Cancer Genetics Overview (PDQ®)

Levels of Cancer Risk

Different hereditary cancer genes are associated with varying levels of cancer risk. This cancer risk can also vary among pathogenic/likely pathogenic (P/LP) variants in the same gene. Rare genetic variants are generally associated with higher cancer risks than common genetic variants. This is depicted in Figure 1.

In some genes, P/LP variants increase an individual's risk of developing cancer at a younger age than that typically seen in the general population.

Cancer risk is multifactorial. It is influenced by genetic, environmental, medical, and lifestyle factors. These nongenetic factors impact cancer risk in individuals who carry a P/LP variant in genes like BRCA1 or BRCA2. For more information, see the Introduction and the Polygenic risk scores for breast and ovarian cancer sections in Genetics of Breast and Gynecologic Cancers.

Figure 1. Genetic architecture of cancer risk. This graph depicts the general finding of a low relative risk associated with common, low-penetrance genetic variants, such as single-nucleotide variants identified in genome-wide association studies. It also shows a higher relative risk associated with rare, high-penetrance genetic variants, such as pathogenic variants in the BRCA1/BRCA2 genes associated with hereditary breast and ovarian cancer and the mismatch repair genes associated with Lynch syndrome.

Available genetic testing expands as more genetic variants are identified and their associated cancer risks are characterized.

Associated Cancers

Cancer risks vary based on the hereditary cancer gene and pathogenic variant involved. For example, P/LP variants in BRCA1 and BRCA2 are associated with breast, ovarian, pancreatic, and other cancers.

Types of Genetic Variants in the Context of Cancer Genetics

• Germline variant: A variant in a reproductive cell (i.e., egg or sperm) that is in the DNA of every cell in the offspring's body. A variant contained within the germline can be passed from parent to offspring and is, therefore, hereditary.
• Somatic (acquired) variant: In the context of cancer genetics, somatic variant refers to a variant in DNA that occurs before or during tumor development. This type of variant is not present within the germline (i.e., sperm and egg).

[Note: Somatic variants have various nuances, like the concepts of mosaicism, gonadal mosaicism, and clonal hematopoiesis of indeterminate potential.]

For more information about germline genetic testing and somatic genetic testing, see the Clinical Applications section.

Variant Classifications for Hereditary Cancer Genetic Testing

Variants (i.e., genetic changes) are classified as pathogenic, likely pathogenic, of uncertain significance, benign, or likely benign.[1] Variant classification is based on whether a variant is associated with disease. Genetic variant studies research how variants are associated with specific diseases.

Variants may be referred to as secondary or incidental findings:

• Secondary findings: Variants found during genetic testing that were intentionally analyzed but are not related to the indication for which the exome or genome sequencing was ordered. These findings may have clinical utility for the ordering physician, the patient, or the patient's family. (Adapted from the American College of Medical Genetics and Genomic [ACMG]'s recommendations for reporting incidental findings in clinical exome and genome sequencing.) ACMG has adopted secondary findings, rather than incidental findings, as its standard terminology when reporting exome or genome results.
• Incidental findings: Variants that were discovered during genetic testing unintentionally or accidentally.[3]

Inheritance Patterns for Hereditary Cancer Risk

Hereditary cancer risk is inherited differently, depending on the indicated gene. Most hereditary cancer risks are inherited in an autosomal dominant fashion. In autosomal dominant inheritance, an individual is at risk to develop cancer when he/she inherits one pathogenic variant (in one of the two copies of the gene). Some hereditary cancer syndromes have other inheritance patterns (e.g., MUTYH-associated polyposis is inherited in an autosomal recessive fashion).

For more information about how to recognize hereditary cancer inheritance patterns, see the Analysis of the family history section in Cancer Genetics Risk Assessment and Counseling.

