Posts Tagged ‘gliomas’

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM

Reporter: Stephen J. Williams, PhD

 Minisymposium: Evaluating Cancer Genomics from Normal Tissues through Evolution to Metastatic Disease

Oncologic therapy shapes the fitness landscape of clonal hematopoiesis

April 28, 2020, 4:10 PM – 4:20 PM

Kelly L. Bolton, Ryan N. Ptashkin, Teng Gao, Lior Braunstein, Sean M. Devlin, Minal Patel, Antonin Berthon, Aijazuddin Syed, Mariko Yabe, Catherine Coombs, Nicole M. Caltabellotta, Mike Walsh, Ken Offit, Zsofia Stadler, Choonsik Lee, Paul Pharoah, Konrad H. Stopsack, Barbara Spitzer, Simon Mantha, James Fagin, Laura Boucai, Christopher J. Gibson, Benjamin Ebert, Andrew L. Young, Todd Druley, Koichi Takahashi, Nancy Gillis, Markus Ball, Eric Padron, David Hyman, Jose Baselga, Larry Norton, Stuart Gardos, Virginia Klimek, Howard Scher, Dean Bajorin, Eder Paraiso, Ryma Benayed, Maria Arcilla, Marc Ladanyi, David Solit, Michael Berger, Martin Tallman, Montserrat Garcia-Closas, Nilanjan Chatterjee, Luis Diaz, Ross Levine, Lindsay Morton, Ahmet Zehir, Elli Papaemmanuil. Memorial Sloan Kettering Cancer Center, New York, NY, University of North Carolina at Chapel Hill, Chapel Hill, NC, University of Cambridge, Cambridge, United Kingdom, Dana-Farber Cancer Institute, Boston, MA, Washington University, St Louis, MO, The University of Texas MD Anderson Cancer Center, Houston, TX, Moffitt Cancer Center, Tampa, FL, National Cancer Institute, Bethesda, MD

Recent studies among healthy individuals show evidence of somatic mutations in leukemia-associated genes, referred to as clonal hematopoiesis (CH). To determine the relationship between CH and oncologic therapy we collected sequential blood samples from 525 cancer patients (median sampling interval time = 23 months, range: 6-53 months) of whom 61% received cytotoxic therapy or external beam radiation therapy and 39% received either targeted/immunotherapy or were untreated. Samples were sequenced using deep targeted capture-based platforms. To determine whether CH mutational features were associated with tMN risk, we performed Cox proportional hazards regression on 9,549 cancer patients exposed to oncologic therapy of whom 75 cases developed tMN (median time to transformation=26 months). To further compare the genetic and clonal relationships between tMN and the proceeding CH, we analyzed 35 cases for which paired samples were available. We compared the growth rate of the variant allele fraction (VAF) of CH clones across treatment modalities and in untreated patients. A significant increase in the growth rate of CH mutations was seen in DDR genes among those receiving cytotoxic (p=0.03) or radiation therapy (p=0.02) during the follow-up period compared to patients who did not receive therapy. Similar growth rates among treated and untreated patients were seen for non-DDR CH genes such as DNMT3A. Increasing cumulative exposure to cytotoxic therapy (p=0.01) and external beam radiation therapy (2×10-8) resulted in higher growth rates for DDR CH mutations. Among 34 subjects with at least two CH mutations in which one mutation was in a DDR gene and one in a non-DDR gene, we studied competing clonal dynamics for multiple gene mutations within the same patient. The risk of tMN was positively associated with CH in a known myeloid neoplasm driver mutation (HR=6.9, p<10-6), and increased with the total number of mutations and clone size. The strongest associations were observed for mutations in TP53 and for CH with mutations in spliceosome genes (SRSF2, U2AF1 and SF3B1). Lower hemoglobin, lower platelet counts, lower neutrophil counts, higher red cell distribution width and higher mean corpuscular volume were all positively associated with increased tMN risk. Among 35 cases for which paired samples were available, in 19 patients (59%), we found evidence of at least one of these mutations at the time of pre-tMN sequencing and in 13 (41%), we identified two or more in the pre-tMN sample. In all cases the dominant clone at tMN transformation was defined by a mutation seen at CH Our serial sampling data provide clear evidence that oncologic therapy strongly selects for clones with mutations in the DDR genes and that these clones have limited competitive fitness, in the absence of cytotoxic or radiation therapy. We further validate the relevance of CH as a predictor and precursor of tMN in cancer patients. We show that CH mutations detected prior to tMN diagnosis were consistently part of the dominant clone at tMN diagnosis and demonstrate that oncologic therapy directly promotes clones with mutations in genes associated with chemo-resistant disease such as TP53.

