Clinical Cancer Research

Dedifferentiated liposarcoma is a rare mesenchymal malignancy driven by amplification of chromosome 12q13-15, which includes the oncogenes CDK4 and MDM2. CDK4 amplification provides a rationale for targeted therapy with CDK4/6 inhibitors, and abemaciclib has shown the most durable activity reported to date in this disease. Clinical responses, however, are incomplete and often transient, and the cellular features that allow tumor cells to continue proliferating during treatment are not well understood. To address this gap, we performed multiplexed single-cell imaging to quantify 17 cell-cycle regulators in both dedifferentiated liposarcoma cell line Lipo246 and surgically resected primary human cells exposed to abemaciclib. Both models contained a subpopulation of cells that retained phosphorylated retinoblastoma protein, a marker of cell proliferation, at the highest abemaciclib doses. These fractionally resistant cells were defined by selective enrichment of cyclin-dependent kinase 2 (CDK2), cyclin B1, and phosphorylated ribosomal protein S6 (pS6), and showed enhanced sensitivity to the CDK2 inhibitor, tagtociclib. Together, these findings reveal nongenetic cell cycle plasticity as a mechanism of escape from CDK4/6 inhibition in dedifferentiated liposarcoma and nominate CDK2 and the PI3K-mTOR pathway as candidate targets for combination therapy.

PurposeLimited CNS bioavailability and pharmacodynamics are obstacles to effective systemic therapies for glioblastoma. One strategy to overcome these challenges is drug combinations enhancing CNS penetration and/or tumor chemosensitivity. LP-184, a synthetic acylfulvene class alkylator, induces DNA damage and inhibits glioblastoma cell viability in pre-clinical models. LP-184 is a prodrug converted to active metabolites by intracellular prostaglandin reductase 1 (PTGR1) that is over-expressed in >70% of glioblastoma. DNA damage induced by LP-184 is MGMT agnostic and reversed by transcription-dependent NER. PatientsLP-184 was evaluated in a Phase 1a study (NCT05933265) in 63 adult patients with advanced malignancies including 16 patients with recurrent glioblastoma. All patients with glioblastoma received prior standard-of-care therapy and most had received 1 or more additional therapies before enrollment. ResultsPatients with glioblastoma experienced more frequent transaminitis, Grade 1-2 nausea and a trend towards more frequent and severe thrombocytopenia compared to the non-glioblastoma cohort. Otherwise, overall toxicity profiles were similar. Clinical pharmacokinetic analysis combined with published pre-clinical intra-tumoral bioavailability data ([~]20% penetration) predicted that LP-184 at the recommended dose for expansion (RDE) would achieve cytotoxic levels if combined with spironolactone, a BBB permeable ERCC3 degrader and TC-NER inhibitor that sensitizes glioblastoma cells to LP-184 3-6-fold. We show that three daily doses of spironolactone deplete orthotopic glioblastoma PDX ERCC3 protein by [~] 80% and increases tumor LP-184 cytotoxicity 2-fold. ConclusionsLP-184 is well tolerated at the RDE, and we establish a clinically translatable scheme for dosing spironolactone in combination with LP-184 for a future Phase 1b clinical trial. Statement of translational relevanceTreatment failure in glioblastoma reflects inadequate drug brain exposure and DNA repair- mediated resistance. LP-184, a novel acylfulvene alkylator, generates MGMT-independent DNA lesions predominantly repaired by transcription-coupled NER. In a Phase 1a dose finding trial, LP-184 was well-tolerated at the recommended dose for expansion (RDE) in participants with advanced cancers, including recurrent glioblastoma. Plasma drug levels achieved predicted effective systemic exposures but not brain concentrations based on projected 20% brain penetrance. Pharmacokinetic modeling indicates that NER inhibition could increase tumor chemosensitivity with the addition of spironolactone. The optimal dosing regimen for spironolactone combined with LP-184 was identified in orthotopic PDX models, facilitating advancement to Phase 1b/2a testing of LP-184 plus spironolactone.

