1
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Nakase T, Guerra G, Ostrom QT, Ge T, Melin B, Wrensch M, Wiencke JK, Jenkins RB, Eckel-Passow JE, Bondy ML, Francis SS, Kachuri L. Genome-wide Polygenic Risk Scores Predict Risk of Glioma and Molecular Subtypes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.10.24301112. [PMID: 38260701 PMCID: PMC10802631 DOI: 10.1101/2024.01.10.24301112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to find efficient ways of capturing genetic risk factors using available germline data. Methods We developed a novel PRS (PRS-CS) that uses continuous shrinkage priors to model the joint effects of over 1 million polymorphisms on disease risk and compared it to an approach (PRS-CT) that selects a limited set of independent variants that reach genome-wide significance (P<5×10-8). PRS models were trained using GWAS results stratified by histological (10,346 cases, 14,687 controls) and molecular subtype (2,632 cases, 2,445 controls), and validated in two independent cohorts. Results PRS-CS was consistently more predictive than PRS-CT across glioma subtypes with an average increase in explained variance (R2) of 21%. Improvements were particularly pronounced for glioblastoma tumors, with PRS-CS yielding larger effect sizes (odds ratio (OR)=1.93, P=2.0×10-54 vs. OR=1.83, P=9.4×10-50) and higher explained variance (R2=2.82% vs. R2=2.56%). Individuals in the 95th percentile of the PRS-CS distribution had a 3-fold higher lifetime absolute risk of IDH mutant (0.63%) and IDH wildtype (0.76%) glioma relative to individuals with average PRS. PRS-CS also showed high classification accuracy for IDH mutation status among cases (AUC=0.895). Conclusions Our novel genome-wide PRS may improve the identification of high-risk individuals and help distinguish between prognostic glioma subtypes, increasing the potential clinical utility of germline genetics in glioma patient management.
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Affiliation(s)
- Taishi Nakase
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Geno Guerra
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Quinn T. Ostrom
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Beatrice Melin
- Department of Radiation Sciences, Oncology Umeå University, Umeå, Sweden
| | - Margaret Wrensch
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - John K. Wiencke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Robert B. Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Melissa L. Bondy
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen S. Francis
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
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2
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FitzGerald TJ, Bishop-Jodoin M, Laurie F, Iandoli M, Smith K, Ulin K, Ding L, Moni J, Cicchetti MG, Knopp M, Kry S, Xiao Y, Rosen M, Prior F, Saltz J, Michalski J. The Importance of Quality Assurance in Radiation Oncology Clinical Trials. Semin Radiat Oncol 2023; 33:395-406. [PMID: 37684069 DOI: 10.1016/j.semradonc.2023.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
Clinical trials have been the center of progress in modern medicine. In oncology, we are fortunate to have a structure in place through the National Clinical Trials Network (NCTN). The NCTN provides the infrastructure and a forum for scientific discussion to develop clinical concepts for trial design. The NCTN also provides a network group structure to administer trials for successful trial management and outcome analyses. There are many important aspects to trial design and conduct. Modern trials need to ensure appropriate trial conduct and secure data management processes. Of equal importance is the quality assurance of a clinical trial. If progress is to be made in oncology clinical medicine, investigators and patient care providers of service need to feel secure that trial data is complete, accurate, and well-controlled in order to be confident in trial analysis and move trial outcome results into daily practice. As our technology has matured, so has our need to apply technology in a uniform manner for appropriate interpretation of trial outcomes. In this article, we review the importance of quality assurance in clinical trials involving radiation therapy. We will include important aspects of institution and investigator credentialing for participation as well as ongoing processes to ensure that each trial is being managed in a compliant manner. We will provide examples of the importance of complete datasets to ensure study interpretation. We will describe how successful strategies for quality assurance in the past will support new initiatives moving forward.
