Using germline variants to estimate glioma and subtype risks

Eckel‐Passow, Jeanette E.; Decker, Paul A.; Kosel, Matt L.; Kollmeyer, Thomas M.; Molinaro, Annette M.; Rice, Terri; Caron, Alissa; Drucker, Kristen L.; Praska, Corinne; Pekmezci, Melike; Hansen, Helen M.; McCoy, Lucie; Bracci, Paige M.; Erickson, Bradley J.; Lucchinetti, Claudia F.; Wiemels, Joseph L.; Wiencke, John K.; Bondy, Melissa L.; Melin, Beatrice; Burns, Terry C.; Giannini, Caterina; Lachance, Daniel H.; Wrensch, Margaret; Jenkins, Robert B.

doi:10.1093/neuonc/noz009

Cited by 30 publications

(35 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Table 1 shows the characteristics of 5103 cases and 10,915 controls in three studies. The ratios of control to case numbers were 2.65, 3.54, and 0.32 in the current, Labreche's and Eckel-Passow's studies, respectively [9,10]. The proportion of GBM cases was higher in our study (62.7%) than Eckel-Passow's (41.9%) and Labreche's studies (30.0%).…”

Section: Introductioncontrasting

confidence: 45%

“…We used 330 cases and 876 controls from the Swedish Glioma International Case-Control (GICC) study for our analyses. Furthermore, in order to provide robust results, we conducted a meta-analysis to combine our results with two recent large studies [9,10]. The genetic risk variants were then categorized into three groups, based on their pattern of association with glioma molecular subgroups: (1) SNVs in TP53 and TERT associated with all glioma; (2) SNVs in CDKN2B-AS1, EGFR, near EGFR, and RTEL1 associated with IDH-wt glioma; and (3) SNVs in CCDC26, C2orf80, PHLDB1, ETFA, LRIG1, ZBTB16 and MAML2 associated with IDH-mutant glioma.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Genetic Architecture of Gliomagenesis–Genetic Risk Variants Linked to Specific Molecular Subtypes

Johansson

Wibom

et al. 2019

Cancers

Self Cite

View full text Add to dashboard Cite

Genome-wide association studies have identified 25 germline genetic loci that increase the risk of glioma. The somatic tumor molecular alterations, including IDH-mutation status and 1p/19q co-deletion, have been included into the WHO 2016 classification system for glioma. To investigate how the germline genetic risk variants correlate with the somatic molecular subtypes put forward by WHO, we performed a meta-analysis that combined findings from 330 Swedish cases and 876 controls with two other recent studies. In total, 5,103 cases and 10,915 controls were included. Three categories of associations were found. First, variants in TERT and TP53 were associated with increased risk of all glioma subtypes. Second, variants in CDKN2B-AS1, EGFR, and RTEL1 were associated with IDH-wildtype glioma. Third, variants in CCDC26 (the 8q24 locus), C2orf80 (close to IDH), LRIG1, PHLDB1, ETFA, MAML2 and ZBTB16 were associated with IDH-mutant glioma. We therefore propose three etiopathological pathways in gliomagenesis based on germline variants for future guidance of diagnosis and potential functional targets for therapies. Future prospective clinical trials of patients with suspicion of glioma diagnoses, using the genetic variants as biomarkers, are necessary to disentangle how strongly they can predict glioma diagnosis.

show abstract

Section: Introductioncontrasting

confidence: 45%

Section: Introductionmentioning

confidence: 99%

The Genetic Architecture of Gliomagenesis–Genetic Risk Variants Linked to Specific Molecular Subtypes

Johansson

Wibom

et al. 2019

Cancers

Self Cite

View full text Add to dashboard Cite

show abstract

“…Polygenic scores (PRS) can potentially help identify patients with different genetic risk of suffering many diseases 26 , including various forms of cancer. However, given the growing evidence showing widespread associations between the germline and somatic genomes in cancer, we need to better understand how they associate with other important variables, such as tumor subtypes 5 or other somatic alterations 25 .…”

Section: Discussionmentioning

confidence: 99%

“…al. recently identified a subset of 8 glioma risk loci that correlate with somatic IDH1 mutations 25 . Following their findings, we used their classification to deconvolute our brain cancer PRS into two different ones: one PRS specific to IDH1-mutated gliomas and another for non-IDH1 mutated gliomas.…”