Background and ObjectivesIntravesical gemcitabine/docetaxel (Gem/Doce) is an effective therapy for Bacillus Calmette- Guerin (BCG)-unresponsive non-muscle-invasive bladder cancer (NMIBC), achieving 50% complete responses at 2 years. However, the genomic determinants underlying response and resistance to Gem/Doce remain poorly defined. Our objective was to define the mutational landscape of BCG-unresponsive NMIBC and nominate genomic features associated with response or resistance Gem/Doce. MethodsPatients with BCG-unresponsive NMIBC treated with Gem/Doce were classified as responders (recurrence-free survival [RFS] >12 months) or non-responders (RFS <12 months). Whole-exome sequencing was performed on tumors prior to Gem/Doce treatment (n=23). Single nucleotide variants were identified and annotated using a Cancer Genome Analysis pipeline. Copy number alterations were inferred with ABSOLUTE, and clonal architecture was reconstructed using PhylogicNDT. Key Findings and LimitationsResponders demonstrated significantly prolonged time to high-grade recurrence (3.5 vs 42 months, p<0.001) and cystectomy compared with non-responders (9.5 months vs not reached; p<0.001). Non-responders exhibited higher tumor mutational burden (13.66 vs 8.71; p=0.02) and more frequent whole-genome doubling (2/2 non-responders vs 0/1 responders; p=0.33). Phylogenetic analyses revealed clonal BAP1 and subclonal BRCA2 mutations in responders, whereas non-responders harbored clonal FGFR3 mutations. Limitations include small sample size and retrospective design. Conclusions and Clinical ImplicationsDistinct genomic features underlie differential response to Gem/Doce in BCG-unresponsive NMIBC. In responders, alterations in DNA repair pathways (e.g., BRCA2) may sensitize tumors to chemotherapy, while non-responders with FGFR3 mutations may benefit from alternative targeted strategies. These findings warrant validation in larger cohorts and support the development of biomarker-driven clinical trials. Patient summaryIn this report we analyzed bladder tumors and found that some tumors respond well to treatment because they have defects in repairing DNA, making them more vulnerable to chemotherapy. In contrast, tumors that do not respond to chemotherapy harbor different genetic changes that help them survive and grow. These findings may help physicians choose more effective and personalized treatments in the future.

BackgroundMen with aggressive, localized prostate cancer (PC) undergo definitive radiotherapy (RT) with androgen deprivation therapy (ADT). The prospective, phase II ARIEL trial evaluates a quantitative MRI biomarker, Restriction Spectrum Imaging restriction score (RSIrs), at three time points (before treatment, after ADT and after RT) for treatment response assessment. RSIrs highlights intracellular restricted diffusion and is correlated with high-grade PC. DesignParticipants are men with unfavorable-intermediate-risk or high-risk localized PC undergoing definitive RT with neoadjuvant and concurrent ADT, and MRI-RSI acquisitions at three time points: before therapy, after neoadjuvant ADT but before RT, and after RT. The primary aim is to evaluate performance of RSIrs for identifying patients who will experience early biochemical recurrence. Change in RSIrs within visible tumors after ADT and RT is the primary independent variable. Results97 patients met inclusion criteria and received [&ge;]1 MRI. On central review, visible PI-RADS lesions were identified in 88 patients: 80 patients had one lesion, and 8 patients had two lesions. After neoadjuvant ADT, 40% of lesions were not clearly visible. Those still visible had shrank by median 55.8% (IQR: 42.8-69.0%), much more than the prostate volume decrease of 21.5% (11.9-31.6%). RSIrs maximum within visible lesions decreased from mean 329 (SD:185) pre-ADT to 209 (SD:125) pre-RT (p<0.01), and to 107 (SD:61) post-RT (p<0.01). Conventional apparent diffusion coefficient (ADC) changes were less consistent. Follow-up is ongoing to assess whether imaging response is related to future recurrence risk. ConclusionARIEL has completed accrual and preliminary results demonstrate changes in RSIrs after treatment, which may indicate tumor response. Primary results will be presented when the primary endpoint is reached. With neoadjuvant ADT, both pre- and post-ADT MRI are likely necessary for accurate focal RT boost targeting. Concurrent commencement of ADT and RT simplifies workflows and facilitates accurate gross tumor volume delineation.

PURPOSE: Newly-diagnosed glioblastoma (nGBM) is a devastating tumor with median survival of only 14-18 months despite aggressive standard of care (SOC). Dendritic cell (DC) homologous antigenic double-loading provides a powerful pattern-based signal that initiates cDC1-like skewing of monocytic precursors, inducing downstream development of CD8+ memory effectors. Here we report phase I results for DOC1021 (dubodencel), a novel DC vaccine regimen integrated with SOC. METHODS: In this dose-escalating study, DC prepared from mobilized peripheral blood were doubly loaded with autologous tumor lysate and amplified tumor mRNA and administered bilaterally near the deep cervical node chains in three biweekly courses given with weekly peg-IFN after conclusion of chemoradiation. Four dose levels from 3.5x106 to 3.6x107 total cells were tested. Patients with subtotal resection or tumor progression prior to vaccination were not excluded. RESULTS: Eighteen patients (median age 61 years (range 47-73), 94% MGMT unmethylated, 25% subtotal/partial resected) completed vaccination (16 nGBM, 2 recurrent) with no dose-limiting toxicities. Attributable AE were mostly mild and flu-like or injection-site reactions. Twelve-month OS among the newly-diagnosed cohort was 88% compared to an expected ~60% for SOC alone. Patients who received observation rather than reoperation in response to worsening MRI contrast-enhancement demonstrated gradual lesional resolution and improved OS. Immunophenotyping revealed post-vaccination elevations in CD4 and CD8 memory T-cells in peripheral blood, and spatial transcriptomic analysis revealed foci of activated inflammatory complexes at the primary tumor site. CONCLUSIONS: DOC1021 was safe, feasibly integrated within SOC, and associated with more favorable outcomes in this challenging patient population. Patients who received observation rather than reoperation for worsening MRI contrast-enhancement exhibited superior survival, suggesting an immune-reactive tumor microenvironment manifesting as pseudo-progression. These data supported initiation of a randomized Phase II trial (NCT06805305) for nGBM.

