Yanwen Chen

CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2007–2011

The Central Brain Tumor Registry of the United States (CBTRUS), in collaboration with the Centers for Disease Control and Prevention and National Cancer Institute, is the largest population-based registry focused exclusively on primary brain and other central nervous system (CNS) tumors in the United States (US) and represents the entire US population. This report contains the most up-to-date population-based data on primary brain tumors available and supersedes all previous reports in terms of completeness and accuracy. All rates are age-adjusted using the 2000 US standard population and presented per 100,000 population. The average annual age-adjusted incidence rate (AAAIR) of all malignant and non-malignant brain and other CNS tumors was 23.41 (Malignant AAAIR = 7.08, non-Malignant AAAIR = 16.33). This rate was higher in females compared to males (25.84 versus 20.82), Whites compared to Blacks (23.50 versus 23.34), and non-Hispanics compared to Hispanics (23.84 versus 21.28). The most commonly occurring malignant brain and other CNS tumor was glioblastoma (14.6% of all tumors), and the most common non-malignant tumor was meningioma (37.6% of all tumors). Glioblastoma was more common in males, and meningioma was more common in females. In children and adolescents (age 0-19 years), the incidence rate of all primary brain and other CNS tumors was 6.06. An estimated 86,010 new cases of malignant and non-malignant brain and other CNS tumors are expected to be diagnosed in the US in 2019 (25,510 malignant and 60,490 non-malignant). There were 79,718 deaths attributed to malignant brain and other CNS tumors between 2012 and 2016. This represents an average annual mortality rate of 4.42. The five-year relative survival rate following diagnosis of a malignant brain and other CNS tumor was 35.8%, and the five-year relative survival rate following diagnosis of a non-malignant brain and other CNS tumors was 91.5%.

The MicroRNAs, MiR-31 and MiR-375, as Candidate Markers in Barrett's Esophageal Carcinogenesis

There is a critical need to identify molecular markers that can reliably aid in stratifying esophageal adenocarcinoma (EAC) risk in patients with Barretts esophagus. MicroRNAs (miRNA/miR) are one such class of biomolecules. In the present cross‐sectional study, we characterized miRNA alterations in progressive stages of neoplastic development, i.e., metaplasia–dysplasia–adenocarcinoma, with an aim to identify candidate miRNAs potentially associated with progression. Using next generation sequencing (NGS) as an agnostic discovery platform, followed by quantitative real‐time PCR (qPCR) validation in a total of 20 EACs, we identified 26 miRNAs that are highly and frequently deregulated in EACs (≥4‐fold in >50% of cases) when compared to paired normal esophageal squamous (nSQ) tissue. We then assessed the 26 EAC‐derived miRNAs in laser microdissected biopsy pairs of Barretts metaplasia (BM)/nSQ (n = 15), and high‐grade dysplasia (HGD)/nSQ (n = 14) by qPCR, to map the timing of deregulation during progression from BM to HGD and to EAC. We found that 23 of the 26 candidate miRNAs were deregulated at the earliest step, BM, and therefore noninformative as molecular markers of progression. Two miRNAs, miR‐31 and −31*, however, showed frequent downregulation only in HGD and EAC cases suggesting association with transition from BM to HGD. A third miRNA, miR‐375, showed marked downregulation exclusively in EACs and in none of the BM or HGD lesions, suggesting its association with progression to invasive carcinoma. Taken together, we propose miR‐31 and −375 as novel candidate microRNAs specifically associated with early‐ and late‐stage malignant progression, respectively, in Barretts esophagus.

Molecular subtypes of glioblastoma are relevant to lower grade glioma.

