Cancer Research Communications

BackgroundThe ketogenic diet is being explored as an adjuvant intervention in breast cancer because it lowers circulating glucose and elevates ketone bodies such as {beta}-hydroxybutyrate (BHB), but how individual ER+ breast cancer subtypes adapt to these conditions remains poorly characterized. We examined metabolic responses to BHB supplementation under glucose restriction in two ER+ breast cancer cell lines, asking whether metabolic adaptation patterns differ between models. MethodsMCF-7 and T47D cells were cultured under high glucose, glucose-restricted (5% of standard), or glucose-restricted with 10 mM BHB conditions and profiled by comprehensive two-dimensional gas chromatography-mass spectrometry (GCxGC-MS). Pairwise Welchs t-tests with Benjamini-Hochberg false discovery rate (FDR) correction were applied to identify treatment-responsive metabolites. Targeted assays quantified intracellular glycine, SHMT1 protein, and total branched-chain amino acid (BCAA) concentrations across a BHB dose range (2.5-15 mM). Patient tumor transcriptomic data from TCGA (n=1,084) and paired tumor-normal samples from GSE58135 (n=20) were analyzed for genes involved in one-carbon, ketone body, and BCAA metabolism. ResultsMCF-7 and T47D cells exhibited markedly divergent metabolic responses to BHB. In MCF-7 cells, BHB supplementation produced a broad pattern-level metabolic shift: 75% of detected metabolites trended upward when BHB was added to glucose-restricted cultures (C vs. B comparison), with 1,4-butanediol reaching nominal significance (FC=2.35, p=0.016) and a 4.1-fold trend increase in lactic acid (p=0.11), although no individual metabolite survived FDR correction. T47D cells showed essentially no metabolic response to BHB at the global level. Targeted assays detected an elevation in glycine at 5 mM BHB in both cell lines that did not follow a monotonic dose response and was not accompanied by changes in SHMT1 protein expression. Total BCAA levels were elevated by BHB in T47D cells but remained unchanged in MCF-7 cells. In paired patient samples, OXCT1 (log2FC = -1.41), SHMT1 (log2FC = -1.31), and ACAT1 (log2FC = -1.07) were significantly downregulated in ER+ tumors relative to matched normal tissue (adjusted p < 0.001 for all three). ConclusionsER+ breast cancer cell lines show heterogeneous metabolic responses to BHB supplementation under glucose restriction. The broad pattern of metabolite elevation in MCF-7 but not T47D cells suggests that capacity to utilize ketone bodies as metabolic substrate varies between ER+ models. The downregulation of OXCT1, ACAT1, and SHMT1 in ER+ tumors compared to normal tissue identifies these enzymes as candidate biomarkers that may help stratify which patients are likely to benefit from ketogenic interventions. Findings related to individual metabolites should be regarded as exploratory and require validation in larger, adequately powered cohorts.

BackgroundHomozygous deletion of the methylthioadenosine phosphorylase (MTAP) gene is a frequent genetic alteration in cancer. MTAP, which creates adenine from 5-methylthioadenosine (MTA), is constitutively expressed in all tissues throughout the body. Previously, we described a novel strategy to specifically target MTAP-deleted cancer cells by combining the antipurine prodrug 2-fluoroadenine (2FA) with MTA. In vitro, this combination efficiently killed MTAP- cancer cells, but in vivo the combination was much less effective in vivo. Here, we explored the role of xanthine oxidase (XO) in this process. Materials and MethodsVarious combinations of 2FA, MTA, and the xanthine oxidase inhibitor febuxostat (FX) were tested in various cancer cell lines grown in vitro and in mice. LC-MS/MS was used to examine the levels and ratio of intracellular 2-FA-containing nucleotides compared to adenine-containing nucleotides. Results and conclusionsThe treatment of cells with 2FA+MTA in vitro resulted in much higher 2FANP/ANP ratios than the same treatment in vivo. The addition of XO to culture media in vitro effectively abolished the killing by 2FA, and this effect was fully reversed by the addition of febuxostat (FX), a xanthine oxidase inhibitor. In vivo, the addition of FX to 2FA results in increased cell killing and toxicity and a 1000% increase in the amount of 2FA converted to 2-FA-monophosphate (2FAMP). Xenograft studies using MTAP- HT1080 and MiaPaCa-2 cell lines have shown that a 2FA/MTA/FX cocktail can cause tumor regression in vivo. These studies suggest that the combination of 2FA/MTA/FX should be explored as a treatment for MTAP- cancer.