References

1. Richards S, Aziz N, Bale S, et al.: Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17 (5): 405-24, 2015. [PMC free article: PMC4544753] [PubMed: 25741868]
2. Plon SE, Eccles DM, Easton D, et al.: Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results. Hum Mutat 29 (11): 1282-91, 2008. [PMC free article: PMC3075918] [PubMed: 18951446]
3. Kalia SS, Adelman K, Bale SJ, 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 19 (2): 249-255, 2017. [PubMed: 27854360]

References

1. National Comprehensive Cancer Network: NCCN Clinical Practice Guidelines in Oncology: Genetic/Familial High-Risk Assessment: Colorectal. Version 2.2023 . Plymouth Meeting, PA: National Comprehensive Cancer Network, 2023. Available with free registration. Last accessed March 13, 2024.
2. National Comprehensive Cancer Network: NCCN Clinical Practice Guidelines in Oncology: Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic. Version 3.2024. Plymouth Meeting, Pa: National Comprehensive Cancer Network, 2023. Available online with free registration. Last accessed April 11, 2024. [PubMed: 33406487]

References

1. Berliner JL, Cummings SA, Boldt Burnett B, et al.: Risk assessment and genetic counseling for hereditary breast and ovarian cancer syndromes-Practice resource of the National Society of Genetic Counselors. J Genet Couns 30 (2): 342-360, 2021. [PubMed: 33410258]
2. Holter S, Hall MJ, Hampel H, et al.: Risk assessment and genetic counseling for Lynch syndrome - Practice resource of the National Society of Genetic Counselors and the Collaborative Group of the Americas on Inherited Gastrointestinal Cancer. J Genet Couns 31 (3): 568-583, 2022. [PubMed: 35001450]

Linkage Analyses

Since it was recognized that cancer clusters in families, investigators have collected data on families with multiple cancer cases. The investigators aimed to localize cancer susceptibility genes via linkage studies.

Linkage studies are typically performed on high-risk kindreds, in whom multiple cases of a particular disease have occurred, in an effort to identify disease susceptibility genes. Linkage analysis statistically compares the genotypes between affected and unaffected individuals. Linkage analysis also seeks evidence that known genetic markers are inherited along with the disease trait. If such evidence is found (linkage), it provides statistical data that the chromosomal region near the known genetic marker also harbors a disease susceptibility gene. Once a genomic region of interest has been identified through linkage analysis, additional studies are required to prove that there truly is a susceptibility gene at this position. Linkage analysis is affected by the following:

• Family size and having enough family members to contribute DNA.
• The number of disease cases in each family.
• Factors related to age at disease onset (e.g., screening use).
• Gender differences in disease risk (not relevant in gender-specific cancers).
• Heterogeneity of disease in cases (e.g., aggressive vs. nonaggressive phenotype).
• The accuracy of family history information.
• Prevalence of phenocopies.

An additional issue in linkage studies is the background rate of sporadic cancer in the context of family studies. For example, because a man’s lifetime risk of prostate cancer is one in eight,[1] it is possible that families under study have both inherited and sporadic prostate cancer cases. Thus, a male may still develop prostate cancer, even if he does not inherit the prostate cancer susceptibility gene that is present in his family. Hence, studies have addressed inconsistencies between linkage data by requiring strict inclusion criteria that define clinically significant disease.[2-6] This approach attempts to define a homogeneous set of cases/families to increase the likelihood of identifying a linkage signal.

Genome-Wide Association Studies (GWAS)

GWAS are identifying common, low-penetrance susceptibility alleles for many complex diseases,[7] including cancer. This approach can be contrasted with linkage analysis, which searches for genetic-risk variants cosegregating within families that have a high prevalence of disease. While linkage analyses are designed to uncover rare, highly penetrant variants that segregate in predictable heritance patterns (e.g., autosomal dominant, autosomal recessive, X-linked, and mitochondrial), GWAS are best suited to identify multiple, common, low-penetrance genetic polymorphisms. GWAS are conducted under the assumption that the genetic underpinnings of complex phenotypes, such as prostate cancer, are governed by many alleles, each conferring modest risk. Most genetic polymorphisms genotyped in GWAS are common, with minor allele frequencies greater than 1% to 5% within a given population (e.g., men of European ancestry). GWAS capture a large portion of common variation across the genome.[8,9] The strong correlation between many alleles located close to one another on a given chromosome (called linkage disequilibrium) allows one to “scan” the genome without having to test all 10 million known single nucleotide variants (SNVs). With GWAS, researchers can test approximately 1 million to 5 million SNVs per study and ascertain almost all common inherited variants in the genome.