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Affiliation(s)
- Thomas J FitzGerald
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA..
| | | | - Fran Laurie
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - Matthew Iandoli
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - Koren Smith
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - Kenneth Ulin
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - Linda Ding
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - Janaki Moni
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - M Giulia Cicchetti
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA
| | - Michael Knopp
- Department of Radiology, University of Cincinnati, Cincinnati, OH
| | - Stephen Kry
- Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA
| | - Mark Rosen
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Jeff Michalski
- Department of Radiation Oncology, Washington University in St Louis, St Louis, MO
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3
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Bauchet L, Sanson M. Deciphering gliomagenesis from genome-wide association studies. Neuro Oncol 2023; 25:1366-1367. [PMID: 36915962 PMCID: PMC10326471 DOI: 10.1093/neuonc/noad057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Indexed: 03/16/2023] Open
Affiliation(s)
- Luc Bauchet
- Department of Neurosurgery, CHU Montpellier, Montpellier, France
- IGF, University of Montpellier, CNRS, INSERM, Montpellier, France
- French Brain Tumor DataBase, Registre des Tumeurs de l’Hérault, ICM, Montpellier, France
| | - Marc Sanson
- AP-HP, Hôpital de la Pitié-Salpêtrière, Service de Neurologie 2, Paris, France
- Sorbonne Université, INSERM Unité 1127, CNRS UMR 7225, Paris Brain Institute, Paris, France
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4
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Cheng L, Zhang F, Zhao X, Wang L, Duan W, Guan J, Wang K, Liu Z, Wang X, Wang Z, Wu H, Chen Z, Teng L, Li Y, Xiao F, Fan T, Jian F. Mutational landscape of primary spinal cord astrocytoma. J Pathol 2023. [PMID: 37114614 DOI: 10.1002/path.6084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/13/2023] [Accepted: 03/31/2023] [Indexed: 04/29/2023]
Abstract
Primary spinal cord astrocytoma (SCA) is a rare disease. Knowledge about the molecular profiles of SCAs mostly comes from intracranial glioma; the pattern of genetic alterations of SCAs is not well understood. Herein, we describe genome-sequencing analyses of primary SCAs, aiming to characterize the mutational landscape of primary SCAs. We utilized whole exome sequencing (WES) to analyze somatic nucleotide variants (SNVs) and copy number variants (CNVs) among 51 primary SCAs. Driver genes were searched using four algorithms. GISTIC2 was used to detect significant CNVs. Additionally, recurrently mutated pathways were also summarized. A total of 12 driver genes were identified. Of those, H3F3A (47.1%), TP53 (29.4%), NF1 (19.6%), ATRX (17.6%), and PPM1D (17.6%) were the most frequently mutated genes. Furthermore, three novel driver genes seldom reported in glioma were identified: HNRNPC, SYNE1, and RBM10. Several germline mutations, including three variants (SLC16A8 rs2235573, LMF1 rs3751667, FAM20C rs774848096) that were associated with risk of brain glioma, were frequently observed in SCAs. Moreover, 12q14.1 (13.7%) encompassing the oncogene CDK4 was recurrently amplified and negatively affected patient prognosis. Besides frequently mutated RTK/RAS pathway and PI3K pathway, the cell cycle pathway controlling the phosphorylation of retinoblastoma protein (RB) was mutated in 39.2% of patients. Overall, a considerable degree of the somatic mutation landscape is shared between SCAs and brainstem glioma. Our work provides a key insight into the molecular profiling of primary SCAs, which might represent candidate drug targets and complement the molecular atlas of glioma. © 2023 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Lei Cheng
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Fan Zhang
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, PR China
| | - Xingang Zhao
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, PR China
| | - Leiming Wang
- Department of Pathology, Xuanwu Hospital, Capital Medical University, Beijing, PR China
| | - Wanru Duan
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Jian Guan
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Kai Wang
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Zhenlei Liu
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Xingwen Wang
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Zuowei Wang
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Hao Wu
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Zan Chen
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
| | - Lianghong Teng
- Department of Pathology, Xuanwu Hospital, Capital Medical University, Beijing, PR China
| | - Yifei Li
- The Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Fei Xiao
- The Key Laboratory of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Tao Fan
- Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, Beijing, PR China
| | - Fengzeng Jian
- Department of Neurosurgery, Xuanwu Hospital, China International Neuroscience Institute, Capital Medical University, Beijing, PR China
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5
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Tang E, Wiencke JK, Warrier G, Hansen H, McCoy L, Rice T, Bracci PM, Wrensch M, Taylor JW, Clarke JL, Koestler DC, Salas LA, Christensen BC, Kelsey KT, Molinaro AM. Evaluation of cross-platform compatibility of a DNA methylation-based glucocorticoid response biomarker. Clin Epigenetics 2022; 14:136. [PMID: 36307860 PMCID: PMC9617416 DOI: 10.1186/s13148-022-01352-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 10/11/2022] [Indexed: 11/10/2022] Open
Abstract
