Citations

AbstractIntroductionMAPT H1 haplotype is implicated as a risk factor for neurodegenerative diseases including Alzheimer's disease (AD).MethodsUsing Alzheimer's Disease Genetics Consortium (ADGC) genome‐wide association study (GWAS) data (n = 18,841), we conducted a MAPT H1/H2 haplotype–stratified association to discover MAPT haplotype–specific AD risk loci.ResultsWe identified 11 loci—5 in H2‐non‐carriers and 6 in H2‐carriers—although none of the MAPT haplotype–specific associations achieved genome‐wide significance. The most significant H2 non‐carrier–specific association was with a NECTIN2 intronic (P = 1.33E‐07) variant, and that for H2 carriers was near NKX6‐1 (P = 1.99E‐06). The GABRG2 locus had the strongest epistasis with MAPT H1/H2 variant rs8070723 (P = 3.91E‐06). Eight of the 12 genes at these loci had transcriptome‐wide significant differential expression in AD versus control temporal cortex (q < 0.05). Six genes were members of the brain transcriptional co‐expression network implicated in “synaptic transmission” (P = 9.85E‐59), which is also enriched for neuronal genes (P = 1.0E‐164), including MAPT.DiscussionThis stratified GWAS identified loci that may confer AD risk in a MAPT haplotype–specific manner. This approach may preferentially enrich for neuronal genes implicated in synaptic transmission.

Abstract BACKGROUND: The diagnosis of monoclonal B-cell lymphocytosis (MBL) is used to characterize patients who have a circulating population of clonal B-cells, a total B-cell count of &lt;5 x 109/L, and no other features of another B-cell lymphoproliferative disorder. In most patients the immunophenotype is similar to that of chronic lymphocytic leukemia with co-expression of CD19 and CD5. The natural history of CLL-phenotype MBL is unclear. We used the Mayo Clinic hematopathology database to identify patients with CLL-phenotype MBL and abstracted clinical information on natural history and prognostic parameters from clinical and research records. METHODS: Using hematopathology records, we identified a cohort of 1820 patients seen at Mayo Clinic between August 1998 and May 2006 who underwent investigation using peripheral blood flow cytometry and who were found to have a clonal B-cell population of CLL phenotype. After elimination of patients with advanced stage (Rai I-IV), previously treated CLL, and individuals with an established diagnosis of CLL/MBL &gt;1 year prior to flow, 636 unique patients with isolated lymphocytosis were identified. Based on our prior analysis (Leukemia Research 32:1458) demonstrating that nearly all patients with an ALC &gt;10x109/L have a B-cell count &gt;5x109/L, we focused the remaining analysis on the 405/636 patients who had an ALC &lt;10x109/L and the necessary raw data from flow cytometry analysis to determine the B-cell count. RESULTS: Among the 405 individuals with an ALC &lt;10x109/L, 306 (76%) had a B-cell count &lt;5x109/L at the time of flow and met the current criteria for the diagnosis of MBL (BJH 130:325). Median age at the time of MBL diagnosis was 70 years (range 34 – 93). A slight male preponderance was observed (59% male). B-cell counts ranged from 0.02 to 4.99x109/L and ALC ranged from 0.3–9.6 x109/L. CD38 status was available for 268 (88%) MBL patients of whom 20% were CD38 positive using the 30% threshold. Cytogenetic analysis by fluorescent in situ hybridization (FISH) was available in 130 (42%) MBL patients. Using the hierarchical classification system of Dohner and colleagues, 57 (44%) were 13q-, 23 (18%) were trisomy 12, 5 (4%) were 11q-, 4 (3%) were 17p-, and 39 (30%) had no defect detected. ZAP-70 status was available for 77 (25%) patients of whom 13% were ZAP-70+ positive. Patients underwent sequential monitoring with a median follow-up of 1.7 yrs (range 0–8.1 yrs). Time from diagnosis to treatment for patients meeting criteria for MBL (n=306) as compared to Rai stage 0 CLL patients with B-cell counts between 5.0 and 10.0 x109/L (n=99) was not significantly different and is shown in Figure 1 (p=0.87). For individuals with MBL, survival free of treatment was 98% (95%CI: 96–100%) at 1 year, 93% (95%CI: 89–98%) at 2 years, and 77% (95%CI: 67–89%) at 5 years. While gender (p=0.10), and B-cell count as a continuous variable (p=0.15) were not found to be associated with time to treatment (TTT) among MBL patients, age (p=0.02, HR=0.94, 95% CI: 0.89–0.99) and CD38 status (p&lt;0.02, HR: 3.3, 95% CI: 1.2–9.2) did show a relationship with TTT. CONCLUSIONS: Although we observed a low rate of progression among individuals meeting criteria for MBL, TFS was similar to individuals meeting current criteria for Rai stage 0 CLL who had B-cell counts between 5.0 – 10.0 x109/L . Among individuals meeting criteria for MBL, younger patients and those who were CD38 positive had a shorter time to treatment. Additional studies are needed to determine what B-cell count or other characteristics should be used to distinguish between MBL and Rai stage 0 CLL. FIGURE: FIGURE:.