Background: CD8+ tumor-infiltrating lymphocytes (TILs) are established prognostic markers in colorectal cancer, yet the clinical significance of CD103+CD8+ tissue-resident memory-like (TRM-like) T cells in locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (NACRT) remains unknown. Methods: We quantified CD8+ and CD103+CD8+ T-cell densities in stromal and intratumoral compartments of post-NACRT resection specimens from 40 LARC patients using Cu-Cyto, a deep learning-based imaging cytometry platform. Associations with survival, pathological response, and adjuvant chemotherapy (AC) were examined. Treatment-induced T-cell dynamics were assessed in paired pretreatment biopsies and post-NACRT resections (n = 9). Results: High stromal CD103+CD8+ density independently predicted better 5-year RFS (67.4% vs. 12.1%, p < 0.001) and OS (80.0% vs. 26.6%, p = 0.016); intratumoral density showed no prognostic significance. Pathological response correlated with stromal CD8+ but not CD103+CD8+ density. Paired analysis revealed a selective non-expansion of the CD103+ subset: stromal CD8+ T cells increased significantly after NACRT while CD103+CD8+ density remained unchanged. AC may preferentially benefit patients with low stromal CD103+CD8+ density. Conclusions: Stromal CD103+CD8+ T-cell density is a robust independent prognostic biomarker in rectal cancer after NACRT that appears to reflect pre-existing rather than treatment-induced immunity. Given its stability across NACRT, pretreatment biopsy assessment may provide equivalent prognostic information, with potential implications for patient stratification before treatment initiation.

BackgroundConvection-enhanced delivery of topotecan enables sustained local chemotherapy for recurrent glioblastoma and was associated with reduced tumor proliferation in our previous phase 1B clinical trial. That trial incorporated a paired pre- and post-treatment biopsy design - rare in glioblastoma clinical research - enabling tissue-anchored assessment of drug effect without reliance on radiographic or survival endpoints, which are notoriously difficult to interpret in this disease. However, the cellular and molecular consequences of local chemotherapy within the treated tumor microenvironment remain incompletely defined. MethodsWe integrated paired, MRI-localized pre- and post-treatment biopsies from a first-in-human CED-topotecan trial (n=5), leveraging the paired biopsy architecture, in which each patient serves as their own control and post-treatment specimens are spatially annotated relative to the MRI-defined infusion zone, to generate tissue-based evidence of drug effect without requiring large patient numbers. These biopsies were integrated with complementary experimental models, including a time-resolved syngeneic murine glioma CED model, acute patient-derived glioblastoma slice cultures, and in vitro human microglial and glioma systems. Clinical biopsies were analyzed by bulk RNA-seq, cell-type deconvolution, and multiplex immunofluorescence. Murine tumors were analyzed by survival, immunofluorescence, and single-cell RNA-seq; patient-derived slice cultures were profiled by single-cell RNA-seq. ResultsIn paired human biopsies, CED-topotecan induced spatially restricted transcriptional remodeling within the infusion zone, characterized by suppression of proliferative tumor programs and enrichment of inflammatory, interferon, hypoxia, and mesenchymal signatures. Cell-type deconvolution and immunofluorescence linked this response to myeloid remodeling, including enrichment of monocyte-derived tumor-associated macrophage states, increased MARCO-positive myeloid populations, and pH2AX-positive genotoxic stress within Iba1-positive myeloid cells. In the murine CED model, topotecan prolonged survival and reduced tumor cellularity, while also inducing inflammatory and DNA-damage programs in tumor-associated macrophages that evolved by 7-days toward hypoxia, angiogenesis, TGF-{beta} signaling, and mesenchymal/tissue-remodeling programs. Human slice culture and in vitro microglial systems confirmed stress-coupled inflammatory and DNA-damage responses in human myeloid cells. ConclusionsLocal topotecan delivery produces spatially structured tumor cytotoxicity together with a genotoxic, stress-coupled inflammatory myeloid response that evolves toward mesenchymal macrophage remodeling. By integrating paired clinical biopsies with time-resolved and mechanistic experimental models, this study provides a framework for understanding how local chemotherapy reshapes the glioblastoma microenvironment and for future studies evaluating dose, schedule, treatment duration, and combination strategies. These findings demonstrate that paired, spatially annotated tissue sampling from small, precisely characterized clinical cohorts can yield mechanistic insight that conventional radiographic and survival endpoints cannot provide, and support tissue-based response assessment as the appropriate paradigm for evaluating novel locoregional therapies in glioblastoma.