  • therapy resulted also in clonal evolution and saw changes in splice variants and spliceosome
  • therapy promotes current DDR mutations
  • clonal hematopoeisis due to selective pressures
  • mutations, variants number all predictive of myeloid disease
  • deferring adjuvant therapy for breast cancer patients with patients in highest MDS risk group based on biomarkers, greatly reduced their risk for MDS

5704 – Pan-cancer genomic characterization of patient-matched primary, extracranial, and brain metastases

Presenter/AuthorsOlivia W. Lee, Akash Mitra, Won-Chul Lee, Kazutaka Fukumura, Hannah Beird, Miles Andrews, Grant Fischer, John N. Weinstein, Michael A. Davies, Jason Huse, P. Andrew Futreal. The University of Texas MD Anderson Cancer Center, TX, The University of Texas MD Anderson Cancer Center, TX, Olivia Newton-John Cancer Research Institute and School of Cancer Medicine, La Trobe University, AustraliaDisclosures O.W. Lee: None. A. Mitra: None. W. Lee: None. K. Fukumura: None. H. Beird: None. M. Andrews: ; Merck Sharp and Dohme. G. Fischer: None. J.N. Weinstein: None. M.A. Davies: ; Bristol-Myers Squibb. ; Novartis. ; Array BioPharma. ; Roche and Genentech. ; GlaxoSmithKline. ; Sanofi-Aventis. ; AstraZeneca. ; Myriad Genetics. ; Oncothyreon. J. Huse: None. P. Futreal: None.

Abstract: Brain metastases (BM) occur in 10-30% of patients with cancer. Approximately 200,000 new cases of brain metastases are diagnosed in the United States annually, with median survival after diagnosis ranging from 3 to 27 months. Recently, studies have identified significant genetic differences between BM and their corresponding primary tumors. It has been shown that BM harbor clinically actionable mutations that are distinct from those in the primary tumor samples. Additional genomic profiling of BM will provide deeper understanding of the pathogenesis of BM and suggest new therapeutic approaches.
We performed whole-exome sequencing of BM and matched tumors from 41 patients collected from renal cell carcinoma (RCC), breast cancer, lung cancer, and melanoma, which are known to be more likely to develop BM. We profiled total 126 fresh-frozen tumor samples and performed subsequent analyses of BM in comparison to paired primary tumor and extracranial metastases (ECM). We found that lung cancer shared the largest number of mutations between BM and matched tumors (83%), followed by melanoma (74%), RCC (51%), and Breast (26%), indicating that cancer type with high tumor mutational burden share more mutations with BM. Mutational signatures displayed limited differences, suggesting a lack of mutagenic processes specific to BM. However, point-mutation heterogeneity revealed that BM evolve separately into different subclones from their paired tumors regardless of cancer type, and some cancer driver genes were found in BM-specific subclones. These models and findings suggest that these driver genes may drive prometastatic subclones that lead to BM. 32 curated cancer gene mutations were detected and 71% of them were shared between BM and primary tumors or ECM. 29% of mutations were specific to BM, implying that BM often accumulate additional cancer gene mutations that are not present in primary tumors or ECM. Co-mutation analysis revealed a high frequency of TP53 nonsense mutation in BM, mostly in the DNA binding domain, suggesting TP53 nonsense mutation as a possible prerequisite for the development of BM. Copy number alteration analysis showed statistically significant differences between BM and their paired tumor samples in each cancer type (Wilcoxon test, p < 0.0385 for all). Both copy number gains and losses were consistently higher in BM for breast cancer (Wilcoxon test, p =1.307e-5) and lung cancer (Wilcoxon test, p =1.942e-5), implying greater genomic instability during the evolution of BM.
Our findings highlight that there are more unique mutations in BM, with significantly higher copy number alterations and tumor mutational burden. These genomic analyses could provide an opportunity for more reliable diagnostic decision-making, and these findings will be further tested with additional transcriptomic and epigenetic profiling for better characterization of BM-specific tumor microenvironments.

  • are there genomic signatures different in brain mets versus non metastatic or normal?
  • 32 genes from curated databases were different between brain mets and primary tumor
  • frequent nonsense mutations in TP53
  • divergent clonal evolution of drivers in BMets from primary
  • they were able to match BM with other mutational signatures like smokers and lung cancer signatures

5707 – A standard operating procedure for the interpretation of oncogenicity/pathogenicity of somatic mutations