Background Identifying blood-based DNA methylation patterns is a minimally invasive way to detect biomarkers in predicting age, characteristics of certain diseases and conditions, as well as responses to immunotherapies. As microarray platforms continue to evolve and increase the scope of CpGs measured, new discoveries based on the most recent platform version and how they compare to available data from the previous versions of the platform are unknown. The neutrophil dexamethasone methylation index (NDMI 850) is a blood-based DNA methylation biomarker built on the Illumina MethylationEPIC (850K) array that measures epigenetic responses to dexamethasone (DEX), a synthetic glucocorticoid often administered for inflammation. Here, we compare the NDMI 850 to one we built using data from the Illumina Methylation 450K (NDMI 450). Results The NDMI 450 consisted of 22 loci, 15 of which were present on the NDMI 850. In adult whole blood samples, the linear composite scores from NDMI 450 and NDMI 850 were highly correlated and had equivalent predictive accuracy for detecting DEX exposure among adult glioma patients and non-glioma adult controls. However, the NDMI 450 scores of newborn cord blood were significantly lower than NDMI 850 in samples measured with both assays. Conclusions We developed an algorithm that reproduces the DNA methylation glucocorticoid response score using 450K data, increasing the accessibility for researchers to assess this biomarker in archived or publicly available datasets that use the 450K version of the Illumina BeadChip array. However, the NDMI850 and NDMI450 do not give similar results in cord blood, and due to data availability limitations, results from sample types of newborn cord blood should be interpreted with care. Supplementary Information The online version contains supplementary material available at 10.1186/s13148-022-01352-1.
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6
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Wu WYY, Dahlin AM, Wibom C, Björkblom B, Melin B. Prediagnostic biomarkers for early detection of glioma—using case–control studies from cohorts as study approach. Neurooncol Adv 2022; 4:ii73-ii80. [PMID: 36380862 PMCID: PMC9650466 DOI: 10.1093/noajnl/vdac036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background Understanding the trajectory and development of disease is important and the knowledge can be used to find novel targets for therapy and new diagnostic tools for early diagnosis. Methods Large cohorts from different parts of the world are unique assets for research as they have systematically collected plasma and DNA over long-time periods in healthy individuals, sometimes even with repeated samples. Over time, the population in the cohort are diagnosed with many different diseases, including brain tumors. Results Recent studies have detected genetic variants that are associated with increased risk of glioblastoma and lower grade gliomas specifically. The impact for genetic markers to predict disease in a healthy population has been deemed low, and a relevant question is if the genetic variants for glioma are associated with risk of disease or partly consist of genes associated to survival. Both metabolite and protein spectra are currently being explored for early detection of cancer. Conclusions We here present a focused review of studies of genetic variants, metabolomics, and proteomics studied in prediagnostic glioma samples and discuss their potential in early diagnostics.
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Affiliation(s)
- Wendy Yi-Ying Wu
- Department of Radiation Sciences, Oncology, Umeå University , Umeå , Sweden
| | - Anna M Dahlin
- Department of Radiation Sciences, Oncology, Umeå University , Umeå , Sweden
| | - Carl Wibom
- Department of Radiation Sciences, Oncology, Umeå University , Umeå , Sweden
| | | | - Beatrice Melin
- Department of Radiation Sciences, Oncology, Umeå University , Umeå , Sweden
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7
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Mo Z, Xin J, Chai R, Woo PY, Chan DT, Wang J. Epidemiological characteristics and genetic alterations in adult diffuse glioma in East Asian populations. Cancer Biol Med 2022; 19:1440-1459. [PMID: 36350002 PMCID: PMC9630523 DOI: 10.20892/j.issn.2095-3941.2022.0418] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 09/20/2022] [Indexed: 05/06/2024] Open
Abstract
Understanding the racial specificities of diseases-such as adult diffuse glioma, the most common primary malignant tumor of the central nervous system-is a critical step toward precision medicine. Here, we comprehensively review studies of gliomas in East Asian populations and other ancestry groups to clarify the racial differences in terms of epidemiology and genomic characteristics. Overall, we observed a lower glioma incidence in East Asians than in Whites; notably, patients with glioblastoma had significantly younger ages of onset and longer overall survival than the Whites. Multiple genome-wide association studies of various cohorts have revealed single nucleotide polymorphisms associated with overall and subtype-specific glioma susceptibility. Notably, only 3 risk loci-5p15.33, 11q23.3, and 20q13.33-were shared between patients with East Asian and White ancestry, whereas other loci predominated only in particular populations. For instance, risk loci 12p11.23, 15q15-21.1, and 19p13.12 were reported in East Asians, whereas risk loci 8q24.21, 1p31.3, and 1q32.1 were reported in studies in White patients. Although the somatic mutational profiles of gliomas between East Asians and non-East Asians were broadly consistent, a lower incidence of EGFR amplification in glioblastoma and a higher incidence of 1p19q-IDH-TERT triple-negative low-grade glioma were observed in East Asian cohorts. By summarizing large-scale disease surveillance, germline, and somatic genomic studies, this review reveals the unique characteristics of adult diffuse glioma among East Asians, to guide clinical management and policy design focused on patients with East Asian ancestry.
