Citations

Long intergenic non-coding RNA (lincRNA) is a family of gene transcripts, the functions of which are largely unknown. Although cigarette smoking is the main cause for lung cancer, lung cancer in non-smokers is a separate entity and its underlying cause is little known. Growing evidence suggests lincRNAs play a significant role in cancer development and progression; however, such data is lacking for lung cancer in non-smokers, or those who have never smoked. This study conducted comprehensive profiling of lincRNAs from RNA sequencing (RNA-seq) data of non-smoker patients with lung adenocarcinoma. Both known and novel lincRNAs distinctly segregated tumors from normal tissues. Approximately one third of lincRNAs were differentially expressed between tumors and normal samples and most of them were coordinated with their putative protein gene targets. More importantly, lincRNAs defined two clusters of tumors that were associated with tumor aggressiveness and patient survival. We identified a subset of lincRNAs that were differentially expressed and also associated with patient survival. Very high concordance (R2 = 0.9) was observed for the differentially expressed lincRNAs in the Cancer Genome Atlas (TCGA) validation set of 85 transcriptomes and the lincRNAs associated with survival from the discovery set were similarly predictive in the validation set. These lincRNAs warrant further investigation as potential diagnostic and prognostic markers.

The MRI community is using quantitative mapping techniques to complement qualitative imaging. For quantitative imaging to reach its full potential, it is necessary to analyze measurements across systems and longitudinally. Clinical use of quantitative imaging can be facilitated through adoption and use of a standard system phantom, a calibration/standard reference object, to assess the performance of an MRI machine. The International Society of Magnetic Resonance in Medicine AdHoc Committee on Standards for Quantitative Magnetic Resonance was established in February 2007 to facilitate the expansion of MRI as a mainstream modality for multi‐institutional measurements, including, among other things, multicenter trials. The goal of the Standards for Quantitative Magnetic Resonance committee was to provide a framework to ensure that quantitative measures derived from MR data are comparable over time, between subjects, between sites, and between vendors. This paper, written by members of the Standards for Quantitative Magnetic Resonance committee, reviews standardization attempts and then details the need, requirements, and implementation plan for a standard system phantom for quantitative MRI. In addition, application‐specific phantoms and implementation of quantitative MRI are reviewed. Magn Reson Med 79:48–61, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Abstract IFN regulatory factor 4 (IRF4) is a key transcription factor that promotes effector CD8+ T cell differentiation and expansion. The roles of IRF4 in regulating the CD8+ T cell response to cytokines have not been explored. In this article, we show that IL-2 and IL-15 signaling and STAT5 activation regulate IRF4 expression in CD8+ T cells. Gene-expression profile analysis has also revealed that IRF4 is required for expression of the receptors of IL-2 family cytokines CD122 and CD127. We found that IRF4 binds directly to CD122 and CD127 gene loci, indicating that it may directly promote CD122 and CD127 gene transcription. As a consequence, IRF4-deficient CD8+ T cells show diminished sensitivity to IL-2, IL-15, and IL-7 treatment in vitro. Furthermore, we found that IRF4-deficient CD8+ T cells had lower expression of CD122 and CD127 in vivo during influenza virus infection. These data suggest that IRF4 regulates the sensitivity of CD8+ T cells to IL-2 family cytokines, which correlates with the diminished effector and memory CD8+ T cell responses in IRF4-deficient CD8+ T cells.

