The Application of Genetics and Genomics to Cancer Prevention

The Application of Genetics and Genomics to Cancer Prevention Kathleen Calzone,a,b Daniel Wattendorf,b and Barbara K. Dunnc Advances in technology have accelerated the translation of genetics and genomics into the arena of cancer prevention. This provides unique opportunities to individualize cancer risk prediction so early intervention can either modify risk or allow for early diagnosis thereby potentially decreasing the morbidity and mortality of cancer and containing costs. While the full potential of these genetic/genomic discoveries have yet to be realized, many have clear clinical relevance such as the value of family history and/or tumor profiling to identify those who may harbor a mutation in a cancer susceptibility gene and are therefore candidates for genetic testing. Here, we provide an overview of the scope of genetic and genomic influences on cancer risk assessment and the entire spectrum of cancer prevention. Semin Oncol 37:407-418. Published by Elsevier Inc.

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he elucidation of the inherited and acquired genetic and genomic changes associated with cancer susceptibility have improved the identification of at-risk individuals, established risk-reducing interventions, enhanced existing screening, and influenced treatment and dosing, as well as optimal agent selection based on the genetic variation of drug metabolism. As of February 2010, GeneTests reported more than 600 laboratories testing for more than 1,890 diseases, of which greater than 1,600 are clinically available tests, including a wide range of tests associated with cancer susceptibility.1 These discoveries are being translated into clinical practice at an unprecedented rate and provide increasingly personalized cancer risk information that can be used for optimal cancer risk management decision-making. In addition to the expansion of available tests, there is an ongoing proliferation of direct to consumer marketing and genetic testing, with 39 companies offering more than 40 separate tests, including those associated aGenetics

Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD. bOffice of the Air Force Surgeon General, Falls Church, VA. cBasic Prevention Science Research Group, Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD. The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Air Force, the Department of Defense, or the US Government. Address correspondence to Kathleen A. Calzone, RN, MSN, APNG, FAAN, National Cancer Institute, Center for Cancer Research, Genetics Branch, 8901 Wisconsin Ave, Building 8, Room 5101, Bethesda, MD 20889 –5105. E-mail: [email protected] 0270-9295/ - see front matter Published by Elsevier Inc. doi:10.1053/j.seminoncol.2010.05.005

Seminars in Oncology, Vol 37, No 4, August 2010, pp 407-418

with cancer risk.2 Direct to consumer genetic testing is especially complex as the extent of healthcare professional oversight and genetic education/counseling varies widely, if there is any at all. Furthermore, there is some evidence that there is variation in the risk predictions provided by different laboratories for identical diseases.3 The application of genetics to cancer prevention is also transitioning beyond single gene disorders into genomics, the study of the entire genome and the interaction with the environment and other personal factors.4 The international HapMap project, launched in 2002, was designed to identify and catalogue human genetic variation.5 The most common type of genetic variations are single-nucleotide polymorphisms (SNP) of which there are approximately 10,000. SNPs (see Figure 1) consist of a single DNA basepair change, which may have health implications such as influencing disease risk and drug metabolism.6 However, almost 90% of SNPs have linkage disequilibrium, where more than one SNP in a region are inherited together.7 This has enabled the development of Genome Wide Association Studies (GWAS) to correlate SNP variation using representative SNPs with diseases such as cancer. In cancer prevention, this capability resulted in the development of the Cancer Genetic Markers of Susceptibility (CGEMS) project in 2005, which aims to identify common genetic variations associated with cancer susceptibility. Thus far this initiative has identified risk alleles for many common malignancies, including prostate, colon, breast, lung, and pancreatic cancer with work on bladder cancer already underway.8 Further accelerating the relevance of genetics and genomics to cancer prevention is the capacity to se407

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K. Calzone, D. Wattendorf, and B. K. Dunn

Figure 1. Single-nucleotide polymorphism (SNP).6

quence increasingly larger sections of DNA using highthroughput technologies at a steadily decreasing cost, exceeding Moore’s Law (Figure 2).9,10 The next generation of sequencing technologies are already available, permitting rapid full genome sequencing at progressively more affordable costs, with some companies offering this service at $5,000 or less.11,12 While the $1,000 genome remains just out of reach, the application of these platforms in the cancer prevention set-

Figure 2. Cost of DNA sequencing and cumulative number of genomes sequenced as a function of time.10 Reproduced from Snyder et al. Genes & Development 23:423– 431 with permission from Cold Spring Harbor Laboratory Press.

