Genome-wide association studies (GWAS) have led to the identification of more than 100 common, low-penetrance loci for cancer. At these loci, common genetic variants are associated with moderate increases in risk, typically <1.5-fold. Almost all loci lie in genomic regions not previously suspected to be involved in cancer. A plausible functional basis for a few loci, such as FGFR2 for breast cancer and MSMB for prostate cancer, has been elucidated, but the majority are not understood and suggest new mechanisms of carcinogenesis. Most loci are specific to a single cancer type, and are often subtype specific (e.g. ER-positive breast cancer). There are notable differences in the genetic architecture for different cancer types, with a greater contribution of common variants for prostate cancer. The clinical utility of variants to predict individual disease risk of disease is currently limited, but this may change as more variants are identified.
[Show abstract][Hide abstract] ABSTRACT: Objective:
Prostate cancer is the second most frequent cancer in men worldwide and kills over 250,000 men worldwide every year. Prostate cancer is a heterogeneous disease at the clinical and the molecular level. The Scandinavian Twin Registry Study demonstrated that in contrast to most malignancies where environment was the overriding influence, heritable factors account for more than fifty percent of prostate cancers.
Methods and materials:
We review the literature on prostate cancer risk variants (rare and common) including SNPs and Copy Number Variants (CNVs) and discuss the potential implications of significant variants for prostate cancer patient care.
The search for prostate cancer susceptibility genes has included both family-based studies and case-control studies utilizing a variety of approaches from array-based to sequencing-based studies. A major challenge is to identify genetic variants associated with more aggressive, potentially lethal prostate cancer and to understand their role in the progression of the disease.
Future risk models useful in the clinical setting will likely incorporate several risk loci rather than single variants and may be dependent on an individual patient's ethnic background.
"The mutation patterns of ARID1B, CASP8, MAP3K13, NCOR1, SMARCD1, and CDKN1B (p27) suggested that these genes may also be tumor suppressors. Several of these genes, including MAP3K1, CASP8, and TBX3, are recessive cancer genes previously identified in genome wide association studies [25,26]. Interestingly, germline mutation of TBX3 causes ulnar-mammary syndrome, which includes, among other defects, failure of mammary gland development . "
[Show abstract][Hide abstract] ABSTRACT: Recent advances in whole-genome technologies have supplied the field of cancer research with an overwhelming amount of molecular data. Improvements in massively parallel sequencing approaches have led to logarithmic decreases in costs, and so these methods are becoming almost commonplace in the analysis of clinical trials and other cohorts of interest. Furthermore, whole-transcriptome quantification by RNA sequencing is quickly replacing microarrays. However, older chip-based methodologies such as comparative genomic hybridization and single-nucleotide polymorphism arrays have benefited from this technological explosion and are now so accessible that they can be employed in increasingly larger cohorts of patients. The study of breast cancer lends itself particularly well to these technologies. It is the most commonly diagnosed neoplasm in women, giving rise to nearly 230,000 new cases each year. Many patients are given a diagnosis of early-stage disease, for which surgery is the standard of care. These attributes result in excellent availability of tissues for whole-genome/transcriptome analysis. The Cancer Genome Atlas project has generated comprehensive catalogs of publically available genomic breast cancer data. In addition, other studies employing the power of genomic technologies in medium to large cohorts were recently published. These data are now publically available for the generation of novel hypotheses. However, these studies differed in the methods, patient cohorts, and analytical techniques employed and represent complementary snapshots of the molecular underpinnings of breast cancer. Here, we will discuss the convergences and divergences of these reports as well as the scientific and clinical implications of their findings.
Breast cancer research: BCR 07/2013; 15(4):209. DOI:10.1186/bcr3435 · 5.49 Impact Factor
"The strong association between family history and PCa risk suggests a significant genetic contribution, yet unlike breast and colorectal cancer in which highly penetrant predisposition genes have been found, specific PCa susceptibility genes have yet to be identified. Certain single nucleotide polymorphisms (SNPs) are correlated with PCa risk [7-9]. Risk estimates for individual SNPs have been associated with modest risk (OR ~ 1.1-1.8), "
[Show abstract][Hide abstract] ABSTRACT: The strong association between family history and prostate cancer (PCa) suggests a significant genetic contribution, yet specific highly penetrant PCa susceptibility genes have not been identified. Certain single-nucleotide-polymorphisms have been found to correlate with PCa risk; however uncertainty remains regarding their clinical utility and how to best incorporate this information into clinical decision-making. Genetic testing is available directly to consumers and both patients and healthcare providers are becoming more aware of this technology. Purchasing online allows patients to bypass their healthcare provider yet patients may have difficulty interpreting test results and providers may be called upon to interpret results. Determining optimal ways to educate both patients and providers, and strategies for appropriately incorporating this information into clinical decision-making are needed.
A mixed-method study was conducted in Utah between October 2011 and December 2011. Eleven focus group discussions were held and surveys were administered to 23 first-degree relatives of PCa patients living in Utah and 24 primary-care physicians and urologists practicing in Utah to present specific information about these assessments and determine knowledge and attitudes regarding health implications of using these assessments.
Data was independently coded by two researchers (relative Kappa = .88; provider Kappa = .77) and analyzed using a grounded theory approach. Results indicated differences in attitudes and behavioral intentions between patient and provider. Despite the test's limitations relatives indicated interest in genetic testing (52%) while most providers indicated they would not recommend the test for their patients (79%). Relatives expected providers to interpret genetic test results and use results to provide personalized healthcare recommendations while the majority of providers did not think the information would be useful in patient care (92%) and indicated low-levels of genetic self-efficacy.
Although similarities exist, discordance between provider and patient attitudes may influence the effective translation of novel genomic tests into clinical practice suggesting both patient and provider perceptions and expectations be considered in development of clinical decision-support tools.
BMC Health Services Research 07/2013; 13(1):279. DOI:10.1186/1472-6963-13-279 · 1.71 Impact Factor
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