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Affiliation(s)
- Zongchao Mo
- Division of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
- HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Shenzhen 518000, China
| | - Junyi Xin
- Division of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
| | - Ruichao Chai
- Division of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
- Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100070, China
| | - Peter Y.M. Woo
- Department of Neurosurgery, Kwong Wah Hospital, Hong Kong SAR, China
- Hong Kong Neuro-Oncology Society, Hong Kong SAR, China
| | - Danny T.M. Chan
- Division of Neurosurgery, Department of Surgery, Prince of Wales Hospital, Hong Kong SAR, China
| | - Jiguang Wang
- Division of Life Science and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
- HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Shenzhen 518000, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong SAR, China
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8
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Sagberg LM, Fyllingen EH, Hansen TI, Strand PS, Håvik AL, Sundstrøm T, Corell A, Jakola AS, Salvesen Ø, Solheim O. Is intracranial volume a risk factor for IDH-mutant low-grade glioma? A case-control study. J Neurooncol 2022; 160:101-106. [PMID: 36029398 PMCID: PMC9622551 DOI: 10.1007/s11060-022-04120-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 08/17/2022] [Indexed: 11/28/2022]
Abstract
Purpose Risk of cancer has been associated with body or organ size in several studies. We sought to investigate the relationship between intracranial volume (ICV) (as a proxy for lifetime maximum brain size) and risk of IDH-mutant low-grade glioma. Methods In a multicenter case–control study based on population-based data, we included 154 patients with IDH-mutant WHO grade 2 glioma and 995 healthy controls. ICV in both groups was calculated from 3D MRI brain scans using an automated reverse brain mask method, and then compared using a binomial logistic regression model. Results We found a non-linear association between ICV and risk of glioma with increasing risk above and below a threshold of 1394 ml (p < 0.001). After adjusting for ICV, sex was not a risk factor for glioma. Conclusion Intracranial volume may be a risk factor for IDH-mutant low-grade glioma, but the relationship seems to be non-linear with increased risk both above and below a threshold in intracranial volume.
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Affiliation(s)
- Lisa Millgård Sagberg
- Department of Neurosurgery, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway. .,Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Even Hovig Fyllingen
- Department of Radiology and Nuclear Medicine, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tor Ivar Hansen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Physical Medicine and Rehabilitation, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Per Sveino Strand
- Department of Neurosurgery, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Aril Løge Håvik
- Department of Clinical Medicine, University of Bergen, Bergen, Norway.,Department of Neurology, Molde Hospital, Molde, Norway
| | - Terje Sundstrøm
- Department of Clinical Medicine, University of Bergen, Bergen, Norway.,Department of Neurosurgery, Haukeland University Hospital, Bergen, Norway
| | - Alba Corell
- Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden.,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden
| | - Asgeir Store Jakola
- Department of Neurosurgery, Sahlgrenska University Hospital, Gothenburg, Sweden.,Institute of Neuroscience and Physiology, Department of Clinical Neuroscience, University of Gothenburg, Sahlgrenska Academy, Gothenburg, Sweden
| | - Øyvind Salvesen
- Clinical Research Unit, Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ole Solheim
- Department of Neurosurgery, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
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9
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Ding L, Bradford C, Kuo IL, Fan Y, Ulin K, Khalifeh A, Yu S, Liu F, Saleeby J, Bushe H, Smith K, Bianciu C, LaRosa S, Prior F, Saltz J, Sharma A, Smyczynski M, Bishop-Jodoin M, Laurie F, Iandoli M, Moni J, Cicchetti MG, FitzGerald TJ. Radiation Oncology: Future Vision for Quality Assurance and Data Management in Clinical Trials and Translational Science. Front Oncol 2022; 12:931294. [PMID: 36033446 PMCID: PMC9399423 DOI: 10.3389/fonc.2022.931294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/21/2022] [Indexed: 11/13/2022] Open
Abstract
