Citations

Abstract Hypertensive disorders of pregnancy (HDP) are a leading cause of maternal and perinatal morbidity, yet modifiable environmental risk factors remain poorly characterized. Prior studies typically have only examined a limited number of exposures and have rarely distinguished HDP subtypes (ie, gestational hypertension, preeclampsia, eclampsia, and chronic hypertension with or without superimposed preeclampsia) or accounted for residential mobility during pregnancy. To address these gaps, we conducted a spatial and contextual exposome study of HDP using linked electronic health records (EHR) and vital statistics data in Florida. We analyzed 686 412 singleton pregnancies conceived between 2013 and 2018, using computable phenotyping to distinguish HDP subtypes. A total of 245 spatial and contextual exposome measures spanning natural, built, and social environments were linked to residential histories from conception through gestational week 19. Using a two-phase double machine learning (DML) framework with exposure-specific, directed acyclic graph-guided confounder adjustment, we conducted discovery and replication analyses, followed by multi-treatment DML to estimate effect sizes. In Phase 1, 26 exposome measures replicated for gestational hypertension and 34 for overall HDP. In Phase 2, 12 measures remained associated with gestational hypertension and 11 with overall HDP, including air toxicants, meteorological factors, ultraviolet radiation, neighborhood crime indicators, environmental noise, and proximity to coastline. No exposures passed multiple-comparison thresholds for preeclampsia or eclampsia. These findings demonstrate that the spatial and contextual exposome contributes to HDP in a subtype-specific manner. Integrating EHR-linked phenotyping, residential mobility, and causal machine-learning methods offers a scalable framework for identifying environmental factors relevant to HDP prevention.

Abstract Context Mandibular hypoplasia, deafness, progeroid features, and lipodystrophy (MDPL) syndrome is a rare, autosomal dominant disorder due to pathogenic heterozygous variants in POLD1. Clinical features of MDPL vary between patients; however, there is no previously reported genotype-phenotype association. Objective This work reports 14 new patients with lipodystrophy due to POLD1 variants and compares phenotypic differences between those with p.Ser605del and missense variants. Methods Genetic sequencing was performed on DNA of 14 patients for POLD1 variants, including exome (n = 10), genome (n = 1), and candidate gene (n = 3) sequencing. Comparisons of demographic, clinical features, and metabolic complications between carriers of POLD1 p.Ser605del and missense variants in our cases and those reported in the literature were made using the Fisher exact test for categorical variables and the t test for continuous variables. Results A total of 9 different POLD1 variants were identified in our patients, including 3 novel variants: p.Asp25Glufs*16, p.Arg507His, and p.Trp781Cys. Compared to individuals with missense variants (n = 15), those with the p.Ser605del (n = 26) POLD1 variant had significantly increased prevalence of mandibular hypoplasia (57% vs 100%, respectively; P = .015), small mouth (36% vs 100%, respectively; P = .015), crowded teeth (44% vs 91%, respectively; P = .046), and hypogonadism in male patients (0% vs 92%, respectively; P = .046). There were no differences in the prevalence of metabolic complications, such as diabetes, hypertriglyceridemia, and hepatic steatosis, in the two groups. Conclusion Individuals with the heterozygous POLD1 p.Ser605del variant had typical MDPL with more severe phenotype compared to those with missense variants with atypical MDPL.

Convergent framework for future POC cancer diagnostics aligning clinical needs (why), enabling technologies (how), and translational bridges. It integrates lab-on-chip biosensing and analytics for decentralized, early, longitudinal care.

