Cancer Research Communications

BackgroundLong non-coding RNAs (lncRNAs) have emerged as key regulators of tumor biology, however, thus far none have translated to cancer therapies. The lncRNA MALAT1 is overexpressed in more than 20 cancers, including breast cancer and has been shown to function via various mechanisms in a context-dependent manner, in 2D cell lines and mouse models. However, its functional role and therapeutic potential have not been evaluated in clinically relevant patient-derived models. MethodsWe investigated the therapeutic potential of a MALAT1-targeting antisense oligonucleotide (ASO) for breast cancer, using clinically relevant 3D human patient-derived organoids (PDOs) and PDO-xenograft (PDO-X) models. We systematically evaluated the efficiency of MALAT1-targeting ASOs using a biobank of 28 PDO models. Using three independent PDO-X models of triple negative breast cancer (TNBC), we targeted MALAT1 in vivo to study its impact on transcription, alternative splicing, stromal remodeling and metastasis. ResultsAcross PDO-X models, MALAT1 depletion reproducibly drove widespread alternative splicing changes across all event types, particularly intron retention events, accompanied by modest gene expression alterations. Differentially spliced transcripts were enriched for targets of shared cancer-associated transcription factors, and MALAT1 knockdown altered the relative abundance of previously unannotated splicing isoforms. Beyond tumor-intrinsic effects, tumor-specific MALAT1 depletion induced a consistent reduction in macrophage-associated gene signatures and reduced lung metastatic burden. ConclusionsOur data define MALAT1s multifaceted role in TNBC, coordinating alternative splicing, transcriptional fine-tuning, tumor-stroma crosstalk, and metastatic progression. Our study provides strong preclinical evidence supporting MALAT1-targeted ASO therapy and establishes PDO-X models as a clinically relevant platform for functional interrogation of TNBC therapies.

BackgroundDrug responses in pancreatic ductal adenocarcinoma (PDAC) vary sharply across in vitro culture formats, but most 2D-3D comparisons conflate microenvironmental cues with time-dependent cellular adaptation. As a result, conventional assays frequently overestimate drug efficacy and poorly reflect clinical pharmacology. Main findingsWe profiled MiaPaCa-2, PANC-1, and CFPAC-1 grown in an extracellular-matrix (ECM) hydrogel for 1-12 days, defining extended 3D cultures ([&ge;]10 days) as mature tumoroids, and quantified 72 h drug responses to a multi-class oncology panel using growth-rate (GR) metrics to normalize for proliferation across formats and durations. Prolonged 3D pre-culture induced broad tolerance, with typical 10-100x reductions in sensitivity to standards of care (5-fluorouracil, SN38, oxaliplatin, gemcitabine, paclitaxel), following a reproducible susceptibility hierarchy (MiaPaCa-2 > PANC-1 > CFPAC-1) after GR correction. In mature tumoroids, GR values closely approximated clinically observed plasma exposures (e.g., within <4x for 5-FU and <0.5x for gemcitabine), whereas 2D and short-term organoid assays markedly underestimated resistance, often by >100x, thereby overstating drug activity. Notably, CFPAC-1 exhibited increased sensitivity to SN38 and trametinib under mature-organoid conditions, demonstrating that microenvironmental conditioning can invert responses for selected mechanisms. Transcriptomic profiling revealed coordinated up-regulation of multiple ABC transporters with extended 3D residence, tracking resistance phenotypes across lines and implicating transporter-linked tolerance programs. SignificanceTogether, these data identify time-in-3D and the emergence of mature tumoroids as dominant, previously under-controlled determinants of PDAC pharmacology that both induce tolerance and unmask context-dependent vulnerabilities. We propose incorporating both short-term and mature-tumoroid screening arms into preclinical workflows, reporting pre-culture duration alongside GR-normalized effect sizes, and leveraging transporter-informed biomarkers to guide regimen prioritization and sequencing. This framework enhances physiological relevance, reproducibility, and translational fidelity in PDAC drug discovery.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer characterized by a high abundance of cancer-associated fibroblasts (CAFs), which influence therapy response, tumor biology and tumor aggressiveness. CAFs are a heterogeneous cell type and previous single-cell RNA sequencing (scRNAseq) of PDAC tumors identified three main CAF subtypes: myofibroblastic, inflammatory and antigen-presenting CAFs (myCAF, iCAF, apCAF, respectively). However, scRNAseq on large patient cohorts is often not feasible due to costs and technical constraints. Therefore, bulk RNAseq deconvolution can be used to identify cell types within the heterogeneous tumor microenvironment. Here, Statescope deconvolution was used to identify different cell types of the tumor microenvironment within an early onset PDAC cohort, comprising 74 patients aged under 60. Three CAF populations were identified (iCAFs, myCAFs and desmoplastic CAFs), and their correlations with tumor microenvironment components, mutational signatures and survival were examined. iCAFs were associated with classical-like tumor cells, whereas myCAFs and desmoplastic CAFs correlated with basal-like tumor cells. Desmoplastic CAFs are associated with inflammatory granulocytes/neutrophils, while negatively associating with monocyte-derived macrophages and immature/transitional B cells. No associations were observed between mutational signatures and the abundance of CAF and epithelial tumor subtypes. Interestingly, a high abundance of CAFs, and specifically increased iCAF abundance, was associated with improved survival. This iCAF-mediated survival effect was predominantly apparent in female patients. All in all, deconvolution of bulk RNA sequencing data, followed by its integration with clinical and biological parameters, reveals the heterogeneity and prognostic implications of CAF subpopulations in the tumor microenvironment of early onset PDAC patients.