Treatment of advanced head and neck squamous cell carcinoma (HNSCC) often involves radiotherapy combined with chemotherapy, targeted therapy, or immunotherapy. However, due to its anatomical and molecular heterogeneity, identifying the most effective treatment for each patient remains a major clinical challenge. To address this need, we developed a high-throughput organoid-based drug screening platform that uses patient-derived organoids to assess candidate treatment regimens. We validated the platform by establishing bioprinted 3D organoids of human HNSCC cell lines and exposing them to X-ray radiation in combination with various small-molecule drugs and biologics. We quantified viability using ATP release assays and assessed extracellular matrix (ECM) invasion with a machine learning-based brightfield image analysis pipeline. Proof-of-concept experiments with HPV-negative HNSCC lines (HN30 and HN31, established from primary and metastatic disease from the same patient) and HPV-positive HNSCC cells (SCC154) revealed different therapy agents that can radiosensitize each cell line. Image analysis showed that copanlisib, afatinib, and ibrutinib could limit ECM invasion of HN31, while the AKT inhibitor ipatasertib promotes invasion of HN30 cells, consistent with previous studies. Application of the platform to patient-derived HPV+ oropharyngeal tumor organoids showed that they shared sensitivity to several agents while also exhibiting differences against certain therapies. Cetuximab, sorafenib, and nedisertib significantly radiosensitized organoids from two clinical samples. This work demonstrates the feasibility of performing sensitivity screening by integrating bioprinting, conventional viability assays, and advanced image analysis techniques. This platform has the potential to enable a personalized therapeutic pipeline for patients with advanced HNSCC, optimizing responses to radiotherapy and targeted agents to improve clinical outcomes while avoiding modulators that may promote tumor invasion.

BackgroundPrimary and secondary resistance to immune checkpoint blockade (ICB) remains a critical challenge in advanced melanoma. Oncolytic Viruses (OV) selectively lyse tumor cells while generating systemic anti-tumor immune responses with minimal side effects. Yet their clinical use is limited to refractory melanoma patients and are only given in combination with second-line ICB regimens. ICB can both help and hinder OV efficacy depending on the source of checkpoint interactions across the tumor-immune microenvironment (TiME). However, functional checkpoint interactions are typically inferred from gene or protein expression and rarely contextualized within myeloid- and antigen presenting cell-associated immune niches during OV therapy, despite these populations dominating melanoma TiMEs and serving as key regulators of anti-viral immunity. MethodsAn integrated multi-omics framework combining Nanostring GeoMx digital spatial profiling (DSP), COMET sequential immunofluorescence (seqIF) and functional oncology mapping (FuncOmap) was applied to melanoma patient tissues collected pre- and post-neoadjuvant Talimogene Laherparepvec (T-VEC) to characterize immune remodeling and directly quantify checkpoint interaction dynamics associated with pathologic responses to OV therapy. ResultsT-VEC induced broad lymphocyte- and myeloid-associated immune transcriptional activation across melanoma TiMEs; however, pathologic responses could not be defined by bulk transcriptomics or cellular deconvolution alone. COMET seqIF analysis identified that HSV-associated M1/APC-like tumor-associated macrophages (TAMs) were enriched in complete pathologic response (CR) tissues and were a major source of PD-1/PD-L1 interaction niches. While partial (PR) and non-pathologic response (NR) tissues retained melanoma-centered PD-1/PD-L1 interaction niches and were enriched for B cell and M2-like TAM populations. FuncOmap analysis indicated that post-T-VEC PD-1/PD-L1 interaction states were consistently elevated in tumor bed, but not in lymph node tissues, across all pathologic response groups. Suggesting that immune checkpoint interactions may benefit T-VEC therapeutic responses depending on their spatial and immune context relative to OV infection. ConclusionsThese findings highlight the importance of integrated transcriptomic and functional proteomic analyses for resolving the spatial distribution and functional status of immune niches during OV therapy. Resolving PD-1/PD-L1 interaction states to specific M1/APC-like TAM and B cell niches may define mechanisms of responses and resistance to OV therapy.

The clinical heterogeneity of cancer poses a major challenge for precision medicine. Limited cohort sizes across evolving assay platforms impede reliable biomarker discovery. Here, we systematically evaluate how to integrate data from four transcriptomics platforms: bulk and single-cell (sc) RNA sequencing (RNA-seq), NanoString, and microarray for predictive modeling in cancer. We use high-grade serous carcinoma (HGSC) of tube-ovarian origin as a model system, as it is highly heterogeneous in both biology and assay data. We find that using fold-change of gene expression in patients with matched pre- and post-neoadjuvant chemotherapy samples reduces inter-patient and inter-assay variability but is insufficient to overcome platform-specific biases. Microarray and scRNA-seq data exhibit systematic biases, while RNA-seq and NanoString show the most promise for combination into a single training cohort. To mitigate inter-assay limitations, we generate a new data set of HGSC tumor samples profiled with both RNA-seq and NanoString, and use it to identify the limits of detection and optimal harmonization strategies. Our approaches enable integration of cohorts for separate and combined RNA-seq and NanoString predictive models of disease recurrence (test-set AUROCs > 0.8), validated in external microarray cohorts. We leverage single-cell and bulk RNA-seq network-based analyses to provide mechanistic context for genes in the predictive models. Our models indicate that GBP4 expression is a key predictor of recurrence and marks immune remodeling towards cytotoxicity. We provide an interactive web portal to facilitate exploration of data and results. These findings guide cross-assay harmonization of transcriptomic data and enable improved predictive modeling in heterogeneous cancers. Statement of SignificanceWe present a framework for integrating RNA-seq, NanoString, microarray, and single-cell transcriptomic data for predictive modeling, enabling robust biomarker discovery in heterogeneous cancers and identifying GBP4 as a marker of immune remodeling.

