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Original Investigation
April 21, 2022

Association of Pathogenic Variants in Hereditary Cancer Genes With Multiple Diseases

Author Affiliations
  • 1National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
  • 2Center for Precision Medicine, Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
  • 3Department of Biostatistics, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, Tennessee
  • 4Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas
  • 5Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, Wisconsin
  • 6Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
  • 7Clinical and Translational Hereditary Cancer Program, Division of Genetic Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee
  • 8Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle
  • 9School of Graduate Studies and Research, Meharry Medical College, Nashville, Tennessee
  • 10Department of Microbiology, Immunology and Physiology, Meharry Medical College, Nashville, Tennessee
  • 11Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
  • 12PRecisiOn Medicine Translational Research (PROMoTeR) Center, Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts
  • 13Department of Population Medicine, Harvard Medical School, Boston, Massachusetts
  • 14Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
  • 15Centre Universitaire de Santé McGill, McGill University Health Centre, Montreal, Quebec, Canada
  • 16Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, Massachusetts
  • 17Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston
  • 18Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York
  • 19Genetic Services and Kaiser Permanente Washington Health Research Institute, Kaiser Permanente of Washington, Seattle
  • 20Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
  • 21Division of Human Genetics, Department of Pediatrics, The University of Pennsylvania Perelman School of Medicine, Philadelphia
  • 22Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
  • 23Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
  • 24Department of Medicine (Medical Genetics), University of Washington, Seattle
  • 25Department of Genome Sciences, University of Washington, Seattle
  • 26Brigham and Women’s Hospital, Broad Institute, Ariadne Labs and Harvard Medical School, Boston, Massachusetts
  • 27Department of Pediatrics, Columbia University, New York, New York
  • 28Department of Medicine, Columbia University, New York, New York
  • 29Division of Nephrology, Department of Medicine, Columbia University Irving Medical Center, New York, New York
  • 30Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York, New York
  • 31Broad Institute of MIT and Harvard, Cambridge, Massachusetts
  • 32Medical & Population Genetics Program and Genomics Platform, Broad Institute of MIT and Harvard Cambridge, Cambridge, Massachusetts
  • 33Center for Genomic Medicine, Massachusetts General Hospital, Boston
  • 34Department of Pathology, Harvard Medical School, Boston, Massachusetts
  • 35Divisions of Cardiovascular Medicine and Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
  • 36Department of Pharmacology, Vanderbilt University, Nashville, Tennessee
JAMA Oncol. 2022;8(6):835-844. doi:10.1001/jamaoncol.2022.0373
Key Points

Question  What is the range of conditions associated with hereditary cancer genes?

Findings  This phenome-wide association study used genetic and phenotypic data derived from health-related data from electronic health records in 3 cohorts comprising 214 020 participants to identify 19 new diseases and conditions associated with pathogenic variants in 13 hereditary cancer genes. These new phenotypes included both neoplastic and nonneoplastic diseases.

Meaning  These findings contribute to recognition and understanding of the full clinical spectrum of hereditary cancer syndromes, which can facilitate early detection of cancers and better management.

Abstract

Importance  Knowledge about the spectrum of diseases associated with hereditary cancer syndromes may improve disease diagnosis and management for patients and help to identify high-risk individuals.

Objective  To identify phenotypes associated with hereditary cancer genes through a phenome-wide association study.

Design, Setting, and Participants  This phenome-wide association study used health data from participants in 3 cohorts. The Electronic Medical Records and Genomics Sequencing (eMERGEseq) data set recruited predominantly healthy individuals from 10 US medical centers from July 16, 2016, through February 18, 2018, with a mean follow-up through electronic health records (EHRs) of 12.7 (7.4) years. The UK Biobank (UKB) cohort recruited participants from March 15, 2006, through August 1, 2010, with a mean (SD) follow-up of 12.4 (1.0) years. The Hereditary Cancer Registry (HCR) recruited patients undergoing clinical genetic testing at Vanderbilt University Medical Center from May 1, 2012, through December 31, 2019, with a mean (SD) follow-up through EHRs of 8.8 (6.5) years.