Presenter/AuthorsPeter Horak, Malachi Griffith, Arpad Danos, Beth A. Pitel, Subha Madhavan, Xuelu Liu, Jennifer Lee, Gordana Raca, Shirley Li, Alex H. Wagner, Shashikant Kulkarni, Obi L. Griffith, Debyani Chakravarty, Dmitriy Sonkin. National Center for Tumor Diseases, Heidelberg, Germany, Washington University School of Medicine, St. Louis, MO, Mayo Clinic, Rochester, MN, Georgetown University Medical Center, Washington, DC, Dana-Farber Cancer Institute, Boston, MA, Frederick National Laboratory for Cancer Research, Rockville, MD, University of Southern California, Los Angeles, CA, Sunquest, Boston, MA, Baylor College of Medicine, Houston, TX, Memorial Sloan Kettering Cancer Center, New York, NY, National Cancer Institute, Rockville, MDDisclosures P. Horak: None. M. Griffith: None. A. Danos: None. B.A. Pitel: None. S. Madhavan: ; Perthera Inc. X. Liu: None. J. Lee: None. G. Raca: None. S. Li: ; Sunquest Information Systems, Inc. A.H. Wagner: None. S. Kulkarni: ; Baylor Genetics. O.L. Griffith: None. D. Chakravarty: None. D. Sonkin: None.AbstractSomatic variants in cancer-relevant genes are interpreted from multiple partially overlapping perspectives. When considered in discovery and translational research endeavors, it is important to determine if a particular variant observed in a gene of interest is oncogenic/pathogenic or not, as such knowledge provides the foundation on which targeted cancer treatment research is based. In contrast, clinical applications are dominated by diagnostic, prognostic, or therapeutic interpretations which in part also depends on underlying variant oncogenicity/pathogenicity. The Association for Molecular Pathology, the American Society of Clinical Oncology, and the College of American Pathologists (AMP/ASCO/CAP) have published structured somatic variant clinical interpretation guidelines which specifically address diagnostic, prognostic, and therapeutic implications. These guidelines have been well-received by the oncology community. Many variant knowledgebases, clinical laboratories/centers have adopted or are in the process of adopting these guidelines. The AMP/ASCO/CAP guidelines also describe different data types which are used to determine oncogenicity/pathogenicity of a variant, such as: population frequency, functional data, computational predictions, segregation, and somatic frequency. A second collaborative effort created the European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of molecular Targets to provide a harmonized vocabulary that provides an evidence-based ranking system of molecular targets that supports their value as clinical targets. However, neither of these clinical guideline systems provide systematic and comprehensive procedures for aggregating population frequency, functional data, computational predictions, segregation, and somatic frequency to consistently interpret variant oncogenicity/pathogenicity, as has been published in the ACMG/AMP guidelines for interpretation of pathogenicity of germline variants. In order to address this unmet need for somatic variant oncogenicity/pathogenicity interpretation procedures, the Variant Interpretation for Cancer Consortium (VICC, a GA4GH driver project) Knowledge Curation and Interpretation Standards (KCIS) working group (WG) has developed a Standard Operating Procedure (SOP) with contributions from members of ClinGen Somatic Clinical Domain WG, and ClinGen Somatic/Germline variant curation WG using an approach similar to the ACMG/AMP germline pathogenicity guidelines to categorize evidence of oncogenicity/pathogenicity as very strong, strong, moderate or supporting. This SOP enables consistent and comprehensive assessment of oncogenicity/pathogenicity of somatic variants and latest version of an SOP can be found at https://cancervariants.org/wg/kcis/.

  • best to use this SOP for somatic mutations and not rearangements
  • variants based on oncogenicity as strong to weak
  • useful variant knowledge on pathogenicity curated from known databases
  • the recommendations would provide some guideline on curating unknown somatic variants versus known variants of hereditary diseases
  • they have not curated RB1 mutations or variants (or for other RBs like RB2? p130?)


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Insight into Gliomas

Curator: Larry H. Bernstein, MD, FCAP



New Gene Therapy Treatment Stops Deadly Brain Cancer in its Tracks

May 17, 2016



Brain cancers called Diffuse Intrinsic Pontine Gliomas (DIPGs) are often a death sentence. These aggressive, fast-growing, drug-resistant tumors are deadly and they originate from glial cells in the brain.

However a recent report published in the journal Cancer Cell details an experimental gene therapy that stops DIPGs in their tracks. This study included researchers from several different institutions, but was led by scientists at Cincinnati Children’s Hospital Medical Center. The study examined human cancer cells and a mouse model of DIPG.

DIPGs seem to require a gene called Olig2 (which encodes a transcription factor) to grow and survive. The majority of gliomas express the protein encoded by the Olig2 gene and removing this gene halts tumor growth and liquidating Olig2-producing cells inhibits tumor formation. This collaborative team designed a technique scientists found a way to use a gene therapy to shut down Olig2 expression.

“We find that elimination of dividing Olig2-expressing cells blocks initiation and progression of glioma in animal models and further show that Olig2 is the molecular arbiter of genetic adaptability that makes high-grade gliomas aggressive and treatment resistant,” said Qing Richard Lu, PhD, lead investigator and scientific director of the Brain Tumor Center at Cincinnati Children’s. “By finding a way to inhibit Olig2 in tumor forming cells, we were able to change the tumor cells’ makeup and sensitize them to targeted molecular treatment. This suggests a proof of principle for stratified therapy in distinct subtypes of malignant gliomas.”

DIPGs originate from supporting brain cells called oligodendrocytes. Oligodendrocytes make the insulation that surrounds the axons of various nerves in the central nervous system. Olig2 expression appears at the early stages of brain cell development, and is also present in the early-stage dividing and replicating cells in tumors. Olig2 also participates in the transformation of normal oligodendrocyte progenitor cells (OPCs) into cancer cells that divide uncontrollably. Olig2 also facilitates the adaptability of gliomas that helps them evade chemotherapeutic regimens. Indeed, clinically speaking, DIPGs may initially respond to chemotherapeutic agents, but they tend to quickly adapt to these drugs and develop high-levels of resistance to them.