Background Cancer treatment response is highly variable, even among patients with the same tumor type and treatment. Exceptional responders (ERs), who are individuals who experience unusually favorable outcomes, provide critical insights into the biological factors driving treatment success. While prior studies have highlighted the role of somatic changes, the contribution of germline rare variants remains underexplored. This study aimed to uncover the genetic underpinnings of exceptional responses by identifying rare, non-silent and predicted deleterious germline mutations enriched among ERs compared to typical cancer patients. Methods The Network of Enigmatic Exceptional Responders (NEER) project collected clinical and germline whole-genome sequencing (WGS) data from 53 ERs. After quality control procedures and ancestry background checks, 51 ERs were left for final analysis. While non-silent mutations were identified based on allele frequencies and mutation types, multiple pathogenicity predictors were applied for predicted deleterious variants. These were compared to a harmonized and comparable subset from the Pan-Cancer Analysis of Whole Genomes (PCAWG) cohort (n=414) using Fisher's exact tests. Kaplan-Meier survival analysis applied to evaluate prognostic associations in PCAWG patients. Additionally, Fisher's exact tests were conducted stratified by cancer type and treatment regimen to identify potential associations between rare germline variants and therapeutic responses. Results Variants in immune-related genes such as CCL26 and GPRC5D were prevalent, suggesting enhanced immune regulation among ERs. Fourteen genes with non-silent and eight with predicted deleterious mutations showed significantly different frequencies between NEER and PCAWG cohorts (FDR < 0.05). IRX3 emerged as a protective gene enriched in ERs, whereas OR6B2 was associated with poor survival in PCAWG lung cancer patients. Moreover, rare non-silent germline variants in drug target genes were enriched among ERs treated with cisplatin and doxorubicin, implicating altered DNA repair and drug-binding mechanisms in their remarkable outcomes. Conclusions This study reveals a distinctive germline mutation landscape in exceptional cancer responders, marked by immune-related and drug-target-associated variants that may enhance therapy response and prolong survival. The findings highlight potential novel prognostic biomarkers, such as IRX3 and OR6B2, providing a foundation for developing personalized cancer treatments informed by rare genetic variation.

BackgroundObesity significantly increases the risk of prognosis and clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). While research on the interactions between obesity and the tumor microenvironment (TME) is mostly confined to a few interactions at a time, leaving a gap in the comprehensive understanding of obesity-driven PDAC. We set out to develop a cell-type-resolved model of obesity-driven PDAC using bulk transcriptomic data to investigate TME changes. MethodsWe conducted an integrated transcriptomic analysis of PDAC patients from the CPTAC-3 cohort (n=140) stratified by BMI. A custom immune and stromal functional gene signature database covering 65 cell types was constructed, followed by LLM-assisted review, overlap control, and validation. BayesPrism deconvolution using matched single-cell references was used to derive expression profiles for each cell type. Stabl, a machine-learning algorithm, was used to identify BMI-associated signatures. Bayesian hierarchical modeling, using both continuous and categorical BMI change, was applied to estimate effect sizes and assess the statistical credibility of the signature changes using the 95% Highest Density Interval (HDI) excluding zero. Virtual multiplex immunofluorescence was generated from whole-slide H&E images using gigaTIME to assess the spatial manifestation of BMI-associated TME changes in tissue ResultsBulk pathway analysis showed that ECM homeostasis and primary immunodeficiency pathways deteriorated with increasing BMI. However, Bayesian modeling revealed cell-type-specific, non-linear dynamics. Stromal populations in overweight (OW) individuals were altered, with changes in ECM synthesis and inflammatory signaling that stabilized rather than intensified during obesity. Immune compartments also showed diverse trajectories: CD4+ T cells remained functional in OW but collapsed in obesity; CD8+ T cells progressed linearly from activation to chronic exhaustion. NK cells exhibited non-monotonic behavior, and monocyte and B cell lineages became impaired prior to clinical obesity. Cell-cell interaction analysis showed a shift from a T cell and dendritic cell-centric adaptive interactome in normal weight patients to a neutrophil-dominated inflammatory network in OW. Spatial analysis showed stromal-trapped CD8+ T cells were compressed closer to the tumor boundary with rising BMI. ConclusionsOverweight status represents a critical tipping point in tumor microenvironmental reprogramming, challenging linear models of obesity-associated immune modulation and suggesting that early metabolic interventions may prevent PDAC functional deterioration. Model is available at https://obese-pdac-model.streamlit.app/ O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/721695v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@b1c8cdorg.highwire.dtl.DTLVardef@1f61b7forg.highwire.dtl.DTLVardef@876c60org.highwire.dtl.DTLVardef@dc32b2_HPS_FORMAT_FIGEXP M_FIG C_FIG