Ionizing radiation can be an effective therapy for prostate cancer. Unfortunately, however, more aggressive prostate cancers such as neuroendocrine prostate cancer (NEPC) are often radiation resistant, which contributes to their high degree of morbidity and mortality. In this study, we used an unbiased approach to identify novel mechanisms that contribute to resistance to radiation and that are associated with neuroendocrine differentiation. Specifically, we compared the expression of cell surface proteins by mass spectrometry in prostate cancer cell lines that had been either untreated or treated with radiation to induce resistance, a process that also promotes neuroendocrine differentiation. Among the proteins identified by this screen, we focused on folate receptor (FR) because of its known biological functions and the fact that it is a validated therapeutic target. Our data reveal that FR has a causal role in enabling prostate cancer cells to resist radiation. Importantly, we also demonstrate that the expression of FR is regulated by HIF-1, which also has a causal role in radiation resistance and neuroendocrine differentiation. Given that the ability of cells to resist damage and death in response to ionizing radiation depends largely on their ability to buffer the substantial increase in reactive oxygen species (ROS) that is generated by radiation, we also demonstrate that the folate-FR axis promotes radiation resistance by sustaining intracellular glutathione levels that buffer this increase in ROS. In summary, the data reported here highlight a novel role for FR in resistance to ionizing radiation that is intimately associated with the hypoxic microenvironment of NEPC and the ability of the folate-FRa axis to maintain redox homeostasis.

Anti-hormonal therapies such as selective estrogen receptor modulators like tamoxifen or aromatase inhibitors like letrozole represent a cornerstone for breast cancer prevention and therapy of estrogen receptor-positive breast cancer. Therapeutic monitoring can include blood tests and imaging; however, genetically-based approaches are not yet in practice. Ideally, a test would be able to detect a positive molecular response across different estrogen pathway-suppressive approaches. Circular RNAs are a species of non-coding RNAs detectable in plasma that have been proposed as non-invasive therapeutic biomarkers. To determine whether a set of specific circular RNAs is altered across estrogen-suppressive pathway approaches, we analyzed mammary gland-specific total RNA sequencing data from two individual genetically engineered mouse models (GEMMs) of estrogen pathway-induced breast cancer, with or without exposure to tamoxifen or letrozole. The nf-core/circrna pipeline was used to identify circRNAs that were differentially expressed in response to either tamoxifen or letrozole. We then screened for circRNAs that were differentially regulated by both anti-hormonals. Four up-regulated and 31 down-regulated circRNAs with host genes known to be expressed in human breast epithelial cells were identified as showing reproducible differential regulation in response to anti-hormonal treatment.

BackgroundHead and neck squamous cell carcinoma (HNSCC) comprises a heterogeneous group of epithelial malignancies associated with poor survival ({approx}50%), limited therapeutic options, and a lack of predictive biomarkers. Concurrent chemoradiotherapy (CRT) remains the standard treatment for advanced disease; however, many patients fail to respond, develop resistance, or eventually relapse. The development of three-dimensional organoid technology has enabled the generation of patient-derived organoids (PDOs), offering a promising platform for personalized therapeutic testing. MethodsWe established a biobank of HNSCC PDOs from fresh laryngeal and pharyngeal tumor samples, including human papillomavirus-positive (HPV+) cases. Organoid formation and expansion rates were analyzed in relation to clinical parameters. Selected representative PDOs were histologically and molecularly characterized. Additionally, several models were exposed to cisplatin and radiation to evaluate treatment response, and a subset was assessed for tumorigenicity in subcutaneous mouse models. ResultsFifty-seven PDO models were successfully established, long-term expanded, and cryopreserved. Prior chemotherapy and/or radiotherapy was identified as an independent negative predictor of organoid outgrowth and expansion capacity compared with treatment-naive samples. Histological features, including differentiation grade and immunohistochemical markers, were largely preserved and strongly correlated with the original tumors. PDOs displayed heterogeneous responses to cisplatin and radiotherapy, with HPV-positive models showing greater sensitivity, consistent with clinical observations. Global transcriptomic profiling revealed molecular subtypes concordant with established HNSCC classifications and suggested an additional subtype characterized by low MYC and mTORC1 transcriptional activity. ConclusionHNSCC PDOs faithfully recapitulate tumor histology and molecular diversity, providing a robust platform to investigate tumor biology and therapeutic response.