Background Gliomas are the most common primary malignant brain tumors in adults with great heterogeneity in histopathology and clinical course. The intent was to evaluate the relevance of known glioblastoma (GBM) expression and methylation based subtypes to grade II and III gliomas (ie. lower grade gliomas). Methods Gene expression array, single nucleotide polymorphism (SNP) array and clinical data were obtained for 228 GBMs and 176 grade II/II gliomas (GII/III) from the publically available Rembrandt dataset. Two additional datasets with IDH1 mutation status were utilized as validation datasets (one publicly available dataset and one newly generated dataset from MD Anderson). Unsupervised clustering was performed and compared to gene expression subtypes assigned using the Verhaak et al 840-gene classifier. The glioma-CpG Island Methylator Phenotype (G-CIMP) was assigned using prediction models by Fine et al. Results Unsupervised clustering by gene expression aligned with the Verhaak 840-gene subtype group assignments. GII/IIIs were preferentially assigned to the proneural subtype with IDH1 mutation and G-CIMP. GBMs were evenly distributed among the four subtypes. Proneural, IDH1 mutant, G-CIMP GII/III s had significantly better survival than other molecular subtypes. Only 6% of GBMs were proneural and had either IDH1 mutation or G-CIMP but these tumors had significantly better survival than other GBMs. Copy number changes in chromosomes 1p and 19q were associated with GII/IIIs, while these changes in CDKN2A, PTEN and EGFR were more commonly associated with GBMs. Conclusions GBM gene-expression and methylation based subtypes are relevant for GII/III s and associate with overall survival differences. A better understanding of the association between these subtypes and GII/IIIs could further knowledge regarding prognosis and mechanisms of glioma progression.

Descriptive epidemiology of pituitary tumors in the United States, 2004-2009.

OBJECT Pituitary tumors are abnormal growths that develop in the pituitary gland. The Central Brain Tumor Registry of the United States (CBTRUS) contains the largest aggregation of population-based data on the incidence of primary CNS tumors in the US. These data were used to determine the incidence of tumors of the pituitary and associated trends between 2004 and 2009. METHODS Using incidence data from 49 population-based state cancer registries, 2004-2009, age-adjusted incidence rates per 100,000 population for pituitary tumors with ICD-O-3 (International Classification of Diseases for Oncology, Third Edition) histology codes 8040, 8140, 8146, 8246, 8260, 8270, 8271, 8272, 8280, 8281, 8290, 8300, 8310, 8323, 9492 (site C75.1 only), and 9582 were calculated overall and by patient sex, race, Hispanic ethnicity, and age at diagnosis. Corresponding annual percent change (APC) scores and 95% confidence intervals were also calculated using Joinpoint to characterize trends in incidence rates over time. Diagnostic confirmation by subregion of the US was also examined. The overall annual incidence rate increased from 2.52 (95% CI 2.46-2.58) in 2004 to 3.13 (95% CI 3.07-3.20) in 2009. Associated time trend yielded an APC of 4.25% (95% CI 2.91%-5.61%). When stratifying by patient sex, the annual incidence rate increased from 2.42 (95% CI 2.33-2.50) to 2.94 (95% CI 2.85-3.03) in men and 2.70 (95% CI 2.62-2.79) to 3.40 (95% CI 3.31-3.49) in women, with APCs of 4.35% (95% CI 3.21%-5.51%) and 4.34% (95% CI 2.23%-6.49%), respectively. When stratifying by race, the annual incidence rate increased from 2.31 (95% CI 2.25-2.37) to 2.81 (95% CI 2.74-2.88) in whites, 3.99 (95% CI 3.77-4.23) to 5.31 (95% CI 5.06-5.56) in blacks, 1.77 (95% CI 1.26-2.42) to 2.52 (95% CI 1.96-3.19) in American Indians or Alaska Natives, and 1.86 (95% CI 1.62-2.13) to 2.03 (95% CI 1.80-2.28) in Asians or Pacific Islanders, with APCs of 3.91% (95% CI 2.88%-4.95%), 5.25% (95% CI 3.19%-7.36%), 5.31% (95% CI -0.11% to 11.03%), and 2.40% (95% CI -3.20% to 8.31%), respectively. When stratifying by Hispanic ethnicity, the annual incidence rate increased from 2.46 (95% CI 2.40-2.52) to 3.03 (95% CI 2.97-3.10) in non-Hispanics and 3.12 (95% CI 2.91-3.34) to 4.01 (95% CI 3.80-4.24) in Hispanics, with APCs of 4.15% (95% CI 2.67%-5.65%) and 5.01% (95% CI 4.42%-5.60%), respectively. When stratifying by age at diagnosis, the incidence of pituitary tumor was highest for those 65-74 years old and lowest for those 15-24 years old, with corresponding overall age-adjusted incidence rates of 6.39 (95% CI 6.24-6.54) and 1.56 (95% CI 1.51-1.61), respectively. CONCLUSIONS In this large patient cohort, the incidence of pituitary tumors reported between 2004 and 2009 was found to increase. Possible explanations for this increase include changes in documentation, changes in the diagnosis and registration of these tumors, improved diagnostics, improved data collection, increased awareness of pituitary diseases among physicians and the public, longer life expectancies, and/or an actual increase in the incidence of these tumors in the US population.

