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Jenni R, Klaa H, Khamessi O, Chikhaoui A, Najjar D, Ghedira K, Kraoua I, Turki I, Yacoub-Youssef H. Clinical and genetic spectrum of Ataxia Telangiectasia Tunisian patients: Bioinformatic analysis unveil mechanisms of ATM variants pathogenicity. Int J Biol Macromol 2024; 278:134444. [PMID: 39098699 DOI: 10.1016/j.ijbiomac.2024.134444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 07/28/2024] [Accepted: 08/01/2024] [Indexed: 08/06/2024]
Abstract
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.
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Affiliation(s)
- Rim Jenni
- Laboratory of Biomedical Genomics and Oncogenetics (LR16IPT05), Institut Pasteur de Tunis, University Tunis El Manar, Tunis 1002, Tunisia.
| | - Hedia Klaa
- LR18SP04 and Department of Child and Adolescent Neurology, National Institute Mongi Ben Hmida of Neurology, 1007 Tunis, Tunisia.
| | - Oussema Khamessi
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (BIMS), Institut Pasteur de Tunis (IPT), University of Tunis El Manar, Tunis, Tunisia; Institut de Biotechnologie de Sidi Thabet, Université de la Manouba, Ariana BP-66, Manouba 2010, Tunisia.
| | - Asma Chikhaoui
- Laboratory of Biomedical Genomics and Oncogenetics (LR16IPT05), Institut Pasteur de Tunis, University Tunis El Manar, Tunis 1002, Tunisia.
| | - Dorra Najjar
- Laboratory of Biomedical Genomics and Oncogenetics (LR16IPT05), Institut Pasteur de Tunis, University Tunis El Manar, Tunis 1002, Tunisia.
| | - Kais Ghedira
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (BIMS), Institut Pasteur de Tunis (IPT), University of Tunis El Manar, Tunis, Tunisia.
| | - Ichraf Kraoua
- LR18SP04 and Department of Child and Adolescent Neurology, National Institute Mongi Ben Hmida of Neurology, 1007 Tunis, Tunisia.
| | - Ilhem Turki
- LR18SP04 and Department of Child and Adolescent Neurology, National Institute Mongi Ben Hmida of Neurology, 1007 Tunis, Tunisia.
| | - Houda Yacoub-Youssef
- Laboratory of Biomedical Genomics and Oncogenetics (LR16IPT05), Institut Pasteur de Tunis, University Tunis El Manar, Tunis 1002, Tunisia.
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Xiao T, Kong S, Zhang Z, Hua D, Liu F. A review of big data technology and its application in cancer care. Comput Biol Med 2024; 176:108577. [PMID: 38739981 DOI: 10.1016/j.compbiomed.2024.108577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 05/16/2024]
Abstract
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.
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Affiliation(s)
- Tianyun Xiao
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Shanshan Kong
- College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China.
| | - Zichen Zhang
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China
| | - Dianbo Hua
- Beijing Sitairui Cancer Data Analysis Joint Laboratory, Beijing, 101149, China
| | - Fengchun Liu
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, Hebei, 063210, China; The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, Hebei, 063210, China; College of Science, North China University of Science and Technology, Tangshan, Hebei, 063210, China; Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, Hebei, China; Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan, Hebei, China
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Langton CR, Harmon QE, Baird DD. Family History and Uterine Fibroid Development in Black and African American Women. JAMA Netw Open 2024; 7:e244185. [PMID: 38568693 PMCID: PMC10993075 DOI: 10.1001/jamanetworkopen.2024.4185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/22/2024] [Indexed: 04/05/2024] Open
Abstract
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.
