Clinical Cancer Research

BackgroundOnce the treatment starts, early prediction of treatment benefit and its correlation with overall survival (OS) remains challenging in metastatic castration-resistant prostate cancer (mCRPC). Existing prognostic models require long-term follow-up, limiting their ability to inform timely treatment decisions. To address this gap, we evaluated tumor growth rate (g-rate)-based survival models across multiple treatment lines to assess their ability to predict OS and support early clinical decision-making. MethodsWe developed GxSurv, a Random Survival Forest (RSF)-based framework that incorporates baseline clinical variables and g-rate calculated from serial on-treatment PSA, to construct line-specific prediction models of OS, a direct measure of treatment outcome. Three variants were developed: G3Surv, using the 3-month g-rate; G6Surv, using the 6-month g-rate; and GfSurv, using the final observed g-rate. Model performance was evaluated using Harrells C-index, Unos C-index, Integrated Brier Score (IBS), time-dependent area under the curve (tAUC). Model interpretability was assessed using permutation importance to quantify predictor contributions within the GxSurv framework. FindingsThe study included 15912 treatment records from 11014 patients with mCPRC across four lines of therapy. We found that incorporation of g-rate consistently improved model performance across all treatment lines, with all GxSurv models outperforming Cox proportional hazards (CoxPH). As the earliest prognostic model, our G3Surv demonstrated strong early predictive performance, with Harrells C-index values ranging from 0{middle dot}700 to 0{middle dot}746 and tAUC values of 0{middle dot}766 to 0{middle dot}822 across all lines, representing 5-8% and 4-5% improvements over CoxPH, respectively. These results indicate that G3Surv accurately predicts individual treatment outcomes at 3 months after treatment initiation. Feature importance analyses consistently identified g-rate as a top predictor, followed by baseline PSA and hemoglobin, with relative variation across treatment lines. InterpretationIntegrating g-rate calculated from on-treatment PSA values enables accurate, line-specific prediction of treatment outcomes in mCRPC, with the 3-month g-rate providing robust early prognostic information to support timely, personalized clinical decision-making. FundingU.S. National Science Foundation, National Institutes of Health, American Cancer Society.

Purpose: Limited 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. Patients: LP-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. Results: Patients 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. Conclusions: LP-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.

1.The ABACUS study was a single arm, phase II trial evaluating neoadjuvant atezolizumab in operable urothelial carcinoma (UC). Initial bulk transcriptomic and immunohistochemistry analyses suggested links between immune activation, tissue remodeling, and resistance pathways such as transforming growth factor {beta} (TGF{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 (10x Genomics Visium) were performed on paired tissue samples. In baseline samples, cells residing in lymphoid aggregates and tertiary lymphoid structures (LAs/TLSs) were more abundant in stable disease than in relapse and exhibited gene expression programs associated with improved survival in UC. 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.

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.

PurposeTarlatamab is a DLL3-directed bispecific T-cell engager demonstrating clinically meaningful activity in relapsed small cell lung cancer (SCLC) in the phase II DeLLphi-301 trial. Determinants of tarlatamab sensitivity and resistance are incompletely understood, and thus we sought to identify genomic and transcriptional correlates of tarlatamab sensitivity using a clinical sequencing pipeline at a single comprehensive cancer center. Experimental DesignWe performed a retrospective, single-institution analysis of 12 patients with SCLC treated with tarlatamab. Whole-exome sequencing (WES) and exome-capture whole-transcriptome sequencing (WTS) were performed on 12 samples, and two matched samples after treatment with tarlatamab. Integrative analysis examined correlation between molecular features and clinical outcomes. ResultsThe overall response rate was 50%, which was consistent with outcomes reported in the DeLLphi-301 trial. Differences between SCLC driver alterations and tumor mutational burden were not significant between responders and non-responders, but homologous recombination deficiency scores were higher in responsive tumors. DLL3 expression was significantly greater in responders and demonstrated predictive discrimination for clinical response (AUC 0.83). Tumors responsive to tarlatamab were predominantly ASCL1-driven (SCLC-A) and demonstrated increased immune activation, such as enrichment of cytotoxic T-cell, NK-cell, and T cell transcriptional programs. Transcriptional subtype and a composite metric consisting of DLL3 expression and immune activity (DLI score) further discriminated between responders and non-responders (sensitivity 0.83, specificity 1). Paired post-treatment sample analysis identified loss of ASCL1 lineage and emergence of YAP1 expression and downregulation of DLL3, consistent with lineage plasticity as a mechanism of acquired resistance. ConclusionsSensitivity to tarlatamab is correlated with a combination of increased DLL3 expression, ASCL1-driven lineage, and an increased immune activation. Lineage state reprogramming and decrease in DLL3 expression accompany acquired resistance to tarlatamab. These findings highlight the utility of RNA based biomarkers which integrate target expression, lineage state, and immune context to guide tarlatamab therapy in SCLC. Prospective validation of the whole-transcriptome DLI score and transcriptional subtype will inform tarlatamab response prediction.