Head and neck squamous cell carcinoma (HNSCC) remains associated with substantial morbidity and a 5-year overall survival rate of approximately 60%, reflecting persistent radio- and chemo-resistance and the lack of effective precision medicine strategies. Patient-Derived Tumor Organoids (PDTO) constitute promising functional models that may predict individual treatment response. In this study, we generated PDTO from surgically resected HNSCC of the oral cavity, oropharynx, larynx, and hypopharynx. A total of 20 long-term PDTO lines were established, maintaining growth over seven passages and successfully cryopreserved, capturing the molecular and clinical diversity of the patient cohort. These PDTO faithfully recapitulated histological features, major tumor marker expression, and the genomic and transcriptomic landscapes of their tumors of origin, with stability over time. Functional assays revealed heterogeneous responses to cisplatin and X-rays. Importantly, in vitro sensitivity of PDTO was associated with clinical outcome of patients at 24 months. Cisplatin response of PDTO predicted prognosis with 66.7% sensitivity and 100% specificity, while X-ray response showed 91.7% sensitivity and 75% specificity. Notably, all patients whose PDTO were classified as resistant to both cisplatin and X-rays experienced relapse and/or death within 24 months. Collectively, the successful long-term expansion and cryopreservation of HNSCC PDTO establish a stable and scalable preclinical resource that captures the molecular and clinical heterogeneity of the disease. This biobank provides a valuable platform for mechanistic studies and for the evaluation of innovative therapeutic strategies. This cohort represents one of the largest clinically annotated HNSCC PDTO collections to date, demonstrating a robust association between PDTO response to cisplatin and X-rays and patient prognosis. These findings support the predictive potential of PDTO-based functional assays and argue for their integration into standardized, rapid, and miniaturized precision oncology workflows for HNSCC.

BackgroundGallium (Ga) is a promising anti-tumor agent; however, its precise molecular targets in osteosarcoma remain debated. While current paradigms largely attribute its toxicity to reactive oxygen species (ROS) and ferroptosis, understanding its true mechanism is essential for overcoming therapeutic resistance. This highlights the need for interdisciplinary approaches, such as metabolomics, to unveil novel vulnerabilities in cancer metabolism. MethodsWe employed an interdisciplinary strategy utilizing high-resolution liquid chromatography-mass spectrometry (LC-MS) metabolomics and 13C2-glutamine stable isotope tracing in osteosarcoma cells to elucidate the cytotoxic mechanisms of gallium nitrate. Scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) was utilized for elemental mapping, and in silico modeling was applied to evaluated metal binding dynamics. Furthermore, synergistic effects were tested by combining gallium with the DNA-damaging agent cisplatin. ResultsOur metabolic profiling revealed a profound bifurcation characterized by the systemic depletion of glycolysis and pentose phosphate pathway intermediates, coupled with a novel ribonucleotide accumulation bottleneck. The observed distinct signature strongly implicated ribonucleotide reductase (RNR) as the primary enzymatic target. In silico modeling and SEM-EDS visually and thermodynamically confirmedthat gallium acts as a structural decoy for iron within the RNR active site. The co-localization induces functional iron starvation rather than canonical ferroptosis. Furthermore, isotope tracing confirmed that elevated ROS is a consequence of overall metabolic failure, not the primary driver of cell death. Crucially, gallium functioned as a metabolic DNA repair inhibitor, synergizing potently with cisplatin to prevent the repair of platinum-induced DNA lesions. ConclusionsGallium selectively sensitizes highly proliferative sarcoma cells by disrupting RNR-mediated DNA precursor synthesis, while sparing normal osteoblasts. Leveraging metabolomics to uncover this state of functional iron starvation provides a rational, interdisciplinary framework for developing gallium-based combination therapies designed to break platinum resistance in clinical oncology.