Genome-wide association study of glioma subtypes identifies specific differences in genetic susceptibility to glioblastoma and non-glioblastoma tumors.

Genome-wide association studies (GWAS) have transformed our understanding of glioma susceptibility, but individual studies have had limited power to identify risk loci. We performed a meta-analysis of existing GWAS and two new GWAS, which totaled 12,496 cases and 18,190 controls. We identified five new loci for glioblastoma (GBM) at 1p31.3 (rs12752552; P = 2.04 × 10−9, odds ratio (OR) = 1.22), 11q14.1 (rs11233250; P = 9.95 × 10−10, OR = 1.24), 16p13.3 (rs2562152; P = 1.93 × 10−8, OR = 1.21), 16q12.1 (rs10852606; P = 1.29 × 10−11, OR = 1.18) and 22q13.1 (rs2235573; P = 1.76 × 10−10, OR = 1.15), as well as eight loci for non-GBM tumors at 1q32.1 (rs4252707; P = 3.34 × 10−9, OR = 1.19), 1q44 (rs12076373; P = 2.63 × 10−10, OR = 1.23), 2q33.3 (rs7572263; P = 2.18 × 10−10, OR = 1.20), 3p14.1 (rs11706832; P = 7.66 × 10−9, OR = 1.15), 10q24.33 (rs11598018; P = 3.39 × 10−8, OR = 1.14), 11q21 (rs7107785; P = 3.87 × 10−10, OR = 1.16), 14q12 (rs10131032; P = 5.07 × 10−11, OR = 1.33) and 16p13.3 (rs3751667; P = 2.61 × 10−9, OR = 1.18). These data substantiate that genetic susceptibility to GBM and non-GBM tumors are highly distinct, which likely reflects different etiology.

Aberrant Vimentin Methylation Is Characteristic of Upper Gastrointestinal Pathologies

Background: We have previously established aberrant DNA methylation of vimentin exon-1 (VIM methylation) as a common epigenetic event in colon cancer and as a biomarker for detecting colon neoplasia. We now examine vimentin methylation in neoplasia of the upper gastrointestinal tract. Methods: Using a quantitative real-time methylation-specific PCR assay, we tested for vimentin methylation in archival specimens of esophageal and gastric neoplasia. Results: We find that acquisition of aberrant vimentin methylation is highly common in these neoplasms, but largely absent in controls. The highest frequency of vimentin methylation was detected in lesions of the distal esophagus, including 91% of Barretts esophagus (n = 11), 100% of high-grade dysplasia (HGD, n = 5), and 81% of esophageal adenocarcinoma (EAC, n = 26) but absent in controls (n = 9). Vimentin methylation similarly was detected in 87% of signet ring (n = 15) and 53% of intestinal type gastric cancers (n = 17). Moreover, in tests of cytology brushings vimentin methylation proved detectable in 100% of Barretts esophagus cases (n = 7), 100% of HGD cases (n = 4), and 83% of EAC cases (n = 18) but was absent in all controls (n = 5). Conclusions: These findings establish aberrant vimentin methylation as a highly common epigenetic alteration in neoplasia of the upper gastrointestinal tract and show that Barretts esophagus, even without dysplasia, already contains epigenetic alterations characteristic of adenocarcinoma. Impact: These findings suggest vimentin methylation as a biomarker of upper gastrointestinal neoplasia with potential for development as molecular cytology in esophageal screening. Cancer Epidemiol Biomarkers Prev; 21(4); 594–600. ©2012 AACR.