In approximately half of endometrial carcinoma (EC), PTEN loss-of-function and activating PI3K mutants coexist. Unlike cells with either single mutation, PTEN/PIK3CA coexistent alterations result in elevated membrane phosphatidylinositol (3,4,5)-trisphosphate (PIP3) levels and mTORC1 hyperactivation, rendering PI3K or AKT inhibition ineffective in blocking mTORC1 activity and tumor growth. The bi-steric mTORC1 kinase inhibitor, RMC-6272, suppresses mTORC1 activity and cell growth by reducing protein translation and cell cycle progression. In vivo, RMC-6272, but not PI3K inhibitors, effectively suppressed mTORC1 and growth of EC PDXs with coexistent PTEN/PIK3CA lesions. These findings are consistent with a phase I trial of bi-steric mTORC1 inhibitor RMC-5552, showing anti-tumor activity in patients with EC. PDXs with KRAS co-mutations regrew after RMC-6272 treatment, which was prevented by the addition of the RAS(ON) multi-selective inhibitor RMC-7977. Overall, these data suggest that mTORC1 hyperactivation drives ECs with coexistent PTEN/PIK3CA mutations, explain the limited antitumor activity of PI3K and AKT inhibitors, and support clinical evaluation of mTORC1 inhibitors as potential therapy for EC. SignificanceWe have found the mechanistic consequences of PTEN/PIK3CA co-alterations in endometrial tumors and that these mutations result in a profound hyperactivation of mTORC1 signaling. Single mutant tumors are sensitive to PI3K inhibition but those with both mutations are insensitive to PI3K or AKT inhibition but are exquisitely dependent on mTORC1 kinase. This provides strong preclinical rationale for targeting mTORC1, alone or combined with RAS inhibition (in RAS co-mutant tumors), as an effective therapeutic strategy.