In a GWAS, allele frequency for each SNV is compared between cases and controls. Promising signals—in which allele frequencies deviate significantly in case compared to control populations—are validated in replication cohorts. To have adequate statistical power to identify variants associated with a phenotype, large numbers of cases and controls, typically thousands of each, are studied. Because up to 1 million SNVs are evaluated in a GWAS, false-positive findings are expected to occur frequently when using standard statistical thresholds. Therefore, stringent statistical rules are used to declare a positive finding, usually using a threshold of P < 1 × 10^-7.[10-12]

To date, thousands of cancer-risk variants have been identified by well-powered GWAS and validated in independent cohorts.[13] These studies have revealed consistent associations between specific inherited variants and cancer risk. However, the findings should be qualified with a few important considerations:

1. GWAS reported thus far have been designed to identify relatively common genetic polymorphisms. It is very unlikely that an allele with high frequency in the population by itself contributes substantially to cancer risk. This, coupled with the polygenic nature of tumorigenesis, means that the contribution by any single variant identified by GWAS to date is quite small, generally with an odds ratio for disease risk of less than 1.3. In addition, despite extensive genome-wide interrogation of common polymorphisms in tens of thousands of cases and controls, GWAS findings to date do not account for even half of the genetic component of cancer risk.[14]
2. Variants uncovered by GWAS are not likely to directly contribute to disease risk. As mentioned above, SNVs exist in linkage disequilibrium blocks and are merely proxies for a set of variants—both known and previously undiscovered—within a given block. The causal allele is located somewhere within that linkage disequilibrium block.
3. Admixture by groups of different ancestry can confound GWAS findings (i.e., a statistically significant finding could reflect a disproportionate number of subjects in the cases versus controls, rather than a true association with disease). Therefore, GWAS are typically powered to analyze a single predominant ancestral group. As a result, some populations remain underrepresented in genome-wide analyses.
4. The clinical relevance of variants identified from GWAS remains unclear. [15 - 17]

Implications of these points and how they relate to polygenic risk scores are discussed further in the Polygenic risk scores for breast and ovarian cancer section in Genetics of Breast and Gynecologic Cancers, the Genetic Polymorphisms and CRC Risk section in Genetics of Colorectal Cancer, and the Common Risk Variants and Polygenic Risk Scores for Prostate Cancer section in Genetics of Prostate Cancer.

References

1. American Cancer Society: Cancer Facts and Figures 2024. American Cancer Society, 2024. Available online. Last accessed June 21, 2024.
2. Stanford JL, McDonnell SK, Friedrichsen DM, et al.: Prostate cancer and genetic susceptibility: a genome scan incorporating disease aggressiveness. Prostate 66 (3): 317-25, 2006. [PubMed: 16245279]
3. Chang BL, Isaacs SD, Wiley KE, et al.: Genome-wide screen for prostate cancer susceptibility genes in men with clinically significant disease. Prostate 64 (4): 356-61, 2005. [PubMed: 15754351]
4. Lange EM, Ho LA, Beebe-Dimmer JL, et al.: Genome-wide linkage scan for prostate cancer susceptibility genes in men with aggressive disease: significant evidence for linkage at chromosome 15q12. Hum Genet 119 (4): 400-7, 2006. [PubMed: 16508751]
5. Cancer risks in BRCA2 mutation carriers. The Breast Cancer Linkage Consortium. J Natl Cancer Inst 91 (15): 1310-6, 1999. [PubMed: 10433620]
6. Easton DF, Bishop DT, Ford D, et al.: Genetic linkage analysis in familial breast and ovarian cancer: results from 214 families. The Breast Cancer Linkage Consortium. Am J Hum Genet 52 (4): 678-701, 1993. [PMC free article: PMC1682082] [PubMed: 8460634]
7. Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447 (7145): 661-78, 2007. [PMC free article: PMC2719288] [PubMed: 17554300]
8. The International HapMap Consortium: The International HapMap Project. Nature 426 (6968): 789-96, 2003. [PubMed: 14685227]
9. Thorisson GA, Smith AV, Krishnan L, et al.: The International HapMap Project Web site. Genome Res 15 (11): 1592-3, 2005. [PMC free article: PMC1310647] [PubMed: 16251469]
10. Evans DM, Cardon LR: Genome-wide association: a promising start to a long race. Trends Genet 22 (7): 350-4, 2006. [PubMed: 16713652]
11. Cardon LR: Genetics. Delivering new disease genes. Science 314 (5804): 1403-5, 2006. [PubMed: 17138888]
12. Chanock SJ, Manolio T, Boehnke M, et al.: Replicating genotype-phenotype associations. Nature 447 (7145): 655-60, 2007. [PubMed: 17554299]
13. Chang CQ, Yesupriya A, Rowell JL, et al.: A systematic review of cancer GWAS and candidate gene meta-analyses reveals limited overlap but similar effect sizes. Eur J Hum Genet 22 (3): 402-8, 2014. [PMC free article: PMC3925284] [PubMed: 23881057]
14. Ioannidis JP, Castaldi P, Evangelou E: A compendium of genome-wide associations for cancer: critical synopsis and reappraisal. J Natl Cancer Inst 102 (12): 846-58, 2010. [PMC free article: PMC2886095] [PubMed: 20505153]
15. Park JH, Gail MH, Greene MH, et al.: Potential usefulness of single nucleotide polymorphisms to identify persons at high cancer risk: an evaluation of seven common cancers. J Clin Oncol 30 (17): 2157-62, 2012. [PMC free article: PMC3397697] [PubMed: 22585702]
16. Lindström S, Schumacher FR, Cox D, et al.: Common genetic variants in prostate cancer risk prediction--results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3). Cancer Epidemiol Biomarkers Prev 21 (3): 437-44, 2012. [PMC free article: PMC3318963] [PubMed: 22237985]
17. Wacholder S, Hartge P, Prentice R, et al.: Performance of common genetic variants in breast-cancer risk models. N Engl J Med 362 (11): 986-93, 2010. [PMC free article: PMC2921181] [PubMed: 20237344]