PurposeHPV-associated carcinomas (HPV+ cancers) account for 5% of all cancers. Circulating tumor HPV DNA (ctHPVDNA) assays for HPV+ cancer surveillance have limited prognostic utility at the time of cancer diagnosis. While HPV integration into the host genome is a proven tissue-based biomarker predicting poor clinical outcomes, existing clinically utilized ctHPVDNA assays cannot classify the viral physical state. MethodsWe previously developed HPV-DeepSeek, a multi-feature HPV whole-genome sequencing liquid biopsy with 99% diagnostic accuracy at the time of HPV+ oropharynx cancer diagnosis. We test the diagnostic accuracy of HPV-DeepSeek in a cohort of 235 HPV+ cancers across nine anatomic sites and employ a novel blood-based computational classifier to infer HPV genome physical state from plasma, termed HPV-SIGNAL, to assess its prognostic potential. ResultsHPV-DeepSeek demonstrated a sensitivity and specificity of 99%. In 181 eligible samples, HPV-SIGNAL identified four viral physical states: episomal-only (N = 69), episomal-rearranged (N = 48), integrated-mixed (N = 55), and integrated-clonal (N = 9), which were confirmed and further elucidated via three orthogonal tissue and blood approaches. Patients harboring integrated viral states in the blood exhibited significantly worse progression-free survival (HR 3.28, 95% CI 1.63-6.58, p = 0.00084) and overall survival (HR 2.98, 95% CI 1.16-7.64, p = 0.023) compared to patients with episomal states. ConclusionHPV whole-genome sequencing liquid biopsy has high diagnostic accuracy across HPV+ cancer types and can be used to identify and classify HPV physical state from blood. Patients with integrated viral states detected in the blood demonstrated worse progression-free and overall survival, suggesting blood-based HPV physical state classification could be used as a prognostic tool at the time of cancer diagnosis. Translational RelevanceCurrent circulating tumor HPV DNA assays for HPV-associated cancer surveillance have limited prognostic utility at the time of cancer diagnosis. While HPV integration into the host genome is a proven tissue-based biomarker predicting poor clinical outcomes, existing circulating tumor HPV DNA assays cannot classify the viral physical state. Here, we show that HPV-SIGNAL, a novel blood-based computational classifier to infer HPV genome physical state from plasma using output from HPV-DeepSeek, an HPV whole genome sequencing liquid biopsy, accurately identifies and classifies HPV physical state from blood and is prognostic of progression-free and overall survival across HPV-associated cancer types.

The ABACUS study was a single arm, phase II trial evaluating neoadjuvant atezolizumab in operable urothelial carcinoma. Initial bulk transcriptomic and immunohistochemistry analyses suggested links between immune activation, tissue remodeling, and resistance pathways such as transforming growth factor {beta} that were associated with clinical outcome. To further characterize spatial and phenotypic changes at high resolution, artificial intelligence-assisted digital image analysis of hematoxylin and eosin sections and spatial transcriptomics were performed on paired tissue samples. In baseline samples, cells residing in lymphoid aggregates and tertiary lymphoid structures were more abundant in stable disease than in relapse and exhibited gene expression programs associated with improved survival in urothelial carcinoma. Most spatial features reflected shared pharmacodynamic changes between stable disease and relapse; however, carcinoma-endothelial adjacency was reduced significantly following treatment and differed between groups, accompanied by distinct transcriptional programs. Together, these findings indicate that atezolizumab induces localized immune and stromal remodeling within the tumor microenvironment, while non-response despite immune expansion is associated with persistent spatial immune exclusion and carcinoma-endothelial adjacency. Spatial and phenotypic biomarkers identified here may inform rational combination strategies for immune checkpoint inhibitor-refractory urothelial carcinoma.