Lu and his colleagues and collaborators eliminated Olig2-positive dividing cells from DIPG tumors that were still in the early stages of tumor formation. Lu and his colleagues used an ingenious technique to remove Oligo2 expression: by genetically engineering a herpes simplex virus-based vector, they delivered a suicide gene (Thymidine kinase) into replicating Olig2-positive cancer cells. Since herpes simplex viruses (HSVs) have the ability to grow in neurons that do not divide a great deal, the HSV-vectors are well suited to this purpose. After infecting the early DIPG cells with the HSV vectors, they administered an anti-herpes drug already in clinical use,ganciclovir (GCV), which kills any cells that have the thymidine kinase gene. The Olig2-deleted tumors were not able to grow.

In follow-up work, Lu and his colleagues observed a fascinating fate for the Olig2- tumors. These cells differentiated into astrocyte-like cells that continued to form tumors, but expressed the epidermal growth factor receptor (EGFR) gene at high levels. EGFR is an effective target for several chemotherapy drugs. In repeated tests in mouse models, Olig2 inhibition consistently transformed the glioma-forming cells into EGFR-expressing astrocyte-like cells. Then these tumors were treated with an EGFR-targeted chemotherapy drug called gefitinib. These treatments stopped the growth of new tumor cells and tumor expansion.

According to Dr. Lu, with additional testing, verification, and, of course, refinement, this experimental therapy that he and his colleagues have designed, could help prevent the recurrence of brain cancer in patients who have undergone initial rounds of successful treatment. Lu also added that these new treatments would probably be used in combination with other existing therapies like radiation, surgery, other chemotherapies and targeted molecular treatments.

Lu and his team will continue their research with other human cell lines and “humanized” mouse models of high-grade glioma. Such mouse models use genetically engineered mice that can grow brain tumors derived from the tumor cells of specific human patients. These tumor cells come from the tumors of patients whose families have donated biopsied tumor samples for research. This allows researchers to test different targeted drugs in their therapeutic protocol that may best match the genetic makeup of tumors from specific individuals.

The entire research team cautions the experimental therapeutic approach they describe will require extensive additional research. Therefore, this type of treatment is years away from possible clinical testing. Having said that, Lu said the data are a significant research breakthrough, since this study identifies a definite weakness in these stubborn cancers that almost always relapse and kill the patients who get them.



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Glioma, Glioblastoma and Neurooncology

Curator: Larry H. Bernstein, MD, FCAP



A Korean and American team profiles gene expression patterns in glioblastoma tumors in a PLOS One paper. The researchers scrutinized gene expression patterns in 43 tumor samples obtained from 28 individuals with glioblastoma — a set that included more than a dozen paired primary and recurrent tumors. They saw two transcriptional clusters in the glioblastoma tumors: a G1 sub-type containing tumors with marked expression of proliferation-related genes and a G2 sub-type with gene expression patterns resembling those in neurons. And by folding in information on expression characteristics of the recurrent tumors, the group gained clues to the types of drug resistance typically displayed by each sub-type.

Recurrent Glioblastomas Reveal Molecular Subtypes Associated with Mechanistic Implications of Drug-Resistance

So Mee Kwon, Shin-Hyuk Kang, Chul-Kee Park, Shin Jung, Eun Sung Park, Ju Seog Lee, Se-Hyuk Kim, Hyun Goo Woo
PLoS ONE  2015; 10(10):e0140528   http://dx.doi.org:/10.1371/journal.pone.0140528

Previously, transcriptomic profiling studies have shown distinct molecular subtypes of glioblastomas. It has also been suggested that the recurrence of glioblastomas could be achieved by transcriptomic reprograming of tumors, however, their characteristics are not yet fully understood. Here,to gain the mechanistic insights on the molecular phenotypes of recurrent glioblastomas, gene expression profiling was performed on the 43 cases of glioblastomas including 15 paired primary and recurrent cases. Unsupervised clustering analyses revealed two subtypesof G1 and G2, which were characterized by proliferation and neuron-like gene expression traits, respectively. While the primary tumors were classified as G1 subtype, the recurrent glioblastomas showed two distinct expression types. Compared to paired primary tumors, the recurrent tumors in G1 subtype did not show expression alteration. By contrast, the recurrent tumors in G2 subtype showed expression changes from proliferation type to neuron-like one. We also observed the expression of stemness related genes in G1 recurrent tumors and the altered expression of DNA-repair genes(i.e., AURK, HOX, MGMT, and MSH6) in the G2 recurrent tumors, which might be responsible for the acquisition of drug resistance mechanism during tumor recurrence in a subtype-specific manner. We suggest that recurrent glioblastomas may choose two different strategies for transcriptome reprogramming to escape the chemotherapeutic treatment during tumor recurrence. Our results might be helpful to determine personalized therapeutic strategy against heterogeneous glioma recurrence.

