This study identified a distinct subgroup of glioma tumors characterized by concerted DNA hypermethylation at many loci (G-CIMP). They analyzed 272 glioblastoma samples and found that 8.8% displayed this G-CIMP phenotype. G-CIMP tumors belonged primarily to the proneural subtype and were associated with IDH1 mutations. Patients with G-CIMP tumors were younger at diagnosis and had significantly improved outcomes. These findings establish G-CIMP as a distinct molecular and clinical subtype of gliomas.
The repeated inflammation caused by monthly ovulation exposes the fallopian tube epithelium to oxidative stress, leading to transcription-associated mutations in the TP53 gene. This is believed to be the first step in the development of high grade serous ovarian carcinoma (HGSC). Estrogen and inflammatory cytokines generated during ovulation cause DNA damage in the fallopian tube cells through reactive oxygen species. Mutation of the TP53 gene prevents the cells from undergoing apoptosis in response to this damage, allowing genetic changes to accumulate and cancer to develop.
Understanding cancer -- patient-genetic_backgroundBoadicea123
While a cancer genome profile provides information, it does not tell the full story on its own. A patient's genetic background, which varies between individuals due to inherited and environmental factors, also influences cancer risk and treatment outcomes. This background includes single nucleotide variations across the genome as well as variations in genes related to drug metabolism and cancer susceptibility. Additional epigenetic and non-coding factors can also indirectly affect gene expression and cancer predisposition within a person. Understanding all of these intrinsic patient-specific influences is important for a complete diagnosis and personalized cancer care.
This document summarizes key information about X-linked diseases and mitochondrial DNA diseases. It discusses the X chromosome and genes located on it. It provides details on three specific X-linked diseases: hemophilia A, Duchenne muscular dystrophy, and fragile X syndrome. It describes the inheritance patterns, genetic causes, clinical features, and DNA testing approaches for each. It also provides a brief overview of the structure and function of mitochondrial DNA and its role in oxidative phosphorylation.
Pistoia Alliance US Conference 2015 - 1.5.4 New data - Nikolaus SchultzPistoia Alliance
The document provides an overview of cancer genomics data and tools for analyzing it. It discusses how next-generation sequencing is identifying genetic alterations in cancer at an increasing scale through projects like TCGA. The cBioPortal is highlighted as a tool that provides intuitive access to these complex cancer genomics datasets and helps identify patterns across data to provide clinical insights. It has become widely used and its code is now fully open source to further collaborative cancer research.
1) The study analyzed the radiation survival of 533 human cancer cell lines across 26 cancer types using a high-throughput profiling platform. It found significant variation in survival both across and within lineages, on the order of 5- to 7-fold difference within lineages.
2) The profiling platform was validated against standard clonogenic survival assays, showing a high correlation between results. Sensitivity to radiation was found to have a normal distribution within most lineages studied.
3) Analyzing genomic features, the study found that higher levels of somatic copy number alterations (SCNAs) in a tumor's genome correlated with increased survival after radiation exposure, possibly by enabling more error-prone DNA repair mechanisms. Certain gene mutations and
1) The study analyzed DNA methylation patterns between young (<45 years old) and older (>=45 years old) women with breast cancer using data from The Cancer Genome Atlas.
2) Machine learning identified 17 differentially methylated sites between the two groups, including hypomethylation of the ITPR3 promoter and hypermethylation of the ARID3A gene in young women.
3) Differential methylation of these genes, particularly ITPR3 which stimulates breast cancer cell proliferation, may help explain the increased aggressiveness of breast cancer in young women. Further investigation of these genes is warranted.
Cancer is the fourth highest cause of death in Malaysia. Vitamin D and targeted therapy drugs can help prevent and treat cancer. Vitamin D regulates cell cycle, apoptosis, and adhesion to prevent cancer formation. It controls mutations and cancer cell production. Lenalidomide is a targeted therapy drug that has direct anti-tumor effects through cell cycle arrest, anti-angiogenesis by decreasing VEGF and IL-6, and T-cell activation. Current research continues to improve targeted drugs to reduce side effects and increase efficacy, though challenges remain regarding side effects, resistance, and ethical issues.
Alternative lengthening of telomeres is enriched in, and impacts survival of ...Joshua Mangerel
This study investigated the prevalence and significance of alternative lengthening of telomeres (ALT) in pediatric brain tumors. The researchers screened 517 pediatric brain tumor samples for ALT using the C-circle assay and validated the results with other assays. They found that ALT was detected in a subset of malignant pediatric brain tumors, especially primitive neuroectodermal tumors, choroid plexus carcinomas, and high-grade gliomas. Somatic mutations in TP53 were strongly associated with ALT. ALT attenuated the poor prognosis associated with TP53 mutations in some tumor types. ALT positive tumors had higher survival rates than ALT negative tumors for children with TP53 mutant malignant gliomas and choroid plexus carcinomas. This suggests ALT may impact survival
The repeated inflammation caused by monthly ovulation exposes the fallopian tube epithelium to oxidative stress, leading to transcription-associated mutations in the TP53 gene. This is believed to be the first step in the development of high grade serous ovarian carcinoma (HGSC). Estrogen and inflammatory cytokines generated during ovulation cause DNA damage in the fallopian tube cells through reactive oxygen species. Mutation of the TP53 gene prevents the cells from undergoing apoptosis in response to this damage, allowing genetic changes to accumulate and cancer to develop.
Understanding cancer -- patient-genetic_backgroundBoadicea123
While a cancer genome profile provides information, it does not tell the full story on its own. A patient's genetic background, which varies between individuals due to inherited and environmental factors, also influences cancer risk and treatment outcomes. This background includes single nucleotide variations across the genome as well as variations in genes related to drug metabolism and cancer susceptibility. Additional epigenetic and non-coding factors can also indirectly affect gene expression and cancer predisposition within a person. Understanding all of these intrinsic patient-specific influences is important for a complete diagnosis and personalized cancer care.
This document summarizes key information about X-linked diseases and mitochondrial DNA diseases. It discusses the X chromosome and genes located on it. It provides details on three specific X-linked diseases: hemophilia A, Duchenne muscular dystrophy, and fragile X syndrome. It describes the inheritance patterns, genetic causes, clinical features, and DNA testing approaches for each. It also provides a brief overview of the structure and function of mitochondrial DNA and its role in oxidative phosphorylation.
Pistoia Alliance US Conference 2015 - 1.5.4 New data - Nikolaus SchultzPistoia Alliance
The document provides an overview of cancer genomics data and tools for analyzing it. It discusses how next-generation sequencing is identifying genetic alterations in cancer at an increasing scale through projects like TCGA. The cBioPortal is highlighted as a tool that provides intuitive access to these complex cancer genomics datasets and helps identify patterns across data to provide clinical insights. It has become widely used and its code is now fully open source to further collaborative cancer research.
1) The study analyzed the radiation survival of 533 human cancer cell lines across 26 cancer types using a high-throughput profiling platform. It found significant variation in survival both across and within lineages, on the order of 5- to 7-fold difference within lineages.
2) The profiling platform was validated against standard clonogenic survival assays, showing a high correlation between results. Sensitivity to radiation was found to have a normal distribution within most lineages studied.
3) Analyzing genomic features, the study found that higher levels of somatic copy number alterations (SCNAs) in a tumor's genome correlated with increased survival after radiation exposure, possibly by enabling more error-prone DNA repair mechanisms. Certain gene mutations and
1) The study analyzed DNA methylation patterns between young (<45 years old) and older (>=45 years old) women with breast cancer using data from The Cancer Genome Atlas.
2) Machine learning identified 17 differentially methylated sites between the two groups, including hypomethylation of the ITPR3 promoter and hypermethylation of the ARID3A gene in young women.
3) Differential methylation of these genes, particularly ITPR3 which stimulates breast cancer cell proliferation, may help explain the increased aggressiveness of breast cancer in young women. Further investigation of these genes is warranted.
Cancer is the fourth highest cause of death in Malaysia. Vitamin D and targeted therapy drugs can help prevent and treat cancer. Vitamin D regulates cell cycle, apoptosis, and adhesion to prevent cancer formation. It controls mutations and cancer cell production. Lenalidomide is a targeted therapy drug that has direct anti-tumor effects through cell cycle arrest, anti-angiogenesis by decreasing VEGF and IL-6, and T-cell activation. Current research continues to improve targeted drugs to reduce side effects and increase efficacy, though challenges remain regarding side effects, resistance, and ethical issues.
Alternative lengthening of telomeres is enriched in, and impacts survival of ...Joshua Mangerel
This study investigated the prevalence and significance of alternative lengthening of telomeres (ALT) in pediatric brain tumors. The researchers screened 517 pediatric brain tumor samples for ALT using the C-circle assay and validated the results with other assays. They found that ALT was detected in a subset of malignant pediatric brain tumors, especially primitive neuroectodermal tumors, choroid plexus carcinomas, and high-grade gliomas. Somatic mutations in TP53 were strongly associated with ALT. ALT attenuated the poor prognosis associated with TP53 mutations in some tumor types. ALT positive tumors had higher survival rates than ALT negative tumors for children with TP53 mutant malignant gliomas and choroid plexus carcinomas. This suggests ALT may impact survival
1. Researchers identified a novel isoform of the tumor suppressor gene DLC1 called DLC1-i4 through 5' RACE. DLC1-i4 encodes a 1125 amino acid protein with a distinct N-terminus compared to other DLC1 isoforms.
2. DLC1-i4 expression is frequently downregulated in multiple carcinoma cell lines, including esophageal, gastric, breast, colorectal, cervical and lung cancers. Its promoter region contains CpG islands that are often methylated, silencing expression.
3. Ectopic expression of DLC1-i4 in silenced tumor cells strongly inhibited their growth and colony formation, demonstrating its tumor suppressive
This document lists 20 publications and 2 book chapters by the author. The publications are listed in chronological order from 2006 to 2015 and cover topics related to p63, vitamin D receptor, estrogen signaling, progesterone receptor, endometrial function, and mammary gland development. The author has contributed to understanding the roles of these factors and their regulation using techniques including gene expression analysis, chromatin immunoprecipitation, and mouse models.
The document discusses several key concepts relating to cancer biology and the hallmarks of cancer. It describes the evolutionary process of tumor development and lists some of the important hallmarks identified by Hanahan and Weinberg, including sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. It also mentions several genetic mutations that have been identified in cancers like glioblastomas and lung adenocarcinomas.
A normal cell can be transformed into a cancerous cell. Discuss the therapeutic strategies that are employed to target the cellular transformation process for cancer prevention and treatment.