ting, anticipated in the near future, offers the potential to not only identify at-risk individuals but to increase our understanding of the underlying cancer biology, thereby facilitating discovery of novel cancer prevention strategies and enhancing our ability to predict cancer risk. Taken together, these advances have created a plethora of genetic and genomic data that are increasingly relevant to cancer prevention. The current regulatory environment permits launching a clinical test plus or minus direct to consumer marketing and/or testing even when evidence demonstrating clinical and analytic validity is lacking.13 Initiatives such as the Evaluation of Genomic Applications in Practice and Prevention (EGAPP), established by the Center for Disease Control’s Office of Public Health Genomics (http:// www.egappreviews.org/), are intended to compile systematic evidence reviews on the clinical applications of these discoveries.14 However, the rate of discovery and introduction into practice has to date far outpaced EGAPPs ability to prepare and disseminate evidence reports. Therefore, at the present time the burden for determining the evidence threshold for clinical utility falls squarely on the shoulders of the healthcare provider. Here, we provide a review of the clinical application of genetics and genomics to cancer prevention and provide an overview of future implications.

USING GENETIC/GENOMIC INFORMATION FOR RISK IDENTIFICATION Family History Family history, arguably the cheapest genetic test available, can be used to establish provider/patient

Genetics and genomics of cancer prevention

relationships, learn about social and cultural variables that may influence healthcare decision-making, assess environmental exposures, and inform the differential diagnosis.15 In the context of cancer risk assessment, family history has two primary applications, identifying at-risk individuals, including those who may have an inherited predisposition to cancer and may be a candidate for genetic predisposition testing, as well as serving as a mechanism for individual cancer risk predictions over time, which can inform cancer risk management decisionmaking.16 The optimal approach and extent of family history has yet to be established.15 However, family history cannot be assessed without consideration of environmental factors such as melanoma in a Caucasian family with many members from Australia versus an African American family residing in Minnesota. Access to family history information can be limited in some circumstances such as adoption or lack of reliable health information from earlier generations. The accuracy of self reported family history varies based on the disease and the distance the affected relative is from the patient providing the information. Family history interpretation can be further complicated by small family size, early deaths from unrelated factors, and single gender– dominated families in predominantly gender-specific cancers such as breast, ovarian, or prostate cancers.17 Tools such as the Surgeon General’s My Family Health Portrait Tool http://www.hhs.gov/familyhistory/, available online and in written formats in English and Spanish, can be useful for collecting family history information in a busy practice. Once a family history is collected, there are several critical indicators (Table 1) that help identify individuals with a possible inherited predisposition to cancer.18,19 Whether referring such individuals out to a genetic healthcare professional or having sufficient skills and resources to handle this within your own practice, the critical next step is establishing the differential list of possible cancer syndromes. Lindor et al have published the second edition of the Concise Handbook of Familial Cancer Susceptibility Syndromes, which is an invaluable tool to facilitate establishing a thorough cancer syndrome differential list.18 Table 2 provides a summary of selected cancer syndromes from Lindor et al, including the associated Online Mendelian in Man (OMIM; http://www.ncbi.nlm.nih.gov/omim) reference numbers. Failure to consider the full extent of possible cancer syndromes can result in misdirected testing and/or failure to identify the genetic basis for the cancer in a given individual and/or his/her family. For example, Figure 3 illustrates a family with breast, prostate, gastric, and an unknown cancer in the context of Eastern European Jewish ancestry, which is documented to have a greater prevalence of mutations in BRCA1 and BRCA2, genes associated with hereditary breast/ovar-

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Table 1. Key Indicators of an Inherited Susceptibility to Cancer18,19