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Affiliation(s)
- Christine R. Langton
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina
| | - Quaker E. Harmon
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina
| | - Donna D. Baird
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina
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Tokutomi T, Yoshida A, Fukushima A, Yamamoto K, Ishigaki Y, Kawame H, Fuse N, Nagami F, Suzuki Y, Sakurai-Yageta M, Uruno A, Suzuki K, Tanno K, Ohmomo H, Shimizu A, Yamamoto M, Sasaki M. The Health History of First-Degree Relatives' Dyslipidemia Can Affect Preferences and Intentions following the Return of Genomic Results for Monogenic Familial Hypercholesterolemia. Genes (Basel) 2024; 15:384. [PMID: 38540442 PMCID: PMC10970353 DOI: 10.3390/genes15030384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 03/14/2024] [Accepted: 03/19/2024] [Indexed: 06/14/2024] Open
Abstract
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.
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Affiliation(s)
- Tomoharu Tokutomi
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
- Department of Clinical Genetics, School of Medicine, Iwate Medical University, Morioka 020-8505, Japan
| | - Akiko Yoshida
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
- Department of Clinical Genetics, School of Medicine, Iwate Medical University, Morioka 020-8505, Japan
| | - Akimune Fukushima
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
- Department of Clinical Genetics, School of Medicine, Iwate Medical University, Morioka 020-8505, Japan
| | - Kayono Yamamoto
- Department of Clinical Genetics, School of Medicine, Iwate Medical University, Morioka 020-8505, Japan
| | - Yasushi Ishigaki
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
| | - Hiroshi Kawame
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Nobuo Fuse
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Fuji Nagami
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Yoichi Suzuki
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Mika Sakurai-Yageta
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Akira Uruno
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Kichiya Suzuki
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Kozo Tanno
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
| | - Hideki Ohmomo
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
| | - Atsushi Shimizu
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, Sendai 980-8573, Japan
| | - Makoto Sasaki
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Shiwa 020-3694, Japan; (A.Y.)
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Nienaber-Rousseau C. Understanding and applying gene-environment interactions: a guide for nutrition professionals with an emphasis on integration in African research settings. Nutr Rev 2024:nuae015. [PMID: 38442341 DOI: 10.1093/nutrit/nuae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024] Open
Abstract
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.
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Affiliation(s)
- Cornelie Nienaber-Rousseau
- Centre of Excellence for Nutrition, North-West University, Potchefstroom, South Africa
- SAMRC Extramural Unit for Hypertension and Cardiovascular Disease, Faculty of Health Sciences, North-West University, Potchefstroom, South Africa
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Wang L, He H, Wen A, Moon S, Fu S, Peterson KJ, Ai X, Liu S, Kavuluru R, Liu H. Acquisition of a Lexicon for Family History Information: Bidirectional Encoder Representations From Transformers-Assisted Sublanguage Analysis. JMIR Med Inform 2023; 11:e48072. [PMID: 37368483 PMCID: PMC10337517 DOI: 10.2196/48072] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/25/2023] [Accepted: 06/01/2023] [Indexed: 06/28/2023] Open
Abstract
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.
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Affiliation(s)
- Liwei Wang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Huan He
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Andrew Wen
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Sungrim Moon
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Sunyang Fu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Kevin J Peterson
- Center for Digital Health, Mayo Clinic, Rochester, MN, United States
| | - Xuguang Ai
- Department of Computer Science, University of Kentucky, Lexington, KY, United States
| | - Sijia Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Ramakanth Kavuluru
- Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY, United States
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
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Papadopoulou E, Bouzarelou D, Tsaousis G, Papathanasiou A, Vogiatzi G, Vlachopoulos C, Miliou A, Papachristou P, Prappa E, Servos G, Ritsatos K, Seretis A, Frogoudaki A, Nasioulas G. Application of next generation sequencing in cardiology: current and future precision medicine implications. Front Cardiovasc Med 2023; 10:1202381. [PMID: 37424920 PMCID: PMC10327645 DOI: 10.3389/fcvm.2023.1202381] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 06/12/2023] [Indexed: 07/11/2023] Open
Abstract
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.