Network Signatures of Survival in Glioblastoma Multiforme

To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included “protein kinase cascade,” “IκB kinase/NFκB cascade,” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.

Genome-Wide Methylation Analyses in Glioblastoma Multiforme

Few studies had investigated genome-wide methylation in glioblastoma multiforme (GBM). Our goals were to study differential methylation across the genome in gene promoters using an array-based method, as well as repetitive elements using surrogate global methylation markers. The discovery sample set for this study consisted of 54 GBM from Columbia University and Case Western Reserve University, and 24 brain controls from the New York Brain Bank. We assembled a validation dataset using methylation data of 162 TCGA GBM and 140 brain controls from dbGAP. HumanMethylation27 Analysis Bead-Chips (Illumina) were used to interrogate 26,486 informative CpG sites in both the discovery and validation datasets. Global methylation levels were assessed by analysis of L1 retrotransposon (LINE1), 5 methyl-deoxycytidine (5m-dC) and 5 hydroxylmethyl-deoxycytidine (5hm-dC) in the discovery dataset. We validated a total of 1548 CpG sites (1307 genes) that were differentially methylated in GBM compared to controls. There were more than twice as many hypomethylated genes as hypermethylated ones. Both the discovery and validation datasets found 5 tumor methylation classes. Pathway analyses showed that the top ten pathways in hypomethylated genes were all related to functions of innate and acquired immunities. Among hypermethylated pathways, transcriptional regulatory network in embryonic stem cells was the most significant. In the study of global methylation markers, 5m-dC level was the best discriminant among methylation classes, whereas in survival analyses, high level of LINE1 methylation was an independent, favorable prognostic factor in the discovery dataset. Based on a pathway approach, hypermethylation in genes that control stem cell differentiation were significant, poor prognostic factors of overall survival in both the discovery and validation datasets. Approaches that targeted these methylated genes may be a future therapeutic goal.

The tumor suppressor CDKN3 controls mitosis

A genome-wide screen of phosphatases that control mitosis identified CDKN3, which acts through the CDC2 signaling axis.

The descriptive epidemiology of atypical teratoid/rhabdoid tumors in the United States, 2001–2010

BACKGROUND Atypical teratoid/rhabdoid tumor is a rare malignant CNS tumor that most often affects children ≤ 3 years old. The Central Brain Tumor Registry of the United States contains the largest aggregation of population-based incidence data for primary CNS tumors in the US. Its data were used to describe the incidence, associated trends, and relative survival after diagnosis of atypical teratoid/rhabdoid tumor. METHODS Using data from 50 cancer registries between 2001 and 2010, age-adjusted incidence rates per 100 000 and 95% CIs were calculated by sex, race, Hispanic ethnicity, age at diagnosis, and location of tumor in the CNS for children aged 0 to 19 years. Relative survival rates and 95% CIs were also calculated. RESULTS The average annual age-adjusted incidence rate was 0.07 (95% CI: 0.07, 0.08). Incidence rates did not significantly vary by sex, race, or ethnicity. Age had a strong effect on incidence rate, with highest incidence among children <1 year, and decreasing incidence with increasing age. The 6-month, 1-year, and 5-year relative survival rates for all ages were 65.0%, 46.8%, and 28.3%, respectively. Atypical teratoid/rhabdoid tumor can occur anywhere in the CNS, but supratentorial tumors were more common with increasing age. CONCLUSION We confirm differences in survival by age at diagnosis, treatment pattern, and location of tumor in the brain. This contributes to our understanding of these tumors and may stimulate research leading to improved treatment of this devastating childhood disease.