Importance: While gut dysbiosis is known to impair response to immune checkpoint inhibitors (ICIs), the relative clinical impact of antibiotic timing (pre- vs. post-ICI initiation) remains unclear. Objective: To evaluate whether antibiotic timing differentially influences overall survival (OS) in a large, multi-institutional pan-cancer cohort. Design, Setting, and Participants: This retrospective cohort study utilized deidentified electronic health record data from six academic medical centers within the University of California Health system. We included 21,108 adults with any malignancy who received PD-1, PD-L1, or CTLA-4 inhibitors between January 2014 and December 2024. Exposures: Antibiotic exposure windows were categorized as pre-only (-60 to -1 days), post-only (+1 to +60 days), both windows, or none. Main Outcomes and Measures: The primary outcome was overall survival (OS) calculated from the first ICI dose. Multivariable Cox proportional hazards models adjusted for demographics, tumor type, line of therapy, and baseline health indicators (albumin, NLR, and recent hospitalization). Results: Among 21,108 patients, 17.3% had pre-only exposure, 13.3% had post-only exposure, and 60.6% had no exposure. In the multivariable model, post-only exposure (HR, 1.27; 95% CI, 1.20-1.35) and combined pre- and post- exposure (HR, 1.31; 95% CI, 1.23-1.40) were significantly associated with higher mortality. Pre-only exposure was not significantly associated with OS (HR, 1.04; 95% CI, 0.99-1.10). Subgroup analyses by tumor type showed consistent trends across major malignancies, including head and neck (Post HR, 1.46) and renal cell carcinoma (Post HR, 1.26). Conclusions and Relevance: In contrast to some smaller studies, this large-scale analysis indicates that antibiotic exposure after ICI initiation carries a greater risk than exposure prior to treatment. These findings highlight the need for rigorous antibiotic stewardship strategies specifically during the early phases of immunotherapy treatment.

Breast cancer (BC) remains a leading cause of morbidity and mortality worldwide. Early detection remains the most effective strategy for improving prognosis. We explored the urinary extracellular vesicle (uEV) proteome for changes linked to BC which could also be potential biomarkers. Urine samples were collected from 20 participants across four groups (n = 5 each): newly diagnosed BC patients, benign breast disease (BBD) patients, individuals with breast cancer symptoms (symptom control, SC), and age-matched healthy controls (HC). EVs were isolated using size exclusion chromatography and extracted proteins were analysed using a GeLC proteomic approach. Proteins were identified and quantified using Proteome Discoverer and further analysed using MetaboAnalystR, Funrich and Metascape. A total of 256 proteins were identified from the uEV preparations. BC comparisons with BBD, SC and HC identified 7 proteins differentially expressed proteins (DEP); SERPINB1 -- Serpin family B member 1, LCN1 -- Lipocalin 1, SIRPA -- Signal regulatory protein alpha, ACTB -- Actin, beta, YWHAZ --Tryptophan 5-monooxygenase activation protein zeta, Ig JCHAIN and APOA1 -- Apolipoprotein A1. Receiver Operator Characteristic (ROC) curve assessments suggested that each DEP protein had an area under the curve (AUC) of > 0.8. These findings highlight EV-derived proteins as promising non-invasive biomarkers for breast cancer detection, warranting further validation in larger cohorts.

Intratumoral heterogeneity drives variable drug responses in cancer. Single-cell RNA sequencing (scRNA-seq) enables characterization of such heterogeneity and prediction of drug response at single-cell resolution. Accordingly, various computational models have been developed to infer drug response from scRNA-seq data. However, their performance, robustness, and generalizability across different biological contexts remain insufficiently evaluated. To address this gap, we benchmarked representative single-cell drug response prediction models using 26 curated datasets comprising over 760,000 cells across 12 cancer types and 21 therapeutic agents. We constructed balanced and imbalanced scenarios to reflect realistic drug-response label distributions. To address the lack of ground-truth labels in conventional scRNA-seq datasets, we incorporated lineage-tracing data with experimentally validated drug-response annotations, enabling evaluation in a clinically relevant pre-treatment prediction setting. Our results show that prediction performance was markedly higher in cell lines than in tissue samples. Under imbalanced conditions, most methods exhibited sharp performance declines, whereas scDEAL demonstrated the highest robustness. Independent validation using an in-house pancreatic ductal adenocarcinoma dataset further confirmed scDEALs robustness and ability to capture biologically meaningful state transitions. Label-substitution experiment revealed that this robustness was partially driven by the models specific training-label construction. However, benchmarking with lineage-tracing data revealed a fundamental limitation: most models capture drug-induced transcriptional changes but struggled to predict intrinsic resistance before treatment. In summary, our study defines the performance boundaries of current approaches and highlights their limitations in addressing intratumoral heterogeneity, class imbalance, and intrinsic resistance prediction, emphasizing the need for the next-generation single-cell drug response models with stronger clinical relevance.