Glioblastoma is the most aggressive and frequent primary brain tumor with dismal prognosis [1,2].The incurable outcomeofthe glioblastoma is largely due to high recurrence rate even after total resection of glioblastoma mass [2,3]. Also, highly infiltrative characteristics of the glioblastoma make it impossible to dissect tumor tissues completely and the majority of glioblastomas are destined to recur less than 6 months after surgical resection [4,5].Therefore, new diagnostic and therapeutic strategies for tumor recurrence might be required to improve clinical outcomes of patients. Previously, numerous genomic profiling studies have addressed the marked heterogeneity of glioblastomas [6–9]. Particularly, The Cancer Genome Atlas(TCGA) project recognized four distinct molecular subtypes of proneural, neural, classical, and mesenchymal, which are different inresponseto aggressive therapies [10,11]. In addition, an earlier study has shown that about one third (8 out of 26) of the recurrent glioblastomas shifted their subtypes toward mesenchymal subtype [12]. However,there is a conflicting observation that the molecular subtypes are not altered by recurrence [11],remaining the mechanisms for tumor recurrence still unveiled. With this concern, in the present study, we re-evaluated the alteration of the molecular phenotypes of recurrent glioblastomas bycomparing geneexpression profiles ofpairedprimary and recurrent glioblastomas. We could identify two different modes of transcriptome reprogramming during recurrence of glioblastomas, and which implied subtype-specific mechanisms for the acquisition of drug-resistance by tumor recurrence.Our analysis may provide new mechanistic and clinical insights on the recurrent glioblastoma management.

 Gene ExpressionProfiling Total RNA was extracted from frozen tumor section (10 to 15 mg: mirVanaTM miRNA isolation Kit, Ambion, AM1560) based on the manufacturer’s guideline. The quantification of RNA was performed using the Nanodrop ND-1000 spectrophotometer (Thermo-Fisher) and the quality of total RNA was evaluated using the RNA 6000 nano kit (Agilent Technologies, 5067–1513) and the Agilent 2100 Bioanalyzer (Agilent Technologies). Cut off value of the integrity of RNAs used in RNA amplification is over 7.0 in the RIN level. For microarray experiments, five hundred (500) ng of total RNA per sample was used for complement RNA (cRNA) production by the Illumina TotalPrep RNA amplification kit (Ambion, IL1791) according to the provided protocol. A total of 750 ng cRNA was used for hybridization toa human HT12-v4 Illumina Beadchip gene expression array (Illumina) according to the manufacturer’s protocol. The arrays were scanned and fluorescence signals obtained using Illumina bead Array Reader confocal scanner, and obtained the intensity datawith Genome Studio software. Raw data were normalized by applying log 2 transformation, quantile normalization, and gene and array centering. All of the data processing was performed using the R/Bioconductorpackages. For validation analysis, two independent gene expression data of REMBRANDT [14] and TCGA[11] were obtained fromtheir websites, respectively. To integrate different dataset, preprocessing ofeach data setwas applied including log2 transformation, quantile normalization, and gene and array centering.

Classification of subtypes For subtype prediction, three independent methods of unsupervised hierarchical clustering, consensus clustering[15], and nearest template prediction (NTP) [16] were applied. For consensus clustering, hierarchical clustering with the distance metric by Pearson(1—Pearson correlation) was used. For K ranging from 2 to 6, hierarchical clustering was run over 10,000 iterations with a sub-sampling ratio of 0.8 for estimating the consensus matrix. For the purpose of visualization and cluster identification, hierarchical clustering with the Pearson (1— Pearson correlate) distance metric and the average linkage option was applied to the estimated consensus matrix. NTP analysis was performed using Gene Pattern software (http:// www.genepattern.org). The classifiers for the four class subtypes in TCGA dataset [11] were applied and annotated with the numeric code representing the unique subtype that each gene represents (1, 2, 3, 4, 5 for proneural, neural, classical, mesenchymal, and unclassified subtypes) with statistical significance of Bonferroni p value < 0.05 with 1,000 resampling bootstrap test.

Gene expression profiling reveals two subtypes of recurrent glioblastoma. A total of 28 glioblastoma patients were enrolled for this study. The patients were treated with temozolomide (TMZ) after surgical resection. Overall, the progression free survival time (PFS) of the patients was ranged from 5 to 62.4 months, and the median PFS and median overall survival time were 10.75 and 20.50 months, respectively. Detailed clinical information of the patients were summarized inTable 1. To characterize the gene expression patterns of the primary and recurrent glioblastomas, we performed gene expression profiling of the 43 tumor tissues which included the 15 cases of paired primary and recurrent glioblastomas and 13 unpaired tumor tissues. First, to demonstrate the overall gene expression patterns, unsupervised clustering analysis was performed using most variable 4,650 genes with standard deviation(S.D.) greater than 0.5.This revealed two distinct clusters of G1(n=32) and G2(n=11) subtypes (Fig 1A, top). The consistency of the cluster was validated by applying consensus clustering algorithm implemented in Gene pattern software, which could confirm the robustness of the two clusters showing the same two clusters (Fig 1B).