Abstract Background The Joint Propagation Model (JPM)‐based CenTauR scale was recently introduced to harmonize tau‐PET quantification across different radiotracers. This study examined how CenTauR harmonization enhances the comparability of tau‐PET quantification across matched cohorts using different tracers. The evaluation focused on three key aspects: (1) comparing tau‐PET positivity rates as defined by CenTauR, (2) evaluating its diagnostic accuracy for symptomatic AD, and (3) analyzing longitudinal changes in tau‐PET rates over time. Method The JPM, developed by the CPAD‐led Tau PET Harmonization Working Group (Leuzy et al., Alzheimers Dement. 2024; Figure 1A), models relationships between anchor point subjects and head‐to‐head tau‐PET SUVR data onto the CenTauR scale, providing conversion equations for multiple tracers. JPM equations were applied to [ 18 F]flortaucipir SUVR (Meta‐temporal ROI) data from 561 cognitively impaired participants in the ADNI and OASIS‐3 studies. Using ROC analysis, we determined the CenTauR cut‐off for positivity (T+) that maximized the Youden index for discriminating between FDA‐approved positive/negative visual reads. Three separate cohorts, scanned using [ 18 F]flortaucipir (ADNI), [ 18 F]MK‐6240 (CPAS), and [ 18 F]RO‐948 (BioFINDER‐2), were matched 1:1 by age, MMSE, amyloid‐β, and clinical diagnosis. CenTauR‐based metrics were compared across these cohorts. Result A cut‐off of 18 CenTauRs was found to optimally classify [ 18 F]flortaucipir PET visual reads (Figure 1B). The matching procedure identified 1089 participants (363 per cohort). The frequency of T+ using the 18 CenTauRs cut‐off for the Meta‐Temporal ROI was highly comparable across tracers across groups, except for the Aβ‐positive cognitively unimpaired group, where variability was more pronounced due to a smaller sample size (Figure 2A). Similarly, tau‐PET discriminative accuracy for symptomatic AD vs controls remained similar across [ 18 F]flortaucipir and [ 18 F]MK‐6240 (Figure 2B). In a separate sample of 212 matched participants from ADNI ([ 18 F]flortaucipir) and AIBL ([ 18 F]MK‐6240) with baseline and 1‐year follow‐up tau‐PET scans, 1‐year change in CenTauRs was comparable (Figure 2C). An exploratory voxelwise transformation, utilizing the Meta‐Temporal conversion equation in a representative case scanned with both [ 18 F]flortaucipir and [ 18 F]MK‐6240, demonstrates the potential for voxelwise CenTauR harmonization (Figure 3). Conclusion These analyses suggest that CenTauR harmonization increases the comparability of tau‐PET data acquired with different tracers. Additional validation analyses with larger cohorts including other radiotracers ([ 18 F]PI‐2620) are underway.

ABSTRACT Gain-of-function mutations in the transient receptor potential 6 (TRPC6) channel have recently been recognized as risk factors for both doxorubicin (DOX)-induced cardiomyopathy. Functional evaluation of TRPC6 missense variants is therefore important for cancer patients undergoing anthracycline treatment. However, traditional electrophysiological methods are labor-intensive and time-consuming. In this study, we compared the functional responses of TRPC6 missense variants to 1-oleoyl-2-acetyl-sn-glycerol (OAG), a TRPC6 agonist, using molecular docking and patch clamp recording techniques. For the wild-type (WT) TRPC6 structure (PDB ID: 6UZ8), OAG exhibited a binding energy of -4.49 kcal/mol and a dissociation constant (Kd) of 0.511 mM. Twenty TRPC6 missense variants were identified from cancer patients in the Mayo Clinic database. Of these, fifteen variants had resolvable structures, nine of which displayed increased Kd values and six decreased Kd values compared to WT in molecular docking analysis. Patch clamp recordings revealed that TRPC6 WT and mutant channels were inactive at baseline but were activated upon 50 μM OAG stimulation, except two loss-of-function variants. Moreover, a 24-h treatment with 0.5 μM DOX significantly enhanced OAG-induced channel activation. All three variants identified in patients with heart failure demonstrated gain-of-function properties in both electrophysiological measurements and in-silico predictions. Importantly, the results obtained from molecular docking and patch clamp recordings were strongly correlated, showing an 82% concordance, higher than the predictions from AlphaMissense. These findings indicate that our computational analysis provides a rapid and reliable method for predicting the functional impact of TRPC6 missense variants, which may aid clinical decision-making in cancer patients receiving chemotherapy.