This study aims to evaluate the gene expression profile of FOXE1 mRNA in paired thyroid tumor and non-tumor tissue samples. Preliminary results from the first 10 paired samples show variation in FOXE1 gene expression levels between tumor and non-tumor tissues, with non-tumor tissue appearing to have higher FOXE1 expression compared to tumor tissue. Analysis of more sample pairs is ongoing, and FOXE1 expression levels will be compared to patient clinical data and tumor pathological characteristics. The results so far indicate there may be a profile of FOXE1 expression in thyroid cancer.
Telomeric G-Quadruplexes as Therapeutic Targets in CancerHIRA Zaidi
This document discusses targeting telomeric G-quadruplexes as a potential therapeutic approach for cancer. It describes how G-quadruplex binding molecules can inhibit telomerase and cause cancer cell death by triggering telomere shortening. The document outlines various methodologies used to design G-quadruplex binding molecules and study their interactions with telomeric DNA, including computational modeling, biophysical assays, and structural analysis. Challenges in developing selective G-quadruplex targeting agents are also discussed.
This research article examines the role of sphingosine-1-phosphate (S1P) in regulating the proliferative and stem-like properties of glioblastoma stem cells (GSCs). The results showed that GSCs rapidly consume ceramide and export S1P into the extracellular environment. Extracellular S1P levels reached nanomolar concentrations in response to increased sphingosine. S1P was also found to act as an autocrine factor that promotes GSC proliferation and maintains their stem-like phenotype. This suggests that microenvironmental S1P critically modulates the GSC population by acting as an autocrine signal to support stemness and favor proliferation, survival, and therapeutic resistance of GSCs.
Lung cancer is a major cause of cancer deaths with approximately 80% of cases accounting to nonsmall cell lung cancer (NSCLC) . In NSCLC target therapy, epidermal growth factor receptor (EGFR) is a promising candidate.
This document provides an overview of the molecular foundations of cancer. It discusses how cancer arises from genetic and epigenetic aberrations that accumulate in cells and lead to altered gene expression and the acquisition of hallmark capabilities that allow tumors to form and progress. Key points covered include the types of genomic changes like mutations and chromosome defects that occur; the roles of oncogenes and tumor suppressor genes; how cancer risk can be inherited; and the uses of genomics in cancer diagnosis and targeted treatment.
Dr. David Guillespie - Identificación de dianas de daño en el DNA en la terap...CIBICAN - ULL
Presentación del Dr. David Guillespie, investigador contratado por el CIBICAN - Universidad de La Laguna gracias al Proyecto Europeo IMBRAIN, en relación a los resultados alcanzados durante la ejecución del mismo y los planes de futuro. La misma se presentó durante las Jornadas IMBRAIN llevadas a cabo el 13 de Octubre de 2015 en la Sección de Física en la Universidad de La Laguna
Management of colorectal cancers in older peopleSpringer
This document discusses genetic alterations in colorectal cancer between younger and older patients. It begins by providing background on the multistep model of colorectal cancer development and the genetic mutations that drive each step, such as APC, TP53, and KRAS. It then notes limited data on differences in genetic alterations between younger and older CRC patients. The document goes on to discuss aging and theories of aging, focusing on pathways such as insulin/IGF-1, TOR, AMP kinase, and sirtuins that may affect longevity when inhibited. It also discusses telomere length differences between younger and older CRC patients and prospects for human anti-aging interventions.
Asbestos-related diseases - mechanisms and causation at Helsinki Asbestos 2014Työterveyslaitos
1. Asbestos fibers cause chronic inflammation in the lungs and pleura, which enables the development of cancers through tumor-promoting inflammation and genomic instability from oxidative DNA damage.
2. The tumor microenvironment in asbestos-related cancers is immunosuppressive, allowing tumors to evade immune destruction.
3. Targeting chronic inflammation and harnessing the host immune response, in addition to cytotoxic therapies, may be more effective against asbestos-related cancers than cytotoxic agents alone.
The document discusses epigenetics and its role in environmental diseases. It defines epigenetics as mechanisms that regulate gene expression without changing DNA sequence. Environmental factors can cause epigenetic changes through pathways like DNA methylation and histone modification. Abnormal epigenetic changes have been implicated in diseases like cancer, aging, and neurodevelopmental disorders. Certain environmental exposures are also linked to epigenetic alterations, though causal relationships are difficult to establish.
Ton Beniers_publicatielijst overzicht 2015Ton Beniers
This document lists publications by Beniers and others from 1987-2001 related to experimental cytokine therapy for renal cell carcinoma and Wilms' tumor. It includes 18 journal publications, 5 book chapters, and 1 PhD thesis by Beniers on experimental cytokine therapy for renal cell carcinoma. The publications examine the in vitro and in vivo effects of interferons and tumor necrosis factor on renal tumor cell lines and xenografts, and differential expression of drug resistance and heat shock proteins in histological compartments of nephroblastomas.
The document discusses lung cancer and EGFR. It notes that EGFR is a transmembrane glycoprotein receptor present on cancer cells. Lung cancer occurs when tumors grow in the lungs and can be caused by smoking, radon gas, air pollution, or genetics. There are two main types of lung cancer - small cell and non-small cell. The EGFR gene is located on chromosome 7 and encodes a growth factor receptor with tyrosine kinase activity. Tyrosine kinase activates cell growth and survival signals when ligands bind to the extracellular portion of EGFR.
Cancer arises due to genetic aberrations that accumulate in somatic cells and alter gene expression. There are several types of genomic changes including mutations, chromosome defects, and changes to oncogenes and tumor suppressor genes. Genetic testing can identify inherited cancer risk genes and guide diagnosis and treatment, while gene therapy holds promise for directly treating cancer at the genetic level.
Role of genetics in periodontal diseasesAnushri Gupta
Terminologies in Genetics
Genetic study design
genetic syndrome and disease associated with periodontal diseases, heretibility of periodontal disease, gene library, gene therapy
Clinical Genomics for Personalized Cancer Medicine: Recent Advances, Challeng...Yoon Sup Choi
I reviewed recent advances, challenges, and opportunities to implement clinical cancer genomics. Case studies of advanced systems, such as Foundation Medicine, MI-ONCOSEQ are introduced for benchmark. A few fundamental limitations to establish personalized oncology are also discussed.
Compare the use of Lonza KGM Gold Bullet kit and Rheinwald and Green complete FAD medium in primary human epidermal keratinocytes culture and its applicability cells cultured by these medium in the construction of reconstituted skin equivalent model
Be Aware Webinar - Uniendo fuerzas: administre y proteja al punto finalSymantec LATAM
Este documento presenta las soluciones de Symantec para la administración y protección de endpoints. Describe los desafíos de controlar endpoints móviles y la necesidad de visibilidad y políticas coherentes. Luego introduce las suites IT Management Suite (ITMS) y Symantec Endpoint Protection (SEP) que brindan administración unificada, seguridad en capas y análisis para proteger y gestionar endpoints de forma centralizada. Finalmente, se discuten los servicios de educación de Symantec para capacitar a los clientes.
1. Researchers identified a novel isoform of the tumor suppressor gene DLC1 called DLC1-i4 through 5' RACE. DLC1-i4 encodes a 1125 amino acid protein with a distinct N-terminus compared to other DLC1 isoforms.
2. DLC1-i4 expression is frequently downregulated in multiple carcinoma cell lines, including esophageal, gastric, breast, colorectal, cervical and lung cancers. Its promoter region contains CpG islands that are often methylated, silencing expression.
3. Ectopic expression of DLC1-i4 in silenced tumor cells strongly inhibited their growth and colony formation, demonstrating its tumor suppressive
This document lists 20 publications and 2 book chapters by the author. The publications are listed in chronological order from 2006 to 2015 and cover topics related to p63, vitamin D receptor, estrogen signaling, progesterone receptor, endometrial function, and mammary gland development. The author has contributed to understanding the roles of these factors and their regulation using techniques including gene expression analysis, chromatin immunoprecipitation, and mouse models.
The document discusses several key concepts relating to cancer biology and the hallmarks of cancer. It describes the evolutionary process of tumor development and lists some of the important hallmarks identified by Hanahan and Weinberg, including sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. It also mentions several genetic mutations that have been identified in cancers like glioblastomas and lung adenocarcinomas.
A normal cell can be transformed into a cancerous cell. Discuss the therapeutic strategies that are employed to target the cellular transformation process for cancer prevention and treatment.
This study aims to evaluate the gene expression profile of FOXE1 mRNA in paired thyroid tumor and non-tumor tissue samples. Preliminary results from the first 10 paired samples show variation in FOXE1 gene expression levels between tumor and non-tumor tissues, with non-tumor tissue appearing to have higher FOXE1 expression compared to tumor tissue. Analysis of more sample pairs is ongoing, and FOXE1 expression levels will be compared to patient clinical data and tumor pathological characteristics. The results so far indicate there may be a profile of FOXE1 expression in thyroid cancer.
Telomeric G-Quadruplexes as Therapeutic Targets in CancerHIRA Zaidi
This document discusses targeting telomeric G-quadruplexes as a potential therapeutic approach for cancer. It describes how G-quadruplex binding molecules can inhibit telomerase and cause cancer cell death by triggering telomere shortening. The document outlines various methodologies used to design G-quadruplex binding molecules and study their interactions with telomeric DNA, including computational modeling, biophysical assays, and structural analysis. Challenges in developing selective G-quadruplex targeting agents are also discussed.
This research article examines the role of sphingosine-1-phosphate (S1P) in regulating the proliferative and stem-like properties of glioblastoma stem cells (GSCs). The results showed that GSCs rapidly consume ceramide and export S1P into the extracellular environment. Extracellular S1P levels reached nanomolar concentrations in response to increased sphingosine. S1P was also found to act as an autocrine factor that promotes GSC proliferation and maintains their stem-like phenotype. This suggests that microenvironmental S1P critically modulates the GSC population by acting as an autocrine signal to support stemness and favor proliferation, survival, and therapeutic resistance of GSCs.
Lung cancer is a major cause of cancer deaths with approximately 80% of cases accounting to nonsmall cell lung cancer (NSCLC) . In NSCLC target therapy, epidermal growth factor receptor (EGFR) is a promising candidate.
This document provides an overview of the molecular foundations of cancer. It discusses how cancer arises from genetic and epigenetic aberrations that accumulate in cells and lead to altered gene expression and the acquisition of hallmark capabilities that allow tumors to form and progress. Key points covered include the types of genomic changes like mutations and chromosome defects that occur; the roles of oncogenes and tumor suppressor genes; how cancer risk can be inherited; and the uses of genomics in cancer diagnosis and targeted treatment.
