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AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes

Ahmad RM, Ali BR, Al-Jasmi F, Al Dhaheri N, Al Turki S, Kizhakkedath P, et al.

Hum Genomics, 2024 Sep 11;18(1):99.
PMID: 39256852 DOI: 10.1186/s40246-024-00667-9

Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. Laboratory-based experimental methods for assessing these effects are time-consuming and often impractical, highlighting the importance of in-silico tools for variant impact prediction. However, the performance metrics of currently available tools on breast cancer missense variants from benchmarking databases have not been thoroughly investigated, creating a knowledge gap in the accurate prediction of pathogenicity. In this study, the benchmarking datasets ClinVar and HGMD were used to evaluate 21 Artificial Intelligence (AI)-derived in-silico tools. Missense variants in breast cancer genes were extracted from ClinVar and HGMD professional v2023.1. The HGMD dataset focused on pathogenic variants only, to ensure balance, benign variants for the same genes were included from the ClinVar database. Interestingly, our analysis of both datasets revealed variants across genes with varying penetrance levels like low and moderate in addition to high, reinforcing the value of disease-specific tools. The top-performing tools on ClinVar dataset identified were MutPred (Accuracy = 0.73), Meta-RNN (Accuracy = 0.72), ClinPred (Accuracy = 0.71), Meta-SVM, REVEL, and Fathmm-XF (Accuracy = 0.70). While on HGMD dataset they were ClinPred (Accuracy = 0.72), MetaRNN (Accuracy = 0.71), CADD (Accuracy = 0.69), Fathmm-MKL (Accuracy = 0.68), and Fathmm-XF (Accuracy = 0.67). These findings offer clinicians and researchers valuable insights for selecting, improving, and developing effective in-silico tools for breast cancer pathogenicity prediction. Bridging this knowledge gap contributes to advancing precision medicine and enhancing diagnostic and therapeutic approaches for breast cancer patients with potential implications for other conditions.
Fulltext Success stories in genomic medicine from resource-limited countries

Mitropoulos K, Al Jaibeji H, Forero DA, Laissue P, Wonkam A, Lopez-Correa C, et al.

Hum Genomics, 2015 Jun 18;9:11.
PMID: 26081768 DOI: 10.1186/s40246-015-0033-3

In recent years, the translation of genomic discoveries into mainstream medical practice and public health has gained momentum, facilitated by the advent of new technologies. However, there are often major discrepancies in the pace of implementation of genomic medicine between developed and developing/resource-limited countries. The main reason does not only lie in the limitation of resources but also in the slow pace of adoption of the new findings and the poor understanding of the potential that this new discipline offers to rationalize medical diagnosis and treatment. Here, we present and critically discuss examples from the successful implementation of genomic medicine in resource-limited countries, focusing on pharmacogenomics, genome informatics, and public health genomics, emphasizing in the latter case genomic education, stakeholder analysis, and economics in pharmacogenomics. These examples can be considered as model cases and be readily replicated for the wide implementation of pharmacogenomics and genomic medicine in other resource-limited environments.
Fulltext Prevalence of pharmacogenomic variants in 100 pharmacogenes among Southeast Asian populations under the collaboration of the Southeast Asian Pharmacogenomics Research Network (SEAPharm)

Runcharoen C, Fukunaga K, Sensorn I, Iemwimangsa N, Klumsathian S, Tong H, et al.

Hum Genome Var, 2021 Feb 04;8(1):7.
PMID: 33542200 DOI: 10.1038/s41439-021-00135-z

Pharmacogenomics can enhance the outcome of treatment by adopting pharmacogenomic testing to maximize drug efficacy and lower the risk of serious adverse events. Next-generation sequencing (NGS) is a cost-effective technology for genotyping several pharmacogenomic loci at once, thereby increasing publicly available data. A panel of 100 pharmacogenes among Southeast Asian (SEA) populations was resequenced using the NGS platform under the collaboration of the Southeast Asian Pharmacogenomics Research Network (SEAPharm). Here, we present the frequencies of pharmacogenomic variants and the comparison of these pharmacogenomic variants among different SEA populations and other populations used as controls. We investigated the different types of pharmacogenomic variants, especially those that may have a functional impact. Our results provide substantial genetic variations at 100 pharmacogenomic loci among SEA populations that may contribute to interpopulation variability in drug response phenotypes. Correspondingly, this study provides basic information for further pharmacogenomic investigations in SEA populations.
Global Burden of Elevated LDL-C: Findings From the Global Burden of Disease Study 2023

GBD 2023 LDL Cholesterol Collaborators, Razo C, DeCleene NK, Johnson CO, Stark BA, LeGrand K, et al.

JAMA, 2026 Aug 25;336(8):673-698.
PMID: 42525403 DOI: 10.1001/jama.2026.8628

IMPORTANCE: Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C-related burden is critical for guiding prevention and treatment strategies.
OBJECTIVES: To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends.
DESIGN, SETTING, AND POPULATION: This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals.
EXPOSURE: Population-level LDL-C concentrations.
MAIN OUTCOMES AND MEASURES: Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C-attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals.
RESULTS: In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C-attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings.
CONCLUSIONS AND RELEVANCE: Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.