There is growing impetus to include measures of personal utility, the nonmedical value of information, in addition to clinical utility in health technology assessment (HTA) of genomic tests such as genomic sequencing (GS). However, personal utility and clinical utility are challenging to define and measure. This study aimed to explore what drives patients’ preferences for hypothetically learning medically actionable and non-medically actionable secondary findings (SF), capturing clinical and personal utility; this may inform development of measures to evaluate patient outcomes following return of SF. Semi-structured interviews were conducted with adults with a personal or family cancer history participating in a trial of a decision aid for selection of SF from genomic sequencing (GS) (www.GenomicsADvISER.com). Interviews were analyzed thematically using constant comparison. Preserving health-related and non-health-related quality of life was an overarching motivator for both learning and not learning SF. Some participants perceived that learning SF would help them “have a good quality of life” through informing actions to maintain physical health or leading to psychological benefits such as emotional preparation for disease. Other participants preferred not to learn SF because results “could ruin your quality of life,” such as by causing negative psychological impacts. Measuring health-related and non-health-related quality of life may capture outcomes related to clinical and personal utility of GS and SF, which have previously been challenging to measure. Without appropriate measures, generating and synthesizing evidence to evaluate genomic technologies such as GS will continue to be a challenge, and will undervalue potential benefits of GS and SF.
Subscribe to Journal
Get full journal access for 1 year
only $41.58 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Kalia SS, Adelman K, Bale SJ, Chung WK, Eng C, Evans JP, et al. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics. Genet Med. 2017;19:249–55.
Wilson BJ, Miller FA, Rousseau F. Controversy and debate on clinical genomics sequencing-paper 1: genomics is not exceptional: rigorous evaluations are necessary for clinical applications of genomic sequencing. J Clin Epidemiol. 2017;92:4–6.
Phillips KA, Deverka PA, Sox HC, Khoury MJ, Sandy LG, Ginsburg GS, et al. Making genomic medicine evidence-based and patient-centered: a structured review and landscape analysis of comparative effectiveness research. Genet Med. 2017;19:1081–91.
Ungar W. Next generation sequencing and health technology assessment in autism spectrum disorder. J Can Acad Child Adolesc Psychiatry. 2015;24:123–7.
Bombard Y, Bach PB, Offit K. Translating genomics in cancer care. J Natl Compr Cancer Netw. 2013;11:1343–53.
Goodman CS. HTA 101: introduction to health technology assessment. Bethesda, MD: National Library of Medicine; 2014.
Garfield S, Polisena J, S Spinner D, Postulka A, Y Lu C, Tiwana SK, et al. Health technology assessment for molecular diagnostics: practices, challenges, and recommendations from the medical devices and diagnostics special interest group. Value Health. 2016;19:577–87.
Hamilton JG, Edwards HM, Khoury MJ, Taplin SH. Cancer screening and genetics: a tale of two paradigms. Cancer Epidemiol Biomark Prev. 2014;23:909–16.
Pitini E, De Vito C, Marzuillo C, D’Andrea E, Rosso A, Federici A, et al. How is genetic testing evaluated? A systematic review of the literature. Eur J Hum Genet. 2018;26:605–15.
Botkin JR, Teutsch SM, Kaye CI, Hayes M, Haddow JE, Bradley LA, et al. Outcomes of interest in evidence-based evaluations of genetic tests. Genet Med. 2010;12:228–35.
Veenstra DL, Piper M, Haddow JE, Pauker SG, Klein R, Richards CS, et al. Improving the efficiency and relevance of evidence-based recommendations in the era of whole-genome sequencing: an EGAPP methods update. Genet Med. 2013;15:14–24.
Mighton C, Carlsson L, Clausen M, Casalino S, Shickh S, McCuaig L, et al. Development of patient “profiles” to tailor counseling for incidental genomic sequencing results. Eur J Hum Genet. 2019;27:1008–17.
Kohler JN, Turbitt E, Lewis KL, Wilfond BS, Jamal L, Peay HL, et al. Defining personal utility in genomics: a Delphi study. Clin Genet. 2017;92:290–7.
Lupo PJ, Robinson JO, Diamond PM, Jamal L, Danysh HE, Blumenthal-Barby J, et al. Patients’ perceived utility of whole-genome sequencing for their healthcare: findings from the MedSeq project. Per Med. 2016;13:13–20.
Kohler JN, Turbitt E, Biesecker BB. Personal utility in genomic testing: a systematic literature review. Eur J Hum Genet. 2017;25:662–8.
Morse J, Mitcham C, Hupcey J, Tason M. Criteria for concept evaluation. J Adv Nurs. 1996;24:385–90.
Morse J, Field P. Qualitative research methods for health professionals. Thousand Oaks, CA: Sage Publications; 1995.
Strauss AL, Corbin J. Basics of qualitative research: grounded theory procedures and techniques. Newbury Park, CA: Sage; 1990.
Charmaz KC. Constructing grounded theory: a practical guide through qualitative analysis. London: SAGE Publications Ltd.; 2006.
Shickh S, Clausen M, Mighton C, Casalino S, Joshi E, Glogowski E, et al. Evaluation of a decision aid for incidental genomic results, the Genomics ADvISER: protocol for a mixed methods randomised controlled trial. BMJ Open. 2018;8:e021876.
Bombard Y, Clausen M, Shickh S, Mighton C, Casalino S, Kim THM, et al. Effectiveness of the Genomics ADvISER decision aid for the selection of secondary findings from genomic sequencing: a randomized clinical trial. Genet Med. 2020;22:727–35.
Berg JS, Khoury MJ, Evans JP. Deploying whole genome sequencing in clinical practice and public health: meeting the challenge one bin at a time. Genet Med. 2011;13:499–504.