Dr. David Guillespie - Identificación de dianas de daño en el DNA en la terap...CIBICAN - ULL
Presentación del Dr. David Guillespie, investigador contratado por el CIBICAN - Universidad de La Laguna gracias al Proyecto Europeo IMBRAIN, en relación a los resultados alcanzados durante la ejecución del mismo y los planes de futuro. La misma se presentó durante las Jornadas IMBRAIN llevadas a cabo el 13 de Octubre de 2015 en la Sección de Física en la Universidad de La Laguna
Management of colorectal cancers in older peopleSpringer
This document discusses genetic alterations in colorectal cancer between younger and older patients. It begins by providing background on the multistep model of colorectal cancer development and the genetic mutations that drive each step, such as APC, TP53, and KRAS. It then notes limited data on differences in genetic alterations between younger and older CRC patients. The document goes on to discuss aging and theories of aging, focusing on pathways such as insulin/IGF-1, TOR, AMP kinase, and sirtuins that may affect longevity when inhibited. It also discusses telomere length differences between younger and older CRC patients and prospects for human anti-aging interventions.
Asbestos-related diseases - mechanisms and causation at Helsinki Asbestos 2014Työterveyslaitos
1. Asbestos fibers cause chronic inflammation in the lungs and pleura, which enables the development of cancers through tumor-promoting inflammation and genomic instability from oxidative DNA damage.
2. The tumor microenvironment in asbestos-related cancers is immunosuppressive, allowing tumors to evade immune destruction.
3. Targeting chronic inflammation and harnessing the host immune response, in addition to cytotoxic therapies, may be more effective against asbestos-related cancers than cytotoxic agents alone.
The document discusses epigenetics and its role in environmental diseases. It defines epigenetics as mechanisms that regulate gene expression without changing DNA sequence. Environmental factors can cause epigenetic changes through pathways like DNA methylation and histone modification. Abnormal epigenetic changes have been implicated in diseases like cancer, aging, and neurodevelopmental disorders. Certain environmental exposures are also linked to epigenetic alterations, though causal relationships are difficult to establish.
Ton Beniers_publicatielijst overzicht 2015Ton Beniers
This document lists publications by Beniers and others from 1987-2001 related to experimental cytokine therapy for renal cell carcinoma and Wilms' tumor. It includes 18 journal publications, 5 book chapters, and 1 PhD thesis by Beniers on experimental cytokine therapy for renal cell carcinoma. The publications examine the in vitro and in vivo effects of interferons and tumor necrosis factor on renal tumor cell lines and xenografts, and differential expression of drug resistance and heat shock proteins in histological compartments of nephroblastomas.
The document discusses lung cancer and EGFR. It notes that EGFR is a transmembrane glycoprotein receptor present on cancer cells. Lung cancer occurs when tumors grow in the lungs and can be caused by smoking, radon gas, air pollution, or genetics. There are two main types of lung cancer - small cell and non-small cell. The EGFR gene is located on chromosome 7 and encodes a growth factor receptor with tyrosine kinase activity. Tyrosine kinase activates cell growth and survival signals when ligands bind to the extracellular portion of EGFR.
Cancer arises due to genetic aberrations that accumulate in somatic cells and alter gene expression. There are several types of genomic changes including mutations, chromosome defects, and changes to oncogenes and tumor suppressor genes. Genetic testing can identify inherited cancer risk genes and guide diagnosis and treatment, while gene therapy holds promise for directly treating cancer at the genetic level.
Role of genetics in periodontal diseasesAnushri Gupta
Terminologies in Genetics
Genetic study design
genetic syndrome and disease associated with periodontal diseases, heretibility of periodontal disease, gene library, gene therapy
Clinical Genomics for Personalized Cancer Medicine: Recent Advances, Challeng...Yoon Sup Choi
I reviewed recent advances, challenges, and opportunities to implement clinical cancer genomics. Case studies of advanced systems, such as Foundation Medicine, MI-ONCOSEQ are introduced for benchmark. A few fundamental limitations to establish personalized oncology are also discussed.
Compare the use of Lonza KGM Gold Bullet kit and Rheinwald and Green complete FAD medium in primary human epidermal keratinocytes culture and its applicability cells cultured by these medium in the construction of reconstituted skin equivalent model
Be Aware Webinar - Uniendo fuerzas: administre y proteja al punto finalSymantec LATAM
Este documento presenta las soluciones de Symantec para la administración y protección de endpoints. Describe los desafíos de controlar endpoints móviles y la necesidad de visibilidad y políticas coherentes. Luego introduce las suites IT Management Suite (ITMS) y Symantec Endpoint Protection (SEP) que brindan administración unificada, seguridad en capas y análisis para proteger y gestionar endpoints de forma centralizada. Finalmente, se discuten los servicios de educación de Symantec para capacitar a los clientes.
Mustafa Bin Abdul Waheed has over 6 years of experience in supply chain management and logistics. He is seeking senior level positions and has expertise in import/export operations, documentation, procurement, and fleet management. He has a post-graduate diploma in supply chain and logistics management and is skilled in using various software programs.
El documento describe las características climáticas y geográficas de Tahití y sus islas, así como detalles sobre el hotel InterContinental Tahiti Resort. El hotel se encuentra a solo 2 km del aeropuerto de Tahití y ofrece diversas habitaciones y bungalows con vista a la laguna. Después de llegar al hotel, recorreremos la isla de Tahití en vehículos todo terreno para visitar el valle de Papenoo, Teahupoo y practicar surf.
Empyaema thoracis secondary to intrapleural rupture of pulmonary hydatid cystAbdulsalam Taha
Published by Basra Journal of Surgery in March 1998
Abstract: Pleural hydatidosis is almost always secondary to pulmonary or hepatic hydatid cysts. Primary hydatid disease of the pleura (i.e. originating from larvae transported by blood and landing upon pleural surfaces) is denied to exist . The extrusion of lung hydatid into the pleura is relatively a rare condition. The reported incidence in the literature is 1 out of 189 cases and 2.41 of 246 cases. Emergence of intact small cysts might be possible, but the larger cysts usually rupture. This is followed by massive pneumothorax, as air enters freely via the bronchial openings. Large amounts of fresh hydatid fluid pours over the pleural surfaces and anaphylactic reaction may follow. Untreated bronchopleural fistulae are unlikely to close and empyaema thoracis certainly ensues. Herein, we report a case of empyaema secondary to intrapleural rupture of lung hydatid cyst. The incidence, pathology, symptomatology and methods of management are discussed.
Apply to Host a U.S. Teacher (TEA and ILEP)sarahgdye
The document outlines the process and responsibilities for TEA Alumni to host a U.S. teacher through the TEA program. Key details include: TEA Alumni can apply by August 2nd to host a teacher for 2 weeks in Spring/Summer 2011. Responsibilities prior to the visit include bi-weekly communication to develop an agenda, workshop plan, and needs assessment. During the visit, the TEA Alumni must fulfill the agreed upon agenda and plans. IREX covers travel costs while the teacher receives a modest allowance for other expenses.
Este documento proporciona una introducción a la geomática. Define geomática como una técnica multidisciplinaria que involucra la adquisición, gestión y uso de información geoespacial a través de métodos como fotogrametría, teledetección, cartografía, SIG, geodesia y topografía. También resume brevemente el desarrollo histórico de la geomática y sus principales aplicaciones como la medición de la Tierra, gestión de información espacial y soporte a disciplinas como ingeniería, medio ambiente y planificación.
Respuestas de anticuerpos dependientes del linfocito t copperadorKarla Ch Galindo
Las respuestas de anticuerpos dependientes del linfocito T cooperador pueden generarse frente a antígenos proteínicos. Estas respuestas requieren la cooperación entre linfocitos T colaboradores y células B para producir anticuerpos específicos del antígeno. La memoria inmunitaria también depende de la cooperación entre linfocitos T y B.
The Ite family goes on vacation to Twikkii Island. Bazz works to complete all the vacation activities and memories. He digs for treasure and finds a map to a hidden location. After several attempts, Bazz is able to fix appliances and get a voodoo doll. He also learns dances and builds a castle. Back home, Bazz teaches Omphac surgery skills and applies for college. Omphac and Bazz then leave for college, ending this chapter of the Ite family story.
Aproximación a la producción de aceite y vino en Caesarobriga (Talavera de la...Sergio de la Llave Muñoz
Este documento resume la producción de aceite y vino en Caesarobriga (actual Talavera de la Reina, Toledo) durante la época romana. Describe hallazgos aislados de estructuras vinculadas a la producción de aceite y vino en la región, así como complejos productivos excavados recientemente. También detalla una instalación industrial descubierta en Caesarobriga que incluye un espacio para prensado con un pavimento de opus signinum y una piedra para prensar, usada para la producción de aceite y/o vino
Innovationen für mehr Skivergnügen.
Innovations for even more ski pleasure.
Ski-optimal bedeutet: mehr Spaß, mehr Sport, mehr Unterhaltung,
mehr Service. Das Zillertal zählt zu den Hot Spots in den Alpen. Gäste schätzen die Bergkulisse, die Schneesicherheit und die Gastlichkeit. Urige Hütten, stylishe Lounges und eine Vielzahl an Pisten
sorgen für unvergessliche Tage.
FIRST CLASS FÜR SIE:
• „The best connection“ – Skiverbindung mit Hochfügen
• gratis Parken direkt an der Talstation
• 3 beschneite Talabfahrten
• großzügiges Anfängerskigelände
• Nachtskilauf 1 x pro Woche auf der beleuchteten Talabfahrt
• Kleinkindbetreuung direkt an den Bergstationen
• großzügige Sonnenterrassen und Lounges
• Ski-Ticket-Automaten
• Infotainment Videowalls
Este documento proporciona información sobre viajes a Italia, incluyendo consejos sobre visas Schengen, vuelos desde Ecuador, equipaje permitido, opciones de transporte dentro de Italia, seguros de viaje, paquetes turísticos todo incluido, cruceros por Italia y alquiler de autos. Además, detalla itinerarios específicos de 9 a 12 días que incluyen ciudades como Venecia, Florencia y Roma.
Ponència a càrrec de Jordi Torra, de la UB, presentada a la 19a edició de la Trobada de l'Anella Científica (TAC) al Tecnocampus Mataró-Maresme el 30 de juny de 2015.
Aquesta presentació ha tractat sobre la missió Gaia i el tractament de les dades estel·lars d’aquesta missió espacial.
Este documento presenta una introducción al levantamiento topográfico con fines catastrales. Explica que el objetivo es representar gráficamente la forma, posición y dimensiones de los inmuebles de manera precisa. Detalla los diferentes tipos de levantamientos topográficos y los instrumentos utilizados como la cinta métrica, teodolito y estación total. También presenta conceptos como planimetría, altimetría, ángulos, distancias y coordenadas necesarios para realizar este tipo de levantamientos.