Introduction

Broad-scale genome sequencing approaches, including multigene (panel) testing, whole-exome sequencing (WES), and whole-genome sequencing (WGS), are rapidly being developed and incorporated into a spectrum of clinical oncologic settings, including cancer therapeutics and cancer risk assessment. Several institutions and companies offer tumor sequencing, and some are developing precision medicine programs that sequence tumor genomes to identify driver genetic alterations that are targetable for therapeutic benefit to patients.[1-3] Many of these tumor-based approaches use germline DNA sequences as a reference to discriminate between DNA changes only within the tumor and those that are potentially inherited. In the genetic counseling and cancer risk assessment setting, the use of multigene testing to evaluate inherited cancer risk is becoming more common and may become routine in the near future, with institutions and companies offering multigene testing to detect alterations in a host of cancer risk–associated genes.

These advances in gene sequencing technologies also identify variants in genes related to the primary indication for ordering genetic sequence testing, along with findings not related to the disorder being tested. The latter genetic findings, termed incidental or secondary findings, are currently a source of clinical, ethical, legal, and counseling debate. The American College of Medical Genetics and Genomics (ACMG) and the Presidential Commission for the Study of Bioethical Issues have published literature that address some of these issues and provide guidance and recommendations for their use.[4-7] However, controversy continues about when and what results to provide to patients and their health care providers. This section was created to provide information about genomic sequencing technologies in the context of clinical sequencing and highlights additional areas of clinical uncertainty for which further research and approaches are needed.

Background

DNA sequencing technologies have undergone rapid evolution, particularly since 2005 when massively parallel sequencing, or next-generation sequencing (NGS), was introduced.[8]

Automated Sanger sequencing is considered the first generation of sequencing technology.[9] Sanger cancer gene sequencing uses polymerase chain reaction (PCR) amplification of genetic regions of interest followed by sequencing of PCR products using fluorescently labeled terminators, capillary electrophoresis separation of products, and laser signal detection of nucleotide sequence.[10,11] While this is an accurate sequencing technology, the main limitations of Sanger sequencing include low throughput, a limited ability to sequence more than a few genes at a time, and the inability to detect structural rearrangements.[10]

NGS refers to high throughput DNA sequencing technologies that are capable of processing multiple DNA sequences in parallel.[11] Although platforms differ in template generation and sequence interrogation, the overall approach to NGS technologies involves shearing and immobilizing DNA template molecules onto a solid surface, which allows separation of molecules for simultaneous sequencing reactions (millions to billions) to be performed in a parallel fashion.[10,12] Thus, the major advantages of NGS technologies include the ability to sequence thousands of genes at one time, a lower cost, and the ability to detect multiple types of genomic alterations, such as insertions, deletions, copy number alterations, and rearrangements.[10] Limitations include the possibility that specific gene regions may be missed, turnaround time can be lengthy (although it is decreasing), and informatics support to handle massive amounts of genetic data has lagged behind the sequencing capability. A well-recognized bottleneck to utilizing NGS data is the lack of advanced computational infrastructure to preserve, process, and analyze the vast amount of genetic data. The magnitude of the variants obtained from NGS is exponential; bioinformatics approaches need to evaluate genetic variants for predicted functional consequence in disease biology. There is also a need for user-friendly bioinformatics pipelines to analyze and integrate genetic data to influence the scientific and medical community.[11,13]

