European Genome-phenome Archive Research Articles

Abstract P6-08-24: Age independently predicts worse disease-specific survival in patients diagnosed with invasive breast cancers at 50 years of age or older but not younger

Abstract BACKGROUND It has been reported that women diagnosed with invasive breast cancer (IBC) in their 50s or 60s have better survival than either younger or older women. Young women with IBCs often have a more aggressive phenotype which contributes to worse survival. But there is supporting evidence in the literature for both sides whether age alone is an intrinsic driver of the poor outcome in young women. The availability of large-scale IBC data provided a good opportunity to address this issue. METHODS Clinicopathologic and gene expression data from 2 public datasets, including METABRIC from the European Genome-Phenome Archive (n=1992) and The Cancer Genome Atlas-Breast Cancer project (TCGA-BC, n=980) from National Cancer Institute, were used in this study. PAM50 was used to derive intrinsic subtypes based on microarray and RNA-Seq data. For a given phenotype, Fisher’s exact test was used for its association with age, and 2-sample test for equality of proportions with continuity correction between young and older patients. Kolmogorov-Smirnov’s test was used for equality of age distributions between phenotypes. Disease-specific survival (DSS) was examined for relationship with age, adjusted for race, AJCC stage, nodal metastasis, tumor size, grade, and subtype where applicable. Kaplan-Meier estimate and log-rank test were used to generate and compare survival curves, respectively. Cox proportional hazards model was used for univariate and multivariate analyses and to calculate hazard ratios (HRs). RESULTS Firstly, we confirmed that more aggressive IBCs were enriched in young patients. Younger patients (<50 years) were diagnosed with more basal-like subtype (P<0.05) and more node+ diseases (P<0.05) compared with their older counterparts (≥50 years) in both datasets. Secondly, we found no significant difference in DSS between these 2 age groups after adjusting for subtype and other clinicopathologic variables. Finally, we examined the effect of age on DSS within the young and older patient groups separately. In the young patient group, age was not predictive of DSS in multivariate models in either datasets. However, in the older patient group, after adjusting for the effects of other clinicopathologic variables in the METABRIC dataset, the continuously increasing age was associated with worse DSS in all subtypes (HR=1.018, P=0.0076), as well as Luminal A (HR=1.041, P=0.00074) and Luminal B (HR=1.021, P=0.047) but not HER2+ or basal-like subtype in this age group. The results were validated in the TCGA-BC data (HR=1.055 and P=0.0052 in all older patients; HR=1.070 and P=0.066 in Luminal A; HR=1.081 and P=0.034 in Luminal B). CONCLUSION Our results supported that age was not an independent predictor of DSS for patients diagnosed at age <50 years, and the worse outcome of this age group was apparently a surrogate for more aggressive IBC phenotype. Our observation that age was an independent predictor of worse DSS in the older patient group, especially patients with Luminal A and B but not the other subtypes, warrants further molecular studies. The views expressed in this abstract are those of the authors and do not reflect the official policy of the Department of Defense, or U.S. Government. Citation Format: Yuanbin Ru, Richard J Mural, Patricia S Steeg, Hallgeir Rui, Craig D Shriver, Hai Hu. Age independently predicts worse disease-specific survival in patients diagnosed with invasive breast cancers at 50 years of age or older but not younger [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-08-24.

Functional characterization of breast cancer using pathway profiles.

BackgroundThe molecular characteristics of human diseases are often represented by a list of genes termed “signature genes”. A significant challenge facing this approach is that of reproducibility: signatures developed on a set of patients may fail to perform well on different sets of patients. As diseases are resulted from perturbed cellular functions, irrespective of the particular genes that contribute to the function, it may be more appropriate to characterize diseases based on these perturbed cellular functions.MethodsWe proposed a profile-based approach to characterize a disease using a binary vector whose elements indicate whether a given function is perturbed based on the enrichment analysis of expression data between normal and tumor tissues. Using breast cancer and its four primary clinically relevant subtypes as examples, this approach is evaluated based on the reproducibility, accuracy and resolution of the resulting pathway profiles.ResultsPathway profiles for breast cancer and its subtypes are constructed based on data obtained from microarray and RNA-Seq data sets provided by The Cancer Genome Atlas (TCGA), and an additional microarray data set provided by The European Genome-phenome Archive (EGA). An average reproducibility of 68% is achieved between different data sets (TCGA microarray vs. EGA microarray data) and 67% average reproducibility is achieved between different technologies (TCGA microarray vs. TCGA RNA-Seq data). Among the enriched pathways, 74% of them are known to be associated with breast cancer or other cancers. About 40% of the identified pathways are enriched in all four subtypes, with 4, 2, 4, and 7 pathways enriched only in luminal A, luminal B, triple-negative, and HER2+ subtypes, respectively. Comparison of profiles between subtypes, as well as other diseases, shows that luminal A and luminal B subtypes are more similar to the HER2+ subtype than to the triple-negative subtype, and subtypes of breast cancer are more likely to be closer to each other than to other diseases.ConclusionsOur results demonstrate that pathway profiles can successfully characterize both common and distinct functional characteristics of four subtypes of breast cancer and other related diseases, with acceptable reproducibility, high accuracy and reasonable resolution.

