With the completion of the Human Genome Project and the development of high throughput technologies, such as next-generation sequencing, the use of multiplex genetic testing, in which multiple genes are sequenced simultaneously to test for one or more conditions, is growing rapidly. Reflecting underlying heterogeneity where a broad range of genes confer risks for one or more cancers, the development of genetic cancer panels to assess these risks represents just one example of how multiplex testing is being applied clinically. There are a number of issues and challenges to consider when conducting genetic testing for cancer risk assessment, and these issues become exceedingly more complex when moving from the traditional single-gene approach to panel testing. Here, we address the practical considerations for clinical use of panel testing for breast, ovarian, and colon cancers, including the benefits, limitations and challenges, genetic counseling issues, and management guidelines.

The rate of meiotic recombination in the yeast Saccharomyces cerevisiae varies widely in different regions of the genome with some genes having very high levels of recombination (hotspots). A variety of experiments done in yeast suggest that hotspots are a feature of chromatin structure rather than a feature of primary DNA sequence. We examined the effects of mutating a variety of enzymes that affect chromatin structure on the recombination activity of the well-characterized HIS4 hotspot including the Set2p and Dot1p histone methylases, the Hda1p and Rpd3p histone deacetylases, the Sin4p global transcription regulator, and a deletion of one of the two copies of the genes encoding histone H3-H4. Loss of Set2p or Rpd3p substantially elevated HIS4 hotspot activity, and loss of Hda1p had a smaller stimulatory effect; none of the other alterations had a significant effect. The increase of HIS4 hotspot activity in set2 and rpd3 strains is likely to be related to the recent finding that histone H3 methylation by Set2p directs deacetylation of histones by Rpd3p. IntroductionThe rate of meiotic recombination varies considerably at different positions in the yeast genome (1,2). This variation reflects differences in the frequency of local meiosis-specific doublestrand DNA breaks (DSBs), the recombination-initiating lesion (3,4) catalyzed by Spo11p and associated proteins (5). There are several lines of evidence that the frequency of DSBs is regulated by elements of chromatin structure rather than primary DNA sequence (2). First, recombination hotspots exhibit hypersensitivity to nucleases (6-10). Some loci (8,9) undergo meiosis-specific alterations in nuclease sensitivity prior to DSB formation, although no such changes are observed at other loci (6). Since all nuclease-hypersensitive regions are not meiotic Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Conflicts of interest None. NIH Public AccessAuthor Manuscript DNA Repair (Amst) (6,10), "open" chromatin appears necessary, but not sufficient, for meiotic recombination hotspot activity. Second, insertion of a nucleosome-excluding sequence into the yeast genome creates a recombination hotspot (11). Third, the activity of some hotspots requires the binding of transcription factors, but not high levels of transcription (2). This result has been interpreted as indicating that chromatin modifications associated with transcription factor binding stimulate both transcription and recombination. Finally, some mutants that affect chromatin modifications reduce the frequency of meiosis-specific DSBs. In the yeast S. pombe, mutatio...

This is the first genome-wide association study of weight loss response to RYGB. Variation in weight loss outcomes after RYGB may be influenced by several common genetic variants.

Background The ability to establish genetic risk models is critical for early identification and optimal treatment of breast cancer. For such a model to gain clinical utility, more variants must be identified beyond those discovered in previous genome wide association studies (GWAS). This is especially true for women at high risk because of family history, but without BRCA1/2 mutations. Methods This study incorporates three datasets in a GWAS analysis of women with Ashkenazi Jewish (AJ) homogeneous ancestry. Two independent discovery cohorts were comprised of 239 and 238 AJ women with invasive breast cancer or preinvasive ductal carcinoma in situ and strong family histories of breast cancer, but lacking the three BRCA1/2 founder mutations, along with 294 and 230 AJ controls, respectively. An independent, third cohort of 203 AJ cases with familial breast cancer history and 263 healthy controls of AJ women was used for validation. Results A total of 19 SNPs were identified as associated with familial breast cancer risk in AJ women. Among these SNPs, 13 were identified from a panel of 109 discovery SNPs, including an FGFR2 haplotype. Additionally, 6 previously identified breast cancer GWAS SNPs were confirmed in this population. Seven of the 19 markers were significant in a multivariate predictive model of familial breast cancer in AJ women, 3 novel SNPs [rs17663555(5q13.2), rs566164(6q21), and rs11075884(16q22.2)], the FGFR2 haplotype, and 3 previously published SNPs [rs13387042(2q35), rs2046210(ESR1), and rs3112612(TOX3)], yielding moderate predictive power with an area under the curve (AUC) of the ROC (receiver-operator characteristic curve) of 0.74. Conclusion Population-specific genetic variants in addition to variants shared with populations of European ancestry may improve breast cancer risk prediction among AJ women from high-risk families without founder BRCA1/2 mutations.

Genetic differences among individuals contribute to differential susceptibility to cancer and, undoubtedly, to variable efficacy and toxicity of pharmacological-based therapeutics. Many of the specific molecular processes involved in human tumorigenesis have been elucidated and accurately modeled in mice. However, the current models used for drug testing do not accurately predict how new treatments will fare in clinical trials. More sophisticated models that treat cancer as a complex disease present within heterogenous patient populations will provide better predictive power to identify patients that may benefit from specific therapies or that may develop potential drug-induced toxicities.