Genetic factors associated with prostate cancer conversion from active surveillance to treatment

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Summary

Men diagnosed with low-risk prostate cancer (PC) are increasingly electing active surveillance (AS) as their initial management strategy. While this may reduce the side effects of treatment for PC, many men on AS eventually convert to active treatment. PC is one of the most heritable cancers, and genetic factors that predispose to aggressive tumors may help distinguish men who are more likely to discontinue AS. To investigate this, we undertook a multi-institutional genome-wide association study (GWAS) of 5,222 PC patients and 1,139 other patients from replication cohorts, all of whom initially elected AS and were followed over time for the potential outcome of conversion from AS to active treatment. In the GWAS we detected 18 variants associated with conversion, 15 of which were not previously associated with PC risk. With a transcriptome-wide association study (TWAS), we found two genes associated with conversion ( MAST3, p = 6.9 × 10 ⁻⁷ and GAB2, p = 2.0 × 10 ⁻⁶). Moreover, increasing values of a previously validated 269-variant genetic risk score (GRS) for PC was positively associated with conversion (e.g., comparing the highest to the two middle deciles gave a hazard ratio [HR] = 1.13; 95% confidence interval [CI] = 0.94–1.36); whereas decreasing values of a 36-variant GRS for prostate-specific antigen (PSA) levels were positively associated with conversion (e.g., comparing the lowest to the two middle deciles gave a HR = 1.25; 95% CI, 1.04–1.50). These results suggest that germline genetics may help inform and individualize the decision of AS—or the intensity of monitoring on AS—versus treatment for the initial management of patients with low-risk PC.

Abstract

Genetic factors may distinguish who should receive active surveillance (AS) versus treatment following prostate cancer diagnosis. We undertook the first study to investigate this and report novel variants, genes, and risk scores associated with AS outcomes. These findings could help inform and individualize the decision of AS.

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Most cited references 83

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Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

Paul A. Harris, Robert Taylor, Robert Thielke … (2009)

Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.

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The REDCap consortium: Building an international community of software platform partners

Paul Harris, Robert Taylor, Brenda Minor … (2019)

The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.

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Fast model-based estimation of ancestry in unrelated individuals.

David H. Alexander, John Novembre, Kenneth Lange (2009)

Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.

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Author and article information

Contributors

John S. Witte

Journal

Journal ID (nlm-ta): HGG Adv

Journal ID (iso-abbrev): HGG Adv

Title: Human Genetics and Genomics Advances

Publisher: Elsevier

ISSN (Electronic): 2666-2477

Publication date PMC-release: 19 November 2021

Publication date Collection: 13 January 2022

Publication date (Electronic): 19 November 2021

Volume: 3

Issue: 1

Electronic Location Identifier: 100070

Affiliations

[1 ]Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA

[2 ]Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA

[3 ]Division of Biostatistics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA

[4 ]Fred Hutchinson Cancer Research Center, Cancer Prevention Program, Public Health Sciences, Seattle, WA 98109, USA

[5 ]Department of Urology, University of Washington, Seattle, WA 98195, USA

[6 ]Fred Hutchinson Cancer Research Center, Cancer Epidemiology Program, Public Health Sciences, Seattle, WA 98109, USA

[7 ]Department of Epidemiology, University of Washington, School of Public Health, Seattle, WA 98195, USA

[8 ]Division of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada

[9 ]Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA

[10 ]Departments of Genitourinary Medical Oncology and Urology, University of Texas MD Anderson Cancer Center, Houston, TX, USA

[11 ]Department of Urology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

[12 ]Odette Cancer Centre, Sunnybrook Health and Sciences Centre, University of Toronto, Toronto, ON, Canada

[13 ]Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada

[14 ]Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA

[15 ]Department of Urology, University of California, San Francisco, San Francisco, CA, USA

[16 ]Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA

[17 ]Department of Urology, University of Michigan, Ann Arbor, MI, USA

[18 ]Department of Pathology, University of Michigan, Ann Arbor, MI, USA

[19 ]Glickman Urological and Kidney Institute, Cleveland Clinic Lerner College of Medicine, Cleveland Clinic, Cleveland, OH, USA

[20 ]Department of Urology, Vanderbilt University Medical Center, Nashville, TN, USA

[21 ]Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CA, USA

[22 ]Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA

[23 ]Department of Urology, Erasmus Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands

[24 ]Department of Urology, Emory University School of Medicine, Atlanta, GA, USA

[25 ]Department of Urologic Surgery, University of California, Davis Medical Center, Sacramento, CA, USA

[26 ]Department of Urology, Royal Melbourne Hospital and University of Melbourne, Melbourne, VIC, Australia

[27 ]Department of Urology, University of Alabama at Birmingham, Birmingham, AL, USA

[28 ]Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA

[29 ]Department of Urology, Weill Cornell Medicine, New York-Presbyterian Hospital, New York, NY, USA

[30 ]Mayo Clinic Department of Urology, Rochester, MN, USA

[31 ]Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA

[32 ]Department of Urology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

[33 ]Department of Urology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA

[34 ]University of Chicago Comprehensive Cancer Center, Chicago, IL, USA

[35 ]Urological Research Network, Miami Lakes, FL, USA

[36 ]Departments of Urology and Population Health, New York University Langone Health and Manhattan Veterans Affairs Medical Center, New York, NY, USA

[37 ]Department of Urology, Oregon Health and Science University, Portland, OR, USA

[38 ]Genesis Healthcare Partners, Department of Urology, University of California, San Diego, CA, USA

[39 ]Division of Urology, NorthShore University Health System, Evanston, IL, USA

[40 ]Departments of Epidemiology and Population Health, Biomedical Data Science, and Genetics, Stanford University, Stanford, CA, USA

Author notes

[∗ ]Corresponding author jswitte@ 123456stanford.edu

[41]

These authors contributed equally

[42]

These authors contributed equally

Article

Publisher Item ID: S2666-2477(21)00051-8 Publisher ID: 100070

DOI: 10.1016/j.xhgg.2021.100070

PMC ID: 8725988

PubMed ID: 34993496

SO-VID: bfbcf0d8-99f8-483e-b665-99ed26dc1670

License:

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Genetic factors associated with prostate cancer conversion from active surveillance to treatment

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Most cited references 83

Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

The REDCap consortium: Building an international community of software platform partners

Fast model-based estimation of ancestry in unrelated individuals.

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