Clinical Applications

NGS has multiple clinical applications. In oncology, the two dominant applications are: 1) the assessment of somatic alterations in tumors to inform prognosis and/or targeted therapeutics; and 2) the assessment of the germline to identify cancer risk alleles.

Governance, Interpretation, and Institutional Oversight of Next-Generation Sequencing (NGS)

The ACCE model uses four main components to evaluate new genetic tests: analytic validity; clinical validity; clinical utility; and ethical, legal, and social issues.[17]

• Analytic validity reflects the accuracy and reliability of the measurement of the genotype.
• Clinical validity refers to how accurately the finding (such a genetic variation) predicts the outcome of interest (such as cancer risk).
• Clinical utility considers whether patient outcome is improved by knowledge of the test result. In 2015, the ACMG issued a policy statement regarding the need to expand the definition of clinical utility to consider the utility of genetic and genomic services for individual patients, families, and society.[5]
• Ethical, legal, and social issues, including informed consent issues, can arise with each new genetic test.

The ACCE model's framework has been adopted worldwide for the evaluation of genetic tests.

Several layers of complexity exist in managing NGS in the clinical setting. At the purely technical level, improvements in the sequencing technique have allowed for sequencing across the entire genome, not merely the exome. As the costs decrease, exomic and genomic sequencing of tumor and normal tissue can be expected to become more routine.

With routine use of WGS, major challenges in interpretation emerge. Foremost is the matter of determining which sequence variations in known cancer predisposition genes are pathologic, which are harmless, and which variations require further evaluation as to their significance. This is not a new challenge. Various groups are developing processes for the interpretation and curation of a growing database of variants and their significance. For example, the International Society for Gastrointestinal Hereditary Tumors has developed such a process for the MMR genes in concert with the Human Variome Project and International Mismatch Repair Consortium.

These processes may serve as a framework for the emerging challenge of interpreting the significance of sequence variations in genes of uncertain or unknown function in regulation of neoplastic progression or other diseases. Larger cancer predisposition multigene tests have been developed by commercial laboratories, with their own process for interpretation. To the extent that increasingly larger multigene tests include genes of unknown significance, governance of the interpretation process requires that academic institutions offering their own multigene tests or using external proprietary panels develop a deliberative process for managing the quality assurance for test performance (including Clinical Laboratory Improvement Amendments [CLIA], where appropriate) and interpretation.

ACMG has issued the following updated guidelines for achieving accountability in interpreting and reporting secondary findings:[4,18]

• Constitutional variants in a list of 56 genes should be reported by the laboratory to the ordering clinician, regardless of the indication for which the clinical sequencing was ordered and regardless of patient age. This includes the normal sample of a tumor-normal sequenced dyad. Patients may opt out of such analysis after clear understanding of the ramifications and full informed consent prior to clinical sequencing.
• Only variants previously reported and a recognized cause of the disorder, or variants previously unreported but of the type expected to cause the disorder, should be reported.
• When clinical genome-scale sequencing is performed, written informed consent should be obtained by a qualified genetics health-care professional describing the nature of the test and addressing issues such as interpretive uncertainty, privacy, familial impact, and generation of secondary findings.
• Clinicians should be familiar with basic attributes and limitations of clinical sequencing.
• ACMG, together with content experts and other professional organizations, should continue to refine and update such guidelines annually.

