paywalled: Potential Usefulness of Single Nucleotide Polymorphisms to Identify Persons at High Cancer Risk: An Evaluation of Seven Common Cancers [Statistics in Oncology]

Potential Usefulness of Single Nucleotide Polymorphisms to Identify Persons at High Cancer Risk: An Evaluation of Seven Common Cancers [Statistics in Oncology]:

Purpose
To estimate the likely number and predictive strength of cancer-associated single nucleotide polymorphisms (SNPs) that are yet to be discovered for seven common cancers.

Methods
From the statistical power of published genome-wide association studies, we estimated the number of undetected susceptibility loci and the distribution of effect sizes for all cancers. Assuming a log-normal model for risks and multiplicative relative risks for SNPs, family history (FH), and known risk factors, we estimated the area under the receiver operating characteristic curve (AUC) and the proportion of patients with risks above risk thresholds for screening. From additional prevalence data, we estimated the positive predictive value and the ratio of non–patient cases to patient cases (false-positive ratio) for various risk thresholds.

Results
Age-specific discriminatory accuracy (AUC) for models including FH and foreseeable SNPs ranged from 0.575 for ovarian cancer to 0.694 for prostate cancer. The proportions of patients in the highest decile of population risk ranged from 16.2% for ovarian cancer to 29.4% for prostate cancer. The corresponding false-positive ratios were 241 for colorectal cancer, 610 for ovarian cancer, and 138 or 280 for breast cancer in women age 50 to 54 or 40 to 44 years, respectively.

Conclusion

Foreseeable common SNP discoveries may not permit identification of small subsets of patients that contain most cancers. Usefulness of screening could be diminished by many false positives. Additional strong risk factors are needed to improve risk discrimination.

open access - Revie: Unravelling modifiers of breast and ovarian cancer risk for BRCA1 and BRCA2 mutation carriers: update on genetic modifiers - Journal of Internal Medicine (references to Lynch Syndrome/Familial Melanoma)

Unravelling modifiers of breast and ovarian cancer risk for BRCA1 and BRCA2 mutation carriers: update on genetic modifiers - Journal of Internal Medicine

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Genetic variants associated with breast cancer risk for BRCA1 mutations carriers

Genetic variants associated with breast cancer risk for BRCA2 mutations carriers

Patterns of association and tumour characteristics

Genetic modifiers of ovarian cancer risk

Environmental, hormonal and reproductive modifiers of risk

Common alleles and cancer risks for mutation carriers

Future challenges     "Over the past 5 years, there has been substantial progress in our understanding of genetic factors that modify breast and ovarian cancer risk for BRCA1 and BRCA2 mutation carriers. This was made possible to a great extent because of the availability of large numbers of mutation carriers from the CIMBA consortium and GWAS data. However, the five loci described in this review that are associated with breast cancer risk for BRCA1 mutation carriers are estimated to explain only approximately 3% of the genetic variability in breast cancer risk for BRCA1 mutation carriers. Similarly, the 11 SNPs associated with breast cancer risk for BRCA2 mutation carriers are estimated to account for approximately 6% of the genetic variability in breast cancer risk for BRCA2 mutation carriers. Therefore, the majority of the genetic variability in breast cancer risk for mutation carriers still remains unexplained. Several more breast and ovarian cancer susceptibility alleles have been identified through GWAS in the general population, but have not yet been investigated in mutation carriers [61, 63, 72, 75]. Given the observed association patterns in mutation carriers with previously identified loci, it is expected that at least a subset of these will also be associated with breast or ovarian cancer risk for mutation carriers. Additional genetic modifiers of risk may also be identified through not only the ongoing GWAS in BRCA1 and BRCA2 mutation carriers but also other GWAS from the general population or by GWAS focusing on specific cancer subtypes such as oestrogen-receptor-negative or triple-negative breast cancers, or serous ovarian cancer. However, it is likely that several of the alleles identified through population-based GWAS may be associated with modest relative risks in the range of 1.05–1.10. Despite sample sizes of approximately 15 000 BRCA1 and 10 000 BRCA2 mutation carriers, CIMBA would still be underpowered to detect modifying polymorphisms conferring such modest relative risks. Given the rarity of BRCA1 and BRCA2 mutations, increasing sample sizes is currently only possible through increased collaboration between studies and through continued recruitment of mutation carriers........

Conclusions

As more cost-effective mutation screening techniques become available, the number of identified BRCA1 and BRCA2 mutation carriers in the population is likely to increase. Therefore, it will be important that all mutation carriers are provided with accurate information on their risk of developing breast and ovarian cancer, so that informed decisions on clinical management are made. Our understanding of factors influencing cancer risk variability in mutation carriers has increased over the last few years and is likely to improve further in the near future. Therefore, we are getting closer to the goal of being able to provide more individualized clinical management. Understanding how cancer risks are modified in BRCA1 and BRCA2 mutation carriers will also provide further insights for studying the biological mechanisms of cancer development in mutation carriers. These may lead to the development of novel therapies and more accurate prediction of breast and ovarian cancer progression in mutation carriers.