 Family member with documented deleterious mutation in a cancer susceptibility gene  Cancer in two or more close relatives (on same side of family)  Early age at cancer diagnosis  Multifocal disease in a single organ  Multiple primary tumors  Bilateral cancer in paired organs  Multiple rare cancers in biologically related relatives  Disease in the gender not predominantly affected (ie, male breast cancer)  Constellation of tumors consistent with known hereditary syndrome (ie, colon and endometrial cancer and Lynch syndrome)  Evidence of a form of Mendelian transmission (i.e. dominant, recessive or X-linked)  Cancer associated with other genetic traits (i.e. congenital hypertrophy of the retinal pigment epithelium (CHRPE) and familial adenomatous polyposis), congenital defects, precursor lesions, and/or other nonmalignant manifestations associated with an inherited cancer syndrome (ie, trichilemmomas and Cowden syndrome)  Infertility and/or unexplained pregnancy loss

ian cancer syndrome.20 However, a detailed assessment of the family history, including review of pathology reports, reveals that the breast cancers in the family are exclusively lobular carcinomas and the gastric cancer reported in the mother was poorly differentiated diffuse gastric cancer with evidence of linitis plastica. These breast and gastrointestinal histologic features are most consistent with hereditary diffuse gastric carcinoma associated with mutations in CDH1.18 Without this critical family history analysis, the tendency may be to test only for mutations in genes that are most commonly associated with a particular cancer, resulting in needless testing expense and a potential missed opportunity for enhanced cancer risk management in the unaffected proband. Several statistical models have been developed to help calculate the prior probability of a gene mutation being associated with the cancer in a family. However, use of these tools, which are gene(s)-specific, is hinged in large part on the provider identifying the accurate differential diagnosis.21 Existing models also vary with regard to whether the calculation is for the individual or the family. Furthermore, all of these tools are limited

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Table 2. Selected Hereditary Cancer Syndromes18 Syndrome Breast/ovarian cancer syndrome

Gene

OMIM No.

Mode of Inheritance

Clinical Manifestations

113705

Autosomal dominant

Breast, ovary, fallopian tube, pancreas and possibly prostate cancer

BRCA2 PTEN

600185 158350, 601728

Autosomal dominant Autosomal dominant

APC

175100, 611731, 135290

Autosomal dominant

Gorlin syndrome (basal cell nevus syndrome) Li-Fraumeni syndrome

PTCH

109400, 601309

Autosomal dominant

p53

Autosomal dominant

Lynch syndrome (hereditary nonpolyposis colorectal cancer)

MLH1 MSH2 MSH3 MSH6 PMS1 PMS2 CDKN2A CDK4

151623, 191170, 609265, 604373, 609266, 202300 609310, 276300, 608089, 158320, 120436, 120435, 609309, 600258, 600259, 600678, 600887

Breast, ovary, fallopian tube, prostate, and pancreas cancer, melanoma Multiple skin manifestations including trichilemmomas, acral keratoses, papillomas. Gastrointestinal hamartomas, fibrocystic disease, lipomas, fibromas, uterine fibroids, as well as breast, endomentrial, thyroid cancer (non-medullary, often follicular), colon cancer, and macrocephaly (⬎97th percentile) Multiple (⬎100) colon and/or rectal adenomatous polyps, polyps in the upper gastrointestinal tract, osteomas, epidermoid cysts, desmoid tumors, congenital hypertrophy of retinal pigment, supernumerary teeth, congenitally absent teeth, dentigerous cyst, and cancers of the thyroid, small bowel, hepatoblastoma, and brain tumors Basal cell carcinoma, medulloblastoma, ovarian fibrosarcoma, odontogenic keratocyst, palmar/ plantar pits, macrocephaly (⬎97th percentile), cardiac or ovarian fibroma, lymphomesenteric or pleural cysts Breast, adrenocortical, and brain cancer, sarcoma, leukemia, and lymphoma

Autosomal dominant

Cancers of the colon, rectum, stomach, small bowel, urinary and biliary tract, brain, endometrium and ovary; and possibly pancreatic cancer

Autosomal dominant

Melanoma, astrocytoma, pancreatic cancer

Autosomal dominant

Pancreatic and duodenal neuroendocrine tumors, gastrinomas, malignant islet cell tumors, and carcinoids, non-medullary thyroid cancer, pituitary and adrenal cortical adenomas, lipomas, collagenomas, facial angiofibromas, meningiomas, ependymomas, and leiomyomas Medullary thyroid cancer, pheochromocytoma, papillary thyroid cancer, ganglioneuromas, mucosal neuromas Neurofibromas, optic gliomas, café-au-lait macules, axillary or inguinal freckles, iris hamartomas, sphenoid wing dysplasia or congenital bowing/thinning of long bone cortex Neurofibromas, gliomas, vestibular schwannoma, schwannomas of other cranial and peripheral nerves, meningioma, ependymonas, astrocytomas Prostate cancer, other cancers not fully defined