The future of radiation oncology is exceptionally strong as we are increasingly involved in nearly all oncology disease sites due to extraordinary advances in radiation oncology treatment management platforms and improvements in treatment execution. Due to our technology and consistent accuracy, compressed radiation oncology treatment strategies are becoming more commonplace secondary to our ability to successfully treat tumor targets with increased normal tissue avoidance. In many disease sites including the central nervous system, pulmonary parenchyma, liver, and other areas, our service is redefining the standards of care. Targeting of disease has improved due to advances in tumor imaging and application of integrated imaging datasets into sophisticated planning systems which can optimize volume driven plans created by talented personnel. Treatment times have significantly decreased due to volume driven arc therapy and positioning is secured by real time imaging and optical tracking. Normal tissue exclusion has permitted compressed treatment schedules making treatment more convenient for the patient. These changes require additional study to further optimize care. Because data exchange worldwide have evolved through digital platforms and prisms, images and radiation datasets worldwide can be shared/reviewed on a same day basis using established de-identification and anonymization methods. Data storage post-trial completion can co-exist with digital pathomic and radiomic information in a single database coupled with patient specific outcome information and serve to move our translational science forward with nimble query elements and artificial intelligence to ask better questions of the data we collect and collate. This will be important moving forward to validate our process improvements at an enterprise level and support our science. We have to be thorough and complete in our data acquisition processes, however if we remain disciplined in our data management plan, our field can grow further and become more successful generating new standards of care from validated datasets.
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Affiliation(s)
- Linda Ding
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Carla Bradford
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - I-Lin Kuo
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Yankhua Fan
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Kenneth Ulin
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Abdulnasser Khalifeh
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Suhong Yu
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Fenghong Liu
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Jonathan Saleeby
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Harry Bushe
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Koren Smith
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Camelia Bianciu
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Salvatore LaRosa
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Fred Prior
- Department of Biomedical Informatics, University of Arkansas, Little Rock, AR, United States
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, United States
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Mark Smyczynski
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Maryann Bishop-Jodoin
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Fran Laurie
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Matthew Iandoli
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Janaki Moni
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - M. Giulia Cicchetti
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
| | - Thomas J. FitzGerald
- Department of Radiation Oncology, UMass Chan Medical School, Worcester, MA, United States
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10
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Eckel-Passow JE, Lachance DH, Decker PA, Kollmeyer TM, Kosel ML, Drucker KL, Slager S, Wrensch M, Tobin WO, Jenkins RB. Inherited genetics of adult diffuse glioma and polygenic risk scores-a review. Neurooncol Pract 2022; 9:259-270. [PMID: 35859544 PMCID: PMC9290891 DOI: 10.1093/nop/npac017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Knowledge about inherited and acquired genetics of adult diffuse glioma has expanded significantly over the past decade. Genomewide association studies (GWAS) stratified by histologic subtype identified six germline variants that were associated specifically with glioblastoma (GBM) and 12 that were associated with lower grade glioma. A GWAS performed using the 2016 WHO criteria, stratifying patients by IDH mutation and 1p/19q codeletion (as well as TERT promoter mutation), discovered that many of the known variants are associated with specific WHO glioma subtypes. In addition, the GWAS stratified by molecular group identified two additional novel regions: variants in D2HGDH that were associated with tumors that had an IDH mutation and a variant near FAM20C that was associated with tumors that had both IDH mutation and 1p/19q codeletion. The results of these germline associations have been used to calculate polygenic risk scores, from which to estimate relative and absolute risk of overall glioma and risk of specific glioma subtypes. We will review the concept of polygenic risk models and their potential clinical utility, as well as discuss the published adult diffuse glioma polygenic risk models. To date, these prior genetic studies have been done on European populations. Using the published glioma polygenic risk model, we show that the genetic associations published to date do not generalize across genetic ancestries, demonstrating that genetic studies need to be done on more diverse populations.