Importance: Glioblastoma (GBM) cells integrate into neuronal circuits, and preclinical work implicates multiple neurotransmitter (NT) networks as key drivers of invasion and treatment resistance. Whether the integration of GBM within NT-defined large-scale brain networks conveys prognostic information for overall survival (OS) is unknown. Objective: To determine whether NT-specific network involvement of GBM is associated with OS in patients with newly diagnosed Isocitrate dehydrogenase (IDH)-wildtype(wt) GBM. Design, Setting, and Participants: In this observational multicenter cohort study, we analyzed two independent cohorts of adults with histopathologically confirmed IDH-wt GBM. Cohort 1 included 153 patients treated at the University Medical Center Hamburg-Eppendorf, Germany (2012-2024), and cohort 2 comprised 264 patients from the University of Pennsylvania Health System, USA (2006-2018). Preoperative contrast-enhanced MRI was used to derive individual tumor masks, which were spatially mapped onto normative NT-informed structural connectomes spanning 19 receptor and transporter systems. Exposures: Preoperative contrast-enhancing GBM lesions, quantified as patient-specific involvement scores (0-1) within each NT-defined brain network. Statistics: We used partial least-squares regression for variable selection and multivariable Cox proportional-hazards models alongside regularized logistic regression with out-of-sample prediction, adjusted for age, methylguanine methyltransferase (MGMT) promoter methylation, and extent of resection, to test associations between NT-specific GBM network involvement and OS. Results: Across 417 patients in two cohorts, greater GBM involvement within cholinergic networks, defined by normative vesicular acetylcholine transporter (VAChT)-weighted as well as dopaminergic D2 receptor involvement, was consistently associated with reduced OS, independent of age, MGMT status, and resection extent. Further, cholinergic network involvement showed the strongest contribution to the prediction models. Other NT networks did not show reproducible prognostic effects across cohorts. Tumor-intrinsic hypomethylation of acetylcholine receptor-associated regions correlated with imaging-based cholinergic network involvement and mirrored its prognostic relevance. Conclusion and Relevance: Tumor integration into neurotransmitter-specific brain networks is an independent predictor of poorer survival in GBM. By combining routine clinical MRI with normative NT-informed connectome data, this approach delineates a novel systems-level marker of tumor aggressiveness and supports cholinergic inhibition as a putative therapeutic target in GBM.

BackgroundImmune checkpoint inhibitors (ICIs) have revolutionized cancer therapy but can cause serious immune-related adverse events (irAEs), with pneumonitis (ICI-P) being among the most severe. Early identification of high-risk patients before ICI initiation is critical to close monitoring, enable timely intervention, and optimize outcomes. PurposeTo develop and validate a deep learning foundation model to predict ICI-P from baseline CT scans in patients with lung cancer. MethodsWe designed the Checkpoint-Inhibitor Pneumonitis Hazard EstimatoR (CIPHER), a deep learning-powered foundation model combining contrastive learning with a transformer-based masked autoencoder to predict ICI-P from baseline CT scans in lung cancer patients. Using self-supervised learning, CIPHER was pre-trained on 590,284 CT slices from 2,500 non-small cell lung cancer (NSCLC) patients, to understand heterogeneous lung parenchyma. Following pre-training, the model was fine-tuned on an internal NSCLC cohort for ICI-P risk prediction, with images from 254 patients used for model development and from 93 patients for internal validation. We compared CIPHER with classical radiomic models. We also validated CIPHER on an external NSCLC cohort of 116 patients. ResultsIn our internal immunotherapy cohort, CIPHER consistently distinguished patients at elevated risk of ICI-P from those without the event, with AUCs ranging from 0.77 to 0.85. In head-to-head benchmarking, CIPHER achieved an AUC of 0.83, outperforming radiomic model. In the external validation cohort, CIPHER maintained high performance (AUC=0.83; balanced accuracy=81.7%), exceeding the radiomic models (Delong p=0.0318) and demonstrating superior specificity without sacrificing sensitivity. By contrast, radiomic model, despite high sensitivity (85.0%), showed markedly lower specificity (45.8%). Confusion matrix analyses confirmed CIPHERs robust classification, correctly identifying 80 of 96 non-ICI-P cases and 16 of 20 ICI-P cases. ConclusionsWe developed and externally validated CIPHER for predicting future risk of developing ICI-P from pre-treatment CT scans. With prospective validation, CIPHER can be incorporated into routine patient management to improve outcomes. HighlightsO_LIThe first chest CT AI foundation model for immune toxicity - We introduce CIPHER (Checkpoint-Inhibitor Pneumonitis Hazard EstimatoR), a transformer-based masked autoencoder trained through self-supervised contrastive learning on 590,284 CT slices from 4,242 NSCLC patients scans. This large-scale pretraining enables CIPHER to learn intrinsic lung parenchymal representations linked to immune toxicity risk. C_LIO_LIEarly risk prediction prior to therapy initiation - CIPHER predicts the likelihood of ICI-induced pneumonitis directly from baseline CT scans, offering the first non-invasive foundation model for early risk assessment before ICI. C_LIO_LIRobust validation and benchmarking - We fine-tuned and evaluated CIPHER across independent internal and external NSCLC immunotherapy cohorts, achieving AUCs of 0.77- C_LIO_LI0.85 internal cross validation and 0.83 external testing, surpassing conventional radiomic models in both performance and generalizability. C_LIO_LIInterpretability and clinical readiness - We demonstrate how model-derived attention maps align with clinically relevant pulmonary patterns, enhancing interpretability and enabling seamless integration into radiology workflows. C_LIO_LITranslational potential - CIPHERs performance and scalability underscore its potential as decision-support tool to guide treatment planning, pre-emptive monitoring, and toxicity mitigation in immunotherapy practice. C_LI

