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Walton NA, Hafen B, Graceffo S, Sutherland N, Emmerson M, Palmquist R, Formea CM, Purcell M, Heale B, Brown MA, Danford CJ, Rachamadugu SI, Person TN, Shortt KA, Christensen GB, Evans JM, Raghunath S, Johnson CP, Knight S, Le VT, Anderson JL, Van Meter M, Reading T, Haslem DS, Hansen IC, Batcher B, Barker T, Sheffield TJ, Yandava B, Taylor DP, Ranade-Kharkar P, Giauque CC, Eyring KR, Breinholt JW, Miller MR, Carter PR, Gillman JL, Gunn AW, Knowlton KU, Bonkowsky JL, Stefansson K, Nadauld LD, McLeod HL. The Development of an Infrastructure to Facilitate the Use of Whole Genome Sequencing for Population Health. J Pers Med 2022; 12:jpm12111867. [PMID: 36579594 PMCID: PMC9693138 DOI: 10.3390/jpm12111867] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/29/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
The clinical use of genomic analysis has expanded rapidly resulting in an increased availability and utility of genomic information in clinical care. We have developed an infrastructure utilizing informatics tools and clinical processes to facilitate the use of whole genome sequencing data for population health management across the healthcare system. Our resulting framework scaled well to multiple clinical domains in both pediatric and adult care, although there were domain specific challenges that arose. Our infrastructure was complementary to existing clinical processes and well-received by care providers and patients. Informatics solutions were critical to the successful deployment and scaling of this program. Implementation of genomics at the scale of population health utilizes complicated technologies and processes that for many health systems are not supported by current information systems or in existing clinical workflows. To scale such a system requires a substantial clinical framework backed by informatics tools to facilitate the flow and management of data. Our work represents an early model that has been successful in scaling to 29 different genes with associated genetic conditions in four clinical domains. Work is ongoing to optimize informatics tools; and to identify best practices for translation to smaller healthcare systems.
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Affiliation(s)
- Nephi A. Walton
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
- Correspondence:
| | - Brent Hafen
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Sara Graceffo
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Nykole Sutherland
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Melanie Emmerson
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Rachel Palmquist
- Department of Pediatrics, University of Utah, Salt Lake City, UT 84108, USA
- Center for Personalized Medicine, Primary Children’s Hospital, Intermountain Healthcare, Salt Lake City, UT 84113, USA
| | - Christine M. Formea
- Department of Pharmacy, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Maricel Purcell
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Bret Heale
- Humanized Health Consulting, Salt Lake City, UT 84102, USA
| | | | | | - Sumathi I. Rachamadugu
- Department of Bioinformatics and Genomics, Pennsylvania State University, University Park, PA 16802, USA
| | - Thomas N. Person
- John Hopkins Genomics—DNA Diagnostics Laboratory, Department of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | | | - G. Bryce Christensen
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Jared M. Evans
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Sharanya Raghunath
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Christopher P. Johnson
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Stacey Knight
- Department of Cardiology, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Viet T. Le
- Department of Cardiology, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Jeffrey L. Anderson
- Department of Cardiology, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Margaret Van Meter
- Department of Medical Oncology, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Teresa Reading
- Department of Surgery, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Derrick S. Haslem
- Department of Cardiology, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Ivy C. Hansen
- School of Medicine, University of Utah, Salt Lake City, UT 84132, USA
| | - Betsey Batcher
- Department of Endocrinology, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Tyler Barker
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Travis J. Sheffield
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Bhaskara Yandava
- Digital Technology Services, Intermountain Healthcare, Salt Lake City, UT 84130, USA
| | - David P. Taylor
- Digital Technology Services, Intermountain Healthcare, Salt Lake City, UT 84130, USA
| | | | - Christopher C. Giauque
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Kenneth R. Eyring
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Jesse W. Breinholt
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Mickey R. Miller
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Payton R. Carter
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Jason L. Gillman
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Andrew W. Gunn
- Center for Personalized Medicine, Primary Children’s Hospital, Intermountain Healthcare, Salt Lake City, UT 84113, USA
| | - Kirk U. Knowlton
- Department of Cardiology, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Joshua L. Bonkowsky
- Department of Pediatrics, University of Utah, Salt Lake City, UT 84108, USA