Studying genetic modifiers of breast and ovarian cancer risk for BRCA1 and BRCA2 mutation carriers has provided useful insights in study design, analytical methodology and applications, which could be used for studying modifiers of disease in carriers of other high-risk mutations such as the mismatch repair genes MSH2, MLH1, MSH6, PMS2 in colorectal cancer (Lynch Syndrome) and CDKN2A in melanoma but also other noncancer-related diseases.

 

 

 

 

 

 

abstract: Ovarian Cancer Risk Associated with Inherited Inflammation-Related Variants

Abstract

The importance of inflammation pathways to the development of many human cancers prompted us to examine the associations between single-nucleotide polymorphisms (SNPs) in inflammation-related genes and risk of ovarian cancer.
In a multi-site case-control study, we genotyped SNPs in a large panel of inflammatory genes in 930 epithelial ovarian cancer cases and 1,037 controls using a custom array and analyzed by logistic regression. SNPs with p<0.10 were evaluated among 3,143 cases and 2,102 controls from the Follow-up of Ovarian Cancer Genetic Association and Interaction Studies (FOCI) collaboration.
Combined analysis revealed association with SNPs rs17561 and rs4848300 in the interleukin gene IL1A which varied by histologic subtype (heterogeneity p=0.03). For example, IL1A rs17561, which correlates with numerous inflammatory phenotypes, was associated with decreased risk of clear cell, mucinous, and endometrioid subtype, but not with the most common serous subtype. Genotype at rs1864414 in the arachidonate 5-lipoxygenase ALOX5 was also associated with decreased risk.
Thus, inherited variation in IL1A and ALOX5 appears to affect ovarian cancer risk which, for IL1A, is limited to rarer subtypes. Given the importance of inflammation in tumorigenesis and growing evidence of subtype-specific features in ovarian cancer, functional investigations will be important to help clarify the importance of inherited variation related to inflammation in ovarian carcinogenesis.

Retrospective study of the impact of pharmacogenetic variants on paclitaxel toxicity and survival in patients with ovarian cancer.

PURPOSE: Paclitaxel has a broad spectrum of anti-tumor activity and is useful in the treatment of ovarian, breast, and lung cancer. Paclitaxel is metabolized in the liver by CYP2C8 and CYP3A4 and transported by P-glycoprotein. The dose-limiting toxicities are neuropathy and neutropenia, but the interindividual variability in toxicity and also survival is large. The main purpose of this study was to investigate the impact of genetic variants in CYP2C8 and ABCB1 on toxicity and survival.
METHODS: The 182 patients previously treated for ovarian cancer with carboplatin and paclitaxel in either the AGO-OVAR-9 or the NSGO-OC9804 trial in Denmark or Sweden were eligible for this study. Genotyping was carried out on formalin-fixed tissue. The patients' toxicity profiles and survival data were derived from retrospective data. CYP2C8*3, ABCB1 C1236T, G2677T/A, and C3435T were chosen a priori for primary analysis; a host of other variants were entered into an exploratory analysis.
RESULTS: Clinical data and tissue were available from a total of 119 patients. Twenty-two single nucleotide polymorphisms (SNPs) in 10 genes were determined. Toxicity registration was available from 710 treatment cycles. In the primary analysis, no statistically significant correlation was found between CYP2C8*3, ABCB1 C1236T, G2677T/A, and C3435T and neutropenia, sensoric neuropathy, and overall survival.
CONCLUSION: CYP2C8*3 and the ABCB1 SNPs C1236T, G2677T/A, and C3435T were not statistically significantly correlated to overall survival, sensoric neuropathy, and neutropenia in 119 patients treated for ovarian cancer with paclitaxel/carboplatin.

health media: BRCA1 Breast Cancer Risk Linked To Other Genes

"...People who carry certain mutations of the BRCA1 gene are known to have a higher risk of developing breast cancer.

Couch told the media that their findings should be "useful in helping determine individual risk for breast cancer in BRCA1 carriers".

"It also provides insights into hormone-receptor-negative breast cancer in the general population," he added.

For the study, Couch and colleagues conducted four phases of genome-wide association studies (GWAS) that altogether involved 20 research centers in 10 North American and European countries and Israel.

For the first phase, to identify candidate gene variants, they scanned the genomes of 1,193 carriers of BRCA1 mutations who were under 40 and had invasive breast cancer and compared them to scans of about the same number of controls: BRCA1 carriers of similar age who did not have breast cancer.

