Citations

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

The NCCN Guidelines for Prostate Cancer provide a framework on which to base decisions for patients with prostate cancer across the disease spectrum. The Guidelines sections included in this article focus on metastatic castration-sensitive prostate cancer (mCSPC), nonmetastatic castration-resistant prostate cancer (CRPC), and metastatic CRPC (mCRPC). For patients with mCSPC, disease characteristics, such as whether metastases arose synchronously or metachronously and the degree of metastatic burden, impact therapy decisions, including how much treatment intensification is appropriate and when prostate-directed and/or metastasis-directed therapy should be considered. In the mCRPC setting, androgen deprivation therapy is continued with the sequential or concurrent addition of certain androgen receptor pathway inhibitors, chemotherapies, immunotherapies, radiopharmaceuticals, and/or targeted therapies. The NCCN Prostate Cancer Panel emphasizes a shared decision-making approach in all disease settings based on patient preferences, prior treatment exposures, biomarkers, the extent and location of metastases, symptoms, and potential side effects.

Background The Woven EndoBridge (WEB) device represents an innovative solution for cerebral aneurysm occlusion, particularly for challenging wide-neck bifurcation aneurysms. However, factors affecting sustained occlusion remain poorly understood. We utilized machine learning to attempt to identify predictors of favorable long-term outcomes following WEB treatment. Methods In this multicenter retrospective study, we collected patient demographics, aneurysm characteristics, procedural details, and clinical outcomes. The primary endpoint was improvement in occlusion status, defined as maintained Raymond-Roy Occlusion Classification (RROC) grade 1, or improvement from grade 2 to 1, or from grade 3 to either 2 or 1 on final angiographic follow up. The dataset was split into training (75%) and validation (25%) sets. The CatBoost algorithm was selected based on performance metrics, with Shapley Additive exPlanations (SHAP) values calculated to determine feature importance. Furthermore, a multivariable binomial logistic regression model was performed to validate machine learning findings. Results Among 720 aneurysms from 36 hospitals, 84% showed improvement in occlusion at follow up. Both machine learning and multivariable logistic regression identified aneurysm height as the most consistent correlate of nonimprovement (odds ratio (OR) 0.90 per mm, p = 0.022). In the CatBoost model, the highest-ranking features by SHAP included aneurysm height, patient age, treatment acuity, ACom location, WEB-SLS device, bifurcation anatomy, aneurysm multiplicity, baseline modified Rankin Scale, access route, and partial thrombosis. Conclusions Machine-learning and regression analyses identified consistent predictors of occlusion improvement after WEB treatment, with aneurysm height most strongly linked to nonimprovement. These insights may guide patient selection and follow up. Findings require cautious interpretation and external validation in larger cohorts.

Abstract Purpose: Relapse is a major cause of failure in human papillomavirus–independent head and neck squamous cell carcinoma. Clinicopathologic criteria for adjuvant treatment remain imprecise and have not changed for decades. We investigated whether ctDNA in lymphatic exudate collected via surgical drains (“lymph”) 24 hours after surgery identified molecular residual disease (MRD) and compared its performance with time-matched plasma. Experimental Design: Using an ultra-sensitive tumor-informed sequencing approach, tumor variants were called in lymph and plasma to classify patients as ctDNA positive or ctDNA negative, trained in an initial cohort of 36 patients, and replicated in an independent cohort of 37 patients. Progression-free survival was compared in ctDNA(+) versus ctDNA(–) patients. Results: Lymph identified MRD in two independent multi-site cohorts (initial cohort sensitivity = 76% and specificity = 63%; log-rank P = 0.01; replication cohort sensitivity = 65% and specificity = 70%; log-rank P = 0.04). Lymph performance was enhanced in locoregional relapse (sensitivity = 78% and specificity = 67%; log-rank P = 0.0004) and generalized to early-stage patients. Analysis of matched plasma collected at this early timepoint was not predictive of recurrence (sensitivity = 35% and specificity = 72%; log-rank P = 0.7). In patients with intermediate-risk pathology, lymph ctDNA was associated with recurrence (sensitivity = 88% and specificity = 67%; log-rank P = 0.0008), suggesting an opportunity for improved stratification of patients who may benefit from additional adjuvant treatment. Conclusions: Postoperative lymph is a novel, proximal, and early source of MRD with the potential to introduce more precision into adjuvant therapy decision-making and improve outcomes, especially for patients with intermediate-risk human papillomavirus–independent head and neck squamous cell carcinoma.