Exposures  Germline variants in 23 hereditary cancer genes. Pathogenic and likely pathogenic variants for each gene were aggregated for association analyses.

Main Outcomes and Measures  Phenotypes in the eMERGEseq and HCR cohorts were derived from the linked EHRs. Phenotypes in UKB were from multiple sources of health-related data.

Results  A total of 214 020 participants were identified, including 23 544 in eMERGEseq cohort (mean [SD] age, 47.8 [23.7] years; 12 611 women [53.6%]), 187 234 in the UKB cohort (mean [SD] age, 56.7 [8.1] years; 104 055 [55.6%] women), and 3242 in the HCR cohort (mean [SD] age, 52.5 [15.5] years; 2851 [87.9%] women). All 38 established gene-cancer associations were replicated, and 19 new associations were identified. These included the following 7 associations with neoplasms: CHEK2 with leukemia (odds ratio [OR], 3.81 [95% CI, 2.64-5.48]) and plasma cell neoplasms (OR, 3.12 [95% CI, 1.84-5.28]), ATM with gastric cancer (OR, 4.27 [95% CI, 2.35-7.44]) and pancreatic cancer (OR, 4.44 [95% CI, 2.66-7.40]), MUTYH (biallelic) with kidney cancer (OR, 32.28 [95% CI, 6.40-162.73]), MSH6 with bladder cancer (OR, 5.63 [95% CI, 2.75-11.49]), and APC with benign liver/intrahepatic bile duct tumors (OR, 52.01 [95% CI, 14.29-189.29]). The remaining 12 associations with nonneoplastic diseases included BRCA1/2 with ovarian cysts (OR, 3.15 [95% CI, 2.22-4.46] and 3.12 [95% CI, 2.36-4.12], respectively), MEN1 with acute pancreatitis (OR, 33.45 [95% CI, 9.25-121.02]), APC with gastritis and duodenitis (OR, 4.66 [95% CI, 2.61-8.33]), and PTEN with chronic gastritis (OR, 15.68 [95% CI, 6.01-40.92]).

Conclusions and Relevance  The findings of this genetic association study analyzing the EHRs of 3 large cohorts suggest that these new phenotypes associated with hereditary cancer genes may facilitate early detection and better management of cancers. This study highlights the potential benefits of using EHR data in genomic medicine.

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1 Comment for this article
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Risk estimation
Saeed Taheri, M.D. | NLMJ; Lahijan
There is likely some uncertainty in the risk estimations in this extensive study. For example in etable 9, the OR for breast cancer was statistically comparable between carriers of BRCA2 & BRCA1, but we expect the latter to be higher. One reason, as the authors well quoted about, is associated with the referral of patients with specific indications for genetic analyses, which authors tried to adjust the analyses for. However another factor is related to the phenotype expression of any pathogenic variant. As mentioned in etable 2, almost all the cohorts started from the infancy or early childhood, while for most of the cancers, we don't expect them to be expressed until more advanced ages (as is known for the BRCA2 to induce breast cancer at relatively higher ages than BRCA1 [ref]). However the follow up time is limited; one solution to this problem could be analyzing the cumulative odds ratios of cancer diagnosis for carrier states at different age points (or conducting a time (age) dependent analysis). For example, carrying a special variant, what would be the risk of a person to develop a particular cancer at the age of 40, 50, 60, and etc. It would have also been nice if authors could make analyses on the 'likely-pathogenic' and variants of 'uncertain significance' to see if they can confirm pathogenicity of any of them for future definition purposes.

ref:
Momozawa Y, et al. Expansion of Cancer Risk Profile for BRCA1 and BRCA2 Pathogenic Variants. JAMA Oncol. 2022 Jun 1;8(6):871-878. doi: 10.1001/jamaoncol.2022.0476. PMID: 35420638; PMCID: PMC9011177.
CONFLICT OF INTEREST: None Reported
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