In comparing the genomes from the two populations the researchers examined over half a million genetic alterations. They found 96 pieces of DNA called SNPs, or "snips", short for single nucleotide polymorphisms, that they thought would be likely candidates because they differed between the two populations...."cont'd

A Third-Generation Map of Human Genetic Variation

An international consortium has published the largest survey of human genetic variation thus far: a third-generation map that includes data from 11 global populations. The accomplishment will help in the ongoing search for genetic variants associated with complex diseases.

Any 2 people are more than 99% the same at the genetic level. The small variations between people can help explain differences in susceptibility to disease, response to drugs or reaction to environmental factors.
Stretches of DNA sequence tend to be inherited together. Thus, sets of small genetic variations called single nucleotide polymorphisms (SNPs) tend to be grouped. These clusters are called haplotypes. The map of human genetic variation is called a haplotype map, or HapMap.
Previous versions of the HapMap were built on the analysis of DNA collected from 270 volunteers from 4 geographically diverse populations. The first version contained approximately 1 million SNPs. The second-generation map brought that total to more than 3.1 million SNPs.
Over the last few years, researchers conducting genome-wide association studies have relied on data from the HapMap to discover hundreds of common genetic variants associated with complex human diseases, such as cardiovascular disease, diabetes, cancer and many other health conditions. Funding to create the third-generation HapMap was provided by NIH’s National Human Genome Research Institute (NHGRI), National Institute on Deafness and Other Communication Disorders (NIDCD) and the Wellcome Trust.
For the latest version, researchers analyzed about 1.6 million SNPs in a much broader range of samples from around the world. As reported in the September 2, 2010, issue of Nature, the HapMap now includes data from an additional 7 global populations, bringing the total number of volunteers to almost 1,200.
The consortium also carefully sequenced 10 regions totaling about 1 million base pairs in 692 samples. The scientists found that 77% of the SNPs they detected were new. This result shows that many more variants remain to be found, especially rare variants. In addition, the scientists added more than 800 copy-number variants to the resource. These reflect differences in the number of copies of specific DNA regions people harbor.

"The generated HapMap provides an important foundation for studies aiming to find genetic variation related to human diseases," says NHGRI Director Dr. Eric D. Green. "It is now routinely used by researchers as a valuable reference tool in our quest to use genomics for improving human health."

Many of the HapMap researchers are also part of the 1000 Genomes Project, an international public-private consortium launched in 2008 to build an even more detailed map of human genetic variation. The scientists are using next-generation DNA sequencing technologies to build a public database with information from the complete genomes of 2,500 people from 27 populations around the world, many of which were studied in the HapMap project. Researchers will be able to use this data to expand their studies of how common and rarer genetic variations contribute to illness.

Related Links:

• Second-Generation Map of Human Genetic Variation:
  http://www.nih.gov/researchmatters/october2007/10292007variation.htm
• International HapMap Project:

A genomic and transcriptomic approach for a differential diagnosis between primary and secondary ovarian carcinomas in patients with a previous histor

CONCLUSIONS: In patients with ovarian carcinoma and a previous history of breast cancer, SNP array analysis can be used to distinguish primary and secondary ovarian tumors.

A genomic and transcriptomic approach for a differential diagnosis between primary and secondary ovarian carcinomas in patients with a previous history of breast cancer.

ABSTRACT:
BACKGROUND: The distinction between primary and secondary ovarian tumors may be challenging for pathologists. The purpose of the present work was to develop genomic and transcriptomic tools to further refine the pathological diagnosis of ovarian tumor after a previous history of breast cancer.

METHODS: Sixteen paired breast-ovary tumors from patients with a former diagnosis of breast cancer were collected.. ... A hierarchical clustering of these samples was performed, combined with a dataset of well-identified primary and secondary ovarian tumors.

RESULTS: In 12 of the 16 paired tumors analyzed, the comparison of genomic profiles confirmed the pathological diagnosis of primary ovarian tumor (n = 5) or metastasis of breast cancer (n = 7). Among four cases with uncertain pathological diagnosis, genomic profiles were clearly distinct between the ovarian and breast tumors in two pairs, thus indicating primary ovarian carcinomas, and showed common patterns in the two others, indicating metastases from breast cancer.
CONCLUSIONS: In patients with ovarian carcinoma and a previous history of breast cancer, SNP array analysis can be used to distinguish primary and secondary ovarian tumors. Transcriptomic analysis may be used when primary breast tissue specimen is not available.

media article: The inherited malignancy of breast cancer (and ovarian cancer)

Note: good article, albeit complicated subject matter - excerpts of important information: