Correspondence

Polygenes, Risk Prediction, and Targeted Prevention of Breast Cancer

N Engl J Med 2008; 359:1406-1407September 25, 2008

Article

To the Editor:

In their discussion of polygenics and breast cancer, Pharoah et al. (June 26 issue)1 do not mention cost-effectiveness. If we take 3% for the reduction in total disease burden estimated by Pharoah et al. and apply it to the calculated 0.6-year increase in life expectancy if all breast cancers were eliminated,2 current polygenic tests would increase life expectancy by just 1 week for the whole female population.

Although the gain is modest, the relatively low cost of implementing the polygenic approach makes it attractive when one considers the estimated additional quality-adjusted life years (QALYs). Even a comprehensive genomewide scan incorporating 500,000 markers now costs only $1,000,3 so using the figures for health-adjusted life expectancy,2 one arrives at a cost of $67,000 per additional QALY. This compares favorably with other approaches to improving survival among patients with breast cancer4 and is certain to become more favorable as the cost of genome scanning plummets, more risk genes are documented, and many medical professionals use genome scans.

Richard J. Wilkins, Ph.D.
University of Waikato, Hamilton 3240, New Zealand
d.wilkins@waikato.ac.nz

4 References

1. 1

   Pharoah PD, Antoniou AC, Easton DF, Ponder BA. Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med 2008;358:2796-2803
   Full Text | Web of Science | Medline

2. 2

   Manuel DG, Luo W, Ugnat A-M, Mao Y. Cause-deleted health-adjusted life expectancy of Canadians with selected chronic conditions. Chronic Dis Can 2003;24:108-115
   Medline

3. 3

   Feero WG, Guttmacher AE, Collins FS. The genome gets personal -- almost. JAMA 2008;299:1351-1352
   CrossRef | Web of Science | Medline

4. 4

   Stout NK, Rosenberg MA, Trentham-Dietz A, Smith MA, Robinson SM, Fryback DG. Retrospective cost-effectiveness analysis of screening mammography. J Natl Cancer Inst 2006;98:774-782
   CrossRef | Web of Science | Medline

To the Editor:

Pharoah et al. concluded “that genetic risk profiles would improve population-based programs of intervention for breast cancer.” They assumed that there are two copies of each locus in the genome and that the risk conferred by seven breast-cancer susceptibility alleles is allele-dose–dependent. They also estimated the relative risk of breast cancer using a multiplicative model for interaction among seven common susceptibility alleles. However, they did not take into account the effect of copy-number variations. Moreover, three of seven alleles are located in regions of copy-number variations (http://projects.tcag.ca/variation/); for two of them, losses of copy in HapMap controls have been identified. Therefore, the number of possible combinations is higher than estimated, and the relative risk should be reassessed. Copy-number variations have been shown to be associated with common disorders1 and to be responsible for variation in gene expression.2 We would like to emphasize the difficulty of stratifying people according to genetic risk and the complexity of integrating different types of genetic variations in a statistical model at present.

Luis Teixeira, M.D.
Hôpital Saint-Louis, 75010 Paris, France
luis.teixeira@inserm.fr

Cedric Julien, Ph.D.
INSERM Unité 567, 75014 Paris, France

Fabien Guimiot, Ph.D.
Hôpital Robert Debré, 75019 Paris, France

2 References

1. 1

   Estivill X, Armengol L. Copy number variants and common disorders: filling the gaps and exploring complexity in genome-wide association studies. PLoS Genet 2007;3:1787-1799
   CrossRef | Web of Science | Medline

2. 2

   Stranger BE, Forrest MS, Dunning M, et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science 2007;315:848-853
   CrossRef | Web of Science | Medline

To the Editor:

Pharoah et al. report on seven polymorphic markers for breast-cancer risk replicated in genomewide-association studies. Although it is well known that cases of premenopausal and postmenopausal breast cancer have different risk factors, these cases were analyzed as one group. Polymorphisms may alter gene expression, but expression is also regulated by environmental factors, which may lead to epigenetic changes.1 The magnitude of risk modification may be miscalculated when premenopausal and postmenopausal patients are combined and no questionnaire data on relevant environmental risk factors are considered. For example, the protective effect of caffeinated coffee on hereditary and sporadic breast cancer appears to be substantial but limited to women with the CYP1A2*1F C allele.2,3 CYP1A2 is a key enzyme in caffeine and estrogen metabolism.4 It is biologically plausible that various combinations of genetic and nongenetic factors (e.g., coffee) that regulate CYP1A2 expression5 affect risk differently. Combining data from questionnaires and genomewide-association studies in which premenopausal and postmenopausal patients are stratified may yield better risk estimates for the selection of women for screening.

Helena Jernström, Ph.D.
Lund University, SE-221 85 Lund, Sweden
helena.jernstrom@med.lu.se

5 References

1. 1

   Waterland RA, Michels KB. Epigenetic epidemiology of the developmental origins hypothesis. Annu Rev Nutr 2007;27:363-388
   CrossRef | Web of Science | Medline

2. 2

   Kotsopoulos J, Ghadirian P, El-Sohemy A, et al. The CYP1A2 genotype modifies the association between coffee consumption and breast cancer risk among BRCA1 mutation carriers. Cancer Epidemiol Biomarkers Prev 2007;16:912-916
   CrossRef | Web of Science | Medline

3. 3

   Bageman E, Ingvar C, Rose C, Jernstrom H. Coffee consumption and CYP1A2*1F genotype modify age at breast cancer diagnosis and estrogen receptor status. Cancer Epidemiol Biomarkers Prev 2008;17:895-901
   CrossRef | Web of Science | Medline

4. 4

   Lee AJ, Cai MX, Thomas PE, Conney AH, Zhu BT. Characterization of the oxidative metabolites of 17beta-estradiol and estrone formed by 15 selectively expressed human cytochrome p450 isoforms. Endocrinology 2003;144:3382-3398
   CrossRef | Web of Science | Medline

5. 5

   Djordjevic N, Ghotbi R, Bertilsson L, Jankovic S, Aklillu E. Induction of CYP1A2 by heavy coffee consumption in Serbs and Swedes. Eur J Clin Pharmacol 2007;64:381-385
   CrossRef | Web of Science | Medline

Citing Articles (1)

Citing Articles

1. 1

   Ke-Da Yu, Qi Fang, Zhi-Ming Shao. (2011) Combining accurate genetic and clinical information in breast cancer risk model. Breast Cancer Research and Treatment 128:1, 283-285
   CrossRef