Concerns remain that the routine reporting of germline variants in the context of tumor sequencing would require laboratories to conduct results review with germline and tumor genome expertise, which would be expected to increase costs, laboratory efforts, and turnaround time for results reporting. The nature of discussions between oncologists and patients would be altered to include the multiple facets involved with germline testing and potential results. Pre- and post-test discussions would also potentially require involvement of genetic counselors and geneticists, who are a limited resource in oncology practices. Recent expert comment stated that more data are needed about the benefits of return of secondary germline findings to cancer patients undergoing tumor sequencing, citing a need for recommendations by experts in the oncology and genetics communities.[19]

It is still very early in the development processes for oversight at the institutional level. As an example, at one high-volume cancer center, the following process has been used:

1. When considering evaluation of tumor tissue for research purposes, patients are asked to consent to undergo tests of very large panels of somatic tissue that are paired with corresponding panels for constitutional tissue. The key research undertakings involve evaluating for differences in prognosis and treatment response according to variant patterns in tumors.
2. The consent process outlines the possibility that variants suggesting underlying tumor susceptibility may be identified. Patients are asked whether they want to receive this information, should an actionable genetic variant be identified. Patients also receive expedited genetic counseling (via counselors associated with the research protocol). If a patient has chosen not to receive such actionable genetic variant information, findings will nevertheless be reported to the patient’s institutional primary care physician (PCP). Unresolved to date are the circumstances in which a patient may be approached again by his or her PCP regarding pertinent findings, notwithstanding a previous decline to receive such information.
3. When potentially significant results are found, the specific test may be repeated in a CLIA lab.
4. A committee was formed to review variants to determine whether they are considered pathogenic or otherwise actionable.
5. A second committee was formed to provide oversight for the conduct of such protocols, in the interest of deliberately evolving the processes above in a manner consistent with the anticipated routine performance of such panels in cancer patients, balanced against the need for patient autonomy and appropriately detailed informed consent.

Informed Consent Issues Arising From the Application of NGS

Informed consent for the sequencing of highly penetrant disease genes has been conducted since the mid-1990s in the contexts of known or suspected inherited diseases within selected families. However, the best methods and approaches for educating and counseling individuals about the potential benefits, limitations, and harms of genetic testing to facilitate informed decisions have not been fully elucidated or adequately tested. New informed consent challenges arise as NGS technologies are applied in clinical and research settings. Challenges to facilitating informed consent include the following:

• Providing a person-centered appreciation for the breadth and diversity of medical risks potentially identified through genomic sequencing. Establishing informed consent may be particularly challenging in medically underserved populations with less familiarity with the concept of disease risk and in individuals with no previous knowledge, experience, or context of the disease(s) for which they are identified to be at increased risk.
• Sequencing for diseases without clear management algorithms or identified best practices.
• Interpretation of variants of possible but unclear pathogenicity.
• Communicating genetic information that may be of relevance for other family members.
• Additional challenges are anticipated as health care providers not trained in genetic/genomic medicine order and receive results on behalf of their patients with the expectation to return and manage medically actionable results.

The increased availability and decreased cost of NGS technology are expanding the use of genome-wide testing of tumors, with the goal of identifying somatic variants as potential targets for cancer treatment. While identifying germline pathogenic variants may be considered secondary to the main purpose of testing tumors, the possibility of identifying actionable secondary findings of pathogenic variants in cancer predisposition genes supports the need for genetic counseling in this context. Approaches for genetic counseling and informed consent in the context of tumor sequencing have been proposed.[20,21]

Conclusion

Advances in genetic sequencing technologies have dramatically reduced the cost of sequencing an individual's full genome or exome. WGS and WES are increasingly being employed in the clinical setting in testing for both somatic variants and germline variants. In addition, multigene tests are now available commercially or within an institution. Considerable debate surrounds the clinical, ethical, legal, and counseling aspects associated with NGS and gene panels. Future research is warranted to address these issues.