Fig1. Gene expression profiling of primary and recurrent glioblastomas. (A)Unsupervised clustering analysis showed two distinct clusters of G1 and G2 tumors(top). The primary and recurrent glioblastomas were marked with dark blue and dark orange color, respectively (bottom). The 15 paired primary and recurrent glioblastomas were marked based on the defined two clusters, G1 and G2. (B )Heatmap shows the consistency of the consensus clustering analysis with k=2.  http://dx.doi.org:/10.1371/journal.pone.0140528.g001

When we examined the distribution of primary and recurrent glioblastomas from the cluster result, most of the primary glioblastomas were classified into the G1 cluster. However, the recurrent glioblastomas were found in both G1 (n=10) and G2 (n=8) clusters. Recurrent glioblastomas were more frequent in G2 cluster with statistical significance (P =0.031,odd ratio =5.60, Fisher’s exact test), implying the enriched expression of recurrence-related genes in the G2 tumors. To address the functional characteristics of the clusters, we identified differentially expressed genes between G1 and G2 tumors as subtype classifiers (i.e.,94 up-regulated and 318 down-regulated genes, respectively) byapplying permutationt-test (P < 0.001) and fold differences greater than two (S1 Table).The gnes expressed in the G1 cluster were significantly enriched with cell cycle-related gene functions such as M phase, chromosome segregation, cell cycle regulation, and DNA metabolic process, while the genes expressed in the G2 cluster were enriched with neuron development-related genes such as neuron projection morphogenesis, regulation of cell projection organization, ion homeostasis(Fig 2). Comparing to the previous TCGA subtypes [10,11],  this result suggests that theG1 tumors are similar to proliferation type and the G2 tumors are similar to neuronal type, respectively. The expressionof neuronal differentiation-related genes might be a key feature of the transcriptomic switch from primary G1 tumors to the paired recurrent G2 tumors. Next,we compared the gene expression changes between the 15 paired primary and recurrent glioblastomas. Remarkably, we found two distinct behaviors of gene expressions in the recurrent glioblastomas compared to those in the paired primary tumors (Fig 1A, bottom). A totalof 7 outof 15 recurrent glioblastomas showed the cluster migration from G1 to G2 subtype. The other 6 recurrent tumors resided in the same cluster with the paired primary tumors. Exceptionally, only one case of recurrent tumor showed opposite migration from G2 to G1 cluster, and one caseof G2 recurrent tumor did not migrate to other cluster. These results suggest that the recurrent glioblastomas might have at least two distinct patterns of molecular changes after being recurred. The G1 type recurrent tumors (G1R,  n=6) showed no subtype migration, while the G2 type recurrent tumors (G2R, n =7) showed subtype migration from G1 to G2 subtype (see S2 Table).

Table 1.  http://dx.doi.org:/10.1371/journal.pone.0140528.t001

Validation of the subtype classifiers of glioblastoma using independent datasets

Fig 2. Functional characteristics of G1 and G1 subtypes. (A-B) The enriched GO terms of the subtype classifiers are indicated. The significance of the enrichment is plotted as value of—log10 (p-value). (C-D) Unsupervised hierarchical clustering analysis showed the conserved expression patterns of the classifiers in independent dataset, REMBRANDT (C) and TCGA (D). (E) Gene expression similarity with the four subtypes of TCGA are evaluated by applying three different methods of consensus clustering, unsupervised clustering, and nearest template prediction(NTP) as described in the Materials and Methods. The primary and recurrent tumors are indicated with different colors. The predicted four classes of proneuronal, mesenchymal, classical, neural type are indicated. Unclassified tumors are indicated as rest.  http://dx.doi.org:/10.1371/journal.pone.0140528.g002

As shown above, the G1 and G2 classification is clearly associated with the expression migration during tumor recurrence. To further validate the robustness and the significance of our classification, we examined the expression pattern ofour subtype classifiers in the independent two datasets of REMBRANT [14] and TCGA [10]. We could observe that the expressions of the subtype classifiers were well conserved in both data sets stratifying G1-like and G2-like subtypes, respectively (Fig 2C and 2D). This result indicated that our subtype classifiers were well conserved independent of patient cohorts and/or data platforms, and might be useful in predicting the subtypes of tumor recurrence. However, when we evaluated the clinical outcomes of the G1-like and G2-like subtypes by Kaplan-Meir plot analysis, there was no significant difference of overall survival between the groups (S1 Fig). This may indicate that our classification does not represent a prognostic sub-classification, but a classification for different mode of mechanistic pathways for tumor recurrence. Confirming the conserved expression of the classifiers in the independent datasets, we next evaluated the relationship between our subtypes and the TCGA subtypes of mesenchymal, proneural, classical,and neural type [11]. Prediction of the subtypes was performed on the integrated data set of TCGA and ours using the overlapped genes with variable expressions (n=4,378, S.D. > 0.5). By applying three different classification methods of unsupervised hierarchical clustering, consensus clustering, and nearest template prediction (NTP) on the integrated data set (for details  see the Materials and Methods), we could successfully re-identify the four subtypes, respectively (S2 Fig and S3 Table). Unsupervised clustering analysis with the integrated data set could reveal four classes which were compatible with the previous TCGA subtypes (S2A Fig). Consensus clustering analysis also showed four distinct expression subtypes (S2B and S2C Fig). When we compared these classification results with our subtypes of G1 and G2,we could observe that the G2 tumors had similar expression pattern to that of neural subtype,while the G1 tumor was similar to those of other three groups of mesenchymal, proneuronal, and classical subtypes (Fig 2E). This result was consistent with the resul tof GO analysis (seeFig 2B). Taken together, we could suggest that the recurrent glioblastomas have at least two different patterns of G1 and G2 subtype. The G2 subtype is similar to neural subtype, while the G1 subtype is likely to be mixed with the other types.