New Features in Revolution R Enterprise 5.0 to Support Scalable Data AnalysisRevolution Analytics
Revolution R Enterprise 5.0 is Revolution Analytics’ scalable analytics platform. At its core is Revolution Analytics’ enhanced Distribution of R, the world’s most widely-used project for statistical computing. In this webinar, Dr. Ranney will discuss new features and show examples of the new functionality, which extend the platform’s usability, integration and scalability.
Universal Wellhead Services provides wellhead equipment and services to the oil and gas industry. Their product catalog outlines their offerings which include:
- Casing heads, casing spools, tubing heads, and casing hangers to support and seal multiple casing strings.
- Secondary seals, chokes, valves, and other accessories.
- They have manufacturing and service locations across the US and offer custom designs, repairs, and 24/7 service support.
- UWS prides itself on superior customer service, low cost manufacturing, safety, and dependability.
Lessons Learned from the Global Sourcing DecisonJohn Meyerson
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Sex-Based Difference in Gene Alterations and Biomarkers in Anal Squamous Cell...semualkaira
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DNA Methylation and Histone Modification in Low-Grade Gliomas: Current Unders...Ahmad Ozair
Ozair A, Bhat V, Alisch RS, Khosla AA, Kotecha RR, Odia Y, McDermott MW, Ahluwalia MS***. DNA Methylation and Histone Modification in Low-Grade Gliomas: Current Understanding and Potential Clinical Targets. Cancers (Basel). 2023;15(4): 1342. ([Review Article], IF = 6.6, Available from: https://www.mdpi.com/2072-6694/15/4/1342)
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Identification of a CpG Island Methylator Phenotype that Defines a Distinct Subgroup of Glioma
1. Identification of a CpG Island Methylator Phenotype that Defines
a Distinct Subgroup of Glioma
Houtan Noushmehr1,*, Daniel J. Weisenberger1,*, Kristin Diefes2,*, Heidi S. Phillips3, Kanan
Pujara3, Benjamin P. Berman1, Fei Pan1, Christopher E. Pelloski4, Erik P. Sulman4, Krishna
P. Bhat2, Roel G.W. Verhaak5,6, Katherine A. Hoadley7,8, D. Neil Hayes7,8, Charles M.
Perou7,8, Heather K. Schmidt9, Li Ding9, Richard K. Wilson9, David Van Den Berg1, Hui
Shen1, Henrik Bengtsson10, Pierre Neuvial10, Leslie M. Cope11, Jonathan Buckley1,12,
James G. Herman11, Stephen B. Baylin11, Peter W. Laird1,**,†, Kenneth Aldape2,**, and The
Cancer Genome Atlas Research Network
1 USC Epigenome Center, University of Southern California, Los Angeles, CA, 90033 USA
2 Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX
77030, USA
3 Department of Tumor Biology and Angiogenesis, Genentech, Inc., South San Francisco, California
94080, USA
4 Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center,
Houston, TX 77030, USA
5 The Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard
University, Cambridge, Massachusetts 02142, USA
6 Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115,
USA
7 Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
8 Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel
Hill, NC 27599, USA
9 The Genome Center at Washington University, Department of Genetics, Washington University
School of Medicine, St. Louis, Missouri 63108, USA
10 Department of Statistics, University of California, Berkeley, California, USA
11 Department on Oncology, Johns Hopkins School of Medicine, Baltimore, MD, 21231, USA
12 Department of Preventive Medicine, Keck School of Medicine, University of Southern California,
Los Angeles, CA, 90033, USA
†To whom correspondence should be addressed. plaird@usc.edu, FAX: (323) 442-7880.
*These authors contributed equally to this work.
**These authors contributed equally to this work.
Conflict of interest statement
P.W.L. is a shareholder, consultant and scientific advisory board member of Epigenomics, AG, which has a commercial interest in DNA
methylation markers. This work was not supported by Epigenomics, AG. K.A. is a consultant and scientific advisory board member for
Castle Biosciences, which has a commercial interest in molecular diagnostics. This work was not supported by Castle Biosciences.
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers
we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting
proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could
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NIH Public Access
Author Manuscript
Cancer Cell. Author manuscript; available in PMC 2011 May 18.
Published in final edited form as:
Cancer Cell. 2010 May 18; 17(5): 510–522. doi:10.1016/j.ccr.2010.03.017.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
2. Summary
We have profiled promoter DNA methylation alterations in 272 glioblastoma tumors in the context
of The Cancer Genome Atlas (TCGA). We found that a distinct subset of samples displays concerted
hypermethylation at a large number of loci, indicating the existence of a glioma-CpG Island
Methylator Phenotype (G-CIMP). We validated G-CIMP in a set of non-TCGA glioblastomas and
low-grade gliomas. G-CIMP tumors belong to the Proneural subgroup, are more prevalent among
low-grade gliomas, display distinct copy-number alterations and are tightly associated with IDH1
somatic mutations. Patients with G-CIMP tumors are younger at the time of diagnosis and experience
significantly improved outcome. These findings identify G-CIMP as a distinct subset of human
gliomas on molecular and clinical grounds.
Keywords
DNA methylation; glioma; CIMP; IDH1; TCGA
Introduction
Human gliomas present as heterogeneous disease, primarily defined by the histologic
appearance of the tumor cells. Astrocytoma and oligodendroglioma constitute the infiltrating
gliomas (Adamson et al., 2009). The cell or cells of origin have not definitively been identified,
however, the identification of tumorigenic, stem-cell like precursor cells in advanced stage
gliomas suggests that human gliomas may have a neural stem cell origin (Canoll and Goldman,
2008; Dirks, 2006; Galli et al., 2004).
Gliomas are subdivided by the World Health Organization (WHO) by histological grade, which
is an indication of differentiation status, malignant potential, response to treatment and
survival. Glioblastoma (GBM), also described as Grade IV glioma, accounts for more than
50% of all gliomas (Adamson et al., 2009). Patients with GBM have an overall median survival
time of only 15 months (Brandes et al., 2001; Martinez et al., 2010; Parsons et al., 2008). Most
GBMs are diagnosed as de novo, or primary tumors and are more common in males. A subset
of ~5% of GBM tumors, termed secondary GBM, progress from lower grade tumors (grade
II/III), are seen in younger patients, are more evenly distributed among the sexes and exhibit
longer survival times (reviewed in (Adamson et al., 2009; Furnari et al., 2007).
There is currently great interest in characterizing and compiling the genome and transcriptome
changes in human GBM tumors to identify aberrantly functioning molecular pathways and
tumor subtypes. The Cancer Genome Atlas (TCGA) pilot project identified genetic changes
of primary DNA sequence and copy number, DNA methylation, gene expression and patient
clinical information for a set of GBM tumors (The Cancer Genome Atlas Research Network,
2008). TCGA also reaffirmed genetic alterations in TP53, PTEN, EGFR, RB1 NF1, ERBB2,
PIK3R1, and PIK3CA mutations and detected an increased frequency of NF1 mutations in
GBM patients (The Cancer Genome Atlas Research Network, 2008). Recent DNA sequencing
analyses of primary GBM tumors using a more comprehensive approach (Parsons et al.,
2008) also identified novel somatic mutations in isocitrate dehydrogenase 1 (IDH1) that occur
in 12% of all GBM patients. IDH1 mutations have only been detected at the arginine residue
in codon 132, with the most common change being the R132H mutation (Parsons et al.,
2008; Yan et al., 2009), which results in a novel gain of enzyme function in directly catalyzing
α-ketoglutarate to R(−)-2-hydroxyglutarate (Dang et al., 2009). IDH1 mutations are enriched
in secondary GBM cases, younger individuals and coincident with increased patient survival
(Balss et al., 2008; Hartmann et al., 2009; Yan et al., 2009). Higher IDH1 mutation rates are
seen in grade II and III astrocytomas and oligodendrogliomas (Balss et al., 2008; Bleeker et
Noushmehr et al. Page 2
Cancer Cell. Author manuscript; available in PMC 2011 May 18.
NIH-PAAuthorManuscriptNIH-PAAuthorManuscriptNIH-PAAuthorManuscript
3. al., 2009; Hartmann et al., 2009; Yan et al., 2009), suggesting that IDH1 mutations generally
occur in the progressive form of glioma, rather than in de novo GBM. Mutations in the related
IDH2 gene are of lower frequency and generally non-overlapping with tumors containing
IDH1 mutations (Hartmann et al., 2009; Yan et al., 2009).
Cancer-specific DNA methylation changes are hallmarks of human cancers, in which global
DNA hypomethylation is often seen concomitantly with hypermethylation of CpG islands
(reviewed in (Jones and Baylin, 2007)). Promoter CpG island hypermethylation generally
results in transcriptional silencing of the associated gene (Jones and Baylin, 2007). CpG island
hypermethylation events have also been shown to serve as biomarkers in human cancers, for
early detection in blood and other bodily fluids, for prognosis or prediction of response to
therapy and to monitor cancer recurrence (Laird, 2003).
A CpG island methylator phenotype (CIMP) was first characterized in human colorectal cancer
by Toyota and colleagues (Toyota et al., 1999) as cancer-specific CpG island hypermethylation
of a subset of genes in a subset of tumors. We confirmed and further characterized colorectal
CIMP using MethyLight technology (Weisenberger et al., 2006). Colorectal CIMP is
characterized by tumors in the proximal colon, a tight association with BRAF mutations, and
microsatellite instability caused by MLH1 promoter hypermethylation and transcriptional
silencing (Weisenberger et al., 2006).
DNA methylation alterations have been widely reported in human gliomas, and there have
been several reports of promoter-associated CpG island hypermethylation in human GBM and
other glioma subtypes (Kim et al., 2006; Martinez et al., 2009; Martinez et al., 2007; Nagarajan
and Costello, 2009; Stone et al., 2004; Tepel et al., 2008; Uhlmann et al., 2003). Several studies
have noted differences between primary and secondary GBMs with respect to epigenetic
changes. Overall secondary GBMs have a higher frequency of promoter methylation than
primary GBM (Ohgaki and Kleihues, 2007). In particular, promoter methylation of RB1 was
found to be approximately three times more common in secondary GBM (Nakamura et al.,
2001).