Section: Towards Driver-specific Prsmentioning

confidence: 99%

The landscape of interactions between cancer polygenic risk scores and somatic alterations in cancer cells

Porta-Pardo

Sayaman

Ziv

et al. 2020

Preprint

View full text Add to dashboard Cite

Over the last 15 years we have identified hundreds of inherited variants that increase the risk of developing cancer. Polygenic risk scores (PRS) summarize the genetic risk of each individual by accounting for the unique combination of risk alleles in their genome. So far, most studies of PRS in cancer have focused on their predictive value: i.e. to what extent the PRS can predict which individuals will develop a particular cancer type. In parallel, for most cancers, we have identified several subtypes based on their somatic molecular properties. However, little is known about the relationship between the somatic molecular subtypes of cancer and PRS and it is possible that PRS preferentially predict specific cancer subtypes. Since cancer subtypes can have very different outcomes, treatment options and molecular vulnerabilities, answering this question is very important to understand the consequences that widespread PRS use would have in which tumors are detected early. Here we used data from The Cancer Genome Atlas (TCGA) to study the correlation between PRS for different forms of cancer and the landscape of somatic alterations in the tumors developed by each patient. We first validated the predictive power of 8 different PRS in TCGA and describe how PRS for some cancer types are associated with specific molecular subtypes or somatic cancer driver events. Our results highlight important questions that could improve the predictive power of PRS and that need to be answered before their widespread clinical implementation.

show abstract

“…[3] The generation of prognostic models to identify favorable and harmful factors was crucial to improve prognosis prediction of patients with GBM. Based on the ndings from previous studies, factors such as the extent of tumor resection (EOR), [4] Karnofsky Performance Scale (KPS) at diagnosis, [5] molecular biomarkers, [6,7] adjuvant treatment, [5,[8][9][10] and age [11] were associated with the prognosis of patients with GBM.…”

Section: Introductionmentioning

confidence: 99%

A prognostic model of newly diagnosed glioblastoma based on clinical characteristics

Sun¹,

Fan²,

Wang³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background Using isocitrate dehydrogenase (IDH) mutations to classify survival outcomes of patients with glioblastoma multiforme was recommended based on novel histopathological classification of brain tumors. Considering this novel classification, it is unclear whether the extent of tumor resection (EOR) is still important. The aim of this study was to investigate prognostic value of clinical factors (age, sex, EOR, status of IDH mutations, and adjuvant therapy) in patients with newly diagnosed glioblastoma. Methods In total, 269 patients were retrospectively enrolled and randomly divided into training (n = 179) and validation (n = 90) cohorts. Clinical information and survival outcomes were acquired from inpatient records and follow-ups. After adjusting for risk coefficients, the independent prognostic factors were selected in a multivariable analysis to generate a model to evaluate survival outcomes. Additionally, a receiver operating characteristic curve was used to assess accuracy for predicting survival outcomes at 12, 15, 18, and 24 months. Results Total resection of the contrast-enhanced region, age ≤ 60 years, received chemotherapy, and IDH mutations were favorable independent factors for overall survival. Area under the curve (AUC) for prediction of survival in the training cohort was 0.815, 0.851, 0.849, and 0.836 at 12, 15, 18, and 24 months, respectively. In the validation cohort, the AUC for prediction of survival was 0.780, 0.807, 0.836, and 0.849 at 12, 15, 18, and 24 months, respectively. Conclusion Total resection of the contrast-enhanced region is still crucial and recommended for patients with glioblastoma. Our prognostic model was able to predict survival outcomes, especially for long-term survival prediction.

show abstract

Using germline variants to estimate glioma and subtype risks

Cited by 30 publications

References 49 publications

The Genetic Architecture of Gliomagenesis–Genetic Risk Variants Linked to Specific Molecular Subtypes

The Genetic Architecture of Gliomagenesis–Genetic Risk Variants Linked to Specific Molecular Subtypes

The landscape of interactions between cancer polygenic risk scores and somatic alterations in cancer cells

A prognostic model of newly diagnosed glioblastoma based on clinical characteristics

Contact Info

Product

Resources

About