PurposeCirculating tumor DNA (ctDNA) analyses are informative as an early indicator of immunotherapy response in advanced non-small cell lung cancer (NSCLC); however, the clinical value of ctDNA molecular response requires further validation. Patients and MethodsAs part of a prospective clinical protocol (NCT05995821), we conducted targeted error-correction sequencing of ctDNA (n=328) and matched WBC DNA (n=109) from 109 patients with metastatic NSCLC who received anti-PD-(L)1 either as monotherapy or in combination. Following cellular origin resolution of 2,818 variants, landmark molecular response (mR) was defined as undetectable ctDNA within 3-9 weeks of treatment initiation. ResultsPre-treatment ctDNA burden, but not blood tumor mutation burden, predicted survival. Implementing a tumor-naive WBC DNA-informed approach increased the number of evaluable cases without compromising the overall accuracy of landmark ctDNA molecular responses. A direct comparison of single-timepoint on-therapy ctDNA assessment with ctDNA dynamics from baseline to the 3-9-week interval, along with an analysis of heterogeneity in molecular response within the 3-9-week window, showed that undetectable ctDNA at the landmark timepoint can effectively predict survival outcomes. A significant enrichment in landmark ctDNA mR was noted among patients with progression-free survival (PFS) [≥]6 months with immunotherapy (p=2.5e-05) and chemo-immunotherapy (p=0.02). Patients in the landmark mR group had longer progression-free (p=1.6e-06) and overall survival (p=2.5e-05) than those with molecular progression. ConclusionsLandmark ctDNA molecular response provides a real-time, accurate approach for monitoring immunotherapy clinical outcomes. Although not currently validated for regulatory use, these findings demonstrate the potential utility of ctDNA as an early endpoint in clinical trials. Translational RelevanceEmploying circulating tumor DNA (ctDNA) dynamics as an early indicator of immunotherapy response requires a roadmap for the next-generation sequencing approach, definition of molecular response and establishment of its clinical sensitivity. In this study, we introduce the concept of a landmark ctDNA molecular response, determined 3-9 weeks after initiation of immunotherapy, that maximizes the number of evaluable patients without sacrificing the specificity of the approach. Notably, when evaluating heterogeneity in ctDNA detection within the landmark 3-9-week window and assessing the impact of landmark interval dynamics on survival, we found that a single ctDNA assessment performed similarly to multiple ctDNA measurements within the landmark window (most notably, regardless of whether the timepoints were concordant or discordant). Our findings demonstrate that a single assessment of early on-therapy landmark ctDNA molecular response, can identify patients at risk of disease progression and enable future intervention and therapy optimization.

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.

Lower-grade gliomas (World Health Organization [WHO] grades 2-3) exhibit variable treatment responses, yet clinical decisions remain guided by population-level trial results. Standard causal survival forests estimate treatment effects at individual time horizons but lack methodology to synthesize these into interpretable temporal trajectories. Here, we apply the Causal Analysis of Survival Trajectories (CAST) framework, a recently developed extension of causal survival forests that synthesizes horizon-specific causal effect estimates into smooth temporal curves while accounting for between-horizon covariances via bootstrap estimation and Ledoit-Wolf shrinkage. We apply CAST to estimate time-varying, heterogeneous effects of radiotherapy and chemotherapy in 776 patients with lower-grade gliomas from The Cancer Genome Atlas (TCGA; n=512) and the Chinese Glioma Genome Atlas (CGGA; n=264), analyzing six treatment-outcome scenarios and adjusting for age, sex, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19q codeletion, and extent of resection using elastic net propensity scores with overlap weighting. CAST curves reveal that chemotherapy provides consistent, sustained benefits across both cohorts; survival probability gains peak at 0.31 at 72-84 months for TCGA overall survival and 0.46 at 48 months for progression-free survival, with restricted mean survival time gains of 18.4 and 32.5 months at 10 years, respectively. CGGA chemotherapy shows delayed but large positive effects (survival probability peak 0.48 at 108 months). Radiotherapy effects are mixed, with modest E-values indicating sensitivity to residual confounding by indication. Subgroup CAST curves identify age at diagnosis as the dominant driver of treatment effect heterogeneity (46-56% of splits). All findings are robust to placebo permutation, simulated unobserved confounder, and negative control refutation tests. The CAST framework provides a general-purpose tool for temporal treatment effect visualization applicable beyond neuro-oncology.

1Molecular subtyping of cancer is traditionally defined in transcriptomic space, yet routine clinical deployment is limited by the availability and cost of sequencing. Meanwhile, histopathology captures rich morphological information that is known to correlate with molecular state but lacks a principled, mechanistic bridge to gene-level representations. We propose a graph-constrained learning framework that aligns morphology-derived signals with a fixed, data-driven gene network discovered via hierarchical Monte Carlo screening. We can derive new gene sets for classification using random sampling, and use the coexpression network of that graph to enforce the learning of a pure morphology model without using gene expression. The resulting model performs subtype prediction using morphology alone, while being explicitly forced to operate through a gene-structured latent space. Structural alignment is enforced during training. For Moffitt classification in pancreatic cancer using PANCAN and TCGA datasets, the model has a reported 85% AUC using an alternative gene sets network structure, while the alternate gene set itself has an 84% AUC in all patients that were classified with subtyping with pancreatic cancer in the dataset. This framework demonstrates that virtual transcriptomics can provide biologically grounded molecular insights using only routine histopathology slides, potentially expanding access to precision oncology in resource-limited settings.