Hypermethylation of the MGMT promoter-associated CpG island has been shown in a large
percentage of GBM patients (Esteller et al., 2000; Esteller et al., 1999; Hegi et al., 2005; Hegi
et al., 2008; Herman and Baylin, 2003). MGMT encodes for an O6-methylguanine
methyltransferase which removes alkyl groups from the O-6 position of guanine. GBM patients
with MGMT hypermethylation showed sensitivity to alkyating agents such as temozolomide,
with an accompanying improved outcome (Esteller et al., 2000; Esteller et al., 1999; Hegi et
al., 2005; Hegi et al., 2008; Herman and Baylin, 2003). However, initial promoter methylation
of MGMT, in conjunction with temozolomide treatment, may result in selective pressure to
lose mismatch repair function, resulting in aggressive recurrent tumors with a hypermutator
phenotype (Cahill et al., 2007; Hegi et al., 2005; Silber et al., 1999; The Cancer Genome Atlas
Research Network, 2008).
Here we report the DNA methylation analysis of 272 GBM tumors collected for TCGA, extend
this to lower grade tumors and characterize a distinct subgroup of human gliomas exhibiting
CIMP.
Results
Identification of a distinct DNA methylation subgroup within GBM patients
We determined DNA methylation profiles in a discovery set of 272 TCGA GBM samples. At
the start of this study, we relied on the Illumina GoldenGate platform, using both the standard
Cancer Panel I, and a custom-designed array (The Cancer Genome Atlas Research Network,
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4. 2008) (Figure S1A–C, Experimental Procedures), but migrated to the more comprehensive
Infinium platform (Figure 1, Figure S1C), as it became available. DNA methylation
measurements were highly correlated for CpG dinucleotides shared between the two platforms
(Pearson’s r = 0.94, Figure S1D). Both platforms interrogate a sampling of about two CpG
dinucleotides per gene. Although this implies that non-representative CpGs may be assessed
for some promoters, it is likely that representative results will be obtained for most gene
promoters, given the very high degree of locally correlated DNA methylation behavior
(Eckhardt et al., 2006).
We selected the most variant probes on each platform and performed consensus clustering to
identify GBM subgroups (Monti et al., 2003). We identified three DNA methylation clusters
using either the GoldenGate or Infinium data, with 97% concordance (61/63) in cluster
membership calls for samples run on both platforms (Table S1). Cluster 1 formed a particularly
tight cluster on both platforms with a highly characteristic DNA methylation profile (Figure
1, Figure S1), reminiscent of the CpG Island Methylator Phenotype described in colorectal
cancer (Toyota et al., 1999; Weisenberger et al., 2006). CIMP in colorectal cancer is
characterized by correlated cancer-specific CpG island hypermethylation of a subset of genes
in a subset of tumors, and not just a stochastic increase in the frequency of generic CpG island
methylation across the genome (Toyota et al., 1999; Weisenberger et al., 2006). Cluster 1 GBM
samples show similar concerted methylation changes at a subset of loci. We therefore
designated cluster 1 tumors as having a glioma CpG Island Methylator Phenotype (G-CIMP).
Combining Infinium and GoldenGate data, 24 of 272 TCGA GBM samples (8.8%) were
identified as G-CIMP subtype (Table S1).
Characterization of G-CIMP tumors within gene expression clusters
Four gene expression subtypes (Proneural, Neural, Classical and Mesenchymal) have been
previously identified and characterized using TCGA GBM samples (Verhaak et al., 2010). We
compared the DNA methylation consensus cluster assignments for each sample to their gene
expression cluster assignments (Figure 1, Figure 2A, Figure S1A–B, Table S1). The G-CIMP
sample cluster is highly enriched for proneural GBM tumors, while the DNA methylation
clusters 2 and 3 are moderately enriched for classical and mesenchymal expression groups,
respectively. Of the 24 G-CIMP tumors, 21 (87.5%) were classified within the proneural
expression group. These G-CIMP tumors represent 30% (21/71) of all proneural GBM tumors,
suggesting that G-CIMP tumors represent a distinct subset of proneural GBM tumors (Figure
2A and Figure S2A, Table S1). The few non-proneural G-CIMP tumors belong to neural (2/24
tumors, 8.3%), and mesenchymal (1/24 tumors, 4.2%) gene expression groups.
In order to obtain an integrated view of the relationships of G-CIMP status and gene expression
differences, we performed pairwise comparisons between members of different molecular
subgroups (Figure 2B). We calculated the mean Euclidean distance in both DNA methylation
and expression for each possible pairwise combination of the five different subtypes: G-CIMP-
positive proneural, G-CIMP-negative proneural, classical, mesenchymal and neural tumors.
We observed the high dissimilarity of the GP, GN, GC, and GM pairs (Figure 2B), supporting
the hypothesis that G-CIMP-positive tumors are a unique molecular subgroup of GBM tumors,
and more specifically that G-CIMP status provides further refinement of the proneural subset.
Indeed, among the proneural tumors, the G-CIMP-positive tumors are distinctly dissimilar to
the mesenchymal tumors (GM pair), while the G-CIMP-negative proneural tumors are
relatively similar to mesenchymal tumors (PM pair). We focused downstream analyses on
comparisons between G-CIMP-positive versus G-CIMP-negative tumors within the proneural
subset, to avoid misidentifying proneural features as G-CIMP-associated features.
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5. Clinical characterization of G-CIMP tumors
We further characterized G-CIMP tumors by reviewing the available clinical covariates for
each patient. Although patients with proneural GBM tumors are slightly younger (median age,
56 years) than all other non-proneural GBM patients (median age, 57.5 years), this was not
statistically significant (p=0.07). However, patients with G-CIMP tumors were significantly
younger at the time of diagnosis compared to patients diagnosed with non-G-CIMP proneural
tumors (median ages of 36 and 59 years, respectively; p<0.0001; Figure 2C).
The overall survival for patients of the proneural subtype was not significantly improved
compared to other gene expression subtypes (Figure 2D), but significant survival differences
were seen for groups defined by DNA methylation status (Figure 2E, 2F, Figure S2B). We
observed significantly better survival for proneural G-CIMP-positive patients (median survival
of 150 weeks) than proneural G-CIMP-negative patients (median survival of 42 weeks) or all
other non-proneural GBM patients (median survivals of 54 weeks). G-CIMP status remained
a significant predictor of improved patient survival (p=0.0165) in Cox multivariate analysis
after adjusting for patient age, recurrent vs. non-recurrent tumor status and secondary GBM
vs. primary GBM status.
IDH1 sequence alterations in G-CIMP tumors
Nine genes were found to have somatic mutations that were significantly associated with
proneural G-CIMP-positive tumors (Figure S3A, Table S2). IDH1 somatic mutations, recently
identified primarily in secondary GBM tumors (Balss et al., 2008; Parsons et al., 2008; Yan et
al., 2009), were found to be very tightly associated with G-CIMP in our data set (Table 1), with
18 IDH1 mutations primarily observed in 23 (78%) G-CIMP-positive tumors, and 184 G-
CIMP-negative tumors were IDH1-wildtype (p< 2.2×10−16). The five discordant cases of G-
CIMP-positive, IDH1-wildtype are not significantly different in age compared to G-CIMP-
positive, IDH1-mutant (median ages of 34 and 37 years respectively; p=0.873). However, the
five discordant cases of G-CIMP-positive, IDH1-wildtype tumors are significantly younger at
the time of diagnosis compared to patients with G-CIMP-negative, IDH1-wildtype (median
ages of 37 and 59 years respectively; p<0.008). Interestingly, two of the five patients each
survived more than five years after diagnosis. We did not observe any IDH2 mutations in the
TCGA data set. Tumors displaying both G-CIMP-positive and IDH1 mutation occurred at low
frequency in primary GBM, but were enriched in the set of 16 recurrent (treated) tumors, and
to an even greater degree in the set of four secondary GBM (Table 1). We also identified
examples of germline mutations (nine genes) and loss of heterozygosity (six genes) that were
significantly associated with proneural G-CIMP-positive tumors (Figure S3B-C, Table S2).
Copy number variation (CNV) in proneural G-CIMP tumors
In order to elucidate critical alterations within proneural G-CIMP-positive tumors, we analyzed
gene-centric copy number variation data (see Supplemental Information). We identified
significant copy number differences in 2,875 genes between proneural G-CIMP-positive and
G-CIMP-negative tumors (Figure 3A, Table S3). Although chromosome 7 amplifications are
a hallmark of aggressive GBM tumors (The Cancer Genome Atlas Research Network, 2008),
copy number variation along chromosome 7 was reduced in proneural G-CIMP-positive
tumors. Gains in chromosomes 8q23.1-q24.3 and 10p15.3-p11.21 were identified (Figure 3B
and 3C). The 8q24 region contains the MYC oncogene, is rich in sequence variants and was
previously shown as a risk factor for several human cancers (Amundadottir et al., 2006;
Freedman et al., 2006; Haiman et al., 2007a; Haiman et al., 2007b; Schumacher et al., 2007;
Shete et al., 2009; Visakorpi et al., 1995; Yeager et al., 2007). Accompanying the gains at
chromosome 10p in proneural G-CIMP-positive tumors, we also detected deletions of the same
chromosome arm in G-CIMP-negative tumors (Figure 3C). Similar copy number variation
results were obtained when comparing all G-CIMP-positive to G-CIMP-negative samples
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6. (Figure S4). These findings point to G-CIMP-positive tumors as having a distinct profile of
copy number variation when compared to G-CIMP-negative tumors.
Identification of DNA methylation and transcriptome expression changes in proneural G-
CIMP tumors
To better understand CpG island hypermethylation in glioblastoma, we investigated the
differentially methylated CpG sites of these samples (Figure 1B). Among 3,153 CpG sites that
were differentially methylated between proneural G-CIMP positive and proneural G-CIMP-
negative tumors, 3,098 (98%) were hypermethylated (Figure 4A). In total, there were 1,550
unique genes, of which 1,520 were hypermethylated and 30 were hypomethylated within their
promoter regions. We ranked our probe list by decreasing adjusted p-values and increasing
beta-value difference in order to identify the top most differentially hypermethylated CpG
probes within proneural G-CIMP-positive tumors (Table S3).
The Agilent transcriptome data were used to detect genes showing both differential expression
and G-CIMP DNA methylation, in G-CIMP-positive and negative proneural samples. Gene
expression values were adjusted for regional copy number changes, as described in
Supplemental Information. A total of 1,030 genes were significantly down-regulated and 654
genes were significantly up-regulated among proneural G-CIMP-positive tumors (Figure 4B,
Table S3). The differentially down-regulated gene set was highly enriched for polysaccharide,
heparin and glycosaminoglycan binding, collagen, thrombospondin and cell morphogenesis
(p<2.2E-04, Table S3). In addition, the significantly up-regulated gene set was highly enriched
among functional categories involved in regulation of transcription, nucleic acid synthesis,
metabolic processes, and cadherin-based cell adhesion (p<6.2E-04). Zinc finger transcription
factors were also found to be highly enriched in genes significantly up-regulated in expression
(p=3.1E-08). Similar findings were obtained when a permutation analysis was performed and
when Affymetrix gene expression data were used (data not shown). We also identified 20
miRNAs that showed significant differences in their gene expression between proneural G-
CIMP-positive and proneural G-CIMP-negative tumors (Figure S5, Table S3).
