Family history assessment significantly enhances delivery of precision medicine in the genomics era

Clinical and genetic spectrum of Ataxia Telangiectasia Tunisian patients: Bioinformatic analysis unveil mechanisms of ATM variants pathogenicity

Ataxia Telangiectasia (AT) is a rare multisystemic neurodegenerative disease caused by biallelic mutations in the ATM gene. Few clinical studies on AT disease have been conducted in Tunisia, however, the mutational landscape is still undefined. Our aim is to determine the clinical and genetic spectrum of AT Tunisian patients and to explore the potential underlying mechanism of variant pathogenicity. Sanger sequencing was performed for nine AT patients. A comprehensive computational analysis was conducted to evaluate the possible pathogenic effect of ATM identified variants. Genetic screening of ATM gene has identified nine different variants from which six have not been previously reported. In silico analysis has predicted a pathogenic effect of identified mutations. This was corroborated by a structural bioinformatics study based on molecular modeling and docking for novel missense mutations. Our findings suggest a profound impact of identified mutations not only on the ATM protein stability, but also on the ATM-ligand interactions. Our study characterizes the mutational landscape of AT Tunisian patients which will allow to set up genetic counseling and prenatal diagnosis for families at risk and expand the spectrum of ATM variants worldwide. Furthermore, understanding the mechanism that underpin variant pathogenicity could provide further insights into disease pathogenesis.

A review of big data technology and its application in cancer care

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.

Family History and Uterine Fibroid Development in Black and African American Women

Importance

Uterine fibroids are an understudied condition, with earlier onset in Black than White women. Prior studies of the importance of family history on fibroid development are limited by reliance on hospital-based participant selection, poorly defined measures of family history, and nonsystematic fibroid assessment.

Objective

To examine whether family history is a risk factor for fibroid development using prospective ultrasonography data to identify incident fibroids and measure fibroid growth and standardized methods to ascertain family history.

Design, Setting, and Participants

This prospective community cohort of Black and African American women from the Detroit, Michigan, area was conducted from January 1, 2010, to December 31, 2018, using 4 standardized ultrasonographic examinations during 5 years to detect fibroids 0.5 cm or larger in diameter and measure fibroid growth. Data analysis was performed between May 2022 and January 2024.

Exposures

Maternal fibroid history data were gathered directly from participants' mothers when possible (1425/1628 [88%]), and 2 exposure variables were created: maternal history of fibroids (diagnosed vs not diagnosed) and age at maternal fibroid diagnosis (20-29, 30-39, or ≥40 years vs not diagnosed).

Main Outcomes and Measures

Fibroid incidence was assessed using multivariable Cox proportional hazards regression models; fibroid growth was calculated as change in log-volume per 18 months for fibroids matched at successive ultrasonograms.

Results

A total of 1610 self-identified Black and/or African American women aged 23 to 35 years (mean [SD] age, 29.2 [3.4] years) with no prior clinical diagnosis of fibroids at enrollment were available for analysis. Of 1187 fibroid-free participants at enrollment, 442 (37%) had mothers who were diagnosed with fibroids. Compared with participants without a maternal history of fibroids, those reporting maternal history had an adjusted hazard ratio (AHR) of 1.21 (95% CI, 0.96-1.52). Risk was strongest in those whose mothers were diagnosed at a younger age (20-29 years: AHR, 1.56; 95% CI, 1.11-2.21; 30-39 years: AHR, 1.03; 95% CI, 0.71-1.49; ≥40 years: AHR, 1.11; 95% CI, 0.81-1.52; P = .053 for trend). Fibroid growth rates were higher when mothers were diagnosed with fibroids vs not diagnosed (8.0% increased growth; 95% CI, -1.2% to 18.0%).

Conclusions and Relevance

In this prospective cohort study, results supported maternal history of fibroids as a risk factor for incident fibroids, especially when mothers were diagnosed at a younger age. Maternal history was also associated with increased fibroid growth. Asking patients about their family history of fibroids could encourage patient self-advocacy and inform care.

The Health History of First-Degree Relatives' Dyslipidemia Can Affect Preferences and Intentions following the Return of Genomic Results for Monogenic Familial Hypercholesterolemia

Genetic testing is key in modern healthcare, particularly for monogenic disorders such as familial hypercholesterolemia. This Tohoku Medical Megabank Project study explored the impact of first-degree relatives' dyslipidemia history on individual responses to familial hypercholesterolemia genomic results. Involving 214 participants and using Japan's 3.5KJPN genome reference panel, the study assessed preferences and intentions regarding familial hypercholesterolemia genetic testing results. The data revealed a significant inclination among participants with a family history of dyslipidemia to share their genetic test results, with more than 80% of participants intending to share positive results with their partners and children and 98.1% acknowledging the usefulness of positive results for personal health management. The study underscores the importance of family health history in genetic-testing perceptions, highlighting the need for family-centered approaches in genetic counseling and healthcare. Notable study limitations include the regional scope and reliance on questionnaire data. The study results emphasize the association between family health history and genetic-testing attitudes and decisions.

