A variable age of onset segregation model for linkage analysis, with correction for ascertainment, applied to glioma

The genomic landscape of familial glioma

Glioma is a rare brain tumor with a poor prognosis. Familial glioma is a subset of glioma with a strong genetic predisposition that accounts for approximately 5% of glioma cases. We performed whole-genome sequencing on an exploratory cohort of 203 individuals from 189 families with a history of familial glioma and an additional validation cohort of 122 individuals from 115 families. We found significant enrichment of rare deleterious variants of seven genes in both cohorts, and the most significantly enriched gene was HERC2 (P = 0.0006). Furthermore, we identified rare noncoding variants in both cohorts that were predicted to affect transcription factor binding sites or cause cryptic splicing. Last, we selected a subset of discovered genes for validation by CRISPR knockdown screening and found that DMBT1, HP1BP3, and ZCH7B3 have profound impacts on proliferation. This study performs comprehensive surveillance of the genomic landscape of familial glioma.

Molecular Characterization of Differentiated-Resistance MSC Subclones by Single-Cell Transcriptomes

Background: The mechanism of tumorigenicity potentially evolved in mesenchymal stem cells (MSCs) remains elusive, resulting in inconsistent clinical application efficacy. We hypothesized that subclones in MSCs contribute to their tumorgenicity, and we approached MSC-subclones at the single-cell level.

Methods: MSCs were cultured in an osteogenic differentiation medium and harvested on days 12, 19, and 25 for cell differentiation analysis using Alizarin Red and followed with the single-cell transcriptome.

Results: Single-cell RNA-seq analysis reveals a discrete cluster of MSCs during osteogenesis, including differentiation-resistant MSCs (DR-MSCs), differentiated osteoblasts (DO), and precursor osteoblasts (PO). The DR-MSCs population resembled cancer initiation cells and were subjected to further analysis of the yes associated protein 1 (YAP1) network. Verteporfin was also used for YAP1 inhibition in cancer cell lines to confirm the role of YAP1 in MSC--involved tumorigenicity. Clinical data from various cancer types were analyzed to reveal relationships among YAP1, OCT4, and CDH6 in MSC--involved tumorigenicity. The expression of cadherin 6 (CDH6), octamer-binding transcription factor 4 (OCT4), and YAP1 expression was significantly upregulated in DR-MSCs compared to PO and DO. YAP1 inhibition by Verteporfin accelerated the differentiation of MSCs and suppressed the expression of YAP1, CDH6, and OCT4. A survey of 56 clinical cohorts revealed a high degree of co-expression among CDH6, YAP1, and OCT4 in various solid tumors. YAP1 inhibition also down-regulated HeLa cell viability and gradually inhibited YAP1 nuclear localization while reducing the transcription of CDH6 and OCT4.

Conclusions: We used single-cell sequencing to analyze undifferentiated MSCs and to discover a carcinogenic pathway in single-cell MSCs of differentiated resistance subclones.

Genome-wide analysis of high-risk primary brain cancer pedigrees identifies PDXDC1 as a candidate brain cancer predisposition gene

BACKGROUND

There is evidence for an inherited contribution to primary brain cancer. Linkage analysis of high-risk brain cancer pedigrees has identified candidate regions of interest in which brain cancer predisposition genes are likely to reside.

METHODS

Genome-wide linkage analysis was performed in a unique set of 11 informative, extended, high-risk primary brain cancer pedigrees identified in a population genealogy database, which include from 2 to 6 sampled, related primary brain cancer cases. Access to formalin-fixed paraffin embedded tissue samples archived in a biorepository allowed analysis of extended pedigrees.

RESULTS

Individual high-risk pedigrees were singly informative for linkage at multiple regions. Suggestive evidence for linkage was observed on chromosomes 2, 3, 14, and 16. The chromosome 16 region in particular contains a promising candidate gene, pyridoxal-dependent decarboxylase domain-containing 1 (PDXDC1), with prior evidence for involvement with glioblastoma from other previously reported experimental settings, and contains the lead single nucleotide polymorphism (rs3198697) from the linkage analysis of the chromosome 16 region.

CONCLUSIONS

Pedigrees with a statistical excess of primary brain cancers have been identified in a unique genealogy resource representing the homogeneous Utah population. Genome-wide linkage analysis of these pedigrees has identified a potential candidate predisposition gene, as well as multiple candidate regions that could harbor predisposition loci, and for which further analysis is suggested.