Exome capture sequencing reveals new insights into hepatitis B virus-induced hepatocellular carcinoma at the early stage of tumorigenesis

Hepatocellular carcinoma (HCC), the most common type of liver cancer, is the third primary cause of cancer-related mortality worldwide. The molecular mechanisms underlying the initiation and formation of HCC remain obscure. In the present study, we performed exome sequencing using tumor and normal tissues from 3 hepatitis B virus (HBV)-positive BCLC stage A HCC patients. Bioinformatic analysis was performed to find candidate protein-altering somatic mutations. Eighty damaging mutations were validated and 59 genes were reported to be mutated in HBV-related HCCs for the first time here. Further analysis using whole genome sequencing (WGS) data of 88 HBV-related HCC patients from the European Genome-phenome Archive database showed that mutations in 33 of the 59 genes were also detected in other samples. Variants of two newly found genes, ZNF717 and PARP4, were detected in more than 10% of the WGS samples. Several other genes, such as FLNA and CNTN2, are also noteworthy. Thus, the exome sequencing analysis of three BCLC stage A patients provides new insights into the molecular events governing the early steps of HBV-induced HCC tumorigenesis.

Abstract 3967: The Cancer Genome Project high throughput analysis pipeline

Abstract The Cancer Genome Project (CGP) was set up in 2000 to use systematic mutation screening methods to increase our understanding of human cancer. With the advent of next generation sequencing, and the large volumes of data that it generates, a new suite of software was required to rapidly and accurately screen these data for somatic changes. We have built an analysis pipeline to track and analyse large numbers of tumour samples, using in-house and externally available tools. The analysis pipeline is built around a ∼2,000 node compute farm and Lustre filesystem which outputs into our archive and data storage system, FileTrk. FileTrk holds the raw data files (BAM, CEL etc), the results of the analysis and any versioning information about the software used to generate these results. Sample lanes are aligned back to the genome using Burrows-Wheeler Aligner (BWA) and lane-to-lane comparisons are made to ensure data integrity. Lanes from each sample are merged into a single sample BAM file and once 30 - 40x coverage is reached and the lanes have been quality assessed the sample is locked and ready for analysis. Mutation callers detect point mutations (Caveman, in-house software), small insertions/deletions (Pindel), breakpoints (BRASS, in-house software) and copy number changes (ASCAT & PICNIC, in-house software). The resulting mutations are post-processed to remove false positives, annotated to the RNA and protein level using standard nomenclature (Vagrent, in-house software) and uploaded to a database. Interfaces have been developed to enable the selection of random sets of mutations for validation, the outcome of the validations is recorded so specificity can be calculated for each sample in the system. IT systems are being developed to automatically export lists of somatic changes to COSMIC, the ICGC data portal and raw data to the European Genome-Phenome Archive (EGA). Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3967. doi:1538-7445.AM2012-3967

Replication of the association of the COG6 gene with juvenile idiopathic arthritis (JIA)

Methods Two SNPs, rs7993214, near the COG6 gene and rs1010824, near the ANGPT1 gene, were genotyped in UK JIA cases (n=817) and the genotype and allele frequency data compared with those for healthy controls (n=5196) extracted from the Wellcome Trust case control consortium 2 (WTCCC2) European Genomephenome Archive (EGA). The Cochrane-Armitage trend test implemented in PLINK was performed and allelic odds ratios (ORs) and their 95% confidence intervals (CIs) calculated.

Single nucleotide polymorphism (SNP) analysis demonstrates a significant association of tumour necrosis factor-alpha (TNFA) with primary immune thrombocytopenia among Caucasian adults

T-helper 1 polarization in patients with primary immune thrombocytopenia (ITP) is well documented. However, the genetic contribution to this imbalance remains unclear. To address this question, we selected six candidate single nucleotide polymorphisms within cytokine or cytokine receptor genes for association testing among Caucasian adults. Patients from the United Kingdom Adult ITP Registry were gender-matched (1∶3) with healthy controls from the Wellcome Trust Case Control Consortium. Variants IL10 −819 c>t, TNFA −308 g>a, TGFB1 −509 c>t, IL1A −889 c>t, IL10 −592 c>t, and IL4R q576r were measured in cases and retrieved for controls from the European Genome-phenome Archive. Associations were evaluated using logistic regression models. In total, 206 patients with primary ITP were matched with 618 controls. A significant per allele odds ratio of 1·34 (95% confidence interval, 1·03–1·75; P = 0·03) was observed for TNFA −308 g>a, implicating an increased disease susceptibility among Caucasian carriers of the rare allele.

The European Nucleotide Archive.

The European Nucleotide Archive (ENA; http://www.ebi.ac.uk/ena) is Europe’s primary nucleotide-sequence repository. The ENA consists of three main databases: the Sequence Read Archive (SRA), the Trace Archive and EMBL-Bank. The objective of ENA is to support and promote the use of nucleotide sequencing as an experimental research platform by providing data submission, archive, search and download services. In this article, we outline these services and describe major changes and improvements introduced during 2010. These include extended EMBL-Bank and SRA-data submission services, extended ENA Browser functionality, support for submitting data to the European Genome-phenome Archive (EGA) through SRA, and the launch of a new sequence similarity search service.