Cowden syndrome (multiple harmartoma syndrome) Familial adenomatous polyposis

Melanoma

Multiple endocrine neoplasm type I

MEN1

123829, 155600, 155601, 155755, 600160, 606660, 606719, 608035, 609048 131100, 600778

Multiple endocrine neoplasm type II Neurofibromatosis

RET

171400, 155240, 162300

Autosomal dominant

NF1

162200, 162210, 193520, 609291, 611431 101000, 607379

Autosomal dominant

176807, 601518, 602759, 300147, 603688, 608656, 153622

Varied: autosomal dominant, recessive, X-linked

NF2 Prostate cancer

BRCA1 BRCA2 As well as candidate genes: RNASEL ELAC2 MSR1 As well as regions: PCAP HPCX HPC20 CAPB Several other loci under study

Autosomal dominant

K. Calzone, D. Wattendorf, and B. K. Dunn

BRCA1

Basal and squamous cell skin cancer, melanoma, sarcoma, cancers of the brain, lung, uterus, breast, stomach, kidney, testicle and leukemia, conjunctival papillomas, actinic keratosis, lid epitheliomas, keratoacanthomas, angiomas, fibromas Autosomal recessive

Autosomal dominant

607102, 194070, 194071, 605982, 601363, 194090, 601583 278700, 133510, 278720, 126340, 600811, 133520, 133530, 603968

Clinical Manifestations Mode of Inheritance

Autosomal dominant 193300, 608537

RB1

VHL

WT1

XPA ERCC3 XPC ERCC2 DDB2 ERCC4 ERCC5 POLH

Von Hippel-Lindau

Wilm’s tumor

Xeroderma pigmentosum

Syndrome

Retinoblastoma

Table 2. Continued

Gene

180200

OMIM No.

Autosomal dominant

Retinoblastoma, soft tissue and osteosarcoma, lung cancer, sebaceous carcinomas, retinomas, and lipomas Renal cell cancer, pancreatic islet cell carcinomas, carcinoid, pheochromocytomas, endolymphatic sac tumors and hemangioblastomas, renal cysts, and adrenal adenomas and paragangliomas and epididymal cysts Wilm’s tumor, nephrogenic rests

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by the constructs used in the model development and therefore are not optimal for use in every family. The National Cancer Institute’s Division of Cancer Control and Population Sciences has developed a website, http://riskfactor.cancer.gov/cancer_risk_prediction/ about.html, that reviews the plethora of models for use in prior probability and absolute cancer risk calculations for a wide range of cancers from both peer- and non–peer reviewed sources.22 Decision support tools also are in development such as the GRAIDS (Genetic Risk Assessment on the Internet with Decision Support).23,24 However, the detailed assessment of personal and family history coupled with clinical judgment remains essential in evaluating any patient and establishing the value of additional model calculations.21

Histologic Features Reliance on family history to identify at-risk individuals who may be candidates for genetic testing has several inherent limitations, not the least of which is the ability of the patient to obtain the detailed family history needed. In addition, there is an emerging literature demonstrating that use of family history criteria, even precise criteria developed for a specific hereditary cancer syndrome, will still miss individuals with an inherited predisposition.25–27 For this reason, tumor-specific characteristics are emerging as an adjunct to family history in identifying individuals who may be a candidate for genetic testing. Lynch Syndrome, also known as hereditary nonpolyposis colon cancer (HNPCC), is associated with colon, endometrial and other cancers. Tumors associated with Lynch syndrome are associated with mutations in DNA mismatch repair genes which result in tumor microsatellite instability (MSI). MSI refers to the somatic accumulation of variations in the length of short repeat DNA sequences, resulting from the failure of specific enzymes to appropriately repair mismatched DNA. This failure occurs when a DNA repair enzyme is encoded by a DNA mismatch repair gene that contains a mutation. Immunohisotchemical (IHC) staining is often combined with MSI testing to assess the presence of the protein products of these DNA-repair genes. Performing MSI and IHC tests on newly diagnosed colorectal and endometrial cancers identified individuals who were candidates for genetic testing that would not have met existing family history criteria.26,28 The Bethesda Guidelines, established to identify Lynch syndrome, have been revised to include MSI.29 However, almost one quarter of individuals did not meet the revised Bethesda Guidelines but were found to have a Lynch-associated mutation based on MSI screening of their colon tumor at the time of diagnosis.25 Debate continues on whether to perform MSI alone, IHC alone, or both as screening tests prior to gene sequencing.

