Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics.

Polygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of Europeans are known to have substantially reduced predictive accuracy in non-European populations, limiting their clinical utility and raising concerns about health disparities across ancestral populations. Here, we introduce a statistical framework named X-Wing to improve predictive performance in ancestrally diverse populations. X-Wing quantifies local genetic correlations for complex traits between populations, employs an annotation-dependent estimation procedure to amplify correlated genetic effects between populations, and combines multiple population-specific PRS into a unified score with GWAS summary statistics alone as input. Through extensive benchmarking, we demonstrate that X-Wing pinpoints portable genetic effects and substantially improves PRS performance in non-European populations, showing 14.1%-119.1% relative gain in predictive R2 compared to state-of-the-art methods based on GWAS summary statistics. Overall, X-Wing addresses critical limitations in existing approaches and may have broad applications in cross-population polygenic risk prediction.

Translational strategies and systems biology insights for blood-brain barrier opening and delivery in brain tumors and Alzheimer's disease.

The blood-brain barrier (BBB) plays a critical role in determining the effectiveness of systemic treatments for brain diseases. Over the years, several innovative approaches in BBB opening and drug delivery have been developed and progressed into clinical testing phases, including focused ultrasound (FUS) with circulating microbubbles, mannitol-facilitated delivery of anti-neoplastic drugs, receptor-mediated transcytosis (RMT) by antibody-drug conjugates (ADCs), and viral vectors for gene therapy. We provided a comprehensive review of the most recent clinical applications of these approaches in managing brain tumors and Alzheimer's disease (AD), two major devastating brain diseases. Moreover, the spatial-temporal molecular heterogeneity of the BBB under disease states emphasized the importance of utilizing emerging spatial systems biology approaches to unravel novel targets for intervention within BBB and tailor strategies for enhancing drug delivery to the brain. SEARCH STRATEGY AND SELECTION CRITERIA: Data for this Review were identified by searches of clinicaltrials.gov, MEDLINE, Current Contents, PubMed, and references from relevant articles using the search terms "blood-brain barrier", "CNS drug delivery", "BBB modulation", "clinical trials", "systems biology", "primary or metastatic brain tumors", "Alzheimer's disease". Abstracts and reports from meetings were included only when they related directly to previously published work. Only articles published in English between 1980 and 2023 were included.

Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures.

We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.