Background and ObjectivePatients with upper urinary tract urothelial carcinoma (UTUC) undergoing radical surgery are at high risk of developing intravesical recurrences (IVR). The biology of IVR after surgery for UTUC is poorly understood, and urine markers to replace cystoscopic surveillance of the bladder are lacking. Here, we characterized the genomic landscape of UTUC and paired IVR to discover therapeutic targets and identify diagnostic markers for IVR. MethodsWe performed targeted next-generation DNA-sequencing of 571 genes in a cohort of 276 retrospectively and 138 prospectively enrolled UTUC patients who received radical surgery. Clonality and evolution were assessed in 79 paired UTUC-IVR cases. Key Findings and LimitationsMutations in TERT (72%) and FGFR3 (50%) were highly prevalent in UTUC, while mutations in KMT2C were associated with reduced risk of IVR. The mutually exclusive mutational profile of UTUC revealed five genomic subtypes with distinct clinicopathological and molecular characteristics, but none were associated with elevated IVR risk. Clonal evolution of paired UTUC-IVR occurred in 92% of cases via four evolutionary paths, with FGFR3 as a key driver in the largest path (36%). Additionally, hotspot mutations in the TERT promoter, and FGFR3 and HRAS genes were identified as potential markers for noninvasive surveillance by urine testing. Limitations include cohort heterogeneity and the selected gene-targeted sequencing approach. Conclusions and Clinical ImplicationsThe high FGFR3 mutation rate in UTUC and its association with IVR development support anti-FGFR targeted therapy to reduce IVR risk. The clonal relationship between UTUC and IVR underscores the potential for patient-friendly noninvasive urine tests for surveillance after radical surgery. SummaryUpper urinary tract urothelial carcinoma (UTUC) is a rare cancer with a high recurrence rate after surgery. We found that the FGFR3 gene is a potential therapeutic target to reduce the risk of recurrence, while recurrent mutations in TERT, FGFR3 and HRAS could serve as potential markers for noninvasive surveillance by urine testing after surgery for UTUC.

Background: Predicting 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 forecast 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 methods: SCOPE was trained on 54 treatment lines from patients with metastatic colorectal cancer (mCRC, n=15) and metastatic pancreatic ductal adenocarcinoma (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. Results: Predicted 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 outperformed 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 clinical data. Conclusion: By combining functional organoid drug screening with clinical modeling, SCOPE generates calibrated efficacy predictions for both established regimens and novel agents without prior clinical 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.

