Citations

Abstract Peanut allergy is a leading cause of food-induced allergy (prevalence of ∼1%). The diagnosis relies on evaluating clinical symptoms consistent with peanut exposure, supported by the detection of total peanut specific IgE (sIgE) and peanut component IgE antibodies (Ara h1, Ara h2, Ara h3, Ara h6, Ara h8, Ara h9 and Bet v2) through serum or skin testing. However, sensitization patterns across different ages and regions in the United States remain largely unknown. In this study, we investigated regional and demographic patterns of peanut component sensitization in a large U.S. population tested for suspected peanut allergy. Retrospective data were collected from 13,702 individuals tested at Mayo Clinic Laboratories. sIgEs were measured using the ImmunoCAP system (Thermo Fisher Scientific). Samples with detectable total peanut sIgE (≥0.1 kU/L) were reflexed to testing for seven peanut component IgEs. Statistical analyses including linear regression and correlation matrix analysis were conducted using R Studio. Regional differences were assessed using care provider zip code information. Among the study population, 92.0% (12,604 of 13,702) had detectable total peanut sIgE, with the Western U.S. region exhibiting the highest detectable rate (95.6%), and the Southeast the lowest (85.6%) (p < 0.001). On average, 3.38 different component IgEs were detectable per person with a detectable total peanut sIgE; 7.1% (892) had no detectable component IgEs, possibly due to antibodies against other peanut components. Correlation analysis revealed strong associations between Ara h2 and Ara h6 (r = 0.94), with moderate correlations observed between Ara h2, Ara h3, and Ara h6 to Ara h1 (r ranging from 0.72 to 0.77). Age-stratified analysis indicated higher total peanut sIgE detectable rates in children and adolescents (95.2%, ≤18 years) compared to adults (76.5%, >18 years). Interestingly, adults were more likely than children to have negative results for all component IgEs. Sensitization rates to different peanut components varied, with Ara h2 (77.5%) and Ara h6 (73.2%) being the most frequently detected, followed by Ara h1 (56%), and Ara h3 (47.3%). Lower detectable rates were observed for Ara h8, Ara h9, and Bet v2 (34.8%, 26.1% and 22.7%, respectively). Regional analysis showed consistently high detectable rates for Ara h2 and Ara h6 (>70%) across most areas (p < 0.001) with slightly lower rates in the Southwest (<63%). Bet v2, despite its overall low prevalence (<24%), had a relatively higher detectable rate in the Southwest (29%). This study provides valuable insights into potential demographic and geographic factors influencing peanut allergy sensitization. Our study demonstrates that peanut sensitization patterns vary by age and geographic location. Children and adolescents exhibit higher detectable rates for total peanut sIgE and component IgEs compared to adults, with Ara h2 and Ara h6 as the most frequently detected and highly correlated components.