BackgroundIn RAS-mutant tumors, ERK phosphorylates the mitochondrial fission GTPase DRP1 to promote mitochondrial fission. DRP1 activity is tumor-promoting in pancreatic and other RAS-driven cancers, but its role in therapeutic resistance is unknown. MethodsWe developed a panel of patient-derived pancreatic cancer cell lines resistant to the MEK inhibitor trametinib. We used immunofluorescence imaging, in vitro growth assays and orthotopic xenografts to determine the role of DRP1 in trametinib resistance. ResultsWe find that trametinib-resistant cells exhibit increased expression and phosphorylation of DRP1 compared to sensitive counterparts. Quantitative analysis of mitochondrial structure reveals that mitochondria in resistant cells are morphologically distinct and relatively smaller than sensitive cells treated with trametinib. Genetic and pharmacological inhibition of both c-Myc and CDK6 are sufficient to block DRP1 phosphorylation in resistant cells, suggesting that activation of a c-Myc-CDK6 signaling axis drives reactivation of mitochondrial fission in the absence of MAPK signaling. Importantly, deletion of DRP1 leads to either growth inhibition or re-sensitization to trametinib in resistant lines. ConclusionThese findings suggest DRP1 contributes to drug resistance, and that inhibition of mitochondrial fission might be a promising therapeutic strategy to combat resistance to MAPK and RAS inhibitors.

Metastasis remains the major cause of cancer-related mortality, and circulating tumor cells (CTCs) are both candidate liquid-biopsy biomarkers and plausible intermediates of metastatic dissemination. Because CTCs are extremely rare in peripheral blood, platform comparisons have often focused solely on recovery. That focus is insufficient for applications that depend on the quality of the recovered material, including single-cell profiling, short-term culture, and functional testing. Here, we compared four CTC isolation approaches: TellDx CTC System, Genesis System, RosetteSep, and flow cytometry, using spike-in experiments in human blood. Capture efficiency was evaluated across all four platforms; purity was assessed for TellDx, Genesis, and RosetteSep; and post-isolation GFP signal persistence in culture was assessed for TellDx and Genesis as an exploratory proxy for short-term post-isolation preservation. Under the conditions tested, TellDx showed the highest recovery (88.1% {+/-} 3.7%), followed by Genesis (40.6% {+/-} 12.1%), RosetteSep (36.5% {+/-} 9.0%), and flow cytometry (7.6% {+/-} 4.5%). TellDx also showed the highest purity score (3.76), whereas Genesis (2.25) and RosetteSep (2.09) did not differ substantially. In the short-term culture assay, TellDx-derived samples retained a higher normalized GFP signal than Genesis-derived samples at 48 h and 72 h. To synthesize these readouts, we propose the Recovery Performance Index (RPI), a composite score integrating recovery, purity, and post-isolation signal persistence. Within this experimental framework, TellDx achieved the highest RPI. These data support two conclusions. First, platform benchmarking for CTC workflows benefits from multidimensional evaluation rather than recovery alone. Second, under this spike-in model and within the specific workflows used here, TellDx performed best among the platforms tested. The principal contribution of this study is therefore the establishment of a practical benchmarking framework that can be expanded in future work using clinical samples, multiple CTC phenotypes, and orthogonal viability assays.

While Trastuzumab emtansine (T-DM1) and other HER2-targeting antibody-drug conjugates (ADCs) are used to treat cancer patients with HER2-amplified tumors, there is a need to improve the efficacy through the understanding of their mechanism of uptake into cells. Flotillin-2 (FLOT2) regulates the internalization of epidermal growth factor receptor (EGFR), leading us to investigate FLOT2 effects on HER2 internalization. Higher FLOT2 expression in nine HER2 amplified cell lines correlated with a higher T-DM1 IC50 in vitro, and breast cancer patients with high FLOT2 expression had worse survival when receiving either T-DXd (16.2 months (m) vs 18.3 m, p=0.04) or T-DM1 (38.0 m vs 41.3 m, p=0.1) in real-world Caris Life Sciences data. FLOT2 interacts with HER2 and positively regulates HER2 activation and downstream signaling, while FLOT2 knockdown reduces the viability of HER2 amplified cancer cells. FLOT2 knockdown results in increased HER2 internalization upon binding of T-DM1, mediated by ubiquitination by the Cbl ubiquitin ligases. We investigated the effects of various small molecules and discovered that zoledronic acid binds to FLOT2 and disrupts the HER2/FLOT2 interaction, which enhances T-DM1 internalization and cytotoxicity. In conclusion, FLOT2 regulates the internalization and cytotoxicity of T-DM1 mediated by Cbl-dependent ubiquitination of HER2. Zoledronic acid disrupts the HER2/FLOT2 interaction, therefore increasing the internalization and cytotoxicity of T-DM1, providing proof of principle that a small molecule inhibitor of the HER2/FLOT2 interaction can enhance the activity of the HER2-targeting ADC.

