased both DNA oxidation and double strand breaks. Epithelialization also associated with redistribution of NRF2 and an altered interplay among p38, ATM, and the other kinases regulating the DNA damage response. Hence, hormonal regulation of OVOLs and chronic stress might permit epithelial differentiation and retard exit from dormancy, while altering redox homeostasis and permitting DNA damage accumulation, which may awaken dormant tumors.

cancer biology↗

bioRxiv2025

TMPRSS2-ERG confers resistance to antiandrogens: mechanism and therapeutic implications

Approximately 50% of prostate cancer (PCa) patients harbor fusions involving the TMPRSS2 and ERG genes. Despite this, tailored therapies targeting the fused gene, tERG, remain undeveloped. Our study analyzed biopsy samples from two clinical trials assessing the efficacies of androgen receptor (AR) signaling inhibitors (ARSIs). The results revealed that tERG promotes resistance to ARSIs and is associated with elevated levels of the glucocorticoid receptor (GR). Subsequent assays showed that GR directly interacts with tERG, alleviates allosteric autoinhibition and prevents chemotherapy-induced tERG degradation. In PCa models, either inhibiting GR or lowering cortisol levels suppressed tumor growth in tERG-positive models, but not in fusion-negative models. In addition, patient-derived fusion-positive xenografts displayed enhanced sensitivity to combined GR and AR inhibitors. Collectively, these findings highlight TMPRSS2-ERG as a new biomarker and propose that simultaneous inhibition of GR and AR may specifically benefit tERG-positine patients. However, GR stimulatory corticosteroid therapies may not be advisable for this patient subgroup.

cancer biology↗

bioRxiv2024

IMMClock reveals immune aging and T cell function at single-cell resolution

The aging of the immune system substantially impacts individual immune responses, yet accurately quantifying immune age remains a complex challenge. Here we developed IMMClock, a novel immune aging clock that uses gene expression data to predict the biological age of individual CD8 T cells, CD4 T cells, and NK cells. The accuracy of IMMClock is first validated across multiple independent datasets, demonstrating its robustness. Second, utilizing the IMMClock, we find that intrinsic cellular aging processes are more strongly altered during immune aging than differentiation processes. Thirdly, our analysis confirms the strong associations between immune aging and established processes such as cellular senescence, exhaustion, and telomere length at the single cell level. Furthermore, immune aging is accelerated under several disease conditions such as type 2 diabetes, heart disease, and cancer. Finally, we apply IMMClock to analyze a perturb-seq gene activation screen of T cell functionality. We find that the post-perturbation immune age of individual T cells is strongly correlated with their pre-perturbation immune age. Furthermore, the immune age at resting state of individual T cells is strongly predictive of their post-stimulation activation state. Overall, IMMClock advances our understanding of immune aging by providing precise, single-cell level age estimations. Its future applications hold promise for identifying interventions that concomitantly rejuvenate and activate T cells, potentially enhancing efforts to counteract age-related immune decline.

bioinformatics↗