BackgroundSezary syndrome (SS) represents an aggressive leukemic variant of cutaneous T-cell lymphoma (CTCL) with distinct clinical behavior compared with other CTCL subtypes. While prior studies have identified recurrent genomic alterations in CTCL, a systematic pathway-centric comparison between SS and non-SS CTCL remains limited. We applied our conversational artificial intelligence (AI) platform for precision oncology, to accelerate hypothesis generation and integrative interpretation of public genomic data. MethodsWe performed a secondary analysis of somatic mutation and clinical data from the Columbia University CTCL cohort available via cBioPortal. Samples were stratified into SS (n=26) and non-SS CTCL (n=17). High-impact coding variants were retained and annotated to curated functional gene groups and signaling pathways relevant to CTCL biology. Pathway-level mutation frequencies were compared using Fishers exact test, with effect sizes summarized by odds ratios. Tumor mutation burden (TMB) was compared using Wilcoxon rank-sum testing. Subtype-specific gene-gene co-mutation patterns were assessed using pairwise association testing and visualized with heatmaps and oncoplots, with our conversational AI agents facilitating interactive exploration and prioritization of results. ResultsOverall TMB did not differ between SS and non-SS CTCL (p=0.83), indicating comparable global mutational burden. Pathway-level analyses revealed enrichment of alterations affecting epigenetic regulators, tumor suppressor and cell-cycle control genes, NFAT signaling, and apoptosis/immune regulation in SS, whereas MAPK and JAK-STAT pathway alterations were relatively more frequent in non-SS CTCL. Co-mutation analysis demonstrated fewer but more focused gene-gene interactions in SS compared with broader co-mutation networks in non-SS CTCL, suggesting divergent evolutionary constraints. Several genes (including ERBB2, WWC1, POSTN) showed borderline subtype-specific enrichment, warranting further validation. ConclusionsConversational AI-enhanced analysis reveals that SS is distinguished from other CTCL subtypes not by higher mutational load, but by qualitative differences in pathway involvement, particularly epigenetic dysregulation, immune escape, and transcriptional control. These findings generate testable hypotheses for downstream validation in patient-level datasets and demonstrate the utility of conversational AI agents as accelerators of translational cancer genomics.

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.

Background. Pancreatic ductal adenocarcinoma (PDAC) has a five-year survival rate of approximately 12%, largely because it is typically diagnosed at an advanced stage. CT-based computational methods for early detection exist but rely on black-box deep learning or large texture feature sets without tissue-specific interpretability. Methods. We developed Virtual Spectral Decomposition (VSD), which applies six parameterized sigmoid functions S(HU) = 1/(1+exp(-alpha x (HU - mu))) to standard portal-venous CT, decomposing each pixel into tissue-specific response channels for fat (mu=-60), fluid (mu=10), parenchyma (mu=45), stroma (mu=75), vascular (mu=130), and calcification (mu=250). Dendritic Binary Gating identifies structural content per channel using morphological filtering, enabling co-firing analysis and lone firer identification. A 25-feature signature was extracted per patient. Three independent datasets were analyzed: NIH Pancreas-CT (n=78 healthy), Medical Segmentation Decathlon Task07 (n=281 PDAC, paired tumor/adjacent tissue), and CPTAC-PDA from The Cancer Imaging Archive (n=82, multi-institutional, with DICOM time point tags). The same six sigmoid parameters were used across all datasets without retraining. Results. VSD achieved AUC 0.943 for field effect detection (healthy vs cancer-adjacent parenchyma) and AUC 0.931 for patient-stratified tumor specification on MSD. On CPTAC-PDA, VSD achieved AUC 0.961 (6 features) and 0.979 (25 features) for distinguishing healthy from cancer-bearing pancreas on scans obtained prior to pathological diagnosis. All significant features replicated across datasets in the same direction: z_fat (d=-2.10, p=3.5e-27), z_fluid (d=-2.76, p=2.4e-38), fire_fat (d=+2.18, p=1.2e-28). Critically, VSD severity did not correlate with days-from-diagnosis (r=-0.008, p=0.944) across a range of day -1394 to day +249. Patient C3N-01375, scanned 3.8 years before pathological diagnosis, had VSD severity 1.87, well above the healthy mean of 0.94 +/- 0.33. The tissue transformation signature was temporally stable, indicating an early, persistent tissue state rather than a progressively worsening process. Conclusions. VSD with Dendritic Binary Gating detects a stable pancreatic tissue composition signature on standard CT that is present years before clinical diagnosis, validated across three independent datasets without parameter adjustment. The six sigmoid channels map to biologically meaningful tissue components through a fully transparent interpretability chain. The temporal stability of the signal implies a detection window of 3-7 years, consistent with known PanIN-3 microenvironment transformation timelines. VSD functions as a single-scan screening tool applicable to any abdominal CT performed during the pre-clinical window.

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.