BackgroundRisk stratification for prostate cancer (PCa) progression or aggressiveness is often based on clinicopathologic features, some of which may be influenced by genetic factors. We developed a novel, germline polygenic risk score (PRSagg) to predict likelihood of developing aggressive PCa. MethodsPRSagg was developed using data from 38,688 patients with PCa (case-only analysis) from the Million Veteran Program (MVP) through a genome-wide search for variants associated with PCa grade group at diagnosis. We tested associations of PRSagg with grade group using the entire MVP dataset using the .632 bootstrap method. In an MVP cohort with localized PCa that was initially monitored without treatment, we tested PRSagg for association with unfavorable outcomes (subsequent development of grade group 4-5, metastasis, and/or biochemical recurrence after definitive treatment). We performed external validation in data from patients in the PRACTICAL Consortium (n=45,214) and from participants in the ProtecT randomized trial who underwent active monitoring (n=316). Odds ratios (ORs) were calculated per standard deviation (SD) increase with 95% confidence intervals, while adjusting for age, genetic ancestry, a previously developed polygenic score for risk of PCa (PHS601), and a polygenic score for benign elevated prostate-specific antigen (PRSPSA). For the outcome of metastasis, we additionally adjusted for PSA at diagnosis. ResultsIn the MVP training dataset, PRSagg (172 variants) was associated with higher grade group at diagnosis (OR = 1.53 [1.51-1.56]) and with increased risk of unfavorable outcomes during monitoring (OR = 1.13 [1.09-1.18]). These findings were confirmed in the external datasets. PRSagg was associated with greater odds of higher grade group at diagnosis (OR = 1.09 [1.06-1.11]). Among ProtecT participants undergoing active monitoring, PRSagg was associated with higher risk of metastasis (OR = 2.15 [1.02-3.88]). Among MVP participants with high polygenic risk of developing any PCa, the risk of aggressive disease was highest in men with high PRSagg and low genetic risk of PSA elevation. ConclusionsAmong men who develop PCa, a weighted sum of common germline variants (PRSagg) is independently associated with PCa aggressiveness. These findings may inform future study of germline influence on tumor evolution and risk-stratified intensity of active surveillance.

Suppressor of cytokine signaling 1 (SOCS1) negative regulates inflammatory cytokine production and attenuates oncogenic growth factor signaling pathways. Reduced SOCS1 protein expression in human prostate cancer correlates with greater disease severity. To define the physiological functions of SOCS1 functions in the prostate, we conditionally ablated Socs1 in prostate epithelial cells of C57BL/6 mice. These Socs1{Delta}PE mice exhibited normal prostate development, maturation and lobular architecture. However, adult Socs1{Delta}PEmice developed progressive epithelial hyperplasia and inflammatory cell infiltration that were temporally and spatially distinct. SOCS1-deficient prostate showed increased epithelial cell proliferation and elevated oxidative stress markers, and prostate organoids recapitulated this hyperplasia phenotype. Diet-induced obesity exacerbated both hyperplasia and inflammation in SOCS1-deficient prostate. Upon transurethral infection with uropathogenic Escherichia coli UPEC1677 expressing the genotoxin colibactin, Socs1{Delta}PE mice developed invasive prostate cancer with complete loss of lobular architecture, whereas control mice developed hyperplasia and pre-neoplastic lesions. In vitro, SOCS1-deficient prostate organoid-derived epithelial cells exhibited increased DNA damage following exposure to UPEC1677. Deletion of the colibactin biosynthetic gene clbP in UPEC1677 abolished its ability to induce DNA damage in SOCS1-deficient cells and to drive prostate cancer in vivo. Proteomic analysis of prostate organoids revealed dysregulation of basal and luminal epithelial lineage markers and signaling pathway proteins that could promote neoplasia in SOCS1-deficient cells. Collectively, these findings establish an essential, epithelial cell-intrinsic role for SOCS1 in maintaining prostate tissue homeostasis by restraining proliferation, regulating lineage plasticity, limiting inflammation and oxidative stress, and conferring protection against genotoxic injury and neoplastic transformation.

MotivationThe tumor microenvironment (TME) dictates cancer progression and therapeutic response, yet translating TME subtypes into robust clinical biomarkers remains a significant challenge. Existing classification models typically rely on static gene signatures and cohort-dependent normalization, making them ill-suited for application to the small, unbalanced datasets common in early-phase clinical trials. To better guide drug development, methods are required that offer the flexibility to target specific biological contexts and bridge the gap between the discovery of tumor archetypes and their robust translation to individual patient samples. ResultsWe developed TumorArchetypeR, a modular R package that unifies unsupervised subtype discovery with the generation of rank-based, single-sample classifiers. By leveraging a systematic parameter grid search, the framework identifies stable, data-driven subtypes rather than relying on arbitrary defaults. Crucially, to ensure clinical translatability, the package includes a module to train a robust classifier using binary gene-pair rules, enabling prediction without cohort-level preprocessing. Applying TumorArchetypeR to colorectal cancer, we resolved the heterogeneity of fibrotic tumors, distinguishing an immunosuppressive "Immune-enriched/Fibrotic" state from an immune-excluded "Fibrotic/Myeloid" phenotype. Furthermore, we identified a distinct "Th/B-cell enriched" archetype associated with superior survival, a group largely obscured by existing pan-cancer models. With our rank-based classifier demonstrating robust performance on previously unseen samples, these findings highlight TumorArchetypeR as a scalable, end-to-end solution for refining patient stratification and optimizing precision oncology strategies. The TumorArchetypeR package and documentation are openly available on GitHub at https://github.com/lutgem/TumorArchetypeR.

