Citations

Study Objective The extended‐release ( ER ) form of venlafaxine is preferred because of improved patient adherence, but the immediate‐release (IR) form is frequently used after Roux‐en‐Y gastric bypass ( RYGB ) surgery because of concerns for malabsorption. The objective of this study was to determine whether a statistically significant and predictable change in the bioavailability of venlafaxine ER capsules occurs after RYGB . Design Prospective nonblinded single‐dose pharmacokinetic study. Setting Clinical research unit at a large tertiary care medical practice. Patients Ten adult pre–bariatric surgery patients who met the criteria for noncomplicated RYGB were enrolled and served as their own controls. Interventions Patients were administered one venlafaxine ER 75‐mg capsule at two visits—the first visit at least 1 week before undergoing RYGB and the second visit 3–4 months after RYGB . Blood samples were collected at predetermined intervals over 48 hours after each dose, and the pharmacokinetics of venlafaxine were measured. Measurements and Main Results Plasma levels of venlafaxine and its primary metabolite, O ‐desmethylvenlafaxine ( ODV ), were compared at baseline and 3–4 months after RYGB . The areas under the serum concentration–time curves from 0–24 hours ( AUC 0–24 ) for venlafaxine (mean ± SD 734 ± 602 vs 630 ± 553 ng·hr/ml, p=0.22) and ODV (mean ± SD 894 ± 899 vs 1083 ± 972 ng·hr/ml, p=0.07) were similar before and after RYGB . Using a bioequivalence approach, differences in pre‐ RYGB and post‐ RYGB values of AUC 0–24 , peak serum concentration, and time to peak serum concentration were not statistically significant for either venlafaxine or ODV. Conclusion This study suggests that RYGB does not significantly alter the amount of venlafaxine or its active metabolite, ODV , absorbed from a venlafaxine ER capsule or the time over which it is absorbed.

Abstract Background Thirty-four single nucleotide polymorphisms (SNPs) are associated with CLL risk to-date. Moreover, family history (FH) of hematological malignancy has been consistently found to be associated with CLL, with an 8.5-fold increased risk of CLL among first-degree relatives. However, there has not been an evaluation of the interactive effects among genetic factors and FH with CLL risk. Methods We pooled data from 8 CLL case-control studies within the InterLymph Consortium (1499 CLL cases and 2601 controls). We computed a polygenic risk score (PRS), a weighted average of the number of risk alleles across the 34 SNPs, with the weights being the log of the previously reported odds ratio (OR) for each SNP. We categorized the PRS by quintiles using the cutoff points based on the distribution of all InterLymph controls (N=8228). Self-reported FH data was available for 60% of cases and 73% of controls. FH was defined as any hematological malignancy in one or more first-degree relative. Logistic regression was used to estimate ORs and 95% confidence intervals (CIs) adjusted for age, sex, socioeconomic status and study. Results The median age at diagnosis of CLL was 63 years and median age of consent was 60 years in the controls. 67% were male in CLL cases and 57% in controls. As expected, FH was associated with CLL risk (OR= 2.14, CI= 1.60-2.86). The median PRS in the cases was 0.40 and in the controls was -0.36 with the frequency of CLL cases in the upper PRS quintile as 48% while in the lowest quintile only 6%. The PRS was strongly associated with CLL risk (OR= 2.90, CI= 2.35-3.56 for upper versus middle quintile). When jointly modeling FH with PRS, a significant interaction was observed (P=0.03). When stratifying by FH, the upper quintile of the PRS had an 11.8-fold (CI= 3.97-34.8) increased risk relative to those in the middle quintile in the FH+ strata, while a 3.11-fold (CI= 2.35-4.10) increased risk was observed in the FH- strata. Conclusions Our data suggest that the PRS has a strong association with CLL risk and this association varies with FH status. Among those with FH-, the risk of CLL was 3-fold for those with many inherited variants while the CLL risk was much higher than that in those with a FH+. Studies are needed to see whether this PRS stratifies risk among those with monoclonal B-cell lymphocytosis, the CLL precursor condition that affects 5-7% of the general population. Citation Format: Geffen Kleinstern, Silvia de Sanjosé, Nicola Camp, Claire M. Vajdic, Timothy G. Call, Mark Liebow, Dennis Robinson, Neil E. Kay, Julie Cunningham, Yolanda Benavente, Alain Monnereau, John Spinelli, James R. Cerhan, Susan L. Slager. Association of polygenic risk scores and family history with the risk of chronic lymphocytic leukemia (CLL) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4267. doi:10.1158/1538-7445.AM2017-4267