Abstract BACKGROUND: The diagnosis of CLL among asymptomatic patients without lymphadenopathy or cytopenias has historically been based on documenting a characteristic lymphocyte clone and the presence of lymphocytosis. Currently, there are no data regarding what lymphocyte type (absolute lymphocyte count [ALC] or B-cell count) or threshold should be used for this diagnosis. We analyzed the relationship of these lymphocyte counts to clinical outcome in a cohort of 459 patients to determine whether the CLL diagnosis: should be based on ALC or B-cell count what lymphocyte threshold should be used for diagnosis whether any lymphocyte counts has independent prognostic value after accounting for molecular prognostic markers. METHODS: We used the Mayo Clinic CLL database to identify all Rai stage 0 patients diagnosed with CLL between 1/1/00 and 12/31/07 who were evaluated and had flow cytometry at Mayo within 12 months of diagnosis. All patients had lymphocytosis (ALC ≥5 x 109/L) and fulfilled the 1996 criteria for CLL. Raw data from the peripheral blood flow analysis was used in conjunction with blood counts to determine ALC and absolute B-cell count at diagnosis. Estimates of survival were calculated using the Kaplan-Meier method. RESULTS: To assess whether B-cell count or ALC more strongly related to clinical outcome, we evaluated the relationship of these variables with treatment free survival (TFS) and overall survival (OS; n=459). When treated as continuous variables (i.e. measuring risk of each 1.0 x 109/L increase in cell count), both ALC and B-cell count were related to TFS (hazard ratio [HR] ALC=1.02; p&lt;0.0001; HR B-cell count=1.02; p&lt;0.0001) and OS (HR ALC=1.02; p=0.04; HR B-cell count=1.02; p=0.02). Because B-cell count and ALC related to TFS and OS as a continuous variables, we next evaluated what threshold (nearest 1.0 x109/L) at diagnosis best related to an individual’s risk of requiring chemotherapeutic treatment and/or dying of CLL using the HR (log rank statistic) and c-statistic [c=1 indicates perfect discrimination between poor survivors and good survivors; c=0.5 equivalent to chance]. The B-cell threshold that best predicted OS was 11 x 109/L (HR=2.36, p=0.01; c=0.60). This threshold also predicted TFS (HR=3.02; p&lt;0.0001; c=0.64). With respect to ALC, a threshold of 12 x 109/L was able to predict TFS (HR=2.28, p=0.003; c=0.62) but not OS (HR=1.62, p=0.12; c=0.56). The B-cell threshold used in the current diagnostic criteria (5 x 109/L) was able to predict TFS (HR=3.24, p&lt;0.0001; c=0.63), but not OS (HR=1.64, p=0.13; c=0.55) Finally, we evaluated the ability of B-cell count to predict TFS independent of IGHV mutation status, ZAP-70 status, CD38 status, and FISH. Since not all patients had all prognostic tests performed, the predictive value of B-cell count (&lt; or ≥11 x 109) independent of the other prognostic variable was assessed for each variable independently. B-cell count retained prognostic value independent of IGHV mutation status, ZAP-70 status, CD38 status, and FISH (all p≤0.001) [Table]. CONCLUSIONS: Although B-cell count and ALC have similar ability to predict TFS and OS as continuous variables, B-cell count may be a better predictor of TFS and OS when a defined lymphocyte threshold is used. These findings provide evidence to justify the recent proposal by Hallek et al (Blood 111:5446) to base the diagnosis of CLL on B-cell count rather than ALC. The results also provide justification for retaining the size of the B-cell count as part of the diagnostic criteria even in the era of molecular/biologic prognostic markers, but imply a threshold of 11 x 109 is the B-cell count that best predicts patient’s TFS and risk of death. Differentiating between CLL and MBL based on a patient’s likelihood of developing clinical symptoms and dying of disease could minimize unnecessary psychologic distress caused by labeling asymptomatic individuals at low risk for adverse clinical consequences as having leukemia. TABLE: TREATMENT FREE SURVIVAL HR p-value CD38 + 3.20 0.0002 B-cell ≥11 3.10 0.0001 ZAP + 8.33 &lt; 0.0001 B-cell ≥11 6.86 &lt; 0.0001 Unmutated 5.26 &lt; 0.0001 B-cell ≥11 3.06 0.001 FISH (17p- or 11q-) 3.97 0.01 B-cell ≥11 3.67 &lt; 0.0001