BackgroundRetrotransposable elements (RE) comprise approximately 45% of the human genome and are typically repressed by DNA methylation to preserve genomic integrity. In cancer, global DNA hypomethylation can lead to RE derepression, resulting in genomic instability and activation of innate immune pathways through viral mimicry. While individual RE classes have been examined in clear cell renal cell carcinoma (ccRCC), the integrated epigenetic landscape of multiple RE families and their clinical relevance remain incompletely characterized. MethodsWe performed a genome-wide prediction of DNA methylation across three major RE classes (Alu, LINE-1, and LTR elements) using a validated computational framework applied to Illumina methylation array data from two independent ccRCC tumor cohorts. Integrated unsupervised clustering of RE methylation profiles was used to define the epigenetic subtypes. Associations with clinicopathologic variables, tumor immune microenvironment composition (DNA Methylation-derived), hypoxia signaling, innate immune activation, and overall survival were evaluated. Prognostic relevance was assessed using multivariable Cox regression models adjusting for age, sex, AJCC stage or AUA risk group, and immune and angiogenic tumor microenvironment features. Key findings were then externally validated in CPTAC-ccRCC and independently replicated in an institutional Dartmouth Cancer Center (DCC) cohort with matched methylation and RNA-sequencing data. ResultsIntegrated clustering identified three reproducible RE methylation subtypes, Repressed, Transient, and Active. In the discovery cohort, the Active subtype showed significantly worse overall survival than the Repressed subtype, with a graded survival pattern across RE methylation states that persisted after multivariable adjustment. RE hypomethylation was associated with reduced EPAS1 (HIF2A) expression, increased immune infiltration, elevated PD-1 expression, and heightened cGAS-STING and interferon signaling, consistent with an immune-inflamed yet immunosuppressed tumor state. In the external CPTAC validation cohort, RE methylation subtypes recapitulated key molecular features and showed supportive survival trends. In the independent DCC replication cohort, an Active RE state was again associated with poorer survival, lower EPAS1 expression, increased PD-1 expression, greater CD8 T-cell and Treg infiltration, and elevated T-cell exhaustion signatures, supporting the reproducibility of the prognostic and immune-exhausted phenotype across cohorts. ConclusionsWe identified RE methylation subtypes with distinct molecular, immunologic, and prognostic features in ccRCC. External validation in CPTAC and independent replication in DCC support the robustness of this RE methylation framework across large-scale and institutional cohorts. These findings highlight the prognostic potential of RE methylation profiles and support their integration into molecular classification strategies to improve risk stratification in ccRCC.

Background & AimsHepatocellular carcinoma (HCC) frequently exhibits resistance to immune checkpoint inhibitors (ICIs), particularly in {beta} -catenin-driven tumors characterized by immune exclusion. While the Unfolded Protein Response (UPR) and the Integrated Stress Responses (ISR) enable tumor adaptation to metabolic stress their role in shaping tumor immunogenicity remains incompletely understood. We investigated whether ATF4, a central effector of the integrated stress response, couples metabolic reprogramming to suppression of anti-tumor immunity in HCC. MethodsWe combined transcriptomic analyses across three independent human HCC cohorts with mechanistic studies using an immunotherapy-resistant MYC/{beta}-catenin-driven murine HCC model. We integrated CRISPR/Cas9-mediated deletion of Atf4 with RNA-sequencing and targeted metabolomics. The impact of tumor-derived metabolites on macrophage differentiation and polarization was evaluated using primary bone marrow-derived cells. Therapeutic responses were evaluated in orthotopic and subcutaneous models treated with anti-PD-1 and anti-VEGFA. ResultsATF4 and XBP1 transcriptional signatures are selectively enriched in human HCC and associate with poor prognosis, vascular invasion, and an immunosuppressive myeloid-enriched tumor microenvironment. Genetic ablation of Atf4 markedly suppressed tumor growth in immunocompetent but not immunodeficient hosts, establishing a requirement for immune-mediated tumor control. Mechanistically, Atf4 loss downregulated Aldh18a1 and disrupted proline biosynthesis, resulting in extracellular proline depletion. This proline-deficient environment abrogated monocyte-to-macrophage differentiation and decreased M2 polarization, thereby reshaping the tumor microenvironment toward enhanced T cell infiltration and activation. Functionally, Atf4-deficient tumors exhibited restored sensitivity to anti-PD-1 monotherapy and showed pronounced responses to combined anti-PD-1/anti-VEGFA treatment in aggressive orthotopic models. ConclusionATF4 programs a proline-dependent metabolic axis that sustains macrophage-mediated immunosuppression and immune evasion in {beta}-catenin-driven HCC. Disruption of this pathway converts immune-excluded tumors into T cell-inflamed states and restores responsiveness to immunotherapy. By governing proline homeostasis and macrophage-mediated immunosuppression, ATF4 is a key metabolic checkpoint for immune evasion, linking stress adaptation to immune escape and a candidate therapeutic target in HCC. Impact and implicationsWe identify ATF4 as a crucial metabolic-immune orchestrator that sustains myeloid-driven immune evasion in {beta}-catenin-dependent HCC through proline-dependent circuitry. Disrupting the ATF4-proline axis converts immune-desert tumors into T cell-inflamed lesions by blocking macrophage differentiation, thereby sensitizing tumors to immune checkpoint therapy. This work positions ATF4 as a tractable therapeutic target to overcome immunotherapy resistance in HCC. Graphical abstract Highlights- ATF4 orchestrates an immunosuppressive tumor microenvironment in HCC by coupling metabolic stress adaptation to immune evasion. - Ablation of ATF4 disrupts proline biosynthesis, leading to a marked depletion of extracellular proline. - Cancer cell-derived proline availability contributes to macrophage differentiation and M2 polarization; its loss restores T cell-mediated anti-tumor surveillance and sensitizes beta-catenin-driven HCC to immune checkpoint blockade.