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Figure 3. Example pedigree.

However, in contrast to many other hereditary cancer syndromes, MSI and IHC testing can be performed on a tumor block (colon, endometrium, or even sebaceous adenoma); therefore, if a consult is determined to be at high risk of carrying a mutation, but has no living affected relative, then testing of a deceased family member’s tumor block is often very useful. Research continues to define the optimal approach to identifying Lynch syndrome patients, but it is likely that family history coupled with molecular and/or tumor specific features will emerge as the standard. Use of histologic features is not limited to colon cancer and in fact extends to non-malignant histologies. For example, trichillemomas, commonly identified by the dermatologist, are a clinical manifestation of Cowden syndrome. In breast cancer, germline mutations in BRCA1 have long been recognized to be associated with estrogen, progesterone and HER-2–negative breast cancers (triple negative).30 Data from studies of women selected for testing based on the diagnosis of a triple-negative breast cancer regardless of family history have identified women with germline mutations who would not have otherwise met any family history criterion.31,32

Moderate-Penetrance Genes Mutations in highly penetrant cancer susceptibility alleles are associated with a maximum of 5% to 10% of

most malignancies and therefore affect the minority of the at-risk population. However, cancer is a complex disease of genetic instability that is polygenic in origin. As such, ongoing efforts to discover low- to moderate-penetrance cancer susceptibility genes that may have higher frequencies in the general population are expected to provide a vehicle for improving population-based cancer risk assessment and management.33,34 In breast cancer, for example, several moderately penetrant susceptibility genes have already been identified, including CHEK2, ATM, BRIP1, and PALB2.35–37 CHEK2, a gene involved in the repair of DNA damage, will be used as an illustration. One specific mutation, 1100delC in CHEK2 has been found in families with breast cancer with an odds ratio (OR) of 2.34 (95% confidence interval [CI], 1.72–3.20).36 This CHEK2 mutation was also more common in women with a firstdegree relative affected with breast cancer and there was a trend for the OR to be greatest in women diagnosed with breast cancer at younger ages (OR 7.91 for women diagnosed at ⬍30 years old).36 A recent metaanalysis found that the risk of breast cancer in CHEK2 1100delC mutation carriers was 37% (95% CI, 26%– 56%) by age 70.38 However, there is some evidence of variation in penetrance based on the population studied.39

Genetics and genomics of cancer prevention

To date there is a paucity of data on the medical management of individuals at moderate risk for breast cancer, including whether the risk reaches the threshold warranting interventions such as chemoprevention, breast magnetic resonance imaging screening and/or consideration of risk reducing surgery.39 This is of real concern given the variation in CHEK2 1100delC incidence as well as penetrance based on the population assessed.39 Studies in CHEK2 as well as the other moderately penetrant breast cancer susceptibility alleles largely have been conducted in select, predominantly Caucasian populations. Therefore, the consistency of these findings in a broader, more diverse population is uncertain at this time.

Low-Penetrance Genes The identification of low-penetrance cancer susceptibility alleles for all forms of cancer has expanded greatly with the advent of genome-wide association studies (GWAS).40 In general, most of these low-penetrance genes confer lower risks of cancer but are frequent in the general population and therefore have implications for a broader segment of the population. As single polymorphisms, most of these discoveries influence cancer risk only a small amount. However, a combination of different low-penetrance alleles may significantly increase cancer risk for a single individual, raising the value of multiplex testing for cancer risk assessment.40 Prostate cancer illustrates the potential of this approach. In a Swedish case– control study (2,893 prostate cancer cases, 1781 controls), 16 SNPs in five chromosomal regions were studied for their association with prostate cancer. In the face of strong linkage disequilibrium, one SNP from each of the five regions with greatest association with prostate cancer was selected as representative of that region for further multivariate analysis. When compared to men without any of the five SNPs, men with one to five SNPs had an increasing likelihood of being affected with prostate cancer. This cumulative association was sustained after controlling for age, geographic region, and family history. Indeed, when prostate cancer family history was incorporated as a risk factor in addition to the five SNPs, any combination of five of these six risk factors was found to have the greatest association with prostate cancer (OR ⫽ 9.46; 95% CI, 3.62–24.72), accounting for 46% of prostate cancers in Swedish men.41 Clearly, prostate cancer represents a very heterogeneous disease, which adds to the complexity of translating these discoveries into practice. Furthermore, although it is likely that multi-allele testing models will continue to emerge, in this case the findings can only be applied to the population studied and are not generalizable. However, a study building on this prior work assessed prostate cancer risk associated with a

