Novel Common Genetic Susceptibility Loci for Colorectal Cancer

BACKGROUND: Previous genome-wide association studies (GWAS) have identified 42 loci (P < 5 × 10-8) associated with risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. METHODS: We conducted a GWAS in European descent CRC cases and control subjects using a discovery-replication design, followed by examination of novel findings in a multiethnic sample (cumulative n = 163 315). In the discovery stage (36 948 case subjects/30 864 control subjects), we identified genetic variants with a minor allele frequency of 1% or greater associated with risk of CRC using logistic regression followed by a fixed-effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P < 5 × 10-8) were tested for replication in separate European ancestry samples (12 952 case subjects/48 383 control subjects). Next, we examined the generalizability of discovered variants in East Asians, African Americans, and Hispanics (12 085 case subjects/22 083 control subjects). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. RESULTS: The discovery GWAS identified 11 variants associated with CRC at P < 5 × 10-8, of which nine (at 4q22.2/5p15.33/5p13.1/6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/20q13.13) independently replicated at a P value of less than .05. Multiethnic follow-up supported the generalizability of discovery findings. These results demonstrated a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% confidence interval [CI] = 7.9% to 13.7%; known variants) to 11.9% (95% CI = 9.2% to 15.5%; known and novel variants). A polygenic risk score identified 4.3% of the population at an odds ratio for developing CRC of at least 2.0. CONCLUSIONS: This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screenin

The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

The Science Performance of JWST as Characterized in Commissioning

This paper characterizes the actual science performance of the James Webb Space Telescope (JWST), as determined from the six month commissioning period. We summarize the performance of the spacecraft, telescope, science instruments, and ground system, with an emphasis on differences from pre-launch expectations. Commissioning has made clear that JWST is fully capable of achieving the discoveries for which it was built. Moreover, almost across the board, the science performance of JWST is better than expected; in most cases, JWST will go deeper faster than expected. The telescope and instrument suite have demonstrated the sensitivity, stability, image quality, and spectral range that are necessary to transform our understanding of the cosmos through observations spanning from near-earth asteroids to the most distant galaxies.Comment: 5th version as accepted to PASP; 31 pages, 18 figures; https://iopscience.iop.org/article/10.1088/1538-3873/acb29

Quantitative DNA Methylation Analysis of Candidate Genes in Cervical Cancer

<div><p>Aberrant DNA methylation has been observed in cervical cancer; however, most studies have used non-quantitative approaches to measure DNA methylation. The objective of this study was to quantify methylation within a select panel of genes previously identified as targets for epigenetic silencing in cervical cancer and to identify genes with elevated methylation that can distinguish cancer from normal cervical tissues. We identified 49 women with invasive squamous cell cancer of the cervix and 22 women with normal cytology specimens. Bisulfite-modified genomic DNA was amplified and quantitative pyrosequencing completed for 10 genes (<i>APC</i>, <i>CCNA</i>, <i>CDH1</i>, <i>CDH13</i>, <i>WIF1</i>, <i>TIMP3</i>, <i>DAPK1</i>, <i>RARB</i>, <i>FHIT</i>, and <i>SLIT2</i>). A Methylation Index was calculated as the mean percent methylation across all CpG sites analyzed per gene (~4-9 CpG site) per sequence. A binary cut-point was defined at >15% methylation. Sensitivity, specificity and area under ROC curve (AUC) of methylation in individual genes or a panel was examined. The median methylation index was significantly higher in cases compared to controls in 8 genes, whereas there was no difference in median methylation for 2 genes. Compared to HPV and age, the combination of DNA methylation level of <i>DAPK1</i>, <i>SLIT2</i>, <i>WIF1</i> and <i>RARB</i> with HPV and age significantly improved the AUC from 0.79 to 0.99 (95% CI: 0.97–1.00, <i>p-value</i> = 0.003). Pyrosequencing analysis confirmed that several genes are common targets for aberrant methylation in cervical cancer and DNA methylation level of four genes appears to increase specificity to identify cancer compared to HPV detection alone. Alterations in DNA methylation of specific genes in cervical cancers, such as <i>DAPK1</i>, <i>RARB</i>, <i>WIF1</i>, and <i>SLIT2</i>, may also occur early in cervical carcinogenesis and should be evaluated.</p></div

The proportion of cancers correctly classified (true positives) and the proportion of normal tissues incorrectly classified (false positives) using an <i>a priori</i> threshold ≥15% methylation.

<p>a. AUC = Area under the curve from Receiver Operator Curve (ROC); FP = False positive rate; PPV = Positive predictive value; NPV = negative predictive value; Sensitivity = true positive rate</p><p>The proportion of cancers correctly classified (true positives) and the proportion of normal tissues incorrectly classified (false positives) using an <i>a priori</i> threshold ≥15% methylation.</p

Distribution of DNA methylation Index among ten tumor suppressor genes in cervical cancer cases and controls.

<p>a. Number of cases and controls per gene vary due to exhausted DNA and/or inability to generate pyrosequencing data</p><p>b. Summary statistics with a nonparametric Mann-Whitney test comparing cases and controls for each gene</p><p>Distribution of DNA methylation Index among ten tumor suppressor genes in cervical cancer cases and controls.</p

Receiver Operator Curve (ROC) Analysis of DNA Methylation Index and HPV.

<p>ROC of predicted sensitivity and 1-specificity when DNA MI for <i>DAPK1</i>, <i>RARB</i>, <i>SLIT2</i> and <i>WIF1</i> genes was added to a model with HPV status and age. The test of the equality between AUC for HPV and age (AUC = 79%, dashed line) compared to methylation index for <i>DAPK1</i>, <i>RARB</i>, <i>SLIT2</i> and <i>WIF1</i>, HPV, and age (AUC = 98%, solid line), <i>p-value</i> = 0.0002.</p

Box-plot of methylation indices for each candidate gene analyzed by pyrosequencing.

<p>Genes are ordered by Mann-Whitney <i>p-value (</i>** p-value<0.0001 and * p-value<0.05). Methylation index (MI) of each gene is presented for normal cervical sample (N) and cancer (T) as a boxplot. Whiskers of the boxplot mark the 5^th and 95^th percentiles, the box marks the 25^th (low boundary of box), median, and 75th (upper boundary of box) percentiles, and extreme values (●).</p

Demographics and clinical characteristics of women with invasive cervical cancer and frequency matched controls.

<p>a. Numbers may not add up to total due to missing data.</p><p>b. Differences between cases and controls determined using the Fisher exact Chi2 test or T-test.</p><p>Demographics and clinical characteristics of women with invasive cervical cancer and frequency matched controls.</p