Diffuse large B-cell lymphoma (DLBCL), the most common aggressive lymphoma, encompasses histologically similar but genetically distinct cancers. Recent genetic studies have defined at least six molecular subtypes, yet none account for Epstein-Barr virus (EBV), despite 5-15% of DLBCLs being EBV-associated. By reanalyzing published whole-exome and RNA-sequencing data from 481 tumors, we identified 19 EBV-positive cases. These were significantly enriched in the BN2 subtype (6/19), while most (11/19) remained unclassified. In BN2 tumors, several subtype-defining mutations were reduced in frequency among EBV-positive cases, supporting the hypothesis that EBV oncogenes substitute for specific cellular alterations and may confound DLBCL classification algorithms. Extending our analysis to cell lines, we found that the widely used Val cell line harbors the B95-8 laboratory EBV strain; other EBV-positive lines appeared authentic but modeled only non-BN2 subtypes and expressed an atypical viral latency III program, whereas some DLBCL tumors expressed the atypical latency III program and others latency I or II. Together, these findings demonstrate that EBV-positive DLBCL, like DLBCL itself, is not a single disease, and that current in vitro models only partially capture its biological heterogeneity. Key pointsO_LIEBV-positive DLBCL is not a single disease and EBV status can impact genetic-based classifications. C_LIO_LICurrent EBV-positive DLBCL cell lines do not adequately capture tumor complexity; we determined that Val is a problematic cell line. C_LI

BackgroundTherapeutic agents targeting the PD1-PDL1 interaction are of great clinical value, however accurately predicting which patients are most likely to benefit is challenging. Improved predictive biomarkers for anti-PD1 therapy are clearly needed. Quantifying PD1 saturation by PDL1 in tumor tissue has the potential to serve as such a biomarker. Here we report a novel bioassay called the PD1 Ligand Receptor Complex Aptamer (LIRECAP) assay and demonstrate it can be used to quantify the saturation of PD1 by PDL1 in formalin-fixed paraffin-embedded tumor biospecimens. ResultsThe PD1 LIRECAP assay was developed by identifying a pair of RNA aptamers. One aptamer preferentially binds to unoccupied PD1 (P aptamer) and the other to the PD1-PDL1 complex (C aptamer). P and C aptamers were added together to a formalin-fixed sample, and bound aptamer extracted. A 2-color qRT-PCR assay using a single set of primers was used to determine the ratio of the sample-bound C to P aptamers (C:P ratio) which reflected PD1 saturation by PDL1 in the sample. Quantification of PD1 saturation by PDL1 as determined by the PD1 LIRECAP assay correlated closely with PD1-mediated signaling and PD1-PDL1 proximity. Analysis of sarcoma FFPE biospecimens confirmed the assay is technically reproducible on clinical biospecimens. There were significant differences in PD1 saturation by PDL1 between patients as well as considerable intratumoral heterogeneity. ConclusionsThe PD1 LIRECAP assay is novel assay that can be used to quantify PD1 saturation by PDL1 in clinical biospecimens. The assay is technically feasible, reproducible, and has the potential to serve as a superior predictive biomarker for PD1/PDL1-based therapy. Similar assays based on this platform could be used in other systems and settings to quantify interaction between two molecules.

Reliable, minimally invasive biomarkers for predicting immunotherapy response in head and neck squamous cell carcinoma (HNSCC) remain an unmet clinical need. Here, using patients from a prospective, multi-institutional phase II clinical trial (NCT02641093), we performed whole genome sequencing of 185 plasma cell-free DNA (cfDNA) samples collected longitudinally from 68 patients with locally advanced, surgically resectable HNSCC undergoing neoadjuvant and adjuvant pembrolizumab treatment. We developed the regional motif diversity score (rMDS), a novel fragmentomic metric quantifying the entropy of cfDNA 5' end motifs across genomic regions. Remarkably, unsupervised analysis revealed that rMDS robustly distinguished immunotherapy responders from non-responders, outperforming established cfDNA fragmentomic metrics and copy number alterations, while demonstrating independence from technical confounders. Longitudinal analysis revealed dynamic rMDS changes in genomic regions enriched for immune, lectin, and keratinization-related genes, hallmarks of squamous cell carcinoma, reflecting the interplay between tumor and peripheral immunity during the immunotherapy treatment. Interestingly, the regions with the most dynamic rMDS changes were highly enriched in telomere proximal loci, suggesting a novel link between telomere biology and cfDNA fragmentation. A machine learning classifier based on rMDS achieved robust predictive performance across multiple validation settings (AUC 0.89-0.99), with the highest accuracy at post-treatment timepoints and superior to PD-L1 expression and tumor fraction in the same sample. Predicted responders demonstrated significant trends toward improved disease-free survival (log rank test p=0.035, hazard ratio: 2.67, 95% confidence interval: 1.03-6.92), underscoring the clinical utility of rMDS-based stratification. These findings position rMDS as a biologically meaningful and clinically actionable biomarker for immunotherapy response in HNSCC, supporting its integration into future risk assessment frameworks and broader cancer care.