Integration of the normalized gene expression and DNA methylation gene lists identified a
total of 300 genes with both significant DNA hypermethylation and gene expression changes
in G-CIMP-positive tumors compared to G-CIMP-negative tumors within the proneural subset.
Of these, 263 were significantly down-regulated and hypermethylated within proneural G-
CIMP-positive tumors (Figure 4C, lower right quadrant). To validate these differentially
expressed and methylated genes, we replicated the analysis using an alternate expression
platform (Affymetrix), and derived consistent results (Figure S5). Among the top ranked genes
were FABP5, PDPN, CHI3L1 and LGALS3 (Table 2), which were identified in an independent
analysis to be highly prognostic in GBM with higher expression associated with worse outcome
(Colman et al., 2010). Gene ontology analyses showed G-CIMP-specific down-regulation of
genes associated with the mesenchyme subtype, tumor invasion and the extracellular matrix
as the most significant terms (Table S3). Genes with roles in transcriptional silencing,
chromatin structure modifications and activation of cellular metabolic processes showed
increased gene expression in proneural G-CIMP-positive tumors. Additional genes
differentially expressed in proneural G-CIMP-positive samples are provided in Table S3.
To extend these findings, the differentially silenced genes were subjected to a NextBio
(www.nextbio.com) meta-analysis to identify data sets that were significantly associated with
our list of 263 hypermethylated and down-regulated genes. There was an overlap with down-
regulated genes in low- and intermediate-grade glioma compared to GBM in a variety of
previously published datasets (Ducray et al., 2008; Liang et al., 2005; Sun et al., 2006) (Figure
S5, Table S3). The overlap of the 263-gene set with each of these additional datasets was
unlikely to be due to chance (all analyses p<0.00001). To further characterize this gene set, we
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7. tested the survival association of these gene expression values in a collection of Affymetrix
profiling data from published and publicly available sources on which clinical annotation was
available. This dataset included cohorts from the Rembrandt set (Madhavan et al., 2009) as
well as other sources, and did not include TCGA data (since TCGA data were used to derive
the gene list). In this combined dataset, the expression of the 263-gene set was significantly
associated with patient outcome (Figure S5G). Together, these findings suggest that G-CIMP-
positive GBMs tumors have epigenetically-related gene expression differences which are more
consistent with low-grade gliomas as well as high grade tumors with favorable prognosis.
Validation of G-CIMP in GBM and incidence in low-grade gliomas
To validate the existence of G-CIMP loci and better characterize the frequency of G-CIMP in
gliomas, MethyLight was used to assay the DNA methylation levels in eight G-CIMP gene
regions in seven hypermethylated loci (ANKRD43, HFE, MAL, LGALS3, FAS-1, FAS-2, and
RHO-F) and one hypomethlyated locus, DOCK5, in the tumor samples. These eight markers
were evaluated in paraffin embedded tissues from 20 TCGA samples of known G-CIMP status
(10 G-CIMP-postive and 10 G-CIMP negative). We observed perfect concordance between
G-CIMP calls on the array platforms versus with the MethyLight markers, providing validation
of the technical performance of the platforms and of the diagnostic marker panel. These 20
samples were excluded from the validation set described below. A sample was considered G-
CIMP positive if at least six genes displayed a combination of DOCK5 DNA hypomethylation
and/or hypermethylation of the remaining genes in the panel. Using these criteria, we tested
an independent set of non-TCGA GBM samples for G-CIMP status. Sixteen of 208 tumors
(7.6%) were found to be G-CIMP-positive (Figure 5A), very similar to the findings in TCGA
data.
To further expand these observations, we determined the IDH1 mutation status for an
independent set 100 gliomas (WHO grades II, III and IV). Among 48 IDH1-mutant tumors,
35 (72.9%) were G-CIMP. However, only 3/52 cases (5.8%) without an IDH1 mutation were
G-CIMP positive (odds ratio= 42; 95% confidence interval (CI), 11–244; Figure S3D),
validating the tight association of G-CIMP and IDH1 mutation.
Based on the association of G-CIMP status with features of the progressive, rather than the de
novo GBM pathway, we hypothesized that G-CIMP status was more common in the low- and
intermediate-grade gliomas. We extended this analysis by evaluating 60 grade II and 92 grade
III gliomas for G-CIMP DNA methylation using the eight gene MethyLight panel. Compared
to GBM, grade II tumors showed an approximately 10-fold increase in G-CIMP-positive
tumors, while grade III tumors had an intermediate proportion of tumors that were G-CIMP-
positive (Figure 5A, Figure S3E). When low- and intermediate-grade gliomas were separated
by histologic type, G-CIMP positivity appeared to be approximately twice as common in
oligodendrogliomas (52/56, 93%) as compared to astrocytomas (43/95, 45%). G-CIMP
positive status correlated with improved patient survival within each WHO-recognized grade
of diffuse glioma, indicating that the G-CIMP status was prognostic for glioma patient survival
(p<0.032, Figure 5B). G-CIMP status was an independent predictor (p<0.01) of survival after
adjustment for patient age and tumor grade (Figure S3F). Together, these findings show that
G-CIMP is a prevalent molecular signature in low grade gliomas and confers improved survival
in these tumors.
Stability of G-CIMP at recurrence
Since epigenetic events can be dynamic processes, we examined whether G-CIMP status was
a stable event in glioma or whether it was subject to change over the course of the disease. To
test this, we obtained a set of samples from 15 patients who received a second surgical
procedure following tumor recurrence, with time intervals of up to eight years between initial
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8. and second surgical procedures. We used the eight-gene MethyLight panel to determine their
G-CIMP status and found that eight samples were G-CIMP-positive, while seven were G-
CIMP-negative. Interestingly, among the G-CIMP-positive cases, 8/8 (100%) recurrent
samples retained their G-CIMP positive status. Similarly, among seven G-CIMP-negative
cases, all seven remained G-CIMP-negative at recurrence, indicating stability of the G-CIMP
phenotype over time (Figure 5C).
Discussion
In this report, we identified and characterized a distinct molecular subgroup in human gliomas.
Analysis of epigenetic changes from TCGA samples identified the existence of a proportion
of GBM tumors with highly concordant DNA methylation of a subset of loci, indicative of a
CpG island methylator phenotype (G-CIMP). G-CIMP-positive samples were associated with
secondary or recurrent (treated) tumors and tightly associated with IDH1 mutation. G-CIMP
tumors also showed a relative lack of copy number variation commonly observed in GBM,
including EGFR amplification, chromosome 7 gain and chromosome 10 loss. Interestingly, G-
CIMP tumors displayed copy-number alterations which were also shown in gliomas with
IDH1 mutations in a recent report (Sanson et al., 2009). Integration of the DNA methylation
data with gene expression data showed that G-CIMP-positive tumors represent a subset of
proneural tumors. G-CIMP-positive tumors showed a favorable prognosis within GBMs as a
whole and also within the proneural subset, consistent with prior reports for IDH1 mutant
tumors (Parsons et al., 2008; Yan et al., 2009). Interestingly, of the five discordant cases of G-
CIMP-positive, IDH1-wildtype tumors, two patients survived more than five years after
diagnosis, suggesting that G-CIMP-positive status may confer favorable outcome independent
of IDH1 mutation status. However, studies with many more discordant cases will be needed
to carefully dissect the effects of G-CIMP status versus IDH1 mutation on survival. To a large
extent, the improved prognosis conferred by proneural tumors (Phillips et al., 2006) can be
accounted for by the G-CIMP-positive subset. These findings indicate that G-CIMP could be
use to further refine the expression-defined groups into an additional subtype with clinical
implications.
G-CIMP is highly associated with IDH1 mutation across all glioma tumor grades, and the
prevalence of both decreases with increasing tumor grade. Tumor grade is defined by
morphology only, and therefore can be heterogeneous with respect to molecular subtypes.
Within grade IV/glioblastoma tumors are a subset of patients who tend to be younger and have
a relatively favorable prognosis. It is only through molecular characterization using markers
such as IDH1 and G-CIMP status that one could prospectively identify such patients.
Conversely, these markers could also be used to identify patients with low- and intermediate-
grade gliomas who may exhibit unfavorable outcome relative to tumor grade.
In the non-TCGA independent validation set examined in this study, an IDH1 mutation was
detected in 40/43 (93%) low- and intermediate-grade gliomas, but only 7/57 (13%) of primary
GBMs. Similarly, we detected nearly 10-fold more G-CIMP-positive gliomas in grade II
tumors as compared to grade IV GBMs. The improved survival of G-CIMP gliomas at all tumor
grades suggests that there are molecular features within G-CIMP gliomas that encourage a less
aggressive tumor phenotype. Consistent with this, we identified G-CIMP-specific DNA
methylation changes within a broad panel of genes whose expression was significantly
associated with patient outcome. We observed that this large subset of differentially silenced
genes were involved in specific functional categories, including markers of mesenchyme,
tumor invasion and extracellular matrix. This concept builds upon our prior finding of a
mesenchymal subgroup of glioma which shows poor prognosis (Phillips et al., 2006).
According to this model, a lack of methylation of these genes in G-CIMP-negative tumors
would result in a relative increase in expression of these genes, which in turn would promote
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9. tumor progression and/or lack of response to currently available treatment modalities. A
comparison of the G-CIMP gene list with prior gene expression analyses (meta-analyses)
suggests that G-CIMP positive tumors may be less aggressive due to silencing of key
mesenchymal genes.
We found that a minority of genes with significant promoter hypermethylation showed a
concomitant significant decrease in associated gene expression (293/1520, 19%). This is
consistent with previous reports, in which we found similarly low frequencies of inversely
correlated promoter hypermethylation and gene expression (Houshdaran et al., 2010; Pike et
al., 2008). The lack of an inverse relationship between promoter hypermethylation and gene
expression for most genes may be attributed to several scenarios, including the lack of
appropriate transcription factors for some unmethylated genes and the use of alternative
promoters for some genes with methylated promoters. Epigenetics controls expression
potential, rather than expression state.
