Kosel, Matthew

Abstract Glioblastoma (GBM), isocitrate dehydrogenase wild-type (IDHwt) and central nervous system diffuse large B-cell lymphoma (CNS-DLBCL) are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. Deep learning on T1 postcontrast and T2-weighted MRI sequences were used to differentiate GBM and CNS-DLBCL. A three-stage temporal study design was utilized. Model development was performed on 146 patients with CNS-DLBCL and 146 age-matched, sex-matched, and MRI year–matched patients with GBM diagnosed at Mayo Clinic between 1998 and 2019. Models were tested on independent temporal test cohorts. Initial testing included 240 independent GBM diagnosed at Mayo Clinic between 1998 and 2019. The prospective test cohort included 37 patients with CNS-DLBCL and 256 patients with GBM diagnosed at Mayo Clinic after January 1, 2020, and 36 patients with CNS-DLBCL diagnosed at an external institution. Of the patients diagnosed at Mayo Clinic, 47% had MRIs generated from non-Mayo institutions. Two different model approaches were compared: (i) ensemble approach using area under the receiver operating characteristic curve (AUC) and cross-validation for model selection and (ii) loss approach minimizing cross-entropy loss and cross-validation to evaluate prediction performance. The AUCs on the prospective test cohort were 0.84 [95% confidence interval (CI), 0.78–0.90] and 0.83 (95% CI, 0.77–0.88) for the ensemble and loss approaches, respectively. Stability of ensemble prediction improved with the increasing number of models. Stratified AUC analysis demonstrated consistent performance across sex and age. We utilized a robust temporal study design and applied 2 different analytic approaches to develop a classification model. The findings confirm the feasibility of using MRI-based deep learning models to differentiate GBM from CNS-DLBCL. Significance: GBM, IDHwt and CNS-DLBCL are aggressive brain tumors with overlapping MRI features, yet distinct treatment approaches. Noninvasive tools are needed to aid in differential diagnosis. We developed MRI-based deep learning models to differentiate GBM, IDHwt from CNS-DLBCL using a rigorous three-stage temporal design that included prospective validation. The model AUC on a prospective cohort was 0.84.

Abstract Purpose: Navtemadlin is a potent small-molecule inhibitor of MDM2, which has completed a phase 0 window-of-opportunity study in glioblastoma (GBM). To optimally interpret the clinical data, a detailed analysis of navtemadlin pharmacokinetics (PK), pharmacodynamics, and efficacy was performed in GBM patient-derived xenografts (PDX). Experimental Design: Response to navtemadlin was characterized in vitro and in vivo in GBM PDXs with and without MDM2 amplification. Efficacy in vivo was integrated with measured plasma and intra-tumoral drug levels to develop a translational PK/efficacy model comparing exposure effective in PDX to exposure achieved in phase 0 patient samples. Results: In vitro, navtemadlin showed robust on-target activity in TP53–wild-type GBM. In vivo efficacy strongly correlated with MDM2 amplification status. In subcutaneous PDXs, navtemadlin significantly extended survival when dosed at 25 mg/kg in an MDM2-amplified PDX compared with 100 mg/kg in a non-amplified PDX. Central nervous system distribution was limited by blood–brain barrier efflux (Kp_brain = 0.009). In an MDM2-amplified orthotopic PDX model, navtemadlin was ineffective at 100 mg/kg; when established in mice with deficient blood–brain barrier efflux (Rag−/−Abcb1a−/−Abcg2−/−), 25 mg/kg doubled survival. A tumor PK/efficacy model was built to define target exposure for efficacy in GBM, using the effective 25 mg/kg dose. Modeled exposures exceeded this threshold in three (of 16) tumor samples from phase 0 study patients at the 240 mg dose level. Conclusions: Navtemadlin efficacy was highly dependent on adequate brain penetration. Our translational PK/efficacy model suggests that the minimum effective tumor exposures were achieved only in a minority of patients with GBM.

BACKGROUND: Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRS CHD ) for 5 genetic ancestry groups. METHODS: We derived ancestry-specific and multi-ancestry PRS CHD based on pruning and thresholding (PRS PT ) and ancestry-based continuous shrinkage priors (PRS CSx ) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRS CHD in 176,988 individuals across 9 diverse cohorts. RESULTS: Multi-ancestry PRS PT and PRS CSx outperformed ancestry-specific PRS PT and PRS CSx across a range of tuning values. Two best-performing multi-ancestry PRS CHD (ie, PRS PTmult and PRS CSxmult ) and 1 ancestry-specific (PRS CSxEUR ) were taken forward for validation. PRS PTmult demonstrated the strongest association with CHD in individuals of South Asian ancestry and European ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41–3.14], 1.65 [1.59–1.72]), followed by East Asian ancestry (1.56 [1.50–1.61]), Hispanic/Latino ancestry (1.38 [1.24–1.54]), and African ancestry (1.16 [1.11–1.21]). PRS CSxmult showed the strongest associations in South Asian ancestry (2.67 [2.38–3.00]) and European ancestry (1.65 [1.59–1.71]), lower in East Asian ancestry (1.59 [1.54–1.64]), Hispanic/Latino ancestry (1.51 [1.35–1.69]), and the lowest in African ancestry (1.20 [1.15–1.26]). CONCLUSIONS: The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRS CHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African ancestry. This highlights the need for larger genome-wide association study datasets of underrepresented populations to enhance the performance of PRS CHD .