Glypican-3 (GPC3) is an oncofetal protein widely being explored as a diagnostic and therapeutic target in hepatocellular carcinoma (HCC). Given that radiotherapy in the form of external beam and radioembolization are standard-of-care treatments for HCC, we aimed to determine whether there was any relationship between GPC3 and response to radiotherapy. Here, we demonstrate that GPC3 expression confers radioresistance in liver cancer through integrated in vitro, in vivo, and patient-level clinical analyses. Stable GPC3-knockout in liver cancer cell lines (HepG2, Hep3B, Huh7) and ectopic GPC3 expression in GPC3-negative liver cancer cells (SNU449), as well as in non-hepatic A431 cells, demonstrated that GPC3-mediated radioresistance is not restricted to hepatic lineage. Following irradiation, GPC3-deficient cells exhibited reduced proliferation, impaired clonogenic survival, persistent DNA damage, prolonged G2/M arrest, and increased apoptosis. Transcriptomic profiling demonstrated enrichment of cell-cycle and DNA damage response pathways in irradiated GPC3-deficient cells compared with GPC3-positive cells, and protein analyses confirmed sustained activation of the ATM/CHK2 axis. In vivo, GPC3 deletion markedly enhanced radiation-induced tumor growth delay in both HepG2 and A431 xenograft models. Consistent with these findings, high GPC3 expression was associated with inferior clinical outcomes in patients with HCC undergoing external-beam radiotherapy or radioembolization. Together, these findings identify GPC3 as a determinant of radioresistance in liver cancer and suggest its potential utility as a biomarker to guide radiotherapeutic strategies. Significance statementRadiotherapy is an important treatment option for HCC, but biomarkers that predict tumor response remain limited. GPC3 is highly expressed in most HCCs and is being investigated as an important biomarker for diagnosis and treatment of this disease, yet its relationship, if any, on radiosensitivity has not been previously reported. Here, we identify GPC3 as a modulator of radioresistance. GPC3 loss enhances radiosensitivity and is associated with persistent unresolved DNA damage, prolonged G2/M arrest, and sustained activation of the ATM/CHK2 pathway, resulting in delayed tumor growth after irradiation. In a clinical cohort of patients treated with radiotherapy, high GPC3 expression was associated with poorer overall survival. These findings suggest that GPC3 expressing tumors may necessitate either more dose-intense radiotherapy, radiobioligically ablative and/or combined with other modalities, or alternative therapeutic modalities to adequately treat HCC.

Cutaneous squamous cell carcinoma (cSCC) is a common skin cancer associated with substantial morbidity and mortality in advanced stages. Despite its well-described stepwise progression from actinic keratosis to invasive disease, robust molecular markers for stage discrimination and clinical decision-making remain limited. We sought to define the transcriptional continuum underlying cSCC progression, identify stage-associated biomarkers, and assess the broader relevance of these programs across human malignancies. Bulk RNA sequencing (HTG EdgeSeq) and spatial transcriptomics (GeoMx) were performed on biopsies from eight patients, each presenting multiple disease stages (healthy skin, premalignant lesion, tumor core, and invasive front) within the same lesion field, enabling within-patient analysis of progression. Spatial transcriptomic analyses identified more than 2,000 differentially expressed genes whose expression varied across disease stages. These genes were organized into 18 coordinated expression programs reflecting progressive biological rewiring during tumor evolution. Proliferation, extracellular matrix remodeling, inflammation, and stress-response pathways were progressively upregulated, whereas epithelial differentiation and metabolic processes, including lipid and amino acid metabolism, were downregulated. Macrophages exhibited distinct metabolic reprogramming, with increased purine metabolism, glycolysis, and pyruvate metabolism across progression. To evaluate the broader clinical relevance of these progression-associated programs, we developed a reproducible Snakemake pipeline to systematically screen 32 solid and hematologic malignancies from The Cancer Genome Atlas (TCGA). A combined cSCC-progression signature was significantly associated with poor overall survival (P < 0.05) in 10 additional cancer types. Finally, we identified 12 stage-informative biomarkers, whose spatially restricted expression patterns were validated using Visium HD. This study provides a spatially resolved and stage-aware transcriptomic map of cSCC progression, identifies coordinated gene programs underlying disease evolution, and defines progression-associated signatures with prognostic relevance across multiple cancers, highlighting their potential translational value.