Prognostic stratification in multiple myeloma (MM) relies on staging systems that assign patients to fixed categories at diagnosis and discard the temporal information that accumulates during treatment. We developed a dynamic multimodal framework that predicts residual overall survival using observation windows ranging from 1 to 18 months post-diagnosis. The model integrates DeepInsight-transformed gene expression representation, longitudinal laboratory measurement trajectories across 10 analytes, and treatment history for three drug classes through an adaptive fusion mechanism that accounts for missing clinical observations. On the MMRF CoMMpass cohort (n = 752), five-fold cross-validation yielded a concordance index (C-index) of 0.773 {+/-} 0.024 and a time-dependent AUC at a 1-year prediction horizon (tdAUC1yr) of 0.789 {+/-} 0.021, outperforming all evaluated baseline methods including DeepSurv (0.633 {+/-} 0.095) and random survival forests (0.636 {+/-} 0.024) on matched cross-validation splits. Modality ablation identified longitudinal laboratory measurements as the strongest individual contributor (C-index 0.693); the DeepInsight spatial encoding of gene expression yielded higher discrimination than a multilayer perceptron (MLP) baseline operating on the same features (0.624 vs. 0.596). Kaplan-Meier analysis showed significant prognostic group separation at all primary landmarks (log-rank p < 0.001; hazard ratios 3.46-3.93). A distilled student model retaining only the DeepInsight representation and five baseline clinical features achieved C-index 0.672 and tdAUC1yr 0.740 on an independent microarray cohort (GSE24080, n = 507) without retraining. Interpretability analysis identified prognostic associations consistent with established myeloma biology, including ubiquitin-proteasome pathway genes, endoplasmic reticulum stress markers, and Interferon Alpha Response pathway enrichment.

Long-chain n-3 polyunsaturated fatty acids (n-3 PUFAs) are candidate preventive agents for breast cancer. With emerging evidence of epigenetic regulation of the tumor microenvironment, tissue-level epigenetic effects may represent an important target for cancer prevention. In a randomized Phase II sub-study (high-dose 5 g/day vs low-dose 1 g/day for 12 months; n = 17; Clinicaltrials.gov: NCT02295059), DNA methylation (DNAm) of the breast environment was profiled by reduced-representation bisulfite sequencing (RRBS). DNAm was assessed genome-wide, at individual gene promoters, and for locus-level heterogeneity which has been linked to epigenetic dysregulation that can precede breast cancer. Both doses induced promoter DNAm changes, but their responses diverged: low-dose samples showed increased CpG variance and more differentially methylated promoters without pathway enrichment, whereas high-dose samples had reduced DNAm heterogeneity and promoter enrichment in inflammation signaling pathways. Many overlapping differentially methylated promoters changed in opposite directions between doses. The finding that high-dose n-3 PUFA affects DNAm fidelity in the breast adipose suggests a new potential mechanism for n-3 PUFA-mediated prevention of breast cancer development. Together with the dose-specific, directionally discordant DNAm responses in breast adipose, this study has important implications for both advancing n-3 PUFA for breast cancer prevention and dose selection in future n-3 PUFA supplementation trials.

Prostate cancer progression is characterized by dysregulated lipid metabolism, with fatty acid synthase (FASN), the rate-limiting step in de novo lipogenesis (DNL), resulting in significant accumulation of saturated lipids. Here, we investigate whether pharmacologic FASN inhibition creates a metabolic state that increases reliance on exogenous polyunsaturated fatty acids (PUFAs). Inhibition of FASN profoundly alters membrane phospholipid composition, driving compensatory incorporation of PUFAs into membrane phospholipids, thus increasing susceptibility to lipid peroxidation and oxidative damage. Combined FASN inhibition and PUFA exposure increased reactive oxygen species, induced mitochondrial hyperpolarization, and enhanced lipid peroxidation in both hormone-sensitive and castration-resistant prostate cancer models. Marked inhibition of human and murine prostate cancer organoids is achieved ex vivo. In genetically engineered, DNL-reliant Hi-Myc mice, a diet enriched in PUFAs significantly inhibited invasive carcinoma compared to a saturated fat-enriched diet. Environmental PUFAs modulate and enhance the therapeutic efficacy of FASN-targeted strategies. These findings set the stage for pharmacologic and dietary intervention in prostate cancer patients.