Genetic alterations in non-syndromic, familial gliomas in first degree relatives: A systematic review

OBJECTIVE

Despite numerous reports in syndromic gliomas, the underlying genetic and molecular basis of familial, non-syndromic gliomas, in first degree relatives, remains unclear. This rare cohort of patients harboring invasive primary brain tumors with poor prognosis may provide a potential substrate of understanding the complex genetic cascade triggering tumorigenesis.

METHODS

A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) 2015 and The Cochrane Handbook of Systematic Reviews of Interventions. PubMed/MEDLINE, Embase and CENTRAL databases were accessed with set inclusion and exclusion criteria.

RESULTS

Following returns of 6756 articles, systematic analysis resulted in 48 papers, with 18 case series, 4 linkage analysis, 3 case-control studies, 1 cohort study, and 22 case reports. A total of 164 first degree relatives of 72 families were analyzed. The most common genetic alterations associated with non-syndromic familial gliomas reported to affect chromosomes 17 (51.1 % germline and 9.3 % tumor mutations), 22 (15.6 % germline and 6 % tumor mutations) and 1 and 19 (4.4 % germline and 9.3 % tumor mutations), with the most commonly affected genes TP53 (8.5 %) and NF2 (3.7 %). Tumor suppressors or cell-cycle regulators, cell signaling and transcription regulation or methylation were the most common gene function categories.

CONCLUSION

Four specific chromosomes (17, 22, 1 and 19) and two specific genes (TP53 and NF2) appear to be most commonly involved. This appears to be the first systematic review of genetic factors underlying non-syndromic glioma clustering in families. The defined list of genetic abnormalities, linked to familial gliomas, may facilitate therapeutic targets and future treatment design.

An Accidental Genetic Epidemiologist

I briefly describe my early life and how, through a series of serendipitous events, I became a genetic epidemiologist. I discuss how the Elston–Stewart algorithm was discovered and its contribution to segregation, linkage, and association analysis. New linkage findings and paternity testing resulted from having a genotyping lab. The different meanings of interaction—statistical and biological—are clarified. The computer package S.A.G.E. (Statistical Analysis for Genetic Epidemiology), based on extensive method development over two decades, was conceived in 1986, flourished for 20 years, and is now freely available for use and further development. Finally, I describe methods to estimate and test hypotheses about familial correlations, and point out that the liability model often used to estimate disease heritability estimates the heritability of that liability, rather than of the disease itself, and so can be highly dependent on the assumed distribution of that liability.

Postoperative standard chemoradiotherapy benefits primary glioblastoma patients of all ages

BACKGROUND

Glioblastoma is the most malignant tumor of the central nervous system. Several prediction models have been produced to aid in prognosis assessment. Age, a primary decision factor for prognosis, is associated with increased genomic alterations, however the exact link between increased age and poor prognosis is unknown.

OBJECTIVE

In this study, we aimed to reveal the underlying cause of poor prognosis in elderly patients.

METHODS

This study included data on 616 primary GBM tumor samples from the CGGA and TCGA databases and 41 nontumor brain tissue samples obtained from GSE53890. Hallmarks and clinicopathological characteristics were evaluated in both tumor and nontumor brain tissues. The association between choice of treatment regimen and age was measured using ANOVA and Student's t test.

RESULTS

Age was a robust predictor of poor prognosis in glioma. No age-related hallmarks of cancer were detected, including pathological characteristics or mutations. However, treatment choice was strongly significantly different between old and young patients. Combined chemo-radiation therapy could benefit old and young GBM patients, however, old patients are currently less likely to choose it.

CONCLUSION

The vast divergence in prognosis between young and old GBM patients is largely caused by choice of treatment rather than age-related tumor genomic characteristics. Postoperative standard radio- and chemotherapy provide strong benefits to primary glioblastoma patients of all ages.

Epidemiology and Molecular Epidemiology

Incidence, prevalence, and survival for diffuse low-grade gliomas and diffuse anaplastic gliomas (including grade II and grade III astrocytomas and oligodendrogliomas) varies by histologic type, age at diagnosis, sex, and race/ethnicity. Significant progress has been made in identifying potential risk factors for glioma, although more research is warranted. The strongest risk factors that have been identified thus far include allergies/atopic disease, ionizing radiation, and heritable genetic factors. Further analysis of large, multicenter epidemiologic studies, and well-annotated "omic" datasets, can potentially lead to further understanding of the relationship between gene and environment in the process of brain tumor development.

Epidemiology of Brain Tumors

Incidence, prevalence, and survival for brain tumors varies by histologic type, age at diagnosis, sex, and race/ethnicity. Significant progress has been made in identifying potential risk factors for brain tumors, although more research is warranted. The strongest risk factors that have been identified thus far include allergies/atopic disease, ionizing radiation, and heritable genetic factors. Further analysis of large, multicenter, epidemiologic studies, as well as well annotated omic datasets (including genomic, epigenomic, transcriptomic, proteomic, or metabolomics data) can potentially lead to further understanding of the relationship between gene and environment in the process of brain tumor development.