Brain tumor-related epilepsy (BTRE) is a common and debilitating symptom of central nervous system (CNS) tumors. The epileptogenic zone, defined as cortex responsible for seizure generation, is located at the peritumoral region for most tumors, and lower-grade intrinsic brain tumors have the highest seizure incidence. Surgery is often the most effective treatment for the reduction in seizures in BTRE. However, surgical decisions have historically often been made exclusively for oncologic reasons, with less emphasis on seizure control. Surgical approaches for all tumor types are reviewed, highlighting relevant risk factors. Adjunctive tools during surgery, such as intraoperative electrocorticography (ECoG), may help identify and remove surrounding brain areas which are epileptogenic. Minimally invasive surgery is also gaining traction, given its utility in treating seizures deep-seated tumors. This review explores epileptogenic brain tumors, surgery for BTRE, and emerging strategies to better achieve seizure control.

Introduction: Apolipoprotein B (ApoB) and coronary artery calcium score (CAC) are well-established risk factors for coronary heart disease (CHD). However, the interaction between these risk factors on CHD risk has not been well-studied. We investigated the association between ApoB levels and CAC with CHD risk. Methods: We analyzed 4,649 participants from the Multiethnic Study of Atherosclerosis cohort (median follow-up 13.9 years) without baseline CHD or statin use. High ApoB was defined as ≥120 mg/dL. Participants were divided into 6 groups; group 1: CAC = 0 with normal ApoB, group 2: CAC = 0 with high ApoB, group 3: CAC 1-99 with normal ApoB, group 4: CAC 1-99 with high ApoB, group 5: CAC ≥100 with normal ApoB, group 6: CAC ≥100 with high ApoB. CHD was defined as myocardial infarction, resuscitated cardiac arrest, or CHD death. Survival analysis using Kaplan-Meier and multivariable Cox proportional hazard models were performed to assess the relationship between ApoB, CAC, and time to first CHD event. Results: In an adjusted model, high ApoB, CAC (1-99), and CAC (≥100) were independently associated with CHD (HR: 1.48; 95% CI: 1.04, 2.11; HR: 2.54; 95% CI: 1.78, 3.64; and HR: 4.41; 95% CI: 3.08, 6.31, respectively). Compared to group 1 (CAC 0 with normal ApoB), CAC 0 and high ApoB (group 2) was not associated with increased risk of CHD (HR: 1.15; 95% CI: 0.59, 2.23). However, a statistically significant greater risk of CHD was observed in those with CAC 1-99 and normal ApoB (group 3) (HR: 2.12; 95% CI: 1.37, 3.28), CAC 1-99 and high ApoB (group 4) (HR: 4.12; 95% CI: 2.42, 7.00), CAC ≥100 and normal ApoB (group 5) (HR: 4.20; 95% CI: 2.78, 6.34), CAC ≥100 and high ApoB (group 6) (HR: 5.72; 95% CI: 3.40, 9.63). Conclusions: In this analysis, ApoB and CAC are independently associated with CHD risk. Individuals with high ApoB and CAC ≥100 had the highest risk of CHD. In contrast, individuals with CAC = 0 and high ApoB had no significant increase in CHD risk compared to individuals with normal ApoB levels. These findings suggest that ApoB and CAC may be complementary tools for CHD risk stratification in the context of primary prevention.

Background: Lipoprotein(a) [Lp(a)] and metabolic syndrome (MetS) are well-established risk factors for cardiovascular disease. Recent evidence suggests that high Lp(a) levels may increase the risk of coronary heart disease (CHD) in patients with diabetes, raising the question of whether similar interactions exist between Lp(a) and metabolic syndrome. We aimed to evaluate the joint association of Lp(a) and MetS with incident CHD risk in a large, multi-ethnic, prospective cohort. Methods: We analyzed 6,664 participants from the MESA (Multiethnic Study of Atherosclerosis) cohort (median follow-up 14.0 years) without baseline CHD. MetS was defined as ≥3 of the following criteria: waist circumference ≥102 cm (men), or ≥88 cm (women), triglycerides ≥150 mg/dL or use of a fibrate medication, HDL-C <40 (men) or <50 (women) or use of a fibrate medication, SBP ≥130 mm Hg and/or DBP ≥85 mm Hg or use of antihypertensive medication, fasting glucose ≥100 mg/dL or use of antidiabetic medication (4). Elevated Lp(a) was defined as a level ≥50 mg/dL. CHD was defined as myocardial infarction, resuscitated cardiac arrest, or CHD death. Participants were divided into four groups: group 1: Normal Lp(a) without MetS, group 2: elevated Lp(a) without MetS, group 3: normal Lp(a) with MetS, group 4: elevated Lp(a) with MetS. Survival analysis using Kaplan-Meier and multivariable Cox proportional hazard models were performed to assess the relationship between Lp(a), MetS, and time to CHD. Results: In an adjusted model, log [Lp(a)] and MetS were both independently associated with CHD (HR: 1.14; 95% CI: 1.03, 1.25) and (HR: 1.75; 95% CI: 1.35, 2.27). Compared to the reference group (normal Lp(a) without MetS), those with elevated Lp(a) without MetS (group 2) did not have increased risk of CHD (HR: 1.30; 95% CI: 0.94, 1.80). However, those with normal Lp(a) with MetS (group 3) had greater risk of CHD (HR: 1.56; 95% CI: 1.18, 2.05). The highest risk for CHD were in those with elevated Lp(a) with MetS (group 4) (HR: 2.37; 95% CI: 1.66, 3.37). Conclusions: In this analysis. elevated Lp(a) and MetS independently increase CHD risk. MetS alone significantly increases risk, whereas elevated Lp(a) without MetS does not demonstrate a significant effect. However, their combination confers the highest risk, suggesting a synergistic interaction. These results emphasize the value of assessing Lp(a) in individuals with MetS to improve cardiovascular risk stratification and guide targeted prevention.

Background: We investigated the impact of genetic risk factors for coronary heart disease (CHD) -polygenic risk score (PRS), familial hypercholesterolemia (FH), and family history (FamHx)- on CHD risk estimates, across the age spectrum, in two diverse cohorts of US adults -eMERGE IV (eIV) and All of Us (AoU). Methods: CHD was defined as myocardial infarction, unstable angina, and coronary revascularization. Self-identified race/ethnicity (SIRE) was used as a population descriptor. We calculated a PRS for CHD (PRS CHD , PGS004698), ascertained FH as the presence of pathogenic/likely pathogenic variants in FH genes, and defined FamHx as early-onset CHD in first-degree relatives. We employed Pooled Cohort Equations (PCE) to estimate the 10y risk of CHD for adults ≥40y and modeled the association of conventional risk factors with CHD in adults <40y. AoU served as the training set, and eIV as the testing set. We analyzed the impact of PRS CHD and FamHx on CHD risk estimates by a) using multivariable logistic regression and Cox proportional hazard models, assessing discrimination and the extent of risk reclassification; and b) net benefit analysis and decision curves to assess the performance of prediction models across actionable thresholds. Results: We analyzed data for 19348 participants from eIV (age 50±15, 68% female, 41% non-White) and 239645 participants from AoU (age 55±17, 61% female, 48% non-White). Genetic risk factors were significantly associated with CHD. PRS CHD performance varied by SIRE groups, while FamHx was consistent. The effects of PRS CHD and FamHx on CHD were independent and additive (Figure 1). In adults ≥40y, incorporating PRS CHD and FamHx into PCE improved discrimination (C-statistic increased from 0.719 to 0.753; P -diff=9.1×10 -3 , Figure 2) and reclassified risk in 19% and 20% of participants at the 7.5% and 10% 10y CHD risk thresholds, respectively. Between the 7.5% and 10% 10y CHD risk thresholds, incorporating PRS CHD and FamHx into the PCE improved the net benefit of the risk prediction models across White, Black, and Hispanic/Latino groups (Figure 3). Conclusion: PRS CHD and FamHx were independently and additively associated with CHD in two large diverse cohorts in the US. Incorporating PRS CHD and FamHx into PCE improved risk discrimination, reclassified risk in a significant portion of participants, and improved net benefit of the PCE across all three major SIRE groups, motivating the addition of these factors to clinical risk calculators.