Abstract High grade serous ovarian cancer (HGSOC) is a complex disease in which initiation and progression have been associated with gene mutation, DNA methylation changes, genetic variation, and epigenetic processes. Variation in several susceptibility regions and cancer-typical global methylation patterns have been observed in HGSOC; however, this knowledge has not been compelling in understanding HGSOC intiation or progression. As ingetration of genomic, epigenomic, and transcriptomic data has increased mechanistic understanding in other cancers, we hypothesized that tumor methylation alone or in combination with germline genetic variation influences tumor gene expression in HGSOC. We examined three nested models using an Elastnic Net (ENET) penalized regression method while adjusting for somatic copy number (CNV): a) germline genotype and tumor DNA methylation (full model), b) genotype only, and c) DNA methylation only. We included 339 cases from The Cancer Genome Atlas (TCGA), 54 cases from Mayo Clinic, and 78 cases from the Australian Ovarian Cancer Study (AOCS). Genotyping and copy number calls on germline DNA, expression, methylation and copy number on somatic samples were collected and analyzed on different platforms separately at each study site. We excluded genes with low overall expression and thus analyzed a total of 11,922 genes available in three datasets ( Ensembl IDs, 500kb window up- and downstream). In general, combining genomic data in HGSOC did not reveal a role for germline genetic variation in altering gene expression. However, in methylation only models 79 genes were associated with differential expression in the TCGA cases (permutation multiple testing adjusted p-val <0.05), in the Mayo cases (unadjusted p-val <0.05) and AOCS cases (unadjusted p-val <0.05). A known tummor suppressor (FBXW7) was associated with differential expression in the three datasets at p-val <0.01. This work demonstrates the feasibility, utility, and statistical power of ENET gene-level analyses incoporating maximal genomic information. Citation Format: Yanina Natanzon, Madalene Earp, Julie M. Cunningham, Kimberly R. Kalli, Stacey J. Winham, Sebastian M. Armasu, Melissa C. Larson, Chen Wang, David D. Bowtell, Dale W. Garsed, Ellen Goode. Omics data integration analysis in high grade serous ovarian cancer: results from three studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3368. doi:10.1158/1538-7445.AM2017-3368

AbstractLarge-scale genotyping studies have identified over 70 single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. However, knowledge regarding genetic risk factors associated with the prognosis is limited. The aim of this study was therefore to investigate the prognostic effect of nine known breast cancer risk SNPs. BC patients (n = 1687) randomly sampled in an adjuvant, randomized phase III trial (SUCCESS A study) were genotyped for nine BC risk SNPs: rs17468277(CASP8), rs2981582(FGFR2), rs13281615(8q24), rs3817198(LSP1), rs889312(MAP3K1), rs3803662(TOX3), rs13387042(2q35), rs4973768(SLC4A7), rs6504950(COX11). Cox proportional hazards models were used to test the SNPsʼ association with overall survival (OS) and progression-free survival (PFS). Additional analyses were carried out for molecular subgroups. rs3817198 in LSP1 (lymphocyte-specific protein 1) was the only SNP that significantly influenced OS (p = 0.01) and PFS (p < 0.01) in the likelihood ratio test comparing the genetic survival model with the clinical survival model. In the molecular subgroups, triple-negative patients with two minor alleles in rs3817198 had a much better prognosis relative to OS (adjusted HR 0.03; 95% CI 0.002 – 0.279) and PFS (HR 0.09; 95% CI 0.02 – 0.36) than patients with the common alleles. The same effect on PFS was shown for patients with luminal A tumors (HR 0.19; 95% CI 0.05 – 0.84), whereas patients with luminal B tumors had a poorer PFS with two minor alleles (HR 2.13; 95% CI 1.02 – 4.40). The variant in rs3817198 has a prognostic effect particularly in the subgroup of patients with triple-negative BC, suggesting a possible link with immunomodulation and BC.

Abstract Background: Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers, and cancer-related traits. Methods: The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers, and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background. Results: The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis. Conclusions: Results from these analyses will enable researchers to identify new susceptibility loci, perform fine-mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental, and lifestyle-related exposures. Impact: Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. Cancer Epidemiol Biomarkers Prev; 26(1); 126–35. ©2016 AACR.

Background The rarity of mutations in PALB2, CHEK2 and ATM make it difficult to estimate precisely associated cancer risks. Population-based family studies have provided evidence that at least some of these mutations are associated with breast cancer risk as high as those associated with rare BRCA2 mutations. We aimed to estimate the relative risks associated with specific rare variants in PALB2, CHEK2 and ATM via a multicentre case-control study. Methods We genotyped 10 rare mutations using the custom iCOGS array: PALB2 c.1592delT, c.2816T>G and c.3113G>A, CHEK2 c.349A>G, c.538C>T, c.715G>A, c.1036C>T, c.1312G>T, and c.1343T>G and ATM c.7271T>G. We assessed associations with breast cancer risk (42 671 cases and 42 164 controls), as well as prostate (22 301 cases and 22 320 controls) and ovarian (14 542 cases and 23 491 controls) cancer risk, for each variant. Results For European women, strong evidence of association with breast cancer risk was observed for PALB2 c.1592delT OR 3.44 (95% CI 1.39 to 8.52, p=7.1×10−5), PALB2 c.3113G>A OR 4.21 (95% CI 1.84 to 9.60, p=6.9×10−8) and ATM c.7271T>G OR 11.0 (95% CI 1.42 to 85.7, p=0.0012). We also found evidence of association with breast cancer risk for three variants in CHEK2, c.349A>G OR 2.26 (95% CI 1.29 to 3.95), c.1036C>T OR 5.06 (95% CI 1.09 to 23.5) and c.538C>T OR 1.33 (95% CI 1.05 to 1.67) (p≤0.017). Evidence for prostate cancer risk was observed for CHEK2 c.1343T>G OR 3.03 (95% CI 1.53 to 6.03, p=0.0006) for African men and CHEK2 c.1312G>T OR 2.21 (95% CI 1.06 to 4.63, p=0.030) for European men. No evidence of association with ovarian cancer was found for any of these variants. Conclusions This report adds to accumulating evidence that at least some variants in these genes are associated with an increased risk of breast cancer that is clinically important.