References

1. Dancey JE, Bedard PL, Onetto N, et al.: The genetic basis for cancer treatment decisions. Cell 148 (3): 409-20, 2012. [PubMed: 22304912]
2. Meric-Bernstam F, Farhangfar C, Mendelsohn J, et al.: Building a personalized medicine infrastructure at a major cancer center. J Clin Oncol 31 (15): 1849-57, 2013. [PMC free article: PMC4878103] [PubMed: 23589548]
3. Sleijfer S, Bogaerts J, Siu LL: Designing transformative clinical trials in the cancer genome era. J Clin Oncol 31 (15): 1834-41, 2013. [PubMed: 23589555]
4. Green RC, Berg JS, Grody WW, et al.: ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med 15 (7): 565-74, 2013. [PMC free article: PMC3727274] [PubMed: 23788249]
5. ACMG Board of Directors: Clinical utility of genetic and genomic services: a position statement of the American College of Medical Genetics and Genomics. Genet Med 17 (6): 505-7, 2015. [PubMed: 25764213]
6. Richards S, Aziz N, Bale S, et al.: Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17 (5): 405-24, 2015. [PMC free article: PMC4544753] [PubMed: 25741868]
7. Presidential Commission for the Study of Bioethical Issues: Anticipate and Communicate: Ethical Management of Incidental and Secondary Findings in the Clinical, Research, and Direct-to-Consumer Contexts. Washington, DC: U.S. Government Printing Office, 2013. Available online. Last accessed February 20, 2024. [PubMed: 25150271]
8. National Human Genome Research Institute: DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP). 2014. Available online. Last accessed February 20, 2024.
9. Sanger F, Nicklen S, Coulson AR: DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A 74 (12): 5463-7, 1977. [PMC free article: PMC431765] [PubMed: 271968]
10. MacConaill LE: Existing and emerging technologies for tumor genomic profiling. J Clin Oncol 31 (15): 1815-24, 2013. [PMC free article: PMC3808234] [PubMed: 23589546]
11. Rizzo JM, Buck MJ: Key principles and clinical applications of "next-generation" DNA sequencing. Cancer Prev Res (Phila) 5 (7): 887-900, 2012. [PubMed: 22617168]
12. Linnarsson S: Recent advances in DNA sequencing methods - general principles of sample preparation. Exp Cell Res 316 (8): 1339-43, 2010. [PubMed: 20211618]
13. Fernald GH, Capriotti E, Daneshjou R, et al.: Bioinformatics challenges for personalized medicine. Bioinformatics 27 (13): 1741-8, 2011. [PMC free article: PMC3117361] [PubMed: 21596790]
14. Mardis ER: The $1,000 genome, the $100,000 analysis? Genome Med 2 (11): 84, 2010. [PMC free article: PMC3016626] [PubMed: 21114804]
15. Schrader KA, Cheng DT, Joseph V, et al.: Germline Variants in Targeted Tumor Sequencing Using Matched Normal DNA. JAMA Oncol 2 (1): 104-11, 2016. [PMC free article: PMC5477989] [PubMed: 26556299]
16. Raymond VM, Gray SW, Roychowdhury S, et al.: Germline Findings in Tumor-Only Sequencing: Points to Consider for Clinicians and Laboratories. J Natl Cancer Inst 108 (4): , 2016. [PMC free article: PMC4849259] [PubMed: 26590952]
17. Haddow J, Palomaki G: A model process for evaluating data on emerging genetic tests. In: Khoury M, Little J, Burke W, eds.: Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease. Oxford University Press, 2004, pp 217-33.
18. ACMG Board of Directors: ACMG policy statement: updated recommendations regarding analysis and reporting of secondary findings in clinical genome-scale sequencing. Genet Med 17 (1): 68-9, 2015. [PubMed: 25356965]
19. Parsons DW, Roy A, Plon SE, et al.: Clinical tumor sequencing: an incidental casualty of the American College of Medical Genetics and Genomics recommendations for reporting of incidental findings. J Clin Oncol 32 (21): 2203-5, 2014. [PMC free article: PMC4188159] [PubMed: 24958819]
20. Everett JN, Gustafson SL, Raymond VM: Traditional roles in a non-traditional setting: genetic counseling in precision oncology. J Genet Couns 23 (4): 655-60, 2014. [PMC free article: PMC4096055] [PubMed: 24578120]
21. Catenacci DV, Amico AL, Nielsen SM, et al.: Tumor genome analysis includes germline genome: are we ready for surprises? Int J Cancer 136 (7): 1559-67, 2015. [PMC free article: PMC4303936] [PubMed: 25123297]

References

1. Bellcross CA, Lemke AA, Pape LS, et al.: Evaluation of a breast/ovarian cancer genetics referral screening tool in a mammography population. Genet Med 11 (11): 783-9, 2009. [PubMed: 19752737]
2. Bellcross C: Further development and evaluation of a breast/ovarian cancer genetics referral screening tool. Genet Med 12 (4): 240, 2010. [PubMed: 20395744]

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PDQ® Cancer Genetics Editorial Board. PDQ Cancer Genetics Overview. Bethesda, MD: National Cancer Institute. Updated <MM/DD/YYYY>. Available at: https://www.cancer.gov/publications/pdq/information-summaries/genetics/overview-hp-pdq. Accessed <MM/DD/YYYY>. [PMID: 26389204]

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