BackgroundImmune checkpoint inhibition has transformed cancer therapy; however, many patients fail to respond to single-agent blockade, and combination strategies are often limited by toxicity. Central nervous system tumors exploit multiple immunosuppressive pathways, including the CD200 and PD-1/PD-L1 axis to evade anti-tumor immunity and support tumor aggressiveness. MethodsWe investigated ARL200, a peptide ligand targeting the CD200 activation receptor (CD200AR) using in vitro immune assays, murine syngeneic tumor models, phosphoproteomics, and correlative studies from a first-in-human trial in recurrent glioblastoma. ResultsARL200 exposure activated DAP10/12-dependent signaling and downregulated multiple inhibitory immune checkpoint receptors, including CD200R1, PD-1, and CTLA-4, and checkpoint ligands, CD200 protein and PD-L1, through suppression of the JAK1/3-SHP-STAT-IKK/{beta}-NF{kappa}B pathway. Distinct ARL200 variant peptides elicited unique immune responses. In patients with recurrent glioblastoma, ARL200 treatment was associated with immune activation, reduced inhibitory checkpoint expression, and evidence of antigen-specific memory responses without treatment-related toxicity. ConclusionsTargeting CD200AR enables coordinated modulation of multiple immune checkpoints with a single agent, representing a next-generation immunotherapeutic strategy opening a new pathway for treating aggressive malignancies. Key PointsO_LIARL200 elicits an active immune response for the development of a potent and durable anti-tumor response C_LIO_LIARL200 abolishes the suppressive effects of multiple immune checkpoint blockades C_LIO_LIDifferent ARL200 sequences drive alternative immune responses. C_LI Importance of the StudyTumors exploit multiple immune checkpoint pathways to suppress antitumor immunity, particularly within the immunosuppressive microenvironment of the central nervous system. Current immune checkpoint inhibitors often require combination therapy to achieve clinical efficacy, frequently at the cost of increased toxicity. In this study, we demonstrate that targeting the CD200 activation receptor (CD200AR) with a peptide ligand provides a novel strategy to simultaneously downregulate multiple inhibitory immune checkpoints, including CD200R1, PD-1, PD-L1, and CTLA-4, through a shared intracellular signaling pathway. ARL200 engagement activates DAP10/12-dependent signaling while suppressing the JAK1/3-SHP-STAT-IKK/{beta}-NF{kappa}B axis, thereby overriding tumor-mediated immunosuppression. Importantly, this multi-checkpoint modulation is achieved with a single therapeutic agent and translates to immune activation and clinical responses in patients with recurrent glioblastoma, with minimal treatment-related toxicity. These findings establish CD200AR targeting as a next-generation immunotherapeutic approach with the potential to improve the safety and efficacy of immune-based therapies for aggressive CNS malignancies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=179 SRC="FIGDIR/small/26345679v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@17a5010org.highwire.dtl.DTLVardef@11e67eborg.highwire.dtl.DTLVardef@1387c07org.highwire.dtl.DTLVardef@156d418_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundThe role of intratumoral plasma cells in immune checkpoint blockade (ICB) therapy has never been tested although their presence is linked with improved patient response and survival. Malignant peripheral nerve sheath tumors (MPNSTs) are deadly sarcomas with minimal responsiveness to ICB therapies. Strikingly, drugs inhibiting cyclin-dependent kinases 4/6 (CDK4/6) and MEK sensitize de novo MPNSTs to immunotherapy targeting programmed death-ligand 1 (PD-L1), which correlates with increased intratumoral plasma cells. Here, we tested if plasma cells mediate the MPNST response to anti-PD-L1 therapy. MethodsAnti-tumor activity of PD-L1 inhibition, with or without CDK4/6-MEK inhibition, was measured in de novo MPNSTs within wild-type versus plasma cell-deficient mice. Plasma cell-dependent effects of CDK4/6-MEK inhibition on priming the MPNST immune environment were determined by single cell transcriptomics and immunostaining. FindingsMPNSTs lacking plasma cells failed to respond to anti-PD-L1 monotherapy and were no longer sensitized to immunotherapy by CDK4/6-MEK inhibition. Plasma cell-deficient MPNSTs exposed to CDK4/6-MEK inhibitors had impaired antigen presentation on major histocompatibility class I (MHC-I) and decreased CD8+ T cell infiltration and activation. Complementary analyses of human sarcomas showed increased intratumoral plasma cell signatures prognose better patient survival. InterpretationPlasma cells favorably remodel the tumor immune environment by increasing CD8+ T cell infiltration and are critical for successful ICB therapy in MPNSTs. This work may help inform ICB treatment strategies and cancer patient stratification for many different tumor types. FundingThis research was supported by University of Iowa Sarcoma Research Program awards and NIH grants T34-GM141143, T32-GM067795, F31-CA281312, P30-CA086862, and R01-NS119322. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSFor many types of cancer, intratumoral plasma cells have been correlated with better patient survival and improved response to immune checkpoint blockade (ICB) therapies. However, the biology underlying those associations is not understood and no study has examined the requirement of plasma cells in immunotherapy response. Compelling data in malignant peripheral nerve sheath tumors (MPNSTs) showed that dual kinase inhibition of oncogenic CDK4/6 and MEK induced intratumoral plasma cell accumulation and sensitized tumors to ICB therapy. While CDK4/6-MEK inhibition is known to enhance antitumor immunity in other tumor types by CD8+ T cells or natural killer (NK) cells, a role for plasma cells has never been explored. Added value of this studyStudies were performed in MPNSTs, an under-researched cancer that normally responds poorly to ICB monotherapies. This is the first investigation to show that intratumoral plasma cells are essential for successful ICB therapy and they support anti-tumor immunity by promoting a pro-inflammatory, CD8+ T cell state involving MHC-I antigen presentation. Findings provide new insight into immunomodulatory effects of CDK4/6-MEK inhibitor therapies, revealing plasma cells are needed for those drugs to activate CD8+ T cell mediated antitumor immunity. Implications of all the available evidenceThe fundamental advance in understanding how plasma cells promote successful ICB immunotherapy is likely applicable to other solid tumors and may guide novel therapeutic strategies in which plasma cell-inducing agents are combined with ICB antibodies. Moreover, an increased presence of intratumoral plasma cells in tumor specimens may streamline clinical decisions regarding which patients are most likely to benefit from ICB therapy.