Abstract Quantifying urine protein is critical for detecting and monitoring kidney disease. However, screens for proteinuria are unable to detect low levels of albuminuria. For this reason, our institution has long measured urinary total protein and osmolality on routine urinalysis specimens (RUS) and calculated a predicted 24-hour protein excretion (PER) based upon the protein to osmolality ratio (POR). Within the last decade, sodium-glucose cotransporter-2 inhibitors (SGLT2i) have been approved by the United States Food and Drug Administration (FDA) as a treatment for adults with type 2 diabetes mellitus, and these agents appear to be beneficial for many forms of proteinuric renal disease and confer renal protection. Therefore, we investigated whether the expected glucosuria and increase in urine osmolality while on SGLT2i would significantly alter the POR, and hence predicted PER, via a retrospective analysis of residual timed and randomly collected urine specimens. Between January 1st, 2014, until January 29th, 2025, 5820 patients with nearly contemporaneous (collected within one week of each other) timed collections and RUS were identified that included 5656 not on SGLT2i (Cohort A) and 164 patients prescribed SGLT2i (Cohort B). Total protein concentrations were assessed using a Roche Cobas c701 by using benzethonium chloride. The osmolality was determined via freezing point depression using a Precision Systems Inc Multi-OSMETTE, whereas glucose measurements were made using either an Arkray AX-4030 with AUTION Stick 9EB test strips or a Clinitek Status with Multistix 10 SG reagent strips. Predicted 24-hour protein results were then calculated from the POR using a published formula based upon prior clinical studies: 10(x·[log(protein/osmolality)]+y where x is 0.908 or 0.953 for males and females, respectively, and y is 2.8254 or 2.9739 for males and females, respectively. In Cohort A, there was good correlation between the predicted and measured 24-hour PER (Spearman correlation coefficient, ρ = 0.78), although the predicted PER underestimated the measured PER (Passing-Bablok regression, y = 1.54x-20.97 mg/24 hr). Importantly, a strong correlation (ρ = 0.93) and similar distribution (p=0.17) were found between the predicted and measured PER in Cohort B, with a much-reduced proportional bias (y = 1.06x+62.96 mg/24 hr). Furthermore, comparison of the difference between the predicted and measured PER between the two cohorts demonstrated the same prediction accuracy (p = 0.65), with a median difference (interquartile range) of 79 mg/24 hr (256) and 109 mg/24 hr (480) for Cohort A and B, respectively. Finally, a strong correlation (ρ = 0.85), similar distribution (p = 0.23), and little bias (y = 0.96x+28.14 mg/24 hr) existed between the predicted and measured PER for all patients with overt glucosuria (urine glucose ≥1000 mg/dL, n = 348). In summary, these data suggest that the established formula for predicted PER on RUS is equally valid for patients on or off SGLT2i, and that this result is a useful clinical estimating tool.

Abstract DDX3X dysfunction causes an X-linked multisystem disorder with high penetrance and variable expressivity. The phenotypic spectrum spans from learning disability without somatic involvement to profound intellectual disability with severe impairments in the central nervous system and other organs. A few multicenter studies and single case reports have previously highlighted some common phenotypic patterns but were unable to delineate correlations between underlying DDX3X variants and phenotypic findings.From the second largest patient cohort published to date, we analysed clinical, psychometric and diagnostic findings of 52 female and 7 male individuals, harbouring de novo and inherited DDX3X variants. These female patients revealed previously unknown quantitative correlations between variant type and localization and specific phenotypic findings (growth features, epilepsy, brain anomalies, dysmorphisms, motor-focused neurological findings). Moreover, by analysing the in silico folding and RNA binding capability of mutant DDX3X monomers, we were able to delineate novel correlations between DDX3X monomer misfolding grade and phenotypic severity.

Abstract The brain avidly consumes glucose to fuel neurophysiology. Cancers of the brain, such as glioblastoma (GBM), relinquish physiological integrity and gain the ability to proliferate and invade healthy tissue. How brain cancers rewire glucose utilization to drive aggressive growth remains elusive. Here, we infused 13C-labeled glucose into patients and mice with brain cancer, coupled with quantitative metabolic flux analysis, to map the fates of glucose-derived carbon in tumor vs. cortex. Through the first direct and comprehensive measurements of carbon and nitrogen labeling in both cortex and glioma tissues, we uncover profound metabolic transformations. In human cortex, glucose carbons fuel essential physiologic processes including TCA cycle oxidation and neurotransmitter synthesis. Conversely, gliomas downregulate these processes and scavenge alternative carbon sources such as amino acids from the environment, repurposing glucose-derived carbons to generate molecules needed for proliferation and invasion. Targeting this metabolic rewiring in mice through dietary amino acid modulation selectively alters GBM metabolism, slows tumor growth, and augments the efficacy of standard-of-care treatments. These findings illuminate how aggressive brain tumors exploit glucose to suppress normal physiological activity in favor of malignant expansion and offer potential therapeutic strategies to enhance treatment outcomes.