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family history of prostate cancer in combination with 27 risk alleles in both the aforementioned Swedish study group, as well as the United States Prostate, Lung, Colon and Ovarian Cancer (PLCO) Screening Trial (1,172 cases, 1,157 controls). The highest risk for prostate cancer was found in Swedish men with a positive family history and ⬎14 risk alleles (OR ⫽ 4.92; 95% CI, 3.64 – 6.64) which was similar to findings from the PLCO study population (OR 3.88; 95% CI, 2.83–5.33).42 This emerging evidence in prostate cancer illustrates the possible value of multiplex testing for identifying those at the highest level of cancer risk who may be candidates for chemoprevention. Some of the current work in this area is associated with the development of models that could aid in cancer risk prediction using SNPs in combination with family history, though clearly considerable research remains to be done in this area before this is pertinent clinically.43

Genetics/Genomics and Risk Models Another similar approach actively being investigated is the use of multiple SNPs in combination with established risk prediction models. Allele frequency and relative risk estimates from seven SNPs associated with breast cancer identified from two prior genome-wide association studies were used to determine whether the addition of these SNPs could improve the accuracy of the widely utilized National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT). The model accuracy, measured by the area under the receiver operating characteristic curve (AUC), did not show significant improvement in the AUC when adding the SNPs.44 More recently, a large study of data from 5,590 women with breast cancer and 5,998 controls ages 50 to 79 looked at risk factors included in the BCRAT in combination with 10 common SNPs all associated with breast cancer to assess whether this improved model performance. Overall, the analysis found that the addition of SNPs to the risk model modestly improved the discriminatory accuracy but that did not translate to significant improvements in risk prediction.45 As with prostate cancer discussed above, breast cancer is a very heterogeneous disease for which many of the risk alleles associated with the disease remain to be identified. This research is in its infancy but illustrates the complexity of this work and the difficulty in translating these developments into practice. However, advancing technology and accumulating evidence are likely to move this field forward to a threshold of clinical relevance.

PHAMACOGENOMICS Chemoprevention The relevance of genomic variants to cancer extends beyond the association of inherited polymorphisms and

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mutations with risk of developing cancer to their complicity in determining response to specific drugs.46,47 A stark example of this is seen in tamoxifen, a standard hormonal therapy that is used to treat metastatic and early-stage breast cancer, as well as ductal carcinomain-situ (DCIS), and to prevent breast cancer in high-risk women. Tamoxifen is a selective estrogen receptor modulator (SERM) that specifically targets estrogen receptor (ER)⫹ cancers by blocking estrogen from binding to the ER, leading to inhibition of ER-regulated breast cancer cell proliferation. However, as demonstrated in the preventive setting in the Breast Cancer Prevention Trial and in adjuvant trials,48,49 only a subset of ER⫹, and no ER⫺, breast cancers are prevented by tamoxifen. A major mechanism believed to underlie resistance of the remaining ER⫹ tumors to this estrogen-targeted intervention is the failure of tamoxifen, a pro-drug, to be converted/ metabolized to endoxifen (4-hydroxy-N-desmethyl tamoxifen), its most active form. Endoxifen combines high affinity for the ER with high concentration, implicating it as the major metabolite responsible for the anti-cancer activity of tamoxifen.50 –53 In some cases of tamoxifen resistance, the inability to metabolize tamoxifen to endoxifen is due to specific polymorphisms in the CYP2D6 gene, which encodes the single most important enzyme involved in the tamoxifen metabolic pathway.54 An extensive pharmacogenetic literature documents the many alleles encoding CYP2D6 isoforms with reduced activity. At least 75 CYP2D6 polymorphisms have been reported to date, but only some of them impart altered activity to their enzyme product.54,55 In contrast to the alleles whose products have normal CYP2D6 activity (called extensive metabolizer [EM] alleles), “null”, or poor metabolizer (PM), alleles, including *3, *4, *5, and *6, exhibit no enzymatic activity. Intermediate metabolizer (IM) alleles, such as *10, *17, and *41, encode enzymes with reduced activity (http:// www.cypalleles.ki.se – CYP2D6, last accessed December 16, 2009).56 In addition, CYP2D6 gene duplications of normal alleles can lead to ultra-rapid CYP2D6 activity and are referred to as ultra-rapid metabolizer (UM) alleles.54 Each allele designation actually refers to a group of haplotypes that share a common polymorphism at the designated site but may differ at other loci within the CYP2D6 gene (http://www.cypalleles.ki.se – CYP2D6, last accessed December 16, 2009). Depending on the particular combination of two CYP2D6 alleles found in an individual woman, her CYP2D6 genotype can be translated into a specific CYP2D6 phenotype.57 These phenotypic states have been classified as poor, intermediate, extensive, and ultra-metabolizers. Yet, almost all women are given the same dose of tamoxifen for either chemoprevention or therapeutic purposes. Women with absent or low CYP2D6 activity due to certain inherited CYP2D6 variants exhibit lower plasma levels of endoxifen58,59 in a manner consistent with a