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Affiliation(s)
- Jeanette E Eckel-Passow
- Corresponding Author: Jeanette E. Eckel-Passow, PhD, Department of Quantitative Health Sciences, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA ()
| | - Daniel H Lachance
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul A Decker
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Thomas M Kollmeyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Matthew L Kosel
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Kristen L Drucker
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Susan Slager
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Margaret Wrensch
- Department of Neurological Surgery, Institute of Human Genetics, University of California, San Francisco, San Francisco, California, USA
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11
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Francis SS, Ostrom QT, Cote DJ, Smith TR, Claus E, Barnholtz-Sloan JS. The Epidemiology of Central Nervous System Tumors. Hematol Oncol Clin North Am 2022; 36:23-42. [PMID: 34801162 DOI: 10.1016/j.hoc.2021.08.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
This article reviews the current epidemiology of central nervous system tumors. Population-level basic epidemiology, nationally and internationally, and current understanding of germline genetic risk are discussed, with a focus on known and well-studied risk factors related to the etiology of central nervous system tumors.
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Affiliation(s)
- Stephen S Francis
- Department of Neurological Surgery, Division of Neuro and Molecular Epidemiology, University of California San Francisco School of Medicine, 1450 3rd Street, HD442, San Francisco, CA 94158, USA.
| | - Quinn T Ostrom
- Department of Neurosurgery, Duke University School of Medicine, 571 Research Drive, MSRB-1, Rm 442, Durham, NC 27710, USA
| | - David J Cote
- Department of Neurosurgery, Keck School of Medicine, University of Southern California, 1200 N State Street, Suite 3300, Los Angeles, CA 90033, USA
| | - Timothy R Smith
- Department of Neurosurgery, Computational Neuroscience Outcomes Center, Brigham and Women's Hospital, Harvard Medical School, 60 Fenwood Avenue, Boston, MA 02115, USA
| | - Elizabeth Claus
- Department of Neurosurgery, Yale University, Yale School of Public Health, Brigham and Women's Hospital, 60 College Street, New Haven, CT 06510, USA
| | - Jill S Barnholtz-Sloan
- Center for Biomedical Informatics and Information Technology, Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute (NCI), NCI Shady Grove, 9609 Medical Center Dr, Rockville, MD 20850, USA
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12
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He Q, Liu Y, Liu M, Wu MC, Hsu L. Random effect based tests for multinomial logistic regression in genetic association studies. Genet Epidemiol 2021; 45:736-740. [PMID: 34403161 DOI: 10.1002/gepi.22427] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 07/31/2021] [Accepted: 08/01/2021] [Indexed: 11/11/2022]
Affiliation(s)
- Qianchuan He
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Yang Liu
- Department of Mathematics and Statistics, Wright State University, Dayton, Ohio, USA
| | - Meiling Liu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Michael C Wu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
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13
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Eckel-Passow JE, Lachance DH, Jenkins RB. Glioma: interaction of acquired and germline genetics. Aging (Albany NY) 2021; 13:19085-19087. [PMID: 34385404 PMCID: PMC8386538 DOI: 10.18632/aging.203428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/10/2021] [Indexed: 01/22/2023]
Affiliation(s)
| | - Daniel H Lachance
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA.,Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Robert B Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
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14
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Manjunath M, Yan J, Youn Y, Drucker KL, Kollmeyer TM, McKinney AM, Zazubovich V, Zhang Y, Costello JF, Eckel-Passow J, Selvin PR, Jenkins RB, Song JS. Functional analysis of low-grade glioma genetic variants predicts key target genes and transcription factors. Neuro Oncol 2021; 23:638-649. [PMID: 33130899 DOI: 10.1093/neuonc/noaa248] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Large-scale genome-wide association studies (GWAS) have implicated thousands of germline genetic variants in modulating individuals' risk to various diseases, including cancer. At least 25 risk loci have been identified for low-grade gliomas (LGGs), but their molecular functions remain largely unknown. METHODS We hypothesized that GWAS loci contain causal single nucleotide polymorphisms (SNPs) that reside in accessible open chromatin regions and modulate the expression of target genes by perturbing the binding affinity of transcription factors (TFs). We performed an integrative analysis of genomic and epigenomic data from The Cancer Genome Atlas and other public repositories to identify candidate causal SNPs within linkage disequilibrium blocks of LGG GWAS loci. We assessed their potential regulatory role via in silico TF binding sequence perturbations, convolutional neural network trained on TF binding data, and simulated annealing-based interpretation methods. RESULTS We built an interactive website (http://education.knoweng.org/alg3/) summarizing the functional footprinting of 280 variants in 25 LGG GWAS regions, providing rich information for further computational and experimental scrutiny. We identified as case studies PHLDB1 and SLC25A26 as candidate target genes of rs12803321 and rs11706832, respectively, and predicted the GWAS variant rs648044 to be the causal SNP modulating ZBTB16, a known tumor suppressor in multiple cancers. We showed that rs648044 likely perturbed the binding affinity of the TF MAFF, as supported by RNA interference and in vitro MAFF binding experiments. CONCLUSIONS The identified candidate (causal SNP, target gene, TF) triplets and the accompanying resource will help accelerate our understanding of the molecular mechanisms underlying genetic risk factors for gliomas.
