Common genetic and clinical risk factors: association with fatal prostate cancer in the Cohort of Swedish Men.


Journal

Prostate cancer and prostatic diseases
ISSN: 1476-5608
Titre abrégé: Prostate Cancer Prostatic Dis
Pays: England
ID NLM: 9815755

Informations de publication

Date de publication:
09 2021
Historique:
received: 28 09 2020
accepted: 18 02 2021
revised: 31 01 2021
pubmed: 17 3 2021
medline: 2 2 2022
entrez: 16 3 2021
Statut: ppublish

Résumé

Clinical variables-age, family history, genetics-are used for prostate cancer risk stratification. Recently, polygenic hazard scores (PHS46, PHS166) were validated as associated with age at prostate cancer diagnosis. While polygenic scores are associated with all prostate cancer (not specific for fatal cancers), PHS46 was also associated with age at prostate cancer death. We evaluated if adding PHS to clinical variables improves associations with prostate cancer death. Genotype/phenotype data were obtained from a nested case-control Cohort of Swedish Men (n = 3279; 2163 with prostate cancer, 278 prostate cancer deaths). PHS and clinical variables (family history, alcohol intake, smoking, heart disease, hypertension, diabetes, body mass index) were tested via univariable Cox proportional hazards models for association with age at prostate cancer death. Multivariable Cox models with/without PHS were compared with log-likelihood tests. Median age at last follow-up/prostate cancer death was 78.0 (IQR: 72.3-84.1) and 81.4 (75.4-86.3) years, respectively. On univariable analysis, PHS46 (HR 3.41 [95% CI 2.78-4.17]), family history (HR 1.72 [1.46-2.03]), alcohol (HR 1.74 [1.40-2.15]), diabetes (HR 0.53 [0.37-0.75]) were each associated with prostate cancer death. On multivariable analysis, PHS46 (HR 2.45 [1.99-2.97]), family history (HR 1.73 [1.48-2.03]), alcohol (HR 1.45 [1.19-1.76]), diabetes (HR 0.62 [0.42-0.90]) all remained associated with fatal disease. Including PHS46 or PHS166 improved multivariable models for fatal prostate cancer (p < 10 PHS had the most robust association with fatal prostate cancer in a multivariable model with common risk factors, including family history. Adding PHS to clinical variables may improve prostate cancer risk stratification strategies.

Sections du résumé

BACKGROUND
Clinical variables-age, family history, genetics-are used for prostate cancer risk stratification. Recently, polygenic hazard scores (PHS46, PHS166) were validated as associated with age at prostate cancer diagnosis. While polygenic scores are associated with all prostate cancer (not specific for fatal cancers), PHS46 was also associated with age at prostate cancer death. We evaluated if adding PHS to clinical variables improves associations with prostate cancer death.
METHODS
Genotype/phenotype data were obtained from a nested case-control Cohort of Swedish Men (n = 3279; 2163 with prostate cancer, 278 prostate cancer deaths). PHS and clinical variables (family history, alcohol intake, smoking, heart disease, hypertension, diabetes, body mass index) were tested via univariable Cox proportional hazards models for association with age at prostate cancer death. Multivariable Cox models with/without PHS were compared with log-likelihood tests.
RESULTS
Median age at last follow-up/prostate cancer death was 78.0 (IQR: 72.3-84.1) and 81.4 (75.4-86.3) years, respectively. On univariable analysis, PHS46 (HR 3.41 [95% CI 2.78-4.17]), family history (HR 1.72 [1.46-2.03]), alcohol (HR 1.74 [1.40-2.15]), diabetes (HR 0.53 [0.37-0.75]) were each associated with prostate cancer death. On multivariable analysis, PHS46 (HR 2.45 [1.99-2.97]), family history (HR 1.73 [1.48-2.03]), alcohol (HR 1.45 [1.19-1.76]), diabetes (HR 0.62 [0.42-0.90]) all remained associated with fatal disease. Including PHS46 or PHS166 improved multivariable models for fatal prostate cancer (p < 10
CONCLUSIONS
PHS had the most robust association with fatal prostate cancer in a multivariable model with common risk factors, including family history. Adding PHS to clinical variables may improve prostate cancer risk stratification strategies.

Identifiants

pubmed: 33723363
doi: 10.1038/s41391-021-00341-4
pii: 10.1038/s41391-021-00341-4
pmc: PMC8387332
mid: NIHMS1675456
doi:

Substances chimiques

Biomarkers, Tumor 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

845-851

Subventions

Organisme : NCI NIH HHS
ID : L30 CA231417
Pays : United States
Organisme : NIBIB NIH HHS
ID : K08 EB026503
Pays : United States

Informations de copyright

© 2021. The Author(s), under exclusive licence to Springer Nature Limited.

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Auteurs

Minh-Phuong Huynh-Le (MP)

Division of Radiation Oncology, George Washington University, Washington, DC, USA.

Roshan Karunamuni (R)

Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.
Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA.

Chun Chieh Fan (CC)

Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA.

Wesley K Thompson (WK)

Division of Biostatistics and Halicioğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA.
Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA.

Kenneth Muir (K)

Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK.
Warwick Medical School, University of Warwick, Coventry, UK.

Artitaya Lophatananon (A)

Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK.

Karen Tye (K)

Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.

Alicja Wolk (A)

Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.

Niclas Håkansson (N)

Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.

Ian G Mills (IG)

Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.

Ole A Andreassen (OA)

NORMENT, KG Jebsen Centre, Oslo University Hospital and University of Oslo, Oslo, Norway.

Anders M Dale (AM)

Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA.
Department of Radiology, University of California San Diego, La Jolla, CA, USA.

Tyler M Seibert (TM)

Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA. tseibert@ucsd.edu.
Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA, USA. tseibert@ucsd.edu.
Department of Radiology, University of California San Diego, La Jolla, CA, USA. tseibert@ucsd.edu.
Department of Bioengineering, University of California San Diego, La Jolla, CA, USA. tseibert@ucsd.edu.

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