RBP1 and G0S2 are the two genes showing the strongest evidence for epigenetic silencing in
G-CIMP tumors. RBP1 has been previously reported to be epigenetically silenced in cancer
cell lines and primary tumors, and the association of its encoded protein with retinoic acid
receptors (RAR) has been well characterized (Esteller et al., 2002). G0S2 gene expression is
regulated by retinoic acid (RA), and encodes a protein that promotes apoptosis in primary cells
suggesting a tumor suppressor role (Kitareewan et al., 2008; Welch et al., 2009). The vitamin
A metabolite RA is important for both embryonic and adult growth. RA has a diverse roles
involving neuronal development and differentiation mediated by RARs (reviewed in (Malik
et al., 2000)). Interestingly, studies in breast cancer cells have shown that silencing of RBP1
plays an important role in RA signaling by lowering all-trans-retinoic acid production and loss
of RAR levels and activity mediated by de-repression of PI3K/Akt signaling pathway, leading
to loss of cell differentiation and tumor progression ((Farias et al., 2005a),(Farias et al.,
2005b)). This mechanism may help describe the molecular features of tumorigenesis in G-
CIMP tumors. Thus, dissecting the gene expression and DNA methylation alterations of G-
CIMP tumors among lower grade gliomas will be helpful to better understand the roles of a
mutant IDH1 and G-CIMP DNA methylation on tumor grade and patient survival.
The highly concerted nature of G-CIMP methylation suggests that this phenomenon may be
caused by a defect in a trans-acting factor normally involved in the protection of a defined
subset of CpG island promoters from encroaching DNA methylation. Loss of function of this
factor would result in widespread concerted DNA methylation changes. We propose that
transcriptional silencing of some CIMP genes may provide a favorable context for the
acquisition of specific genetic lesions. Indeed, we have recently found that IGFBP7 is silenced
by promoter hypermethylation in BRAF-mutant CIMP+ colorectal tumors (Hinoue et al.,
2009). Oncogene-induced senescence by mutant BRAF is known to be mediated by IGFBP7
(Wajapeyee et al., 2008). Hence, CIMP-mediated inactivation of IGFBP7 provides a suitable
environment for the acquisition of BRAF mutation. The tight concordance of G-CIMP status
with IDH1 mutation in GBM tumors is very reminiscent of colorectal CIMP, in which DNA
hypermethylation is strongly associated with BRAF mutation (Weisenberger et al., 2006). We
hypothesize that the transcriptional silencing of as yet unknown G-CIMP targets may provide
an advantageous environment for the acquisition of IDH1 mutation.
In our integrative analysis of G-CIMP tumors, we observed up-regulation of genes functionally
related to cellular metabolic processes and positive regulation of macromolecules. This
expression profile may reflect a metabolic adjustment to the proliferative state of the tumor,
in conjunction with the gain-of-function IDH1 mutation (Dang et al., 2009). Such a metabolic
adjustment may be consistent with Warburg’s observation that proliferating normal and tumor
cells require both biomass and energy production, and convert glucose primarily to lactate,
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10. regardless of oxygen levels, while non-proliferating differentiated cells emphasize efficient
energy production (reviewed in (Vander Heiden et al., 2009)).
In summary, our data indicate that G-CIMP status stratify gliomas into two distinct subgroups
with different molecular and clinical phenotypes. These molecular classifications have
implications for differential therapeutic strategies for glioma patients. Further observation and
characterization of molecular subsets of glioma will likely provide additional information
enabling insights into the the development and progression of glioma, and may lead to targeted
drug treatment for patients with these tumors.
Experimental Procedures
Samples and DNA methylation assays
Genomic DNAs from TCGA GBM tumors were isolated by the TCGA Biospecimen Core
Resource (BCR) and delivered to USC as previously described (The Cancer Genome Atlas
Research Network, 2008). One sample (TCGA-06-0178) with a confirmed IDH1 mutation was
removed from our analyses, since it became clear that an incorrect tissue type had been shipped
for the DNA methylation analysis. Four brain genomic DNA samples from apparently healthy
individuals were included as controls. All tissue samples (patients and healthy individuals)
were obtained using institutional review board–approved protocols from University of
Southern California (TCGA GBM samples), Johns Hopkins School of Medicine (Normal Brain
samples) and The University of Texas MD Anderson Cancer Center (Glioma validation
samples). Tissue samples were deidentified to ensure patient confidentiality. Genomic DNA
methylated in vitro with M.SssI methylase or whole genome amplified (WGA) as positive and
negative controls for DNA methylation, respectively, were also included. Details are in
Supplemental Information.
The GoldenGate assays were performed according to the manufacturer and as described
previously (Bibikova et al., 2006). The GoldenGate methylation assays survey the DNA
methylation of up to 1,536 CpG sites - a total of 1,505 CpGs spanning 807 unique gene loci
are interrogated in the OMA-002 probe set (Bibikova et al., 2006), and 1,498 CpGs spanning
the same number of unique gene regions are investigated in the OMA-003 probe set (The
Cancer Genome Atlas Research Network, 2008).
The Infinium methylation assays were performed according to manufacturer’s instructions.
The assay generates DNA methylation data for 27,578 CpG dinucleotides spanning 14,473
well-annotated, unique gene promoter and/or 5′ gene regions (from −1,500 to +1,500 from the
transcription start site). The assay information is available at www.illumina.com and the probe
information is available on the TCGA Data Portal web site. Data from 91 TCGA GBM samples
(Batches 1, 2, 3 and 10) were included in this analysis. Batches 1–3 (63 samples) were run on
both Inifium and GoldenGate, whereas batches 4–8 (182 samples) were analyzed exclusively
on GoldenGate and batch 10 (28 samples) were analyzed exclusively on Infinium. All data
were packaged and deposited onto the TCGA Data Portal web site
(http://tcga.cancer.gov/dataportal). Figure S1C illustrates a Venn diagram with the overlapping
samples between different DNA methylation platforms. Additional details on DNA
methylation detection protocols and TCGA GBM data archived versions are in Supplemental
Information.
Integrative TCGA data platforms
While ancillary data (expression, mutation, copy number) were available for additional tumor
samples, we only included those samples for which there were DNA methylation profiling
(either GoldenGate or Infinium). Since the Agilent gene expression platform contained a
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11. greater number of genes for which DNA methylation data were available, we limited our
primary analysis to only the Agilent gene expression data set. Where appropriate, we confirmed
results using the Affymetrix gene expression data. Additional details are in Supplemental
Information.
Clustering analysis and measurement of differential DNA methylation and differential gene
expression
Probes for each platform were filtered by removing those targeting the X and Y chromosomes,
those containing a single nucleotide polymorphism (SNP) within five basepairs of the targeted
CpG site, and probes containing repeat element sequences ≥10 basepairs. We next retained the
most variably methylated probes (standard deviation > 0.20) across the tumor set in each DNA
methylation platform. These final data matrices were used for unsupervised Consensus/
Hierarchical clustering analyses. A non-parametric approach (Wilcoxon rank-sum test) was
used to determine probes/genes that are differentially DNA methylated or differentially
expressed between the two groups of interest. Additional information is described in
Supplemental Information.
G-CIMP validation using MethyLight technology
Tumor samples were reviewed by a neuropathologist (K.A.) to ensure accuracy of diagnosis
as well as quality control to minimize normal tissue contamination. MethyLight real-time PCR
strategy was performed as described previously (Eads et al., 2000; Eads et al., 1999). Additional
details are in Supplemental Information.
Pathway and meta-analyses and statistical analyses
Additional tools included the Molecular Signatures Database (MSigDB database v2.5),
Database for Annotation, Visualization and Integrated Discovery (DAVID) and NextBio™.
All statistical tests were done using R software (R version 2.9.2, 2009-08-24, (R Development
Core Team, 2009)) and packages in Bioconductor (Gentleman et al., 2004), except as noted.
Additional details are in Supplemental Information.
Significance
Glioblastoma (GBM) is a highly aggressive form of brain tumor, with a patient median
survival of just over one year. The Cancer Genome Atlas (TCGA) project aims to
characterize cancer genomes to identify means of improving cancer prevention, detection,
and therapy. Using TCGA data, we identified a subset of GBM tumors with characteristic
promoter DNA methylation alterations, referred to as a glioma CpG Island Methylator
Phenotype (G-CIMP). G-CIMP tumors have distinct molecular features, including a high
frequency of IDH1 mutation and characteristic copy-number alterations. Patients with G-
CIMP tumors are younger at diagnosis and display improved survival times. The molecular
alterations in G-CIMP tumors define a distinct subset of human gliomas with specific
clinical features.
Highlights
• Identification of a CpG island methylator phenotype (G-CIMP) in gliomas
• G-CIMP is tightly associated with IDH1 mutation
• G-CIMP patients are younger at diagnosis and display improved survival
• G-CIMP is more prevalent among low- and intermediate-grade gliomas
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12. Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
This work was supported by NIH/NCI grants U24 CA126561 and U24 CA143882-01 (P.W.L and S.B.B.) and grants
from the Brain Tumor Funders’ Collaborative, the V Foundation, the Rose Foundation and SPORE grant
P50CA127001 from NIH/NCI (K.A.). We thank Dennis Maglinte and members of the USC Epigenome Center for
helpful discussions, Andreana Rivera for technical assistance and Marisol Guerrero for editorial assistance.
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16. Figure 1. Clustering of TCGA GBM tumors and control samples identifies a CpG Island
Methylator Phenotype (G-CIMP)
Unsupervised consensus clustering was performed using the 1,503 Infnium DNA methylation
probes whose DNA methylation beta values varied the most across the 91 TCGA GBM
samples. DNA Methylation clusters are distinguished with a color code at the top of the panel:
red, consensus cluster 1 (n=12 tumors); blue, consensus cluster 2 (n=31 tumors); green,
consensus cluster 3 (n=48 samples). Each sample within each DNA Methylation cluster are
colored labeled as described in the key for its gene expression cluster membership (Proneural,
Neural, Classical and Mesenchymal). The somatic mutation status of five genes (EGFR,
IDH1, NF1, PTEN, and TP53) are indicated by the black squares, the gray squares indicate the
absence of mutations in the sample and the white squares indicate that the gene was not screened
in the specific sample. G-CIMP-positive samples are labeled at the bottom of the matrix. A)
Consensus matrix produced by k-means clustering (K = 3). The samples are listed in the same
order on the x and y axes. Consensus index values range from 0 to 1, 0 being highly dissimilar
and 1 being highly similar. B) One-dimensional hierarchical clustering of the same 1,503 most
variant probes, with retention of the same sample order as in Figure 1A. Each row represents
a probe; each column represents a sample. The level of DNA methylation (beta value) for each
probe, in each sample, is represented by using a color scale as shown in the legend; white
indicates missing data. M.SssI-treated DNA (n=2), WGA-DNA (n=2) and normal brain (n=4)
samples are included in the heatmap but did not contribute to the unsupervised clustering. The
probes in the eight control samples are listed in the same order as the y axis of the GBM sample
heatmap. See also Figure S1 and Table S1.