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Affiliation(s)
| | | | | | | | - Georgia Vogiatzi
- Third Department of Cardiology, Sotiria Hospital, Athens, Greece
| | - Charalambos Vlachopoulos
- Unit of Inherited Cardiac Conditions and Sports Cardiology, First Department of Cardiology, National and Kapodistrian University of Athens, Athens, Greece
| | - Antigoni Miliou
- Unit of Inherited Cardiac Conditions and Sports Cardiology, First Department of Cardiology, National and Kapodistrian University of Athens, Athens, Greece
| | | | - Efstathia Prappa
- Second Department of Cardiology, Arrhythmia Unit, Evangelismos General Hospital of Athens, Athens, Greece
| | - Georgios Servos
- Pediatric Cardiology Unit, “P. & A. Kyriakou” Children’s Hospital, Athens, Greece
| | - Konstantinos Ritsatos
- Unit of Inherited and Rare Cardiovascular Diseases, Onassis Cardiac Surgery Center, Athens, Greece
| | - Aristeidis Seretis
- Unit of Inherited and Rare Cardiovascular Diseases, Onassis Cardiac Surgery Center, Athens, Greece
| | - Alexandra Frogoudaki
- Second Department of Cardiology, Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
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Christensen KD, McMahon PM, Galbraith LN, Yeh JM, Stout NK, Lu CY, Stein S, Zhao M, Hylind RJ, Wu AC. Benefits, harms, and costs of newborn genetic screening for hypertrophic cardiomyopathy: Estimates from the PreEMPT model. Genet Med 2023; 25:100797. [PMID: 36727595 PMCID: PMC10168130 DOI: 10.1016/j.gim.2023.100797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 02/02/2023] Open
Abstract
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.
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Affiliation(s)
- Kurt D Christensen
- Department of Population Medicine, Harvard Medical School, Boston, MA; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA.
| | - Pamela M McMahon
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA
| | - Lauren N Galbraith
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA; Department of Epidemiology and Biostatistics, School of Public Health, Boston University, Boston, MA
| | - Jennifer M Yeh
- Division of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, MA; Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School, Boston, MA; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA
| | - Christine Y Lu
- Department of Population Medicine, Harvard Medical School, Boston, MA; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA
| | - Sarah Stein
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA
| | | | - Robyn J Hylind
- Inherited Cardiac Arrhythmia Program, Department of Cardiology, Boston Children's Hospital Boston, MA
| | - Ann Chen Wu
- Department of Population Medicine, Harvard Medical School, Boston, MA; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA; Division of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, MA
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9
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Chiang J, Ngeow JYY. Challenges with mainstreaming genetic testing for metastatic prostate cancer treatment in Singapore. ANNALS OF THE ACADEMY OF MEDICINE, SINGAPORE 2023; 52:111-113. [PMID: 38904487 DOI: 10.47102/annals-acadmedsg.202384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Affiliation(s)
- Jianbang Chiang
- Cancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore
- Duke-NUS Medical School, Singapore
| | - Joanne Yuen Yie Ngeow
- Cancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore
- Duke-NUS Medical School, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
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10
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Haga SB, Orlando LA. Expanding Family Health History to Include Family Medication History. J Pers Med 2023; 13:jpm13030410. [PMID: 36983592 PMCID: PMC10053261 DOI: 10.3390/jpm13030410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/13/2023] [Accepted: 02/22/2023] [Indexed: 03/02/2023] Open
Abstract
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.
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11
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DeLouize AM, Eick G, Karam SD, Snodgrass JJ. Current and future applications of biomarkers in samples collected through minimally invasive methods for cancer medicine and population-based research. Am J Hum Biol 2022; 34:e23665. [PMID: 34374148 PMCID: PMC9894104 DOI: 10.1002/ajhb.23665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/27/2021] [Accepted: 07/28/2021] [Indexed: 02/04/2023] Open
Abstract
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.
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Affiliation(s)
| | - Geeta Eick
- Department of Anthropology, University of Oregon, Eugene, Oregon, USA
| | - Sana D. Karam
- Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - J. Josh Snodgrass
- Department of Anthropology, University of Oregon, Eugene, Oregon, USA
- Center for Global Health, University of Oregon, Eugene, Oregon, USA
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Vanderwall RA, Schwartz A, Kipnis L, Skefos CM, Stokes SM, Bhulani N, Weitz M, Gelman R, Garber JE, Rana HQ. Impact of Genetic Counseling on Patient-Reported Electronic Cancer Family History Collection. J Natl Compr Canc Netw 2022; 20:898-905.e2. [PMID: 35948032 DOI: 10.6004/jnccn.2022.7022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 04/29/2022] [Indexed: 11/17/2022]
Abstract
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.