Abstract BACKGROUND [68Ga]Ga-DOTA-TATE (68Ga-Dotatate) PET is a promising imaging modality for post-operative meningioma delineation and outcome assessments following stereotactic radiosurgery. However, its utility for response assessment following other meningioma therapies, including somatostatin receptor 2 (SSTR2) targeting [177Lu]Lu-DOTA-TATE (177Lu-Dotatate) therapy is insufficiently understood. Here we report the 68Ga-Dotatate PET results, prospectively obtained in patients with treatment-refractory meningioma who enrolled in a phase 2, open-label clinical trial (NCT03971461) evaluating the radiopharmaceutical 177Lu-Dotatate. METHODS Patients underwent 68Ga-Dotatate PET during screening and 24±2 weeks following treatment with 177Lu-Dotatate at 7.4 GBq (200 mCi) every 8 weeks for 4 doses. Up to 5 meningioma lesions per patient were assessed by gadolinium-enhanced T1 MRI and 68Ga-Dotatate PET using lesional Krenning score and lesional to superior sagittal sinus (SSS) maximum standardized uptake value (SUVmax) ratio (SUVR (lesion/SSS)). RESULTS Eight patients with 16 lesions were assessed. By RANO criteria, the best radiographic response was stable disease (SD) in 7 patients (14 lesions) and partial response (PR) in 1 patient (2 lesions). Krenning score was assessed in 14 lesions (Krenning 4/3/2/1 in 3/4/5/2 lesions). Following 177Lu-Dotatate, Krenning score was reduced by ≥1 point in 9 and remained stable in 5 lesions. SUVR (lesion/SSS) was evaluated in 16 lesions. Compared to baseline, SUVR (lesion/SSS) at 6 months was partially reduced (PR, ≥50%) in 2 lesions, minimally reduced (MR, <50% and ≥25%) in 3 lesions, and stable (SD, <25% reduction and <25% increase) in 11 lesions. CONCLUSIONS 68Ga-Dotatate PET is a promising imaging biomarker in patients with advanced meningioma. Treatment with 177Lu-Dotatate led to substantial reduction in SUVR (lesion/SSS) in a subset of patients. In contrast to other reports, 68Ga-Dotatate assessments were generally concordant with post-gad T1 based MRI assessments per RANO. Updated results in an expanded group of patients will be presented.

Abstract BACKGROUND Most meningiomas are considered benign tumors, however, a subset of meningiomas are characterized by a more aggressive clinical course and require multimodal treatment. Beyond surgery and radiation, there are no effective medical treatments available. Somatostatin receptor 2 (SSTR2) is expressed by the majority of meningiomas. [177Lu]Lu-DOTA-TATE (177Lu-Dotatate) is a SSTR2-targeting radioligand therapeutic agent that has been successful in other oncological entities, including neuroendocrine tumors. Here we report the results of a phase 2 clinical trial (NCT03971461) evaluating 177Lu-Dotatate in patients with advanced and treatment-refractory intracranial meningiomas. METHODS In this multicenter, open-label, phase 2 study, adults with advanced intracranial meningiomas received 177Lu-Dotatate at 7.4 GBq (200 mCi) every eight weeks for four doses. [68Ga]Ga-DOTA-TATE (68Ga-Dotatate) PET was performed before and at the end of treatment. The primary endpoint was progression-free survival at 6 months (PFS-6). Correlative studies evaluated the association of PFS-6, objective response rate, progression-free survival, overall survival with radiographic tumor measurements, and 68Ga-Dotatate uptake on PET. RESULTS Thirty-two patients (F=21, M=11) with progressive meningiomas (WHO 1=7, WHO 2=24, WHO 3=1) were enrolled. Median age was 65.5 (range 41-78) years. All patients previously underwent tumor resection and at least one course of radiation. Three patients (9%) had received medical therapy. Treatment with 177Lu-Dotatate was well tolerated, no treatment-limiting toxicities were observed. Of 29 evaluable patients at the data cutoff, 19 patients (66%) had achieved PFS-6 (WHO 1=4, WHO 2=15). Radiographically, the best response was partial response (PR) in 2 patients (6%) and stable disease (SD) in 20 patients (63%). CONCLUSIONS Treatment with SSTR2-targeting 177Lu-Dotatate is well tolerated and 66% of patients achieved PFS-6, comparing favorably to historical benchmarks, 177Lu-Dotatate therefore may be a useful new therapeutic modality for patients with advanced meningiomas and will be further explored in MOMENTUM-1 (NCT#06955169), an open-label, randomized, phase 2 trial.