K. Calzone, D. Wattendorf, and B. K. Dunn

gene– dose effect. These reduced endoxifen levels are, in turn, expected to impact clinical outcomes. In fact, clinical studies have begun to address the actual clinical outcomes as they relate to CYP2D6 phenotype. For example, associations between homozygosity for a poor metabolizing CYP2D6 genotype and higher risk of relapse, shorter time to recurrence, and worse relapse-free survival, but not overall survival, have been reported in women with breast cancer treated with adjuvant tamoxifen.57,60 On the other hand, the updated results of an ongoing multicenter study conducted by the International Tamoxifen Pharmacogenomics Consortium, showed no difference in clinical outcome in relation to CYP2D6 metabolizer phenotype.56 Similarly, observations from several retrospective studies also failed to show an association between tested CYP2D6 genotype and clinical outcome.61– 64 As accumulating data address the impact, if any, of CYP2D6 allele status on clinical outcome, the role for factoring CYP2D6 genotype into therapeutic decisions regarding tamoxifen is being clarified. The application of CYP2D6 genotyping extends to the prevention setting. In a report of 46 women with breast cancer and 136 controls in the Italian Tamoxifen Trial, Bonanni et al observed a higher frequency of the CYP2D6*4/*4 slow-metabolizing genotype in cases than in women free of cancer, and this difference was restricted to participants in the tamoxifen arm of the trial.65 In contrast, a recent report from the International Breast Intervention Study (IBIS)-I tamoxifen prevention trial noted that CYP2D6 genotype was not associated with incident invasive breast cancer.66 Importantly, genomic variants of CYP2D6 are not the only source of perturbation of CYP2D6 activity; concomitant use of drugs that inhibit CYP2D6 activity result in lower measurable levels of endoxifen in plasma and suppression of tamoxifen-associated clinical symptoms.58,59,67,68 In the breast cancer setting, drugs that are commonly prescribed to alleviate tamoxifen-associated hot flashes include selective serotonin reuptake inhibitors (SSRIs, including fluoxetine and paroxetine) and selective noradrenaline reuptake inhibitors (SNRIs). Both SSRIs and SNRIs are potent inhibitors of CYP2D6 activity and thus, have potential to impair tamoxifen conversion to endoxifen, in turn decreasing the efficacy of tamoxifen with respect to the main treatment target, breast cancer. Nevertheless, the clinical significance of using CYP2D6 inhibitors together with tamoxifen remains to be clarified.69 In summary, both CYP2D6 genotype and concomitant drug use should be considered during decisionmaking regarding tamoxifen treatment. The importance of these factors for ensuring tamoxifen efficacy was addressed by the Food and Drug Administration (FDA) in October 2006.70 Continued studies of the relationship between CYP2D6 genotype and clinical outcome should refine the basis for individualizing ta-

Genetics and genomics of cancer prevention

moxifen therapy both for treatment and prevention of breast cancer. A method for interpreting the results generated by available genotyping tests has been proposed and is called a CYP2D6 “activity score.”71 In addition, the potential causal relationship that the CYP2D6 genotype– endoxifen level association has with treatment outcome still remains to be clarified.54

ETHICAL, LEGAL, AND SOCIAL IMPLICATIONS When to Consider Genetic Testing? The American Society of Clinical Oncology (ASCO) has remained consistent in their recommendation that genetic testing be considered when:72,73 ● ● ● ●

●

There is familial or individual evidence of an inherited cancer syndrome. The results of the test being considered can be interpreted. Testing will aide in the diagnosis and/or be used in medical management decision-making. The individual being tested has reached the age of maturity (18 years) or there are pediatric cancer risks and medical management options for children such as in the case of familial adenomatous polyposis. In addition, that genetic testing be performed in conjunction with genetic education, counseling and informed consent.