BackgroundNon-invasive electromagnetic field (EMF)-based therapies offer a potential route to modulate local tumor-immune interactions but their mechanistic basis remains poorly defined. MethodsWe evaluated Asha therapy, a proprietary low-intensity (50khz, 2 mT, 25% duty cycle) alternating magnetic-field treatment in preclinical breast cancer models. Cellular responses in human triple negative breast cancer cell lines (MDA-MB-231 and MDA-MB-468) were evaluated using bulk RNA sequencing, quantitative proteomics, flow cytometry, and cytokine analysis and proteomics analysis. Tumor microenvironment responses in mouse 4T1 breast cancer model was characterized using single-cell CITE-seq analysis. Functional efficacy was assessed in vivo using the murine 4T1 triple-negative breast cancer model, both as monotherapy and in combination with anti-PD1 checkpoint blockade. Clinical relevance was assessed by deriving a 19-gene neutrophil activation signature from Asha-induced transcriptional changes and projecting it onto two independent TNBC patient cohorts (METABRIC n=338, SCAN-B n=874) for survival analysis. ResultsAsha therapy induced endoplasmic reticulum (ER) stress and activated an adaptive unfolded-protein response in tumor cells, triggering robust NF-{kappa}B and interferon signaling and time-dependent secretion of inflammatory cytokines. In vivo, these tumor-intrinsic changes propagated to the tumor microenvironment (TME), reprogramming fibroblasts from contractile states to immune-recruiting, interferon-responsive phenotypes and enriching for interferon-stimulated, metabolically active neutrophils and macrophages. These coordinated innate immune changes occurred without overt cytotoxicity and were associated with significant reductions in metastasis and improved survival. Combination with anti-PD1 therapy markedly enhanced efficacy, reducing lung metastasis and mortality by 88% compared with control. The neutrophil activation signature derived from Asha-treated tumors was associated with improved overall survival in both METABRIC (log-rank p=0.036) and SCAN-B (p=0.048) TNBC cohorts by Kaplan-Meier analysis, with pooled multivariable Cox regression confirming significant survival benefit (HR=0.75, 95% CI 0.59-0.94, p=0.01). ConclusionsAsha therapy triggers a controlled ER stress response in tumor cells that drives interferon-mediated cytokine release and immune reprogramming of the TME, resulting in anti-metastatic and survival benefits. These findings identify electromagnetic-field exposure as a potential non-pharmacologic strategy to activate innate immunity and sensitize tumors to checkpoint blockade, supporting further clinical development of EMF-based immunotherapy.

TP53 mutations occur in 80-90% of triple-negative breast cancers (TNBCs) and drive genomic instability and metastatic progression. Poly (ADP-ribose) polymerase (PARP) is critical for DNA repair and replication fork stability. How oncogenic signaling influences PARP function to sustain proliferation during replication stress remains unclear. Mutant p53 (mtp53) R273H associates tightly with chromatin, forms complexes with PARP, and enhances PARP recruitment to replication forks [1-3]. The C-terminal region of mtp53 mediates mtp53-PARP and mtp53-Poly (ADP-ribose) (PAR) interactions that facilitate S phase progression [4, 5]. The PARP inhibitor talazoparib (TAL) combined with the alkylating agent temozolomide (TMZ) produces synergistic cytotoxicity selectively in mtp53, but not wild-type p53 (wtp53), breast cancer cells and organoids. Herein we evaluated the mechanism of mtp53-associated cell death and tested if this could translate to a preclinical xenograft model. We found that TMZ+TAL treatment induced elevated cleaved PARP and {gamma}H2AX and reduced the metastasis-promoting oncoprotein MDMX. In orthotopic xenografts expressing mtp53 R273H, but not wtp53, combination therapy significantly decreased circulating tumor cells (CTCs) and lung metastases. Transcriptomic profiling of tumors from combination treated animals demonstrated downregulation of MDMX, VEGF, and NF-{kappa}B, consistent with the observed suppression of CTCs and lung metastasis, and increased {gamma}H2AX, indicative of replication stress in mtp53 xenografts. Inhibition of metastasis was also observed in mtp53 R273H WHIM25 and p53-undetectable WHIM6 TNBC patient-derived xenografts (PDX). The mtp53 C-terminal domain (347-393) demonstrated a critical tumor promoting function, as CRISPR-mediated deletion impaired replication fork progression, tumor growth, and metastatic dissemination. DNA fiber combing showed that expression of full-length mtp53 R273H, but not C-terminal deleted {Delta}347-393, supported sustained single-stranded DNA gaps (ssGAPs) following Poly (ADP-ribose) glycohydrolase (PARG) inhibition. These findings support that mtp53 uses C-terminal amino acids to exploit PARP to enable replication stress adaptation and that mtp53 is a predictive biomarker for combined PARP inhibitor and DNA damaging therapies targeting TNBC. Significance statementTP53 mutations are the most common genetic alterations in TNBC and a major driver of replication stress and metastasis. This study shows that missense mutant p53 uses C-terminal amino acids to reprogram PARP activity to maintain tumor cell survival under replication stress. We demonstrate that p53 status governs the response to combined PARP inhibitor (PARPi) and DNA-damaging chemotherapy, establishing an additional molecular basis beyond BRCA1 mutations for treating TNBC with PARPi therapy. These findings reveal a previously unrecognized mechanism by which the mutant p53-PARP axis enables replication stress tolerance and drives cancer metastasis. We show mutation of p53 in TNBC provides an additional biomarker-guided framework to improve PARPi therapeutic outcomes.