Background: Immune 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 for closer monitoring, timely intervention, and improved outcomes. Purpose: To develop and validate a deep learning foundation model to predict ICI-P from baseline CT scans in patients with lung cancer. Methods: We designed the Checkpoint-Inhibitor Pneumonitis Hazard EstimatoR (CIPHER), a deep learning foundation model that combines contrastive learning with a transformer-based masked autoencoder to predict ICI-P from baseline CT scans in patients with lung cancer. Using self-supervised learning, CIPHER was pre-trained on 590,284 CT slices from 2,500 non-small cell lung cancer (NSCLC) patients to capture heterogeneous lung parenchymal patterns. After pre-training, the model was fine-tuned on an internal NSCLC cohort for ICI-P risk prediction, using images from 254 patients for model development and 93 patients for internal validation. We compared CIPHER with classical radiomic models and further evaluated it on an external NSCLC cohort of 116 patients. Results: In the 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 the radiomic models. In the external validation cohort, CIPHER maintained strong performance (AUC = 0.83; balanced accuracy = 81.7%), exceeding the radiomic models (DeLong p = 0.0318) and demonstrating higher specificity without sacrificing sensitivity. By contrast, the radiomic model showed high sensitivity (85.0%) but markedly lower specificity (45.8%). Confusion matrix analysis confirmed the robust classification performance of CIPHER, correctly identifying 80 of 96 non-ICI-P cases and 16 of 20 ICI-P cases. Conclusions: We developed and externally validated CIPHER for predicting future risk of ICI-P from pre-treatment CT scans. With prospective validation, CIPHER may be incorporated into routine patient management to improve outcomes.