Aims Active surveillance (AS) allows selected men with localized prostate cancer to defer curative therapy and reduce treatment morbidity. Conversion from AS to treatment is commonly triggered by Gleason grade group (GGG) upgrading on confirmatory biopsy. We developed and validated a digital pathology artificial intelligence (DPAI) biomarker to predict GGG upgrading in AS-eligible patients. Materials & Methods The DPAI model was trained using histopathology image features from diagnostic biopsies of 998 patients and validated in an independent cohort of 296 patients meeting criteria for AS. Logistic regression estimated the probability of confirmatory-biopsy GGG increase, and feature selection identified the most predictive variables. Results AI-GUR (Artificial Intelligence-Gleason Upgrade Risk) predicted GGG reclassification at confirmatory biopsy (OR 1.60; p=0.0003), and provided information beyond conventional stratification (risk group, CAPRA) and cribriform morphology (all p<0.01). Predicted risks were similar across time from diagnosis (~10-15% to ~85% at 1, 1.5, or 2 years; p for time=0.50), consistent with initial biopsy mischaracterization rather than time-dependent progression. Conclusions AI-GUR provides individualized estimates of confirmatory-biopsy GGG upgrading for AS candidates. Using DPAI may improve shared decision-making by complementing standard clinicopathologic tools and molecular testing using the same biopsy specimen, while informing the likelihood of grade upgrade at confirmation.

BackgroundRadiotherapy efficacy is constrained by an immunosuppressive tumor microenvironment (TME) enriched in extracellular adenosine and suppressive myeloid populations that attenuate cytotoxic T-cell responses. The CD73-adenosine-A2a/A2b receptor axis represents a key metabolic immune checkpoint; however, the relative contributions of tumor cell-intrinsic versus host-derived adenosine signaling to radiotherapy response remain incompletely defined. MethodsUsing orthotopic murine breast carcinoma models, we interrogated radiation-induced adenosine dynamics and downstream immune remodeling through quantitative adenosine measurements, bulk RNA sequencing, and multiparameter flow cytometry. Genetically engineered models were employed to dissect the roles of tumor-derived CD73 and host A2a/A2b receptors in regulating radiosensitivity. Therapeutic studies evaluated combinatorial targeting of CD73 and A2a/A2b receptors with radiotherapy and anti-PD-1, followed by comprehensive immune profiling in breast carcinomas. ResultsTumor cell-intrinsic CD73 and host A2A receptor signaling cooperatively drive radioresistance and tumor progression. Radiotherapy induces a rapid surge in intratumoral adenosine, triggering transcriptional and cellular programs consistent with myeloid-mediated immunosuppression and lymphocyte dysfunction. Although T-cell infiltration increases at later time point post-irradiation, effector function remains constrained. Pharmacologic inhibition of CD73 and A2a/A2b receptors partially restores T-cell functionality but is insufficient for durable tumor control as monotherapy. In contrast, concurrent blockade of adenosine signaling during radiotherapy, followed by adjuvant PD-1 inhibition, amplifies adaptive antitumor immunity and significantly enhances tumor control. ConclusionsThese findings define a mechanistic link between radiation-induced adenosine signaling and immune dysfunction in the TME. Targeting the CD73-A2a/A2b axis in combination with radiotherapy and checkpoint blockade represents a rational strategy to overcome radioresistance and improve antitumor immunity. STATEMENT OF SIGNIFICANCEThe tumor and immune cell contributions to adenosine signaling play a central role in shaping the therapeutic outcomes of tumor irradiation. Therapeutic targeting of the adenosine signaling axis improves radiosensitivity and efficacy of checkpoint blockade.