Background: Current risk stratification for Ewing sarcoma relies heavily on clinical factors such as metastatic status, failing to capture histologic heterogeneity as a potential prognostic indicator. Although pathology reports contain rich biological data, this information remains locked in unstructured narrative text, limiting large-scale retrospective analyses. We aimed to validate the utility of Large Language Models (LLMs) for scalable data abstraction and to identify prognostic histologic features from a large multi-institutional cohort. Methods: We conducted a retrospective cohort study using data from six Children's Oncology Group (COG) clinical trials. We utilized an LLM-based pipeline (OpenAI o3) to extract structured variables, including immunohistochemical (IHC) markers and CD99 staining patterns - from digitized, Optical Character Recognition (OCR)-processed pathology reports. Extraction accuracy was validated against a human-annotated ground truth (n=200) and cross-validated against senior experts (n=48). We assessed the association between extracted features and Overall Survival (OS) using Kaplan-Meier analysis and multivariable Cox proportional hazards regression, adjusting for metastatic status. Findings: We analyzed 931 diagnostic pathology reports spanning over 19-years. The LLM achieved a weighted average accuracy of 94% across 17 IHC markers; in a cross-validation subset, the LLM outperformed human annotators (weighted average accuracy over 15 IHC markers: LLM o3: 98.1%, a resident specialist 91.4%, and a senior expert 95.9%). Survival analysis identified Neuron-Specific Enolase (NSE) and S100 as significant prognostic biomarkers. After adjusting for metastatic status, NSE positivity was associated with significantly inferior survival (HR 2.15, 95% CI 1.15 - 4.02, p=0.016); this risk was most pronounced in patients with non-metastatic disease (HR 5.64, p=0.0055). Conversely, S100 positivity was associated with improved survival (HR 0.58, 95% CI 0.34-1.00, p=0.046). Interpretation: LLM-assisted extraction of pathology variables is highly accurate and scalable, capable of unlocking "dark data" from historical clinical trials. We identified NSE as a potent risk factor and S100 as a protective marker in Ewing sarcoma, particularly in localized disease. These findings suggest that AI-derived histologic data can refine risk stratification and, if validated, warrant inclusion in future prospective trials.

