Citations

Abstract Measurable residual disease (MRD)-testing is often used to evaluate therapy efficacy in chronic lymphocytic leukaemia (CLL), with undetectable MRD (uMRD) often serving as a surrogate for progression-free survival (PFS). However, its accuracy in predicting PFS with Bruton tyrosine kinase inhibitors (BTKis), with or without venetoclax, remains controversial. We searched PubMed, Web of Science, Embase, and Cochrane Library databases up to February 8, 2025 using terms chronic lymphocytic leukemiaOR small lymphocytic lymphomaAND measurable residual diseaseAND progression-free survival. Bayesian Weibull accelerated failure time models were used for individual-level analyses, while trial-level correlations used weighted Spearman rank tests and weighted least squares regression. A total of 41 trials (9,569 subjects) were included. Therapies tested included BTKis (11 trials), venetoclax (12), the combination (5), and other therapies like chemoimmunotherapy and monoclonal antibodies (23). MRD was assessed in blood (21 trials), bone marrow (7), or both (9). At the individual level, subjects with detectable MRD (dMRD) had a higher progression and/or death risk compared to uMRD (Hazard Ratio [HR] = 3.63; 95% Credibility Interval [CrI] 3.30-4.00; P < 0.01). In sensitivity analysis, MRD-testing showed reduced predictive accuracy for PFS in subjects receiving BTKi-based therapy (HR = 1.52, 1.02–2.19) compared to venetoclax-based (HR = 4.46, 3.61–5.50) or other therapies (HR = 3.86, 3.45–4.34). Across subgroups, dMRD remained consistently associated with increased risk. In subjects with del(17p)/TP53 variants, dMRD predicted greater (HR = 6.52; 3.47–12.79) versus those without (HR = 4.34; 3.24–5.81). By IGHV status, dMRD conferred a 3.54-fold risk in IGHV-mutated (95% CrI: 2.42-5.34) and 2.64-fold in unmutated subjects (95% CrI: 2.10-3.34). Among response categories, risk was higher in partial responders (HR = 2.39; 1.70–3.43) than complete responders (HR = 1.64; 1.24–2.17). dMRD also predicted increased risk in relapsed/refractory (R/R) CLL subjects (HR = 4.11, 2.54-6.84) and untreated subjects (HR = 3.59, 3.25-3.98). Bone marrow MRD testing proved a stronger predictor of PFS than blood (HR = 4.27, 3.42-5.35 versus HR = 3.52, 3.17-3.92). The highest risk was seen with MRD testing 12–32 months post-treatment (HR = 5.37, 4.56-6.40), though differences across timepoints were modest after adjusting for analytical method. At the trial level, correlations between MRD and PFS were weak (Spearman's rho [R] = -0.35; Determination Coefficients [R²] = 0.02). In sensitivity analyses, correlations remained low for BTKi-based therapy (R = -0.19 [-0.73, 0.50]; R² = 0.04) and venetoclax-based therapy (R = -0.32 [-0.85, 0.45]; R² = 0.14), but were stronger for other therapies (R = -0.80; R² = 0.58). For other therapies, MRD tests of bone marrow samples showed higher predictive value than blood (R = -0.94; R² = 0.86 versus R = -0.81; R² = 0.63). Despite blood samples and testing at 9-months of other therapies meeting the validity threshold (R² = 0.63, P = 0.21; R² = 0.63, P = 0.06), the associated P-values preclude definitive conclusions. Additional analyses examining sample source, time points and testing method within treatment classes suggest limited accuracy of predicting PFS in heterogeneous conditions (all R < -0.85, R² < 0.60). In subgroup analyses, untreated trials showed weak associations (R = -0.30 [-0.77, 0.13]; R²= 0.02), while R/R CLL trials were insufficient for analysis. MRD-testing of blood showed lower PFS predictive accuracy than bone marrow(R = -0.26 [-0.71, 0.20]; R² = 0.03 versus R = -0.37 [-1.00, 0.62]; R² = 0.03). MRD-testing at 15 months demonstrated slightly better accuracy than 9-month testing (R = -0.59 [-1.00, 0.61]; R² = 0.27 versus R = -0.26 [-0.88, 0.41]; R² = 0.04).MRD-testing by NGS outperformed MPFC (R = -0.54 [-1.00, 1.00]; R² = 0.45 versus R = -0.30 [-0.76, 0.13]; R² = 0.01), though both methods remained below the pre-specified determination coefficient threshold. Our data indicate accuracy of results of MRD-testing to predict PFS depends on whether used at the subject or trial-level, type of therapy and sample tested. Importantly MRD-testing is not an accurate predictor of PFS for BTKis with or without venetoclax. We suggest results of MRD-testing as a surrogate for PFS should not be used as a surrogate endpoint for studies of BTKis with or without venetoclax.

