第809回千葉医学会例会・第一外科教室談話会 64.

<p>RR, relative risk.</p

Novel Luminex Assay for Telomere Repeat Mass Does Not Show Well Position Effects Like qPCR

<div><p>Telomere length is a potential biomarker of aging and risk for age-related diseases. For measurement of relative telomere repeat mass (TRM), qPCR is typically used primarily due to its low cost and low DNA input. But the position of the sample on a plate often impacts the qPCR-based TRM measurement. Recently we developed a novel, probe-based Luminex assay for TRM that requires ~50ng DNA and involves no DNA amplification. Here we report, for the first time, a comparison among TRM measurements obtained from (a) two singleplex qPCR assays (using two different primer sets), (b) a multiplex qPCR assay, and (c) our novel Luminex assay. Our comparison is focused on characterizing the effects of sample positioning on TRM measurement. For qPCR, DNA samples from two individuals (K and F) were placed in 48 wells of a 96-well plate. For each singleplex qPCR assay, we used two plates (one for Telomere and one for Reference gene). For the multiplex qPCR and the Luminex assay, the telomere and the reference genes were assayed from the same well. The coefficient of variation (CV) of the TRM for Luminex (7.2 to 8.4%) was consistently lower than singleplex qPCR (11.4 to 14.9%) and multiplex qPCR (19.7 to 24.3%). In all three qPCR assays the DNA samples in the left- and right-most columns showed significantly lower TRM than the samples towards the center, which was not the case for the Luminex assay (p = 0.83). For singleplex qPCR, 30.5% of the variation in TL was explained by column-to-column variation and 0.82 to 27.9% was explained by sample-to-sample variation. In contrast, only 5.8% of the variation in TRM for the Luminex assay was explained by column-to column variation and 50.4% was explained by sample-to-sample variation. Our novel Luminex assay for TRM had good precision and did not show the well position effects of the sample that were seen in all three of the qPCR assays that were tested.</p></div

Variation of “single gene” measures from different assays by column or row position.

<p>The CT values for the randomly selected single gene MTHFR and the reference gene 36B4 by column are shown in (A) and (B) respectively. The variation of relative abundance of single gene MTHFR (measured as <i>MTHFR/36B4</i> ratio) by column and row is shown in (C) and (D) respectively.</p

Trans-ethnic predicted expression genome-wide association analysis identifies a gene for estrogen receptor-negative breast cancer

<div><p>Genome-wide association studies (GWAS) have identified more than 90 susceptibility loci for breast cancer, but the underlying biology of those associations needs to be further elucidated. More genetic factors for breast cancer are yet to be identified but sample size constraints preclude the identification of individual genetic variants with weak effects using traditional GWAS methods. To address this challenge, we utilized a gene-level expression-based method, implemented in the MetaXcan software, to predict gene expression levels for 11,536 genes using expression quantitative trait loci and examine the genetically-predicted expression of specific genes for association with overall breast cancer risk and estrogen receptor (ER)-negative breast cancer risk. Using GWAS datasets from a Challenge launched by National Cancer Institute, we identified <i>TP53INP2</i> (tumor protein p53-inducible nuclear protein 2) at 20q11.22 to be significantly associated with ER-negative breast cancer (Z = -5.013, p = 5.35×10⁻⁷, Bonferroni threshold = 4.33×10⁻⁶). The association was consistent across four GWAS datasets, representing European, African and Asian ancestry populations. There are 6 single nucleotide polymorphisms (SNPs) included in the prediction of <i>TP53INP2</i> expression and five of them were associated with estrogen-receptor negative breast cancer, although none of the SNP-level associations reached genome-wide significance. We conducted a replication study using a dataset outside of the Challenge, and found the association between <i>TP53INP2</i> and ER-negative breast cancer was significant (p = 5.07x10^-3). Expression of <i>HP</i> (16q22.2) showed a suggestive association with ER-negative breast cancer in the discovery phase (Z = 4.30, p = 1.70x10^-5) although the association was not significant after Bonferroni adjustment. Of the 249 genes that are 250 kb within known breast cancer susceptibility loci identified from previous GWAS, 20 genes (8.0%) were statistically significant associated with ER-negative breast cancer (p<0.05), compared to 582 (5.2%) of 11,287 genes that are not close to previous GWAS loci. This study demonstrated that expression-based gene mapping is a promising approach for identifying cancer susceptibility genes.</p></div

Variation of Telomere products by column.

<p>Column number is shown in x-axis and the quantity of Telomere product is shown in y-axis. CT-values of Telomere product (inversely proportional to PCR product quantity) from SP-qPCR-set1, SP-qPCR-set2 and MP-qPCR are shown in fig (A), (B) and (C) respectively. Quantity of Telomere product measured by Luminex assay is shown in (D).</p

PCR primers and thermocycling conditions.

<p>PCR primers and thermocycling conditions.</p

Regulatory element annotation of variants that predicted expression of <i>TP53INP2</i> using HaploReg [33].

<p>Regulatory element annotation of variants that predicted expression of <i>TP53INP2</i> using HaploReg [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006727#pgen.1006727.ref033" target="_blank">33</a>].</p

Percentage of variation in the data that can be explained by variation in different factors.

<p>Percentage of variation in the data that can be explained by variation in different factors.</p

Top genes with P-values < 10⁻³ in analyses of association between predicted gene expressions and overall breast cancer risk^*.

<p>Top genes with P-values < 10⁻³ in analyses of association between predicted gene expressions and overall breast cancer risk^{<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1006727#t001fn001" target="_blank">*</a>}.</p

GAME-ON replication for SNPs related to the <i>TP53INP2</i> gene.

<p>GAME-ON replication for SNPs related to the <i>TP53INP2</i> gene.</p