Abstract Introduction: Terminal duct lobular unit (TDLU) involution, an age-related physiological process, is characterized by the reduction of size and numbers. Reduced involution is associated with higher mammographic density, a strong breast cancer risk factor. Both factors predict risk of developing breast cancer among women with benign disease. Given that prior studies have been conducted mainly in White women, we compared the extent of TDLU involution among Western and Asian breast cancer cases to describe potential racial heterogeneity in breast cancer etiology. Method: We obtained three TDLU involution metrics (count/100 mm2, mean span, and mean acini count/TDLU; all inversely correlated with TDLU involution) measured in benign breast tissue sections from 379 Chinese (254 luminal A and 125 core basal phenotype [CBP] cases) and 476 Polish (407 luminal A and 69 CBP) breast cancer cases. Polytomous logistic regression was performed using tertiles of TDLU measures as ordinal dependent variable and race as an independent variable. Covariates included age, body mass index, and parity. Analyses were performed separately by age group (<50, ≥50) and subtype (luminal A and CBP). Results: Among luminal A cases, Chinese had significantly greater TDLU count compared with Polish in both age groups (ORtrend= 11.0; 95% CI=2.80-43.2; Ptrend=0.001 for age<50; ORtrend= 12.1; 95% CI=5.12-28.7; Ptrend=1E-08 for age≥50). In addition, Chinese were more likely to have greater mean span and acini count among younger cases (ORtrend=2.5; 95% CI=1.44-4.28; Ptrend=0.001 for mean span and ORtrend=7.4; 95% CI=4.13-13.4; Ptrend=3E-11 for acini count) but not among older cases. Among CBP cases, Chinese were more likely to have greater acini count compared to Polish only among younger women (ORtrend=2.6; 95% CI=1.12-6.95; Ptrend=0.03 for age<50). Conclusion: We found Chinese were more likely to have reduced TDLU involution compared to Polish cases after accounting for potential confounders, with greater differences for luminal A and younger women. Studies of TDLU involution in diverse populations are needed to confirm and understand whether population difference in TDLU involution metrics are related to age at breast cancer onset and risks for specific tumor subtypes. Citation Format: Hyuna Sung, Changyuan Guo, Jennifer Guida, Shan Zheng, Erni Li, Jing Li, Nan Hu, Joseph Deng, Montserrat Garcia-Closas, Jonine Figueroa, Mark Sherman, Gretchen Gierach, Ning Lu, Xiaohong R. Yang. Racial variation in terminal duct lobular unit (TDLU) involution in Chinese and Polish breast cancer patients [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 5290. doi:10.1158/1538-7445.AM2017-5290

Abstract Background: Large-scale genome-wide association studies (GWAS) have identified ~35 loci associated with epithelial ovarian cancer risk. However, these genetic loci together explain only a small portion of the heritability of this malignancy. The large majority of the GWAS-identified variants are located in non-coding regions, thus possible causal genes in these loci remain largely unknown. We performed a transcriptome-wide association study (TWAS) to search for novel genetic loci for ovarian cancer risk and plausible causal genes at GWAS-identified loci. Method: Transcriptome data from normal ovarian tissue samples (n=68) and all tissue samples (n = 369), along with their high-density genotyping data, obtained from only European descendants included in the Genotype-Tissue Expression Project (GTEx), were used to build ovarian linear prediction models and cross-tissue models (to increase statistical power) using the elastic net method. Based on model performance, we evaluated 17,121 genes for their cis-predicted gene expressions in relation to ovarian cancer risk using summary statistics data generated in GWAS of ovarian cancer from 25,509 cases and 40,941 controls. MetaXcan was used to integrate gene expression prediction model with summary statistics. Results: We identified 35 genes with predicted expression levels associated with ovarian cancer risk at P value < 2.2 × 10-6, the Bonferroni corrected significance level for multiple comparisons. Of these, 12 genes at 4 genetic loci are located ≥500kb away from risk SNPs previously reported in GWAS, representing potential novel genetic loci for ovarian cancer risk. The remaining 23 genes at 12 loci are located within known ovarian cancer risk loci. Fifteen of these genes at the 12 loci have not been reported in previous studies. Analyses by ovarian cancer histological subtypes showed that the majority of these 35 genes are associated with serous invasive carcinoma. Several new associations were identified in histological subtype analyses. Conclusion: In this TWAS we identified multiple genes with predicted expressions related to ovarian cancer risk and provide substantial new information to enhance the understanding of ovarian cancer biology and genetics. Citation Format: Yingchang Lu, Joellen M. Schildkraut, Thomas A. Sellers, Lang Wu, Xingyi Guo, Bingshan Li, Y. Ann Chen, Jennifer B. Doherty, Simon Gayther, Ellen L. Goode, Hae Kyung Im, Siddhartha Kar, Kate Lawrenson, Ani W. Manichaikul, Jennifer B. Permuth, Brett M. Reid, Jamie K. Teer, Paul Pharoah, Wei Zheng, Jirong Long. Transcriptome-wide association study among 66,450 women to identify candidate susceptible genes for ovarian cancer risk [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 1308. doi:10.1158/1538-7445.AM2017-1308