Expression of stemness and drug-resistance-related genes might be involved in the subtypes of recurrence glioblastomas

To further gain an insight on the differential molecular determinants in the G1 and G2clusters, a network analysis was applied by using GeneMANIA software (version 3.2)[17]. This revealed CDK1 (cyclin-dependent kinase 1), AURKA (aurorakinase A), and AURKB (aurorakinase B) as key hub regulators for G1 tumors(Fig3 A). Indeed, AURKA is well known to play an important function in tumor development, progression,and patient survival [18–21]. Moreover, AURKA is strongly correlated with survival of glioma stem cells[22]. AURKB has also been associated with TMZ susceptibility [23]and aggressive outcomes of glioblastomas [24]. CDK1 isalso known to play regulatory roles in the self-renewal of mouse embryonic stemcells [25] as well as for cell survival of glioblastoma [26].These findings may support that the selective targeting of these genes for G1 recurrent tumors might be beneficial in the clinic. In addition, when we performed geneset enrichment analysis, the G1 tumors showed significant enrichment of stemness-related genes, ES1 (ES=0.526, P-value < 0.001, False Discovery Rate(FDR) < 0.001) which has been identified previously elsewhere [27]. Among the ES1 genes, HMMR (Hyaluronan-mediated motility receptor) was top ranked (Fig 3B), suggesting its pivotal role in the stem cell-like characteristics of G1 tumors. HMMR has recently been reported to express in the gliomas and to play a crucial role in self-renewal and tumorigenic potential of glioblastoma stem cells[28]. Supporting this, we also observed that HOX genes were enriched and differentially expressed (ES =0.704, P-value < 0.001,FDR < 0.001) in the G1 tumors (Fig 3C), which have been notified as “self- renewal”-associated genes in gliomas [29,30]. Of these, HOXA10 showed marked over-expression in G1 tumors (Fig 3D). HOXA10 has been known to involve in homologous recombinant DNA repair pathway [31], playing a key role inTMZ resistance in glioblastomas [29]. Congruent with these findings, the G1 tumors showed significant enrichment of the DNA_REPAIR genes (ES=0.686, P value < 0.001, FDR < 0.001, S3A Fig). Therefore, we could suggest that resistance tothe chemotherapeutic agent may be attributed by the inherited stem-cell-like characteristics of the G1 tumors. The self-renewal properties and the activated DNA repair system (e.g.,HOXA10) might be responsible for the relapseof the recurrent G1 glioblastomas after resection and adjuvant treatment.

 Fig 3. Expression of stemness-like traits in G1 recurrent tumors. (A) Network analysis using G1 signature genes reveals the CDK and AURK as the key hub genes (top). Pathway(light blue) and physical interactions (light pink) are indicated with different colors. The heatmap of the expression of the keyhub genes (CDK1, AURKA, AURKB, HMMR, RAD45L) are plotted (bottom). (B) The GSEA result show the enrichment of the ES1 signature (top) and the expression of the top 20 differentially expressed genes are shown (bottom). (C) The plots showed the enrichment scores (ES) for the HOX_GENE signature (top) and their expression heatmap is shown(bottom).(D) The expression of HOX10a in G1 and G2 tumors are plotted. Statistical significance is calculated using Welch TwoSampleT-test. http://dx.doi.org:/10.1371/journal.pone.0140528.g003

Differential expression of MGMT and MSH6 genes in the subtypes of recurrentglioblastomas