Correcting for Ascertainment

Data used to study human genetics are often not obtained by simple random sampling, which is assumed by many statistical methods, especially those that are based on likelihood for making inferences. There is a well-developed theory to correct likelihoods based on sibship data whether or not the exact mode of ascertainment is known. In the case of larger pedigrees, however, the problem is much more difficult unless they are recruited into the sample by single ascertainment. There is no one piece of software that analyzes ascertainment in general, so most of this chapter is devoted to theory. A general method by which one general genetic analysis software package corrects pedigree data for ascertainment is briefly described.

Glioma cell VEGFR-2 confers resistance to chemotherapeutic and antiangiogenic treatments in PTEN-deficient glioblastoma

Loss of the tumor suppressor phosphatase and tensin homolog deleted on chromosome 10 (PTEN) is a prerequisite for tumor cell-specific expression of vascular endothelial growth factor receptor (VEGFR)-2 in glioblastoma defining a subgroup prone to develop evasive resistance towards antiangiogenic treatments. Immunohistochemical analysis of human tumor tissues showed VEGFR-2 expression in glioma cells in 19% of specimens examined, mainly in the infiltration zone. Glioma cell VEGFR-2 positivity was restricted to PTEN-deficient tumor specimens. PTEN overexpression reduced VEGFR-2 expression in vitro, as well as knock-down of raptor or rictor. Genetic interference with VEGFR-2 revealed proproliferative, antiinvasive and chemoprotective functions for VEGFR-2 in glioma cells. VEGFR-2-dependent cellular effects were concomitant with activation of 'kappa-light-chain-enhancer' of activated B-cells, protein kinase B, and N-myc downstream regulated gene 1. Two-photon in vivo microscopy revealed that expression of VEGFR-2 in glioma cells hampers antiangiogenesis. Bevacizumab induces a proinvasive response in VEGFR-2-positive glioma cells. Patients with PTEN-negative glioblastomas had a shorter survival after initiation of bevacizumab therapy compared with PTEN-positive glioblastomas. Conclusively, expression of VEGFR-2 in glioma cells indicates an aggressive glioblastoma subgroup developing early resistance to temozolomide or bevacizumab. Loss of PTEN may serve as a biomarker identifying those tumors upfront by routine neuropathological methods.

Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm

BACKGROUND

Barrett's esophagus is often asymptomatic and only a small portion of Barrett's esophagus patients are currently diagnosed and under surveillance. Therefore, it is important to develop risk prediction models to identify high-risk individuals with Barrett's esophagus. Familial aggregation of Barrett's esophagus and esophageal adenocarcinoma, and the increased risk of esophageal adenocarcinoma for individuals with a family history, raise the necessity of including genetic factors in the prediction model. Methods to determine risk prediction models using both risk covariates and ascertained family data are not well developed.

METHODS

We developed a Barrett's Esophagus Translational Research Network (BETRNet) risk prediction model from 787 singly ascertained Barrett's esophagus pedigrees and 92 multiplex Barrett's esophagus pedigrees, fitting a multivariate logistic model that incorporates family history and clinical risk factors. The eight risk factors, age, sex, education level, parental status, smoking, heartburn frequency, regurgitation frequency, and use of acid suppressant, were included in the model. The prediction accuracy was evaluated on the training dataset and an independent validation dataset of 643 multiplex Barrett's esophagus pedigrees.

RESULTS

Our results indicate family information helps to predict Barrett's esophagus risk, and predicting in families improves both prediction calibration and discrimination accuracy.

CONCLUSIONS

Our model can predict Barrett's esophagus risk for anyone with family members known to have, or not have, had Barrett's esophagus. It can predict risk for unrelated individuals without knowing any relatives' information.

IMPACT

Our prediction model will shed light on effectively identifying high-risk individuals for Barrett's esophagus screening and surveillance, consequently allowing intervention at an early stage, and reducing mortality from esophageal adenocarcinoma. Cancer Epidemiol Biomarkers Prev; 25(5); 727-35. ©2016 AACR.

Linkage and related analyses of Barrett's esophagus and its associated adenocarcinomas

Background

Familial aggregation and segregation analysis studies have provided evidence of a genetic basis for esophageal adenocarcinoma (EAC) and its premalignant precursor, Barrett's esophagus (BE). We aim to demonstrate the utility of linkage analysis to identify the genomic regions that might contain the genetic variants that predispose individuals to this complex trait (BE and EAC).