Understanding and applying gene-environment interactions: a guide for nutrition professionals with an emphasis on integration in African research settings

Noncommunicable diseases (NCDs) are influenced by the interplay between genetics and environmental exposures, particularly diet. However, many healthcare professionals, including nutritionists and dietitians, have limited genetic background and, therefore, they may lack understanding of gene-environment interactions (GxEs) studies. Even researchers deeply involved in nutrition studies, but with a focus elsewhere, can struggle to interpret, evaluate, and conduct GxE studies. There is an urgent need to study African populations that bear a heavy burden of NCDs, demonstrate unique genetic variability, and have cultural practices resulting in distinctive environmental exposures compared with Europeans or Americans, who are studied more. Although diverse and rapidly changing environments, as well as the high genetic variability of Africans and difference in linkage disequilibrium (ie, certain gene variants are inherited together more often than expected by chance), provide unparalleled potential to investigate the omics fields, only a small percentage of studies come from Africa. Furthermore, research evidence lags behind the practices of companies offering genetic testing for personalized medicine and nutrition. We need to generate more evidence on GxEs that also considers continental African populations to be able to prevent unethical practices and enable tailored treatments. This review aims to introduce nutrition professionals to genetics terms and valid methods to investigate GxEs and their challenges, and proposes ways to improve quality and reproducibility. The review also provides insight into the potential contributions of nutrigenetics and nutrigenomics to the healthcare sphere, addresses direct-to-consumer genetic testing, and concludes by offering insights into the field's future, including advanced technologies like artificial intelligence and machine learning.

Acquisition of a Lexicon for Family History Information: Bidirectional Encoder Representations From Transformers-Assisted Sublanguage Analysis

BACKGROUND

A patient's family history (FH) information significantly influences downstream clinical care. Despite this importance, there is no standardized method to capture FH information in electronic health records and a substantial portion of FH information is frequently embedded in clinical notes. This renders FH information difficult to use in downstream data analytics or clinical decision support applications. To address this issue, a natural language processing system capable of extracting and normalizing FH information can be used.

OBJECTIVE

In this study, we aimed to construct an FH lexical resource for information extraction and normalization.

METHODS

We exploited a transformer-based method to construct an FH lexical resource leveraging a corpus consisting of clinical notes generated as part of primary care. The usability of the lexicon was demonstrated through the development of a rule-based FH system that extracts FH entities and relations as specified in previous FH challenges. We also experimented with a deep learning-based FH system for FH information extraction. Previous FH challenge data sets were used for evaluation.

RESULTS

The resulting lexicon contains 33,603 lexicon entries normalized to 6408 concept unique identifiers of the Unified Medical Language System and 15,126 codes of the Systematized Nomenclature of Medicine Clinical Terms, with an average number of 5.4 variants per concept. The performance evaluation demonstrated that the rule-based FH system achieved reasonable performance. The combination of the rule-based FH system with a state-of-the-art deep learning-based FH system can improve the recall of FH information evaluated using the BioCreative/N2C2 FH challenge data set, with the F1 score varied but comparable.

CONCLUSIONS

The resulting lexicon and rule-based FH system are freely available through the Open Health Natural Language Processing GitHub.

Application of next generation sequencing in cardiology: current and future precision medicine implications

Inherited cardiovascular diseases are highly heterogeneous conditions with multiple genetic loci involved. The application of advanced molecular tools, such as Next Generation Sequencing, has facilitated the genetic analysis of these disorders. Accurate analysis and variant identification are required to maximize the quality of the sequencing data. Therefore, the application of NGS for clinical purposes should be limited to laboratories with a high level of technological expertise and resources. In addition, appropriate gene selection and variant interpretation can result in the highest possible diagnostic yield. Implementation of genetics in cardiology is imperative for the accurate diagnosis, prognosis and management of several inherited disorders and could eventually lead to the realization of precision medicine in this field. However, genetic testing should also be accompanied by an appropriate genetic counseling procedure that clarifies the significance of the genetic analysis results for the proband and his family. In this regard, a multidisciplinary collaboration among physicians, geneticists, and bioinformaticians is imperative. In the present review, we address the current state of knowledge regarding genetic analysis strategies employed in the field of cardiogenetics. Variant interpretation and reporting guidelines are explored. Additionally, gene selection procedures are accessed, with a particular emphasis on information concerning gene-disease associations collected from international alliances such as the Gene Curation Coalition (GenCC). In this context, a novel approach to gene categorization is proposed. Moreover, a sub-analysis is conducted on the 1,502,769 variation records with submitted interpretations in the Clinical Variation (ClinVar) database, focusing on cardiology-related genes. Finally, the most recent information on genetic analysis's clinical utility is reviewed.

Benefits, harms, and costs of newborn genetic screening for hypertrophic cardiomyopathy: Estimates from the PreEMPT model

PURPOSE

Population newborn genetic screening for hypertrophic cardiomyopathy (HCM) is feasible, however its benefits, harms, and cost-effectiveness are uncertain.