- Center for Personalized Medicine, Primary Children’s Hospital, Intermountain Healthcare, Salt Lake City, UT 84113, USA
| | | | - Lincoln D. Nadauld
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
| | - Howard L. McLeod
- Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA
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Greenberg S, Buys SS, Edwards SL, Espinel W, Fraser A, Gammon A, Hafen B, Herget KA, Kohlmann W, Roundy C, Sweeney C. Population prevalence of individuals meeting criteria for hereditary breast and ovarian cancer testing. Cancer Med 2019; 8:6789-6798. [PMID: 31531966 PMCID: PMC6825998 DOI: 10.1002/cam4.2534] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 08/13/2019] [Accepted: 08/14/2019] [Indexed: 12/21/2022] Open
Abstract
Background Personal cancer diagnosis and family cancer history factor into which individuals should undergo genetic testing for hereditary breast and ovarian cancer (HBOC) syndrome. Family history is often determined in the research setting through kindreds with disease clusters, or clinically from self‐report. The population prevalence of individuals with diagnostic characteristics and/or family cancer history meeting criteria for HBOC testing is unknown. Methods Utilizing Surveillance, Epidemiology, and End Results (SEER) cancer registry data and a research resource linking registry records to genealogies, the Utah Population Database, the population‐based prevalence of diagnostic and family history characteristics meeting National Comprehensive Cancer Network (NCCN) criteria for HBOC testing was objectively assessed. Results Among Utah residents with an incident breast cancer diagnosis 2010‐2015 and evaluable for family history, 21.6% met criteria for testing based on diagnostic characteristics, but the proportion increased to 62.9% when family history was evaluated. The proportion of cases meeting testing criteria at diagnosis was 94% for ovarian cancer, 23% for prostate cancer, and 51.1% for pancreatic cancer. Among an unaffected Utah population of approximately 1.7 million evaluable for family history, 197,601 or 11.6% met testing criteria based on family history. Conclusions This study quantifies the population‐based prevalence of HBOC criteria using objectively determined genealogy and cancer incidence data. Sporadic breast cancer likely represents a portion of the high prevalence of family cancer history seen in this study. These results underline the importance of establishing presence of a deleterious mutation in an affected family member, per NCCN guidelines, before testing unaffected relatives.
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Affiliation(s)
| | - Saundra S Buys
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah.,Department of Internal Medicine, University of Utah, Salt Lake City, Utah
| | | | - Whitney Espinel
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Alison Fraser
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Amanda Gammon
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Brent Hafen
- Intermountain Healthcare, Salt Lake City, Utah
| | | | - Wendy Kohlmann
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | | | - Carol Sweeney
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah.,Department of Internal Medicine, University of Utah, Salt Lake City, Utah.,Utah Cancer Registry, University of Utah, Salt Lake City, Utah
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Anand-Ivell R, Manson J, Wittert G, Wohlgemuth J, Hafen B, Ivell R. 280. INSL3 is a measure of human Leydig cell functionality both during fetal and adult life. Reprod Fertil Dev 2008. [DOI: 10.1071/srb08abs280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Insulin like factor 3 (INSL3) and testosterone are the two major secretory products of the testis, both produced by the interstitial Leydig cells. The Leydig cells of the testis have two distinct generations, one developing before birth (fetal Leydig cells, FLC) and an adult type (adult Leydig cells, ALC) that become differentiated and functional at puberty. Although these two types of Leydig cells represent distinct populations, rodent studies show that both types produce testosterone and INSL3. Both are presumed to have evolved from a common stem cell pool. We measured INSL3 levels in human amniotic fluids collected at various times of gestation and show for the first time that the human male fetus indeed generates INSL3 at a time appropriate for the first transabdominal phase of testicular descent, which appears to be the primary physiological role for the fetal hormone. INSL3 appears to be independent of androgen production. The adult type Leydig cells (in adult men) secrete INSL3 that can be measured in the peripheral circulation at levels ranging from 0.5 to 2.5 ng/mL. We studied a large randomly recruited cohort of 1183 men from South Australia, comparing serum INSL3 concentrations with age, and a variety of endocrine, cognitive and morphological parameters. INSL3 concentration was observed to decline significantly with age. This however, had no correlation with testosterone or components of the HPG axis. INSL3 is an independent measure of Leydig cell function (quality and number), which appears to be independent of acute control via the HPG axis. Its decline with age reflects a decline in the properties of the Leydig cell population only, and emphasises a gonadal component in the age-related decrease in androgen production.
Research supported by ARC Discovery grant DP0773315.
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