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Affiliation(s)
- Mohith Manjunath
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Jialu Yan
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yeoan Youn
- Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Kristen L Drucker
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Thomas M Kollmeyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrew M McKinney
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | - Valter Zazubovich
- Department of Physics, Concordia University, Montreal, Québec, Canada
| | - Yi Zhang
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Joseph F Costello
- Department of Neurological Surgery, University of California San Francisco, San Francisco, California, USA
| | | | - Paul R Selvin
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Robert B Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Jun S Song
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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15
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Eckel-Passow JE, Drucker KL, Kollmeyer TM, Kosel ML, Decker PA, Molinaro AM, Rice T, Praska CE, Clark L, Caron A, Abyzov A, Batzler A, Song JS, Pekmezci M, Hansen HM, McCoy LS, Bracci PM, Wiemels J, Wiencke JK, Francis S, Burns TC, Giannini C, Lachance DH, Wrensch M, Jenkins RB. Adult diffuse glioma GWAS by molecular subtype identifies variants in D2HGDH and FAM20C. Neuro Oncol 2021; 22:1602-1613. [PMID: 32386320 DOI: 10.1093/neuonc/noaa117] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Twenty-five germline variants are associated with adult diffuse glioma, and some of these variants have been shown to be associated with particular subtypes of glioma. We hypothesized that additional germline variants could be identified if a genome-wide association study (GWAS) were performed by molecular subtype. METHODS A total of 1320 glioma cases and 1889 controls were used in the discovery set and 799 glioma cases and 808 controls in the validation set. Glioma cases were classified into molecular subtypes based on combinations of isocitrate dehydrogenase (IDH) mutation, telomerase reverse transcriptase (TERT) promoter mutation, and 1p/19q codeletion. Logistic regression was applied to the discovery and validation sets to test for associations of variants with each of the subtypes. A meta-analysis was subsequently performed using a genome-wide P-value threshold of 5 × 10-8. RESULTS Nine variants in or near D-2-hydroxyglutarate dehydrogenase (D2HGDH) on chromosome 2 were genome-wide significant in IDH-mutated glioma (most significant was rs5839764, meta P = 2.82 × 10-10). Further stratifying by 1p/19q codeletion status, one variant in D2HGDH was genome-wide significant in IDH-mutated non-codeleted glioma (rs1106639, meta P = 4.96 × 10-8). Further stratifying by TERT mutation, one variant near FAM20C (family with sequence similarity 20, member C) on chromosome 7 was genome-wide significant in gliomas that have IDH mutation, TERT mutation, and 1p/19q codeletion (rs111976262, meta P = 9.56 × 10-9). Thirty-six variants in or near GMEB2 on chromosome 20 near regulator of telomere elongation helicase 1 (RTEL1) were genome-wide significant in IDH wild-type glioma (most significant was rs4809313, meta P = 2.60 × 10-10). CONCLUSIONS Performing a GWAS by molecular subtype identified 2 new regions and a candidate independent region near RTEL1, which were associated with specific glioma molecular subtypes.