BackgroundPredicting whether a treatment will demonstrate meaningful clinical benefit before committing to a large-scale trial remains a major unmet need in oncology. Patient-derived organoids (PDOs) recapitulate individual tumor drug sensitivity, but have not been used to fore-cast population-level trial outcomes. We developed SCOPE (Screening-to-Clinical Outcome Prediction Engine), a platform that integrates PDO drug screening with clinical prognostic modeling to predict arm-level median progression-free survival (mPFS) and objective response rate (ORR) without access to any trial outcome data. Patients and methodsSCOPE was trained on 54 treatment lines from 52 independent patients with metastatic colorectal cancer (mCRC, n=15) and metastatic pancreatic ductal adeno-carcinoma (mPDAC, n=39) with matched clinical data and PDO drug screening across 9 compounds. A Clinical Score module captures baseline prognosis; a Drug Screen Score module quantifies treatment-specific organoid sensitivity. To predict trial outcomes, synthetic patient profiles are generated from published eligibility criteria and matched to a biobank of 81 PDO lines. Predictions were externally validated against 32 arms from 23 published trials, treatment ranking was assessed across 8 head-to-head comparisons, and prospective applicability was tested for Daraxonrasib (RMC-6236), a novel pan-RAS inhibitor in mPDAC. ResultsPredicted mPFS strongly agreed with published outcomes (R2=0.85, MAE=0.82 months; Pearson r=0.92, P <0.001), approaching the empirical concordance between two independently measured clinical endpoints (ORR vs. mPFS, R2=0.87). ORR prediction was similarly robust (R2=0.71, MAE=7.3 percentage points). Integrating organoid and clinical data significantly out-performed either alone (P =0.001). SCOPE correctly identified the superior arm in 7 of 8 head-to-head comparisons (88%, P <0.05). Applied to Daraxonrasib prior to phase 3 data availability, the platform predicted superiority over standard chemotherapy in KRAS-mutant mPDAC, consistent with emerging trial data. ConclusionBy combining functional organoid drug screening with clinical modeling, SCOPE generates calibrated efficacy predictions for both established regimens and novel agents without prior trial data. This approach could support clinical trial design, treatment arm selection, and go/no-go decisions, offering a new tool to improve the efficiency of gastrointestinal cancer drug development.

Patient-reported outcomes (PROs) capture the patient voice and have been associated with improved clinical outcomes in oncology, but their prognostic and predictive value remains underutilized due to challenges in interpreting these highly variable and noisy PRO data. Here, we developed a quantitative modeling framework integrating nonlinear mixed-effects (NLME) and item response theory (IRT) to characterize symptom-level PRO trajectories and transform them into clinically actionable predictors. Using longitudinal PRO data from 589 patients with metastatic cancers in the PRO-TECT trial, we modeled 332,920 symptom responses to estimate patient-specific PRO trajectory parameters while accounting for variability and noise. IRT-NLME modeling captured heterogeneous symptom-level PRO dynamics and is more informative than modeling with composite PRO scores. PRO trajectory parameters were strongly associated with overall survival, acute care utilization, and treatment modifications. Machine learning models leveraging these parameters achieved robust prediction of survival (AUC-ROC 0.80) and retained prognostic performance using the first 30 - 180 days of PRO observations, with AUCs of 0.69-0.78. Similar predictive performance was observed for hospitalization (AUC 0.75), emergency department visit (AUC 0.65), treatment discontinuation (AUC 0.71), and dose reduction (AUC 0.67). These findings demonstrate that longitudinal PRO trajectories can serve as early, patient-centered biomarkers of clinical risk. By converting complex symptom data into interpretable and predictive metrics, this quantitative framework provides a practical pathway to integrate the patient voice into clinical decision-making and advance precision oncology. ClinicalTrials.gov registration: NCT03249090

Background: The tumor suppressor gene TP53 and the oncogene KRAS are among the most frequently altered core drivers in human malignancies. Although they cooperatively regulate critical biological processes, the prognostic impact of their co alterations remains poorly defined and exhibits striking inconsistency across different cancer types. Methods: We comprehensively analyzed genomic and clinical data from multi-cancer cohorts sourced from the cBioPortal database and The Cancer Genome Atlas (TCGA). Genetic alterations, including sequence variations and copy number alterations (CNAs), were classified for TP53 and KRAS. Patients were stratified into four subgroups based on individual or combined alteration status. Survival analyses were performed using Kaplan-Meier methods. Integrated multi-omics analyses were conducted to assess the relationship between genetic alterations and mRNA/protein expression, and to characterize co-occurring genetic events and their prognostic implications. Results: Patients harboring concurrent TP53 and KRAS alterations exhibited significantly shorter overall survival in pancreatic cancer, colorectal cancer, and ampullary carcinoma, but surprisingly demonstrated the longest survival in gastric cancer. Distinct KRAS mutation subtype distributions were observed across cancer types: G12D/G12V predominated in pancreatic and colorectal cancers, G12C in non small cell lung cancer, and G13D in gastric cancer, with copy number alterations representing a substantial proportion of KRAS alterations in gastric and lung cancers. Multi-omics analysis revealed a lack of concordance between genetic alterations and mRNA/protein expression, indicating that mutation status alone does not reliably reflect downstream molecular changes. Concurrent genetic events displayed striking cancer-type specificity: CDKN2A alterations frequently co-occurred with TP53/KRAS double alterations in pancreatic cancer and were associated with worse prognosis, whereas APC mutations co-occurred in colorectal cancer and correlated with improved survival. Integrated analysis further demonstrated that KRASaltered/TP53altered patients were highly enriched in pancreatic, colorectal, and lung cancers, each exhibiting unique background genomic landscapes. Conclusions: The prognostic significance of TP53 and KRAS alterations is profoundly cancer-type specific, driven by differences in mutation subtype distribution, copy number alteration patterns, co-occurring genetic events, and the discordance between genotype and functional expression. These findings challenge the simplistic view of dual-gene alterations as universal markers of poor prognosis and underscore the necessity of incorporating cancer-specific molecular contexts into prognostic models and precision oncology strategies.