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17. Figure 2. Characterization of G-CIMP tumors as a unique subtype of GBMs within the proneural
gene expression subgroup
A) Integration of the samples within each DNA methylation and gene expression cluster.
Samples are primarily categorized by their gene expression subtype: P, proneural; N, neural;
C, classical; M, mesenchymal. The number and percent of tumors within each DNA
methylation cluster (red, cluster 1 (G-CIMP); blue, cluster 2; green, cluster 3) are indicated
for each gene expression subtype. B) Scatter plot of pairwise comparison of the gene expression
and DNA methylation clusters as identified in Figure 1 and Figure S1. Same two-letter
represents self-comparison while mixed two-letter represents the pair-wise correlation between
gene expression and DNA methylation. Axes are reversed to illustrate increasing similarity.
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18. C) GBM patient age distribution at time of diagnosis within each gene expression cluster.
Samples are divided by gene expression clusters as identified along the top of each jitter plot,
and further subdivided by G-CIMP status within each expression subgroup. G-CIMP-positive
samples are indicated as red data points and G-CIMP-negative samples are indicated as black
data points. Median age at diagnosis is indicated for each subgroup by a horizontal solid black
line. D–F) Kaplan-Meier survival curves for GBM methylation and gene expression subtypes.
In each plot, the percent probability of survival is plotted versus time since diagnosis in weeks.
All samples with survival data greater than five years were censored. D) Kaplan-Meier survival
curves among the four GBM expression subtypes. Proneural tumors are in blue, Neural tumors
are in green, Classical tumors are in red, and Mesenchymal tumors are in gold. E) Kaplan-
Meier survival curves between the three DNA methylation clusters. Cluster 1 tumors are in
red, cluster 2 tumors are in blue and cluster 3 tumors are in green. F) Kaplan-Meier survival
curves between proneural G-CIMP-positive, proneural G-CIMP-negative and all non-
proneural GBM tumors. Proneural G-CIMP-positive tumors are in red, proneural G-CIMP-
negative tumors are in blue and all non-proneural GBM tumors are in black. See also Figure
S2.
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19. Figure 3. Significant regions of copy number variation in the G-CIMP genome
Copy number variation for 23,748 loci (across 22 autosomes and plotted in genomic
coordinates along the x-axis) was analyzed using 61 proneural TCGA GBM tumors.
Homozygous deletion is indicated in dark blue, hemizygous deletion in light blue, neutral/no
change in white, gain in light red and high-level amplification in dark red. A) Copy number
variation between proneural G-CIMP positive and G-CIMP negative tumors. The Cochran
Armitage test for trend, percent total amplification/deletion and raw copy number values are
listed. The – log10(FDR-adjusted P value) between G-CIMP-positive and G-CIMP negative
proneurals is plotted along the y-axis in the “adjusted P-value pos vs. neg” panel. In this panel,
red vertical lines indicate significance. Gene regions in 8q23.1-q24.3 and 10p15.2-11.21 are
identified by asterisks and are highlighted in panels B and C, respectively. See also Figure S4
and Table S3.
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20. Figure 4. Comparison of transcriptome versus epigenetic differences between proneural G-CIMP-
positive and G-CIMP-negative tumors
A) Volcano Plots of all CpG loci analyzed for G-CIMP association. The beta value difference
in DNA Methylation between the proneural G-CIMP-positive and proneural G-CIMP-negative
tumors is plotted on the x-axis, and the p-value for a FDR-corrected Wilcoxon signed-rank test
of differences between the proneural G-CIMP-positive and proneural G-CIMP-negative
tumors (−1* log10 scale) is plotted on the y-axis. Probes that are significantly different between
the two subtypes are colored in red. B) Volcano plot for all genes analyzed on the Agilent gene
expression platform. C) Starburst plot for comparison of TCGA Infinium DNA methylation
and Agilent gene expression data normalized by copy number information for 11,984 unique
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21. genes. Log10(FDR-adjusted P value) is plotted for DNA methylation (x-axis) and gene
expression (y-axis) for each gene. If a mean DNA methylation β-value or mean gene expression
value is higher (greater than zero) in G-CIMP-positive tumors, −1 is multiplied to log10(FDR-
adjusted P value), providing positive values. The dashed black lines indicates FDR-adjusted
P value at 0.05. Data points in red indicate those that are significantly up- and down-regulated
in their gene expression levels and significantly hypo- or hypermethylated in proneural G-
CIMP-positive tumors. Data points in green indicate genes that are significantly down-
regulated in their gene expression levels and hypermethylated in proneural G-CIMP-positive
tumors compared to proneural G-CIMP-negative tumors. See also Figure S5 and Table S3.
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22. Figure 5. G-CIMP prevalence in grade II, III and IV gliomas using MethyLight
A) Methylation profiling of gliomas shows an association of CIMP status with tumor grade.
Eight markers were tested for G-CIMP DNA methylation in 360 tumor samples. Each marker
was coded as red if methylated and green if unmethylated. One of these markers (DOCK5) is
unmethylated in CIMP, while the remaining seven markers show G-CIMP-specific
hypermethylation. G-CIMP-positive status was determined if ≥6 of the 8 genes had G-CIMP-
defining hyper- or hypomethylation. G-CIMP-positive status is indicated with a black line
(right side of panel), and a grey line indicates non G-CIMP. Samples with an identified
IDH1 mutation is indicated as a black line and samples with no known IDH1 mutation as a
grey line. White line indicates unknown IDH1 status. B) Association of G-CIMP status with
patient outcome stratified by tumor grade. G-CIMP-positive cases are indicated by red lines
and the G-CIMP-negative cases are indicated by black lines in each Kaplan-Meier survival
curve. C) Stability of G-CIMP over time in glioma patients. Fifteen samples from newly
diagnosed tumors were tested for G-CIMP positivity using the eight-marker MethyLight panel.
Eight tumors were classified as G-CIMP-positive (upper left panel), and seven tumors were
classified as G-CIMP-negative (non-G-CIMP, lower left panel). Samples from a second
procedure, ranging from 2–9 years after the initial resection, were also evaluated for the G-
CIMP-positive cases (upper right panel), as well as for the non-G-CIMP cases (lower right
panel). Each marker was coded as red if methylated and green if unmethylated.
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Table 1
G-CIMP and IDH1 mutation status in primary, secondary, and recurrent GBMs
G-CIMP and IDH1 mutation status are compared for All analyzed GBM tumors (p< 2.2x10−16), Primary tumors,
Recurrent tumors, and Secondary tumors. P value is calculated from Fisher’s exact test. See also Figure S3 and
Table S2.
ALL TUMORS
G-CIMP
TOTAL
− +
IDH1
Wild-type 184 5 189
Mutant 0 18 18
TOTAL 184 23 207
PRIMARY TUMORS
G-CIMP
TOTAL
− +
IDH1
Wild-type 171 4 175
Mutant 0 12 12
TOTAL 171 16 187
RECURRENT TUMORS
G-CIMP
TOTAL
− +
IDH1
Wild-type 12 0 12
Mutant 0 4 4
TOTAL 12 4 16
SECONDARY G-CIMP
TOTAL
TUMORS − +
IDH1
Wild-type 1 1 2
Mutant 0 2 2
TOTAL 1 3 4
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Table 2
The top 50 most differentially hypermethylated and down regulated genes in proneural G-
CIMP positive tumors
Genes are sorted by decreasing Gene Expression log2 ratios. Beta value difference indicates differences in mean
of beta values (DNA methylation values) between proneural G-CIMP-positive and proneural G-CIMP-negative.
Fold Change is the log2 ratio of the means of proneural G-CIMP-positive and proneural G-CIMP-negative
normalized expression intensities. See also Table S3.
Gene Name
DNA Methylation Gene Expression
Wilcoxon Rank P-Value Beta Value Difference Wilcoxon Rank P-Value Fold Change
G0S2 2.37E-07 0.76 2.12E-13 −3.92
RBP1 2.37E-07 0.84 1.07E-14 −3.9
FABP5 2.30E-05 0.3 1.39E-12 −3.53
CA3 4.50E-06 0.43 2.06E-06 −2.82
RARRES2 4.74E-07 0.63 6.25E-10 −2.69
OCIAD2 2.06E-04 0.32 1.23E-08 −2.64
CBR1 3.30E-05 0.37 3.77E-09 −2.45
PDPN 3.87E-03 0.23 1.47E-07 −2.43
LGALS3 2.37E-07 0.72 7.97E-09 −2.42
CTHRC1 3.50E-04 0.45 1.44E-07 −2.34
CCNA1 4.66E-03 0.29 2.95E-05 −2.14
ARMC3 1.76E-03 0.31 7.76E-05 −2.13
CHST6 5.74E-04 0.22 8.70E-06 −2.11
C11orf63 2.37E-07 0.64 7.95E-11 −2.05
GJB2 2.16E-03 0.24 2.94E-07 −2.04
KIAA0746 1.66E-06 0.58 2.97E-07 −1.94
MOSC2 2.37E-07 0.66 6.85E-12 −1.91
CHI3L1 7.11E-06 0.13 4.11E-06 −1.9
RARRES1 6.38E-05 0.41 5.93E-09 −1.89
AQP5 4.50E-06 0.43 6.86E-14 −1.87
SPON2 8.68E-05 0.3 2.50E-05 −1.87
RAB36 2.06E-04 0.26 6.49E-11 −1.86
CHRDL2 2.64E-03 0.07 1.15E-07 −1.81
TOM1L1 2.37E-07 0.66 4.57E-14 −1.8
BIRC3 2.37E-07 0.66 6.50E-07 −1.78
LDHA 4.50E-04 0.55 1.83E-07 −1.74
SEMA3E 2.37E-07 0.52 2.36E-04 −1.72
FMOD 6.38E-05 0.42 2.05E-04 −1.72
C10orf107 1.59E-05 0.63 4.11E-06 −1.71
FLNC 2.37E-07 0.74 1.36E-05 −1.67
TMEM22 4.74E-07 0.59 1.63E-08 −1.67
TCTEX1D1 1.66E-06 0.37 2.97E-07 −1.67
DKFZP586H2123 1.59E-05 0.53 4.83E-05 −1.66
TRIP4 4.74E-07 0.49 4.18E-08 −1.65
Cancer Cell. Author manuscript; available in PMC 2011 May 18.