Background Neprilysin is a metalloprotease involved in proteolysis of numerous peptides, including natriuretic peptides, and is of prognostic and therapeutic importance in heart failure with reduced ejection fraction. No studies have investigated circulating neprilysin in the community, its clinical correlates, or its relationship to cardiovascular disease in the general population. Methods and Results Plasma neprilysin was measured in 1536 participants from Olmsted County, Minnesota, using a commercially available sandwich ELISA assay. Clinical and echocardiographic correlates and subsequent outcomes were determined. Soluble neprilysin is non‐normally distributed in the community (median: 3.9 ng/mL; interquartile range: 1.0–43.0 ng/mL). There was no relationship between plasma neprilysin and age (Spearman correlation: −0.04, P =0.16); body mass index (Spearman correlation: −0.04, P =0.16); glomerular filtration rate (Spearman correlation: −0.007, P =0.8); or A‐, B‐, or C‐type natriuretic peptides (Spearman correlation: 0.03, P =0.22; −0.001, P =0.96; 0.01, P =0.67, respectively). Among tertiles of neprilysin, the lowest tertile group had the highest prevalence of smokers ( P <0.001), hypertension ( P =0.04), dyslipidemia ( P =0.03), and diastolic dysfunction ( P =0.02). Soluble neprilysin was not prospectively associated with death or heart failure over a median of 10.7 years. Conclusions In a large community‐based cohort, for the first time, we described the distribution of circulating neprilysin in the general community. We observed that neprilysin does not correlate with natriuretic peptide levels and is not independently associated with adverse outcomes. The novel associations observed between low soluble neprilysin levels and an adverse cardiometabolic and smoking profile requires further investigation.

Objective Obesity is a risk factor for colorectal cancer (CRC), accounting for more than 14% of CRC incidence. Microbial dysbiosis and chronic inflammation are common characteristics in both obesity and CRC. Human and murine studies, together, demonstrate the significant impact of the microbiome in governing energy metabolism and CRC development; yet, little is understood about the contribution of the microbiome to development of obesity-associated CRC as compared to individuals who are not obese. Design In this study, we conducted a meta-analysis using five publicly available stool and tissue-based 16S rRNA and whole genome sequencing (WGS) data sets of CRC microbiome studies. High-resolution analysis was employed for 16S rRNA data, which allowed us to achieve species-level information to compare with WGS. Results Characterisation of the confounders between studies, 16S rRNA variable region and sequencing method did not reveal any significant effect on alpha diversity in CRC prediction. Both 16S rRNA and WGS were equally variable in their ability to predict CRC. Results from diversity analysis confirmed lower diversity in obese individuals without CRC; however, no universal differences were found in diversity between obese and non-obese individuals with CRC. When examining taxonomic differences, the probability of being classified as CRC did not change significantly in obese individuals for all taxa tested. However, random forest classification was able to distinguish CRC and non-CRC stool when body mass index was added to the model. Conclusion Overall, microbial dysbiosis was not a significant factor in explaining the higher risk of colon cancer among individuals with obesity.

Anastrozole is a widely prescribed aromatase inhibitor for the therapy of estrogen receptor positive (ER+) breast cancer. We performed a genome‐wide association study (GWAS) for plasma anastrozole concentrations in 687 postmenopausal women with ER+ breast cancer. The top single‐nucleotide polymorphism (SNP) signal mapped across SLC38A7 (rs11648166, P = 2.3E‐08), which we showed to encode an anastrozole influx transporter. The second most significant signal (rs28845026, P = 5.4E‐08) mapped near ALPPL2 and displayed epistasis with the SLC38A7 signal. Both of these SNPs were cis expression quantitative trait loci (eQTL)s for these genes, and patients homozygous for variant genotypes for both SNPs had the highest drug concentrations, the highest SLC38A7 expression, and the lowest ALPPL2 expression. In summary, our GWAS identified a novel gene encoding an anastrozole transporter, SLC38A7, as well as epistatic interaction between SNPs in that gene and SNPs near ALPPL2 that influenced both the expression of the transporter and anastrozole plasma concentrations.