Key Points Inositol polyphosphate-5-phosphatase E (INPP5E) and type Ig phosphatidylinositol-4-phosphate 5-kinase (PIPKIγ) coordinated the homeostasis of phosphatidylinositol-4-phosphate and phosphatidylinositol-4,5-bisphosphate in primary cilia. Modulating INPP5E or PIPKIγ activity changed the level of polycystin-1 and polycystin-2 in primary cilia. INPP5E inhibition increased the hypomorphic PKD1 variants (PC1-R3277C) in cilia and reduced cystogenesis in the kidney in vitro . Background Autosomal dominant polycystic kidney disease is mainly caused by mutations in PKD1 and PKD2 , which encode polycystin-1 (PC1) and polycystin-2 (PC2), respectively. PC1 and PC2 assemble a cation channel complex enriched in primary cilium, a sensory organelle associated with various developmental diseases, including polycystic kidney disease (PKD). Accumulating evidence supports the necessity of functional polycystin (PC) complex in cilia to prevent cystogenesis in the kidney, indicating that improving their ciliary levels may ameliorate defects underlying PKD pathogenesis. Yet, molecular mechanisms underlying the ciliary targeting and homeostasis of the PC complex are not fully understood. Methods Indirect immunofluorescence microscopy was used to monitor ciliary levels of PC1 and PC2 in renal epithelial cells. Electrophysiology analysis in oocytes was used to determine the channel activity of the PC complex. Cystogenesis in the kidney was measured using in vitro 3D-Matrigel cell models and ex vivo mouse embryonic kidney models. Results Suppressing inositol polyphosphate-5-phosphatase E (INPP5E) or activating type Ig phosphatidylinositol-4-phosphate 5-kinase raised ciliary levels of the PC complex in both normal renal epithelial cells and cells carrying autosomal dominant polycystic kidney disease mutations that interrupt the trafficking of PCs into cilia, including GANAB inactivation and the trafficking PKD1 mutation p.Arg3277Cys (RC). PC1 RC formed a complex with PC2 and exhibited normal channel activity in vitro . An INPP5E that increases PC1 and PC2 in cilia, suppressed in vitro forskolin-induced cystogenesis of inner medullary collecting duct epithelial cell line 3 cells in 3D Matrigel and ex vivo cyst formation in embryonic Pkd1 RC/RC mouse kidneys. Conclusions Our results demonstrated that increasing the ciliary level of PCs, by manipulating a ciliary phosphoinositide signaling axis, enhanced the functionality of PCs and suppressed cystogenesis of renal epithelial cells in vitro .

Background Admixture mapping can be leveraged to identify genomic loci associated with disease traits in admixed individuals such as Hispanic Americans. Methods and Results We characterized fine‐scale population structure of participants (n=105 108) in the multiethnic eMERGE‐III (electronic Medical Records and Genomics) cohort, using reference panels from the 1000 Genomes Project and a Native American cohort. We defined “genetically admixed Hispanic Americans” as those having ≥10% Native American genetic ancestry (n=5025), in addition to European (specifically, Iberian) and African genetic ancestry. This population descriptor was subsequently applied to 3 other cohorts: the MESA (Multi‐Ethnic Study of Atherosclerosis; n=1176), WHI (Women’s Health Initiative; n=2873), and the HCHS/SOL (Hispanic Community Health Study/Study of Latinos; n=7804). We inferred local ancestry along the genome in genetically admixed Hispanic Americans in each cohort using RFMix2 with phased haplotypes from SHAPEIT2. Local ancestry proportions were used for admixture mapping of lipid traits (low‐density lipoprotein cholesterol, high‐density lipoprotein cholesterol, total cholesterol, and triglycerides) using extended linear mixed models that incorporated multiple or single ancestries. After accounting for fixed‐effect covariates and random effects, ancestry proportions at the following loci were associated with lipid traits in the HCHS/SOL cohort: 11q12.2 ( P =6.8×10 −7 ) with low‐density lipoprotein cholesterol, 2p23.3 ( P =5.2×10 −7 ) with total cholesterol, and 11q23.3 ( P =7.3×10 −12 ) with triglycerides. The LDL cholesterol and triglyceride loci replicated in at least one independent cohort, whereas the total cholesterol locus showed suggestive evidence for replication in the eMERGE cohort ( P =0.073). Native American (NAMR) ancestry appeared to drive the LDL‐C association, whereas both NAMR and Iberian population in Spain (IBS) ancestries contributed to the triglyceride signal. Conclusions Admixture mapping in genetically admixed Hispanic Americans identified genomic loci influencing lipid traits and the different ancestral origins driving these associations.