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.

BackgroundTumour-associated macrophages (TAMs) play critical roles within the tumour microenvironment regulating immune evasion and therapeutic response. Previously, we have shown that the combination of Checkpoint kinase 1 inhibitor (CHK1i) with a subclinical dose of hydroxyurea (LDHU) reprograms the tumour immune microenvironment to a pro-inflammatory status. MethodsWe investigated a tumour-restricted Fcgr4 (Cd16.2) expressing macrophage population in multiple murine tumour models and the impact of CHK1i+LDHU on this population, using conventional and imaging flow cytometry as well as single-cell sequencing. ResultsTranscriptional profiling using CITE-seq and single-cell RNA sequencing reveals that Fcgr4 TAMs closely resemble Fcgr4- TAMs but display modest enrichment of interferon-associated and inflammatory gene programs, consistent with a functionally biased state rather than a distinct lineage. Importantly, we show that a highly tumour selective CHK1i+LDHU therapy shifts TAMs toward a more inflammatory phenotype while preserving dominant immunosuppressive features. Depletion of CSF1R macrophages enhanced CD8 T cell activation without influencing tumour growth but significantly augmented therapeutic efficacy of CHK1i+LDHU. ConclusionTogether, these findings define a novel TAM population and establish how targeted therapy reshapes, but does not fully overcome, TAM-mediated immune regulation.

Prostate cancer (PCa) is the second most common cancer and cause of cancer death in American men. Existing risk prediction methods have limited accuracy and reproducibility, resulting in difficulty in predicting disease severity. We demonstrate the development and external validation of an automated multimodal artificial intelligence algorithm using biparametric MRI (bpMRI) and clinical covariates for predicting biochemical recurrence (BCR) after radical prostatectomy (RP) in PCa patients. Development cohort included 80% of patients from center 1 (n = 240) who underwent prostate MRI prior to RP between January 2008 and December 2018 with a minimum of two years of follow-up after RP. Test cohort included the remaining 20% of center 1 patients (n = 71), and the external validation cohort from center 2 (n = 168). Center 2 patients included those who underwent prostate MRI and RP between January 2015 and December 2024 with a minimum of two years of follow-up. Clinical comparisons were CAPRA-S (center 1) and ISUP grade group from post-RP biopsy (center 2). Models developed were a clinical model (M0), an automated clinical model (M1), a radiomics model (M2), and a multimodal model (M3). Clinical variables (M0) included PSA, age, primary Gleason, and ISUP grade group. Automated clinical variables (M1 and M3) included PSA and age. Radiomics features (M2 and M3) were extracted from bpMRI using a lesion detection algorithm. Accuracy, sensitivity, specificity, and AUC were calculated, and log-rank tests compared BCR-free survival to assess the models ability to discriminate relative to clinical standards. Intermediate-risk groups were also assessed. The multimodal model (M3) had the highest AUC across test sets (combined: 0.71; center 1: 0.70; center 2: 0.75) and was the only model to significantly differentiate BCR-free survival outcomes in intermediate-risk groups across both centers (p < 0.05). This automated multimodal model leveraging radiomics and clinical covariates can predict BCR after RP, approaches clinical gold standards, and may enhance imaging-based prognostication following further validation.

Therapeutic resistance remains a major challenge in advanced and recurrent endometrial cancer (EC). Aberrant NOTCH signaling has been associated with aggressive tumor behavior and therapeutic resistance across multiple malignancies, yet its therapeutic significance in EC remains incompletely defined. We investigated whether auranofin (AuR), a noncanonical modulator of NOTCH signaling through the transcriptional effector RBPJ, alters platinum responsiveness in EC models. Elevated NOTCH3 copy-number was associated with poorer overall survival in the TCGA-UCEC cohort. AuR treatment reduced RBPJ occupancy at canonical NOTCH target loci, including HES1 and HES4, across multiple EC models. Stable NOTCH3 depletion altered AuR responsiveness in a context-dependent manner while significantly enhancing cisplatin (CDDP) sensitivity in AN3CA cells. Pharmacologic AuR treatment similarly potentiated CDDP response in AN3CA cells and AN3CA xenografts, resulting in reduced tumor burden and prolonged endpoint-free survival following combination treatment. In contrast, ARK-1 xenografts demonstrated limited additional benefit from combined AuR plus CDDP therapy despite detectable suppression of RBPJ occupancy. Together, these findings identify context-dependent NOTCH-associated therapeutic vulnerabilities in EC and support further development of biomarker-guided AuR-based platinum-sensitization strategies. Statement of significanceAuranofin suppresses RBPJ-associated transcriptional activity and enhances cisplatin response in biologically distinct subsets of endometrial cancer.