METHODS

We developed a microsimulation model to simulate a US birth cohort of 3.7 million newborns. Those identified with pathogenic/likely pathogenic variants associated with increased risk of HCM underwent surveillance and recommended treatment, whereas in usual care, individuals with family histories of HCM underwent surveillance.

RESULTS

In a cohort of 3.7 million newborns, newborn genetic screening would reduce HCM-related deaths through age 20 years by 44 (95% uncertainty interval [UI] = 10-103) however increase the numbers of children undergoing surveillance by 8127 (95% UI = 6308-9664). Compared with usual care, newborn genetic screening costs $267,000 per life year saved (95% UI, $106,000 to $919,000 per life year saved).

CONCLUSION

Newborn genetic screening for HCM could prevent deaths but at a high cost and would require many healthy children to undergo surveillance. This study shows how modeling can provide insights into the tradeoffs between benefits and costs that will need to be considered as newborn genetic screening is more widely adopted.

Expanding Family Health History to Include Family Medication History

The collection of family health history (FHH) is an essential component of clinical practice and an important piece of data for patient risk assessment. However, family history data have generally been limited to diseases and have not included medication history. Family history was a key component of early pharmacogenetic research, confirming the role of genes in drug response. With the substantial number of known pharmacogenes, many affecting response to commonly prescribed medications, and the availability of clinical pharmacogenetic (PGx) tests and guidelines for interpretation, the collection of family medication history can inform testing decisions. This paper explores the roots of family-based pharmacogenetic studies to confirm the role of genes in these complex phenotypes and the benefits and challenges of collecting family medication history as part of family health history intake.

Current and future applications of biomarkers in samples collected through minimally invasive methods for cancer medicine and population-based research

Despite advances in cancer medicine and research, invasive and potentially risky procedures such as biopsies, venous blood tests, imaging, colonoscopy, and pap smear tests are still primarily used for screening, staging, and assessing response to therapy. The development and interdisciplinary use of biomarkers from urine, feces, saliva, scent, and capillary blood collected with minimally invasive methods represents a potential opportunity for integration with biomarker analysis for cancers, both in clinical practice (e.g., in screening, treatment, and disease monitoring, and improved quality of life for patients) and population-based research (e.g., in epidemiology/public health, studies of social and environmental determinants, and evolutionary medicine). In this article, we review the scientific rationale, benefits, challenges, and potential opportunities for measuring cancer-related biomarkers in samples collected through minimally invasive methods.

Impact of Genetic Counseling on Patient-Reported Electronic Cancer Family History Collection

BACKGROUND

Cancer family history is a vital part of cancer genetic counseling (GC) and genetic testing (GT), but increasing indications for germline cancer GT necessitate less labor-intensive models of collection. We evaluated the impact of GC on patient pedigrees generated by an electronic cancer family history questionnaire (eCFHQ).

METHODS

An Institutional Review Board-approved review of pedigrees collected through an eCFHQ was conducted. Paired pre-GC and post-GC pedigrees (n=1,113 each group) were analyzed independently by cancer genetic counselors for changes in patient-reported clinical history and to determine whether the pedigrees met NCCN GT criteria. Discrepancy in meeting NCCN GT criteria between pre-GC and post-GC pedigrees was the outcome variable of logistic regressions, with patient and family history characteristics as covariates.

RESULTS

Overall, 780 (70%) patients had cancer (affected), 869 (78%) were female, and the median age was 57 years (interquartile range, 45-66 years; range, 21-91 years). Of the 1,113 pairs of pre-GC and post-GC pedigrees analyzed, 85 (8%) were blank, 933 (84%) were not discrepant, and 95 (9%) were discrepant in meeting any NCCN GT criteria. Of the discrepant pedigrees, n=79 (83%) became eligible for testing by at least one of the NCCN GT criteria after GC. Patients with discrepant pedigrees were more likely to report no or unknown history of GT (odds ratio [OR], 4.54; 95% CI, 1.66-18.70; P=.01, and OR, 18.47; 95% CI, 5.04-88.73; P<.0001, respectively) and belonged to racially and/or ethnically underrepresented groups (OR, 1.91; 95% CI, 1.08-3.25; P=.02).

CONCLUSIONS

For most patients (84%), a standalone eCFHQ was sufficient to determine whether NCCN GT criteria were met. More research is needed on the performance of the eCFHQ in diverse patient populations.

Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities

Precision medicine is driven by the paradigm shift of empowering clinicians to predict the most appropriate course of action for patients with complex diseases and improve routine medical and public health practice. It promotes integrating collective and individualized clinical data with patient specific multi-omics data to develop therapeutic strategies, and knowledgebase for predictive and personalized medicine in diverse populations. This study is based on the hypothesis that understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic and predictive biomarkers and optimal paths providing personalized care for diverse and targeted chronic, acute, and infectious diseases. This study briefs emerging significant, and recently reported multi-omics and translational approaches aimed to facilitate implementation of precision medicine. Furthermore, it discusses current grand challenges, and the future need of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to accelerate diagnostic and preventive care delivery strategies beyond traditional symptom-driven, disease-causal medical practice.