Coyne IT. Sampling in qualitative research. Purposeful and theoretical sampling; merging or clear boundaries? J Adv Nurs. 1997;26:623–30.
Morse J. The significance of saturation. Qual Health Res. 1995;5:147–9.
Saunders B, Sim J, Kingstone T, Baker S, Waterfield J, Bartlam B, et al. Saturation in qualitative research: exploring its conceptualization and operationalization. Qual Quant. 2018;52:1893–907.
D’Andrea E, Lagerberg T, De Vito C, Pitini E, Marzuillo C, Massimi A, et al. Patient experience and utility of genetic information: a cross-sectional study among patients tested for cancer susceptibility and thrombophilia. Eur J Hum Genet. 2018;26:518–26.
Kaphingst KA, Ivanovich J, Biesecker BB, Dresser R, Seo J, Dressler LG, et al. Preferences for return of incidental findings from genome sequencing among women diagnosed with breast cancer at a young age. Clin Genet. 2016;89:378–84.
Wright MF, Lewis KL, Fisher TC, Hooker GW, Emanuel TE, Biesecker LG, et al. Preferences for results delivery from exome sequencing/genome sequencing. Genet Med. 2014;16:442–7.
Hamilton JG, Shuk E, Genoff MC, Rodriguez VM, Hay JL, Offit K, et al. Interest and attitudes of patients with advanced cancer with regard to secondary germline findings from tumor genomic profiling. J Oncol Pract. 2017;13:e590–601.
Turrini M, Prainsack B. Beyond clinical utility: the multiple values of DTC genetics. Appl Transl Genom. 2016;8:4–8.
Skevington SM, Lotfy M, O’Connell KA. The World Health Organization’s WHOQOL-BREF quality of life assessment: psychometric properties and results of the international field trial. A report from the WHOQOL group. Qual Life Res. 2004;13:299–310.
Ferrans CE, Powers MJ. Psychometric assessment of the Quality of Life Index. Res Nurs Health. 1992;15:29–38.
Grosse SD, Wordsworth S, Payne K. Economic methods for valuing the outcomes of genetic testing: beyond cost-effectiveness analysis. Genet Med. 2008;10:648–54.
Rogowski WH, Grosse SD, John J, Kääriäinen H, Kent A, Kristofferson U, et al. Points to consider in assessing and appraising predictive genetic tests. J Community Genet. 2010;1:185–94.
Rapley M. Quality of life research. London: SAGE; 2003.
Joseph L, Cankovic M, Caughron S, et al. The spectrum of clinical utilities in molecular pathology testing procedures for inherited conditions and cancer: a report of the association for molecular pathology. J Mol Diagn. 2016;18:605–19.
We would like to thank the following individuals for supporting this study: Carolyn Piccinin, Laura Winter-Paquette, Melyssa Aronson, Talia Mancuso, Justin Lorentz, Tracy Graham, Yael Silberman, Rochelle Demsky, Alexandra Volenik, Jeanna McCuaig, Oana Morar, Leslie Ordal, and Nicholas Watkins. We would like to thank Theresa H.M. Kim for her contributions to the statistical analysis and recruitment in the RCT. This research was supported by grants from the Canadian Institutes of Health Research (CIHR) and the University of Toronto McLaughlin Center awarded to YB (#333703 and MC-2016-04 respectively). YB was supported by a CIHR New Investigator Award during this study. CM received support from the Research Training Center at St. Michael’s Hospital, the Canadian Institutes of Health Research (#160968, GSD-164222) and a studentship funded by the Canadian Center for Applied Research in Cancer Control (ARCC). ARCC receives core funding from the Canadian Cancer Society (Grant #2015-703549). Finally, we would like to thank our interview participants for their time and valuable insights.
Incidental Genomics Study Team
Yvonne Bombard (PI),1,2 Susan Randall Armel,4,13 Melyssa Aronson,6,13 Nancy Baxter,1,2,14 Ken Bond,15 José-Mario Capo-Chichi,4,8 June C. Carroll,6,9 Timothy Caulfield,16,17,18 Marc Clausen,2 Tammy J. Clifford,19 Iris Cohn,20 Irfan Dhalla,1,2,5,21,22 Craig C. Earle,7,22,23 Andrea Eisen,7 Christine Elser,4,5,6 Mike Evans,2 Emily Glogowski,10 Tracy Graham,7 Jada G. Hamilton,24 Wanrudee Isaranuwatchai,1,2 Monika Kastner,1,2,9 Raymond H. Kim,4,5,6 Andreas Laupacis,1,2 Jordan Lerner-Ellis,6,8 Chantal F. Morel,4 Michelle Mujoomdar,25 Kenneth Offit,24 Seema Panchal,6 Mark Robson,24 Stephen W. Scherer,5,13,20 Adena Scheer,2,14 Kasmintan Schrader,11,12 Terrence Sullivan1 and Kevin E. Thorpe26,27
YB received funding for this study from Canadian Institutes of Health Research (CIHR, #333703), the University of Toronto McLaughlin Center (MC-2016-04) and a CIHR New Investigator Award. CM received support from the Research Training Center at St. Michael’s Hospital, CIHR (FRN #160968, FRN GSD-164222) and a studentship from the Canadian Center for Applied Research in Cancer Control (ARCC) which receives core funding from the Canadian Cancer Society (Grant #2015-703549).
Conflict of interest
The authors declare that they have no conflict of interest.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Members of the Incidental Genomics Study Team are listed below acknowledgements.
About this article
Cite this article
Mighton, C., Carlsson, L., Clausen, M. et al. Quality of life drives patients’ preferences for secondary findings from genomic sequencing. Eur J Hum Genet 28, 1178–1186 (2020). https://doi.org/10.1038/s41431-020-0640-x