Methods

We genotyped 144 individuals in 42 multiplex pedigrees chosen from 1000 singly ascertained BE/EAC pedigrees, and performed both model‐based and model‐free linkage analyses, using S.A.G.E. and other software. Segregation models were fitted, from the data on both the 42 pedigrees and the 1000 pedigrees, to determine parameters for performing model‐based linkage analysis. Model‐based and model‐free linkage analyses were conducted in two sets of pedigrees: the 42 pedigrees and a subset of 18 pedigrees with female affected members that are expected to be more genetically homogeneous. Genome‐wide associations were also tested in these families.

Results

Linkage analyses on the 42 pedigrees identified several regions consistently suggestive of linkage by different linkage analysis methods on chromosomes 2q31, 12q23, and 4p14. A linkage on 15q26 is the only consistent linkage region identified in the 18 female‐affected pedigrees, in which the linkage signal is higher than in the 42 pedigrees. Other tentative linkage signals are also reported.

Conclusion

Our linkage study of BE/EAC pedigrees identified linkage regions on chromosomes 2, 4, 12, and 15, with some reported associations located within our linkage peaks. Our linkage results can help prioritize association tests to delineate the genetic determinants underlying susceptibility to BE and EAC.

Targeted sequencing in chromosome 17q linkage region identifies familial glioma candidates in the Gliogene Consortium

Glioma is a rare, but highly fatal, cancer that accounts for the majority of malignant primary brain tumors. Inherited predisposition to glioma has been consistently observed within non-syndromic families. Our previous studies, which involved non-parametric and parametric linkage analyses, both yielded significant linkage peaks on chromosome 17q. Here, we use data from next generation and Sanger sequencing to identify familial glioma candidate genes and variants on chromosome 17q for further investigation. We applied a filtering schema to narrow the original list of 4830 annotated variants down to 21 very rare (<0.1% frequency), non-synonymous variants. Our findings implicate the MYO19 and KIF18B genes and rare variants in SPAG9 and RUNDC1 as candidates worthy of further investigation. Burden testing and functional studies are planned.

The epidemiology of glioma in adults: a "state of the science" review

Gliomas are the most common primary intracranial tumor, representing 81% of malignant brain tumors. Although relatively rare, they cause significant mortality and morbidity. Glioblastoma, the most common glioma histology (∼45% of all gliomas), has a 5-year relative survival of ∼5%. A small portion of these tumors are caused by Mendelian disorders, including neurofibromatosis, tuberous sclerosis, and Li-Fraumeni syndrome. Genomic analyses of glioma have also produced new evidence about risk and prognosis. Recently discovered biomarkers that indicate improved survival include O⁶-methylguanine-DNA methyltransferase methylation, isocitrate dehydrogenase mutation, and a glioma cytosine-phosphate-guanine island methylator phenotype. Genome-wide association studies have identified heritable risk alleles within 7 genes that are associated with increased risk of glioma. Many risk factors have been examined as potential contributors to glioma risk. Most significantly, these include an increase in risk by exposure to ionizing radiation and a decrease in risk by history of allergies or atopic disease(s). The potential influence of occupational exposures and cellular phones has also been examined, with inconclusive results. We provide a “state of the science” review of current research into causes and risk factors for gliomas in adults.

Clinical manifestations and imaging characteristics of gliomatosis cerebri with pathological confirmation

OBJECTIVE

To explore the clinical manifestations and imaging characteristics of gliomatosis cerebri to raise the awareness and improve its diagnostic accuracy for patients.

MATERIALS AND METHODS

Clinical data, imaging characteristics and pathological examination of 12 patients with GC from Jan., 2008 to Jan., 2012 were analyzed retrospectively.

RESULTS

Patients with GC were clinically manifested with headache, vomiting, repeated seizures, fatigue and unstable walking, most of whom had more than 2 lesions involving in parietal lobe, followed by temporal lobe, frontal lobe, periventricular white matter and corpus callosum. Magnetic resonance imaging (MRI) showed diffuse distribution, T1-weighted images (T1WI) with equal and low signals and T2-weighted images (T2WI) with bilateral symmetrical high diffuse signals. There was no reinforcement by enhancement scanning and signals were different in diffusion-weighted images (DWI). The higher the tumor staging, the stronger the signals. Pathological examination showed neuroastrocytoma in which tumor tissues were manifested by infiltrative growth in blood vessels and around neurons.

CONCLUSIONS

In clinical diagnosis of GC, much attention should be paid to the diffuse distribution of imaging characteristics, incomplete matching between clinical and imaging characteristics and confirmation by combining with histopathological examination.