As the glioblastoma subtypes were associated with drug-resistance, we hypothesized that different tactics to escape the chemotherapeutics might be involved in recurrent glioblastomasof each subtype. TMZ has been currently emerged as a new standard regimen in glioblastoma. Previous studies have demonstrated that the therapeutic effects of TMZ might be restricted to  the patients whose MGMT (O-6-methylguanine–DNA methyltransferase) promoters were methylated [32,33], which might be due to the MGMT repairing DNA-alkylated adducts could diminish the TMZ cytotoxicity induced by O6-methylguanine-DNA adducts [34]. In addition, it has been suggested that MGMT-independent DNA repair pathway could affect TMZ effectiveness [35–37].Indeed, it has been demonstrated that the activation of DNA mismatch repair (MMR) system could promote TMZ resistance [35–38].With respect to this, we examined the expression of both MGMT and MMR genes (i.e., MLH1,  MSH2, and MSH6). MGMT was significantly up-regulated in the G2 subtype than theG1 subtype  (P=1 .145 x 10−5,Fig  4A). By contrast, the MSH6 expression was significantly down-regulated inG2 subtype implying their decreased activity of MMR pathway (P=4 .45 x10−3). When we compared the paired primary and recurrent tumors, marked change of MGMT expression could be observed in recurrent G2 (G2R) but not in recurrent G1 (G1R) tumors (P<0.005, Fig 4B, left). Vice versa, MSH6 showed significant lower expression in the G2R tumors compared to the G1R tumors (P=0 .0098). Taken together, our results strongly suggest that the G2 but not G1 tumors may acquire TMZ tolerance via altered expression of MGMT and MMR pathway genes. As the G2 subtype showed similar expression pattern with neural subtype (see Fig 2),we next compared the expression of MGMT and MSH6 among the subtypes of TCGA data. As expected, the neural subtype showed significant overexpression of MGMT
(P = 1 .18x 10−3, Fig 4C,  left) and down-expression of MSH6 (P=1 .34x 10−2, Fig 4C, left) compared to the other subtypes, respectively. When we compared the four subtypes of TCGA, the neural subtype showed the highest expression ofMGMT and the lowest expression of MSH6 compared to other subtypes (S4A and S4B Fig). These resulst may support our result showing the subtype specific mechanism of TMZ resistance

Fig 4. Differential expressionof MGMTand MSH6 genes between G1 and G2tumors. (A) The expressions of MGMT (left) and MSH6 (right) were evaluated in G1 and G2 tumors. (B) Paired comparison of MGMT (left) and MSH6 (right) expressions between primary (P) and paired recurrent(R) tumors. Traced lines indicate the expression changes between primary and paired recurrent tumors. (C) The comparison of MGMT (left) and MSH6 (right) expressions between the neural subtype (N) and the other subtypes. The statistical significance is evaluated using Welch Two Sample t-test (*significantatP<0.05,**significantat P<0.005).


In this study, by performing integrative gene expression profile analyses, we have demonstrated that there are two distinct subtypes of transcriptomic reprogramming during recurrence of glioblastomas. From the results,we could suggest that the distinct two different mechanisms might be involved in for the TMZ resistance in each subtype.The G1 recurrent tumors had similar expression with the paired primary tumors, which express stemness and DNA-repair related genes. By contrast, the G2 recurrent tumors showed gene expression migration acquiring neuron-like traits. This may reflect the two different mechanisms might be involved in the acquisition of the recurrence phenotypes. Further interrogation has revealed the differential expression of MGMT and MSH6 between the subtypes (Fig 4B), which suggested the involvement of distinct mechanisms for TMZ resistance during recurrence of glioblastomas. The G1 tumors expressed the stem cell-related “self-renewal” signature including HOX_genes, stemness genes (ES1), CDK, and AURKA/B genes in both the paired primary and recurrent tumors. The G1 recurrent tumors didn’t show subtype migration by recurrence, indicating that the initial gene expression profiles were remained without change even after treatment and disease progression. Thus,the expression of stemness genes might be a possible explanation for the TMZ resistance in G1 recurrent tumors. On the other hand, the G2 tumors showed significant differential expression of MGMT and MSH6 genes compared to the primary tumors. As an  underlying mechanism for the TMZ resistance, it has been addressed that MGMT protein removes the methyl orchloroethyl damage at the O6 position of guanine [40]. In addition,the mismatch repair system (MMR) is also considered to be involved in theTMZ resistance, amending the DNA damage and base mismatches [41]. MMR recognizes unrepaired O6-methylated guanine adduct and induces cytotoxicity. Thus, inactivation of MMR may induce TMZ tolerance [34, 38]. In this regards, the G2 tumors showed the acquired expressions of MGMT and inactivation of MMR system genes (MSH6), which might be responsible for the acquisition of TMZ resistance. It is interesting to find that the G2 recurrent tumors acquire neuron-like features. Indeed, we have previously demonstrated the xenografted tumors in the brain acquire neuron-like expression traits,mimicking neurogenesis during development [42]. The results showed the connection of tumors with brain microenvironment such as neighbor astrocytes can give rise to chemo-resistant nature of brain metastatic tumors. Congruently, our data strongly support that brain environment may contribute to the neuron-like transcriptional reprogramming in G2 recurrent tumors. In addition, we have shown in theprevious study the high concordance between promoter methylation and gene expression profiles, suggesting the contribution of epigenetic events to transcriptome reprogramming [42]. This raises a possibility that the acquisitionof neuron-like trait in the G2 subtype might be related with the methylation reprogramming. However,we could not observe from TCGA data the associations between methylation status and the tumor recurrence subtypes. To address the roles of epigenetic reprogramming to the transcriptomic reprogramming during glioma recurrence accurately, further large scale studies with detailed methylation profiling might be needed.

Current and Emerging Treatments for Brain Metastases

Review ArticleApril 15, 2015Oncology Journal, Brain Tumors

ONCOLOGY  2015; 29(4)
By , , , , and
Conventional methods for treating brain metastasis, such as surgery, WBRT, and SRS, each compete with and complement one another. A plethora of recent studies have helped define and expand the utility of these tools.


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