Abstract BACKGROUND Post-operative prognostication in glioma patients remains a clinical challenge, particularly given the heterogeneity in tumor biology and patient outcomes. We evaluated whether image-derived features from post-operative imaging improve survival modeling beyond established clinicopathologic factors. METHODS We analyzed a cohort of 71 post-surgical glioma patients treated at Princess Margaret Cancer Centre with known clinicopathologic features including age, histological classification, IDH mutation status, MGMT methylation status, extent of resection, and performance status. A baseline Cox proportional hazards model was fit to clinical variables. Radiomic features were extracted using PyRadiomics from post-operative CT and MRI across three regions of interest: whole brain, tumor bed, and peritumoral ring. Features underwent stepwise unsupervised reduction (variance filtering, exclusion of volume-correlated features, and removal of remaining collinear features), followed by dimensionality reduction via principal component analysis. Combined clinical and imaging models were evaluated in the overall cohort and stratified by IDH mutation status. RESULTS In the overall cohort, the clinical model was highly prognostic (C-index 0.74, p ~ 3.2×10⁻⁶). Adding imaging features improved performance (C-index 0.79) but was not significant (p ~ 0.31). Subset analysis revealed differential effects by IDH status. In IDH-mutant gliomas (n=24), neither clinical nor combined models were significant. In IDH wild-type glioblastoma (n=47), the clinical model achieved C-index 0.75 (p=0.04). The addition of imaging features significantly improved model performance (C-index 0.83, p=0.03), with selected imaging components contributing independent prognostic value after adjustment for clinical variables. Post-hoc analysis of radiomic feature importance (random forest ranking) revealed that top contributing features were drawn from both CT and MRI modalities, with most sourced from the whole brain region. CONCLUSIONS In post-surgical patients with IDH wild-type glioblastoma, image-derived features from post-operative imaging can significantly enhance prognostic modeling beyond known clinicopathologic factors. These preliminary findings support the potential role of radiomic biomarkers in refining risk stratification for this patient subgroup.

Abstract Glioblastoma (GBM) is the most common primary tumor in adults with a median survival of 15 months post-treatment. GBM incidence and progression depends on sex, with reports of 1.6x increased incidence and 6-month shorter overall survival in males, compared to females. Regardless of sex, the standard of care for GBM patients includes surgical resection, followed by chemotherapy and radiotherapy, and often dosing of the synthetic glucocorticoid Dexamethasone (DEX) to reduce brain edema post-treatment. Our lab recently found that DEX can suppress or promote GBM growth depending on the time of day the treatment is administered. Because glucocorticoid secretion depends on circadian time and sex, we tested whether endogenous glucocorticoids regulate sex differences in GBM growth. We transduced a murine chromosomal male or female GBM cell line (Nf1-/-DNp53 XX or XY) with a transcriptional luciferase reporter of the circadian clock gene Per2 (Nf1-/-DNp53-P2L). These cells were next treated with DEX at the daily peak or trough of Per2 expression and growth was measured 48 hours post-treatment. We found that administering DEX at peak Per2 suppresses growth in males, while administering DEX at trough Per2 promotes growth in both males and females. We then knocked-down the glucocorticoid receptor (GR) using an shRNA lentiviral construct in two GBM cell models (GL261 and LN229) and implanted WT or GR-KD cells into the basal ganglia of male and female mice. Loss of GR in GBM cells significantly suppressed GBM growth, especially in male mice, as evidenced by postmortem Ki67 staining. These results suggest that GBM growth in males may be more sensitive to endogenous glucocorticoids, and that sensitivity varies with time of day. This work will inform future personalized circadian interventions to improve GBM patient outcomes. Supported by NIH grant R01NS134885.