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Affiliation(s)
| | - Kristen L Drucker
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Thomas M Kollmeyer
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Matt L Kosel
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Paul A Decker
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Annette M Molinaro
- Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, California.,Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California
| | - Terri Rice
- Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, California
| | - Corinne E Praska
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Lauren Clark
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Alissa Caron
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Alexej Abyzov
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Anthony Batzler
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Jun S Song
- Department of Physics, Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois
| | - Melike Pekmezci
- Department of Pathology, University of California San Francisco, San Francisco, California
| | - Helen M Hansen
- Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, California
| | - Lucie S McCoy
- Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, California
| | - Paige M Bracci
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Joseph Wiemels
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California
| | - John K Wiencke
- Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, California.,Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.,Institute of Human Genetics, University of California San Francisco, San Francisco, California
| | - Stephen Francis
- Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, California.,Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California
| | - Terry C Burns
- Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota
| | - Caterina Giannini
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Daniel H Lachance
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.,Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | - Margaret Wrensch
- Department of Neurological Surgery, University of California San Francisco (UCSF), San Francisco, California.,Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California.,Institute of Human Genetics, University of California San Francisco, San Francisco, California
| | - Robert B Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
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16
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Porta‐Pardo E, Valencia A, Godzik A. Understanding oncogenicity of cancer driver genes and mutations in the cancer genomics era. FEBS Lett 2020; 594:4233-4246. [PMID: 32239503 PMCID: PMC7529711 DOI: 10.1002/1873-3468.13781] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Revised: 01/23/2020] [Accepted: 02/09/2020] [Indexed: 12/12/2022]
Abstract
One of the key challenges of cancer biology is to catalogue and understand the somatic genomic alterations leading to cancer. Although alternative definitions and search methods have been developed to identify cancer driver genes and mutations, analyses of thousands of cancer genomes return a remarkably similar catalogue of around 300 genes that are mutated in at least one cancer type. Yet, many features of these genes and their role in cancer remain unclear, first and foremost when a somatic mutation is truly oncogenic. In this review, we first summarize some of the recent efforts in completing the catalogue of cancer driver genes. Then, we give an overview of different aspects that influence the oncogenicity of somatic mutations in the core cancer driver genes, including their interactions with the germline genome, other cancer driver mutations, the immune system, or their potential role in healthy tissues. In the coming years, this research holds promise to illuminate how, when, and why cancer driver genes and mutations are really drivers, and thereby move personalized cancer medicine and targeted therapies forward.
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Affiliation(s)
- Eduard Porta‐Pardo
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
- Josep Carreras Leukaemia Research Institute (IJC)BadalonaSpain
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
- Institucio Catalana de Recerca I Estudis Avançats (ICREA)BarcelonaSpain
| | - Adam Godzik
- Division of Biomedical SciencesUniversity of California Riverside School of MedicineRiversideCAUSA
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17
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The Genetic Architecture of Gliomagenesis-Genetic Risk Variants Linked to Specific Molecular Subtypes. Cancers (Basel) 2019; 11:cancers11122001. [PMID: 31842352 PMCID: PMC6966482 DOI: 10.3390/cancers11122001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 12/05/2019] [Accepted: 12/07/2019] [Indexed: 12/19/2022] Open
Abstract
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.
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18
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Molinaro AM, Taylor JW, Wiencke JK, Wrensch MR. Genetic and molecular epidemiology of adult diffuse glioma. Nat Rev Neurol 2019; 15:405-417. [PMID: 31227792 PMCID: PMC7286557 DOI: 10.1038/s41582-019-0220-2] [Citation(s) in RCA: 386] [Impact Index Per Article: 77.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/15/2019] [Indexed: 12/24/2022]
Abstract
The WHO 2007 glioma classification system (based primarily on tumour histology) resulted in considerable interobserver variability and substantial variation in patient survival within grades. Furthermore, few risk factors for glioma were known. Discoveries over the past decade have deepened our understanding of the molecular alterations underlying glioma and have led to the identification of numerous genetic risk factors. The advances in molecular characterization of glioma have reframed our understanding of its biology and led to the development of a new classification system for glioma. The WHO 2016 classification system comprises five glioma subtypes, categorized by both tumour morphology and molecular genetic information, which led to reduced misclassification and improved consistency of outcomes within glioma subtypes. To date, 25 risk loci for glioma have been identified and several rare inherited mutations that might cause glioma in some families have been discovered. This Review focuses on the two dominant trends in glioma science: the characterization of diagnostic and prognostic tumour markers and the identification of genetic and other risk factors. An overview of the many challenges still facing glioma researchers is also included.
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Affiliation(s)
- Annette M Molinaro
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA.
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
| | - Jennie W Taylor
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - John K Wiencke
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Institute of Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | - Margaret R Wrensch
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- Institute of Human Genetics, University of California, San Francisco, San Francisco, CA, USA
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