Abstract Background The Joint Propagation Model (JPM)‐based CenTauR scale was recently introduced to harmonize tau‐PET quantification across different radiotracers. This study examined how CenTauR harmonization enhances the comparability of tau‐PET quantification across matched cohorts using different tracers. The evaluation focused on three key aspects: (1) comparing tau‐PET positivity rates as defined by CenTauR, (2) evaluating its diagnostic accuracy for symptomatic AD, and (3) analyzing longitudinal changes in tau‐PET rates over time. Method The JPM, developed by the CPAD‐led Tau PET Harmonization Working Group (Leuzy et al., Alzheimers Dement. 2024; Figure 1A), models relationships between anchor point subjects and head‐to‐head tau‐PET SUVR data onto the CenTauR scale, providing conversion equations for multiple tracers. JPM equations were applied to [¹⁸F]flortaucipir SUVR (Meta‐temporal ROI) data from 561 cognitively impaired participants in the ADNI and OASIS‐3 studies. Using ROC analysis, we determined the CenTauR cut‐off for positivity (T+) that maximized the Youden index for discriminating between FDA‐approved positive/negative visual reads. Three separate cohorts, scanned using [¹⁸F]flortaucipir (ADNI), [¹⁸F]MK‐6240 (CPAS), and [¹⁸F]RO‐948 (BioFINDER‐2), were matched 1:1 by age, MMSE, amyloid‐β, and clinical diagnosis. CenTauR‐based metrics were compared across these cohorts. Result A cut‐off of 18 CenTauRs was found to optimally classify [ 18 F]flortaucipir PET visual reads (Figure 1B). The matching procedure identified 1089 participants (363 per cohort). The frequency of T+ using the 18 CenTauRs cut‐off for the Meta‐Temporal ROI was highly comparable across tracers across groups, except for the Aβ‐positive cognitively unimpaired group, where variability was more pronounced due to a smaller sample size (Figure 2A). Similarly, tau‐PET discriminative accuracy for symptomatic AD vs controls remained similar across [ 18 F]flortaucipir and [ 18 F]MK‐6240 (Figure 2B). In a separate sample of 212 matched participants from ADNI ([¹⁸F]flortaucipir) and AIBL ([¹⁸F]MK‐6240) with baseline and 1‐year follow‐up tau‐PET scans, 1‐year change in CenTauRs was comparable (Figure 2C). An exploratory voxelwise transformation, utilizing the Meta‐Temporal conversion equation in a representative case scanned with both [¹⁸F]flortaucipir and [¹⁸F]MK‐6240, demonstrates the potential for voxelwise CenTauR harmonization (Figure 3). Conclusion These analyses suggest that CenTauR harmonization increases the comparability of tau‐PET data acquired with different tracers. Additional validation analyses with larger cohorts including other radiotracers ([¹⁸F]PI‐2620) are underway.

Abstract Description Evidence consistent with immunoediting in human cancer has been identified through associative studies. To provide direct evidence that differences in the neoantigen profile are immune-mediated, we compared the neoantigen profiles of cutaneous squamous cell carcinoma (cSCC) from immunocompetent and immunosuppressed patients. Despite consistency in the overall mutational burden and signature, immunocompetent patients have a lower clonal mutational burden compared to immunosuppressed patients. Furthermore, clonal mutations in cSCC from immunocompetent patients have a lower proportion of neoantigens predicted to bind MHC class I, supporting the role of the immune system in shaping the neoantigen profile. In single-cell RNA sequencing of intratumoral CD8+ T cells from a separate cohort of cSCC from immunocompetent and immunosuppressed patients, we observe an increase in exhausted CD8+ T cells in tumors from immunocompetent patients, suggesting an explanation for the persistence of binding neoantigens subclonally. Finally, we compare clonal and subclonal neoantigens as an unbiased screen for characteristics associated with neoantigen immunogenicity. Neoantigens with a large increase in predicted MHC class I binding relative to the normal peptide are enriched in subclonal neoantigens in immunocompetent patients. Overall, this work supports the immune system’s role in sculpting the neoantigen profile of cSCC. Funding Sources Supported by American Cancer Society - Simone Charitable Foundation - Discovery Boost Grant, AZ-DBG-23-1151311-01-IBCD; NIH F30CA281056; R35GM149235 Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)

Abstract Background The associations between different types of diabetes, characterized by distinct pathophysiology and genetic architecture, and pancreatic ductal adenocarcinoma (PDAC) risk are not understood. Methods We investigated associations of genetic susceptibility to type 2 diabetes (T2D), 8 T2D mechanistic clusters, type 1 diabetes (T1D), and maturity-onset diabetes of the young (MODY) with PDAC risk. We used genome-wide association study (GWAS) summary-level statistics for T2D (242 283 cases, 1 569 734 controls), T1D (18 942 cases, 501 638 controls), and PDAC (10 244 cases and 360 535 controls) in individuals of European ancestry. Results Two-sample Mendelian randomization (MR) using the Robust Adjusted Profile Score (MR-RAPS) method indicated that genetically predicted T2D was associated with PDAC risk (OR = 1.10; 95% CI = 1.05 to 1.15), particularly the T2D obesity (OR = 1.28; 95% CI = 1.15 to 1.42) and lipodystrophy (OR = 1.25; 95% CI = 1.03 to 1.51) clusters. No association was observed for T1D with PDAC risk (OR = 1.01; 95% CI = 0.99 to 1.02). Pathway/gene-set analysis using the summary-based Adaptive Rank Truncated Product (sARTP) method revealed a significant association between the MODY gene-sets and PDAC risk (P = 1.5 × 10−8), which remained after excluding 20 known PDAC GWAS loci (P = 7.6 × 10−4). HNF1A, FOXA3, and HNF4A were the top contributing genes after excluding the previously identified GWAS loci regions. Conclusions Our results from this genetic association study support that T2D, particularly the obesity and lipodystrophy mechanistic clusters, and MODY genomic susceptibility regions play a role in the etiology of PDAC.