Abstract Glioblastoma is a highly lethal malignancy with dismal overall survival and near universal recurrence despite maximal multimodal therapy. Patients with unmethylated O6-methylguanine methyltransferase (MGMT) promoters do not derive survival benefit from adjuvant temozolomide. Immunotherapies have revolutionized cancer care; however, this modality has yet to translate into survival benefits in glioblastoma in part due to both local and systemic immunosuppressive states. Here, we report the results of a phase 1 clinical trial (NCT04015700) evaluating the safety and feasibility of GNOS-PV01, a personalized DNA vaccination platform that safely immunized against up to 40 patient-specific neoantigens in a clinically relevant timescale following surgical resection and radiation for MGMT-unmethylated glioblastoma patients. Multisector tumor sampling at time of resection enabled personalized and spatially representative neoantigen identification through an established pipeline. In the nine participants receiving vaccines, there were no unexpected toxicities or serious adverse events with a median vaccine delivery time of 10 weeks post-radiation. The vaccination was strongly immunogenic and induced activation and expansion of both local infiltrating CD8+ and CD4+ T-cells and circulating peripheral T-cells. T-cell receptor sequencing revealed both expanded pre-existing populations as well as novel neoantigen-specific populations, suggesting that vaccination both reinvigorates pre-existing immunity and generates novel immune reactivity. We developed a new technology to profile T-cells at single-nuclei resolution from frozen tissue revealing that vaccination altered gene expression signatures involved in inflammatory response and lymphocyte activation in the tumor microenvironment. Exposure to dexamethasone greatly suppressed immune reactivity, while PD-1 blockade enhanced local immune responses. With respect to efficacy, one third of patients survived longer than 24 months with one long-term survivor living >40 months from time of diagnosis, exceeding survival of recently published trials. Thus, our study demonstrates the safety, feasibility, and preliminary clinical efficacy of a personalized multivalent neoantigen vaccination platform in a devastating cancer.

Importance Transthyretin amyloid cardiomyopathy (ATTR-CM) is underdiagnosed despite expanding treatment options. Objective To develop and evaluate an artificial intelligence (AI)–augmented clinical program for ATTR-CM detection. Design, Setting, and Participants This nonrandomized clinical trial involved constructing an AI model, ATTRACTnet, using electrocardiogram waveforms, echocardiographic measurements, demographics, and diagnosis codes for orthopedic manifestations of amyloidosis. A single-system, multisite, single-arm, open-label trial was conducted to evaluate its real-world performance. The model was trained and validated at a large academic referral site for ATTR-CM with external validation at an academic site. The trial was conducted as a single-system, multisite trial. Patients with left ventricular (LV) wall thickness 12 mm or more and an ATTRACTnet score 0.5 or higher were eligible. Exclusions included prior ATTR-CM testing, hypertrophic cardiomyopathy, expected life span less than 1 year, nursing home residence, advanced dementia, or LV wall thickness less than 14 mm explained by uncontrolled hypertension or moderate/severe aortic stenosis. Intervention Eligible patients were notified and offered nuclear scintigraphy testing, monoclonal protein testing, and follow-up care on agreement from the treating physician. Main Outcomes and Measure The primary outcome was a diagnosis of ATTR-CM by consensus criteria. ATTR-CM testing positivity was compared with historical and contemporary controls. Results ATTRACTnet was developed in an internal test set of 799 patients (mean [SD] age, 75.1 [11.1] years; 516 [64.7%] male and 283 [35.3%] female) using 5-fold cross-validation with an additional external test set of 422 patients. It had good discrimination for ATTR-CM detection with an area under the receiver operator characteristic curve of 0.85 (5-fold cross-validation, 0.77-0.85) in the internal set and 0.82 (95% CI, .81-0.83) in the external test set with similar performance in Hispanic, non-Hispanic Black, and non-Hispanic White patients. A total of 1471 patients were identified with positive AI model scores 0.5 or more during the study period, with 256 eligible patients who met study criteria. Of these patients, 50 underwent amyloidosis testing after physician and patient approval; 24 (48%) were diagnosed with ATTR-CM, and 21 (88%) initiated treatment within 3 months. The positivity rate was more than 2.8 times higher than historical controls (15.3%; 95% CI, 13.1%-17.9%; P < .001), with an 18% relative increase in new diagnoses vs the prior year. Conclusions and Relevance AI-augmented screening may improve ATTR-CM detection and identify patients who are missed by usual care. Prospective randomized trials are needed to determine if outcomes are improved. Trial Registration ClinicalTrials.gov Identifier: NCT06469372