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Affiliation(s)
- Rebecca A Vanderwall
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Alison Schwartz
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Lindsay Kipnis
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Catherine M Skefos
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Samantha M Stokes
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute
| | - Nizar Bhulani
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute.,Harvard Medical School; and
| | - Michelle Weitz
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Rebecca Gelman
- Harvard Medical School; and.,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Judy E Garber
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute.,Harvard Medical School; and
| | - Huma Q Rana
- Divisions of Population Sciences and Cancer Genetics and Prevention, Department of Medical Oncology, Dana-Farber Cancer Institute.,Harvard Medical School; and
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Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities. Emerg Top Life Sci 2022; 6:215-225. [PMID: 35234253 DOI: 10.1042/etls20210244] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/13/2022] [Accepted: 02/21/2022] [Indexed: 12/12/2022]
Abstract
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.
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Bellcross CA. Hereditary Breast and Ovarian Cancer. Obstet Gynecol Clin North Am 2022; 49:117-147. [DOI: 10.1016/j.ogc.2021.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Ahmed Z. Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2022; 190:101-125. [DOI: 10.1016/bs.pmbts.2022.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Bylstra Y, Lim WK, Kam S, Tham KW, Wu RR, Teo JX, Davila S, Kuan JL, Chan SH, Bertin N, Yang CX, Rozen S, Teh BT, Yeo KK, Cook SA, Jamuar SS, Ginsburg GS, Orlando LA, Tan P. Correction to: Family history assessment significantly enhances delivery of precision medicine in the genomics era. Genome Med 2021; 13:109. [PMID: 34225778 PMCID: PMC8259385 DOI: 10.1186/s13073-021-00916-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Yasmin Bylstra
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore
| | - Weng Khong Lim
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.,Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore.,SingHealth Duke-NUS Genomic Medicine Center, Singapore Health Services, Singapore, Singapore
| | - Sylvia Kam
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.,Department of Paediatrics, KK Women's and Children's Hospital, Singapore, Singapore
| | - Koei Wan Tham
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.,Department of Physiology, National University of Singapore, Singapore, Singapore
| | - R Ryanne Wu
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Jing Xian Teo
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore
| | - Sonia Davila
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.,SingHealth Duke-NUS Genomic Medicine Center, Singapore Health Services, Singapore, Singapore.,Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, Singapore
| | - Jyn Ling Kuan
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore
| | - Sock Hoai Chan
- Cancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Nicolas Bertin
- Centre for Big Data and Integrative Genomics, Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore
| | - Cheng Xi Yang
- National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore, Singapore
| | - Steve Rozen
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.,Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore
| | - Bin Tean Teh
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.,National Cancer Centre Singapore, Singapore, Singapore
| | - Khung Keong Yeo
- Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, Singapore.,Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore
| | - Stuart Alexander Cook
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.,Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, Singapore, Singapore.,Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore
| | - Saumya Shekhar Jamuar
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore.,SingHealth Duke-NUS Genomic Medicine Center, Singapore Health Services, Singapore, Singapore.,Department of Paediatrics, KK Women's and Children's Hospital, Singapore, Singapore.,Paediatric Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Geoffrey S Ginsburg
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Lori A Orlando
- Center for Applied Genomics and Precision Medicine, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.
| | - Patrick Tan
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore Health Services, Singapore, Singapore. .,Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore, Singapore. .,Genome Institute of Singapore, Agency for Science Technology and Research, Singapore, Singapore.
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Affiliation(s)
- Muin J Khoury
- Office of Genomics and Precision Public Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.
| | - Kathryn E Holt
- Monash University, Melbourne, Australia. .,London School of Hygiene and Tropical Medicine, London, UK.
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