BackgroundNeoadjuvant chemoimmunotherapy (nCIT) has become a standard treatment for locally advanced resectable non-small cell lung cancer (NSCLC), yet the spatial biology underlying treatment resistance remains poorly understood. We used spatial transcriptomics to define the microenvironmental architecture of residual cancers in patients who did not achieve major pathologic response (non-MPR) compared with those who did (MPR). MethodsSpatial transcriptomics was performed on 10 formalin-fixed paraffin-embedded (FFPE) tumor blocks (5 MPR, 5 non-MPR) obtained from 8 patients treated with nCIT. A deep learning algorithm was applied to detect viable residual cancer spots from treatment-induced fibrosis and necrosis. Spatial deconvolution, distance modeling, ligand-receptor analysis, and functional pathway scoring were integrated to characterize niche-specific programs. ResultsMPR cancer core displayed an immune-permissive remodeling environment with deep infiltration of cytotoxic CD8+ T cells, mature dendritic cells (LAMP3+, CCR7+), and active efferocytosis signaling (APOE-TREM2), alongside robust MHC class II expression. Non-MPR cancer core, by contrast, exhibited spatial immune exclusion: a dense fibroblast barrier reinforced by TIMP1-CD63 signaling and Treg-enriched boundaries physically restricted effector T cell access to the cancer core. Residual cancer cells in non-MPR samples maintained active cell cycling and independently upregulated cytochrome P450-mediated drug detoxification and DNA damage response pathways without inducing MHC class II expression -- effectively decoupling intrinsic survival from immune recognition. The non-MPR core also showed a hyper-metabolic profile, including elevated glutathione metabolism consistent with antioxidant buffering against chemotherapy-induced oxidative stress. TROP2 was broadly expressed across the non-MPR cancer core and co-localized with DNA damage response and nuclear factor erythroid 2-related factor 2 resistance signatures. ConclusionsResidual cancer cores in non-MPR tumors appear to represent evolved resistant niches sustained by structural immune exclusion, metabolic rewiring, and DNA repair proficiency. These findings highlight the spatial co-localization of epithelial anchors, such as TROP2, with intrinsic resistance pathways, providing a structural rationale for developing novel precision therapeutic strategies to bypass stromal barriers and overcome the cancer cores intrinsic repair capacity.

BackgroundDespite extensive cataloging of carcinogenic exposures by the International Agency for Research on Cancer (IARC) and pharmacogenomic variation by resources such as PharmVar and CPIC, few platforms unify exposure, metabolic activation and detoxification, DNA damage, and genetic annotation within a single interactive visualization framework. This gap limits systematic evaluation of gene-environment interactions in cancer risk assessment. MethodsWe developed the Carcino-Genomic Knowledge Graph, ExposoGraph, an interactive knowledge-graph platform for carcinogen metabolism and DNA damage pathways. The reference graph integrates curated data and annotations from IARC, KEGG, PharmVar, CPIC, CTD, and supporting literature/resources. The current reference graph contains 96 nodes across 5 entity types (Carcinogens, Enzymes, Metabolites, DNA Adducts, and Pathways) and 102 edges across 6 relationship types (activates, detoxifies, transports, forms adduct, repairs, and pathway). ResultsThe first-generation reference graph captures metabolic activation and detoxification pathways for 9 carcinogen classes spanning 15 index carcinogens. It represents 36 enzymes across Phase I activation (n=14), Phase II conjugation and detoxification (n=14), Phase III transport (n=3), and DNA repair (n=5). Interactive exploration supports carcinogen-class filtering, node- and edge-type filtering, metadata-based search, and detailed hover/detail views with provenance and pharmacogenomic annotations. The androgen branch highlights cross-pathway connectivity by linking androgen metabolism to estrogen quinone formation and DNA adduct generation through CYP19A1-mediated aromatization and downstream catechol estrogen chemistry. In the optional androgen-focused extension, additional receptor, tissue, and variant context further connects this branch to androgen receptor signaling and genotype-specific annotations. ConclusionsExposoGraph provides a first-generation integrated, interactive framework linking carcinogenic exposures to metabolic fates and genetic modulators. The platform supports hypothesis generation for gene-environment interaction studies and may inform future individualized risk modeling, while remaining a research-use framework rather than a clinically validated risk-assessment tool.

BackgroundThe composition and function of the gut microbiome have been shown to contribute to both health and disease. One of the most powerful modulators of microbial composition and function is diet. Materials & MethodsUsing the E{micro}-TCL1 murine model of B-cell chronic lymphocytic leukemia (CLL), we assigned male and female mice to a high-fat, high-carbohydrate Western diet (HF) or standard chow (CH) diet. ResultsMice consuming a HF diet had significantly shorter survival than those consuming a CH diet, irrespective of sex, with female mice exhibiting particularly poor outcomes. We also observed a significant increase in splenic involvement by CLL in the HF diet-fed mice at time of sacrifice. Mice receiving the HF diet demonstrated immediate and profound effects on the gut microbiome, marked by reduced alpha diversity and significantly different community composition as measured by beta diversity. Notably, there was a sustained increase in Akkermansia muciniphila and Bacteroidetes thetaiotaomicron in HF diet-fed mice, coupled with a corresponding increase in microbiome functional pathways related to arginine and histidine biosynthesis, chitin degradation, and nucleotide biosynthesis. DiscussionCollectively our data provides evidence of the profound and sustained impact of a high-fat Western diet upon the gut microbiome community and CLL pathogenesis in the E{micro}-TCL1 murine model of CLL.