Abstract Objectives To implementation an automated multi-institutional pipeline that delivers breast-cancer risk integrated with polygenic risk scores, monogenic variants, family history, and clinical factors, emphasizing operational challenges and their solutions. Materials and Methods A five-stage process was executed at ten sites. Data streams from REDCap surveys, PRS and monogenic reports, and MeTree pedigrees were normalized and forwarded through a REDCap plug-in to the CanRisk API. Results Integrated risk was returned to >10 000 women; 3.6% were ≥25 % lifetime risk and 0.9% carried pathogenic variants. Pipeline generated score aligns well with manual generated ones. Major barriers such as heterogeneous pedigree formats, missing data, edge-case handling, and evolving model versions were identified and resolved through mapping rules, imputations, and iterative testing. Discussion Cross-platform data harmonization and stakeholder alignment were decisive for success. Borderline-risk communication and model-version drift remain open issues. Conclusion Large-scale PRS-integrated breast-cancer risk reporting is feasible but requires robust interoperability standards and iterative governance.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS BMI –covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge genome-wide association studies effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Objective: The REDISCOVER consensus conference aimed at developing and validating guidelines on the perioperative care of patients with borderline-resectable (BR-) and locally advanced (LA) pancreatic ductal adenocarcinoma (PDAC). Background: Coupled with improvements in chemotherapy and radiation, the contemporary approach to pancreatic surgery supports the resection of BR-PDAC and, to a lesser extent, LA-PDAC. Guidelines outlining the selection and perioperative care for these patients are lacking. Methods: The Scottish Intercollegiate Guidelines Network (SIGN) methodology was used to develop the REDISCOVER guidelines and create recommendations. The Delphi approach was used to reach a consensus (agreement ≥80%) among experts. Recommendations were approved after a debate and vote among international experts in pancreatic surgery and pancreatic cancer management. A Validation Committee used the AGREE II-GRS tool to assess the methodological quality of the guidelines. Moreover, an independent multidisciplinary advisory group revised the statements to ensure adherence to nonsurgical guidelines. Results: Overall, 34 recommendations were created targeting centralization, training, staging, patient selection for surgery, possibility of surgery in uncommon scenarios, timing of surgery, avoidance of vascular reconstruction, details of vascular resection/reconstruction, arterial divestment, frozen section histology of perivascular tissue, extent of lymphadenectomy, anticoagulation prophylaxis, and role of minimally invasive surgery. The level of evidence was however low for 29 of 34 clinical questions. Participants agreed that the most conducive means to promptly advance our understanding in this field is to establish an international registry addressing this patient population (https://rediscover.unipi.it/). Conclusions: The REDISCOVER guidelines provide clinical recommendations pertaining to pancreatectomy with vascular resection for patients with BR-PDAC and LA-PDAC, and serve as the basis of a new international registry for this patient population.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS BMI –covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge genome-wide association studies effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Abstract Importance Chronic kidney disease (CKD) is a genetically complex disease determined by an interplay of monogenic, polygenic, and environmental risks. The most common forms of monogenic kidney disorders include autosomal dominant polycystic kidney disease (ADPKD), caused by mutations in the PKD1 or PKD2 genes, and COL4A-associated nephropathy (COL4A-AN), caused by mutations in COL4A3, COL4A4, or COL4A5 genes. These disorders have incomplete penetrance and variable expressivity. It is presently unknown if some of the variability in penetrance can be attributed to polygenic factors. Objective Our objectives were to investigate the interplay of polygenic and monogenic risk of kidney disease and to test the utility of polygenic risk scores for risk stratification in ADPKD and COL4-AN. Design We combined exome/genome sequencing, SNP micro-array, and electronic health record data from the UK Biobank (N = 469,835 participants) and the All of Us (N = 98,622 participants) datasets to test the effects of the genome-wide polygenic score (GPS) for CKD in ADPKD and COL4-AN variant carriers defined by strict variant classification criteria. We used the eMERGE-III electronic CKD phenotype to define cases (estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73m2 or kidney failure) and controls (eGFR > 90 mL/min/1.73m2 in the absence of kidney disease diagnoses). The GPS was tested as a predictor of CKD in qualifying variant carriers and non-carriers using logistic regression after adjustment for age, sex, diabetes, and genetic ancestry. Results The GPS was predictive of CKD in ADPKD variant carriers (ORmeta=2.28 per SD, 95%CI: 1.55–3.37, P = 2.6E-05) as well as non-carriers (ORmeta=1.72 per SD, 95% CI = 1.69–1.76, P < E-300) independent of age, sex, diabetes, and genetic ancestry. Compared to the middle tertile of the GPS distribution for non-carriers, ADPKD variant carriers in the top tertile had a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile had only a 3-fold increased risk of CKD. Similarly, the GPS was predictive of CKD in both COL4-AN carriers (ORmeta=1.78, 95% CI = 1.22–2.58, P = 2.38E-03) and non-carriers (ORmeta=1.70, 95%CI: 1.68–1.73 P < E-300). The carriers in the top GPS tertile had a 2.5-fold higher risk of CKD while the risk for carriers in the bottom GPS tertile was not different from the average population risk. Conclusions and Relevance Variable penetrance of kidney disease in ADPKD and COL4-AN is partially explained by the differences in polygenic risk profiles. Accounting for polygenic factors improves risk stratification in monogenic kidney disease and may have clinical implications for genetic counseling.

AbstractObjectiveThe aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used.Materials and MethodsA sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries.ResultsMost of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27.DiscussionDespite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints.ConclusionsThe phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic.