Wang, Xin

Abstract Recurrent or refractory IDH-mutant (mt) gliomas represent a population with significant unmet clinical need. The Notch ligand delta-like 3 (DLL3) is a tumor-associated antigen that is highly and homogeneously expressed in IDH-mt gliomas but absent in normal brain tissue, making it an attractive therapeutic target. Tarlatamab is a half-life extended bispecific T-cell engager that targets DLL3 and CD3 to redirect cytotoxic T cells toward DLL3-expressing tumor cells. Given the specific expression of DLL3 in IDH-mt gliomas and promising activity of tarlatamab in other DLL3-positive tumors, this study investigates the safety, efficacy, and intracranial activity of tarlatamab in this patient population. TARGID is an open-label, phase II trial enrolling adult patients with histologically confirmed IDH1 or IDH2-mt astrocytoma or oligodendroglioma following up to two prior lines of systemic therapies. The study comprises two cohorts. Cohort 1 (n=10) includes patients with resectable disease who will receive up to three cycles of tarlatamab prior to surgery; postoperative continuation is permitted. The primary endpoint for this cohort is the change in CD8+ T-cell infiltration in tumor tissue and blood. Cohort 2 (n=34) includes patients with unresectable disease and uses a Simon’s optimal two-stage design (H0: P ≤ 0.1 versus H1: P ≥ 0.25). In the 1st stage, 13 patients will be accrued. If 2 or more responses are observed, 21 additional patients will be enrolled. Tarlatamab is administered intravenously at 10 mg every two weeks, following a 1 mg priming dose on Cycle 1 Day 1. The primary endpoint is objective response rate by RANO 2.0. Exploratory endpoints include DLL3 expression, circulating biomarkers, and immune correlatives. The trial is currently in activation, with first patient enrollment anticipated in Q3 2025. The study is supported by the provision of study drug from Amgen as an investigator-initiated 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 Mutation of Triggering receptor expressed on myeloid cells 2 (TREM2) impairs the response of microglia to amyloid‐β (Aβ) pathology in Alzheimer's disease (AD), although the mechanism governing TREM2‐regulated microglia recruitment to Aβ plaques remains unresolved. Here, we confirm that TREM2 mutation attenuates microglial migration. Then, using Trem2 −/− mice and an R47H variant mouse model for AD generated for this study, we show that TREM2 deficiency or the AD‐associated R47H mutation results in inhibition of FAK and Rac1/Cdc42‐GTPase signaling critical for cell migration. Intriguingly, treatment with CN04, a Rac1/Cdc42‐GTPase activator, partially enhances microglial migration in response to oligomeric Aβ 42 in Trem2 −/− or R47H microglia both in vitro and in vivo. Our study shows that the dysfunction of microglial migration in the AD‐associated TREM2 R47H variant is caused by FAK/Rac1/Cdc42 signaling disruption, and that activation of this signaling ameliorates impaired microglial migration response to Aβ 42 , suggesting a therapeutic target for R47H‐bearing patients with high risk of AD.