The 2010 update to the ASCO policy statement has been expanded to include the integration of low to moderate penetrance and multiplex genomic tests, as well as consideration of the direct to consumer testing market place.72 This expanded framework (Table 3) recommends that the provider consider the established clinical utility of a given test and secondarily, whether the test was obtained through a healthcare provider or direct to the consumer.72

Selecting the Testing Laboratory The quality assurance of genetic and genomic tests remains highly variable given the limited regulatory oversight. Clinical laboratory testing is subject to The Clinical Laboratory Improvement Act (CLIA), which reg-

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ulates testing that generates diagnostic or other health information by specifying personnel qualifications, quality-assurance standards, documentations, and validation of tests and procedures.74 Under CLIA regulations, laboratories are subject to periodic proficiency testing to verify their ability to perform and interpret highly complex tests.13,75 However, a specialty area for molecular and biologic genetic and genomic tests has yet to be established; therefore, there is no mandated proficiency testing for genetic and genomic testing.75 The FDA regulates test kits, which are manufactured kits that are sold for use by multiple laboratories. However, the vast majority of genetic and genomic tests are developed and assembled within individual laboratories and therefore fall outside FDA oversight.13 To aid the healthcare provider in laboratory selection, GeneTests (http:// www.ncbi.nlm.nih.gov/sites/GeneTests/?db⫽Gene Tests) maintains an international laboratory directory that can be searched by gene, disease, protein name, laboratory name, and/or laboratory director. Laboratory listings include an overview of testing services, analysis method(s), credentials of senior laboratory staff, and contact information including a link to the website if applicable.

Legislative Protections Concern about discrimination has long been a barrier to the consideration of genetic testing. Years in the making, the Genetic Information Nondiscrimination Act (GINA) was signed into law in May 2008 with the final interim regulations effective December, 2009. GINA prohibits health insurers from establishing eligibility, determining coverage, underwriting, or adjusting premiums based on an individual’s genetic information (including family history) or require that an individual or their family undergo a genetic test. This legislation also extends to employers who are prohibited from using genetic information to make hiring, firing, or promotion decisions, as well as limiting an employer’s right to request, require, or purchase an employee’s genetic information.76 GINA does not pre-empt state laws that provide more protection but instead requires all entities to comply with GINA regulations in addition to any more protective state laws.

Table 3. Clinical Utility of Genetic/Genomic Tests72

Professional Role in Testing

Accepted Clinical Utility

Uncertain Clinical Utility

Healthcare professional order No healthcare professional order

High-penetrance gene mutations (ie, BRCA1, BRCA2) High-penetrance gene mutations (ie, BRCA1, BRCA2)

Low- and moderate-penetrance gene mutations (eg, CHEK2) Low- and moderate-penetrance gene mutations

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GINA is a significant step forward. However, there are limitations including that the law does not cover the US military and individuals obtaining healthcare through the Veteran’s Administration or the Indian Health Service. GINA also does not prohibit medical underwriting based on current health status. Lastly, GINA does not cover life insurance, disability insurance, or long-term care insurance though legislation in some states does extend to these insurances.76 The National Human Genome Research Institute maintains a policy and legislative database at http://www.genome. gov/PolicyEthics/LegDatabase/pubsearch.cfm, which is useful to search for your own state laws.

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CONCLUSION One cornerstone of personalized healthcare is the individualized prediction of risk to provide early intervention, risk mitigation, or early diagnosis thereby potentially decreasing morbidity and mortality. The translation of genetics and genomics into the arena of cancer prevention provides unique opportunities to achieve all of these aims. While the translation of research advances has advanced further for highly penetrant single-gene conditions, the ever-increasing knowledge of the contribution of variations in multiple genes, each conferring only a modest effect, when tested together, are at the forefront of individualized prevention for the majority of the population. Furthermore, the ability to target chemoprevention not only to those with the highest risk but also to the most likely to benefit based on their genetic profile remains the ultimate goal. Methodological advances in genomic technologies such as the emergence of individual full-genome sequencing for several thousand dollars should continue to provide new opportunities to shift prevention strategies from a population-based posture to the individual.

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