Targeting class I histone deacetylase 2 in MYC amplified group 3 medulloblastoma

Introduction: Medulloblastoma (MB) is the most frequent malignant brain tumor in children. Four subgroups with distinct genetic, epigenetic and clinical characteristics have been identified. Survival remains particularly poor in patients with Group 3 tumors harbouring a MYC amplification. We herein explore the molecular mechanisms and translational implications of class I histone deacetylase (HDAC) inhibition in MYC driven MBs. Material and Methods: Expression of HDACs in primary MB subgroups was compared to normal brain tissue. A panel of MB cell lines, including Group 3 MYC amplified cell lines, were used as model systems. Cells were treated with HDAC inhibitors (HDACi) selectively targeting class I or IIa HDACs. Depletion of HDAC2 was performed. Intracellular HDAC activity, cellular viability, metabolic activity, caspase activity, cell cycle progression, RNA and protein expression were analyzed. Results: HDAC2 was found to be overexpressed in MB subgroups with poor prognosis (SHH, Group 3 and Group 4) compared to normal brain and the WNT subgroup. Inhibition of the enzymatic activity of the class I HDACs reduced metabolic activity, cell number, and viability in contrast to inhibition of class IIa HDACs. Increased sensitivity to HDACi was specifically observed in MYC amplified cells. Depletion of HDAC2 increased H4 acetylation and induced cell death. Simulation of clinical pharmacokinetics showed time-dependent on target activity that correlated with binding kinetics of HDACi compounds. Conclusions: We conclude that HDAC2 is a valid drug target in patients with MYC amplified MB. HDACi should cover HDAC2 in their inhibitory profile and timing and dosing regimen in clinical trials should take binding kinetics of compounds into consideration

Identification of low and very high-risk patients with non-WNT/non-SHH medulloblastoma by improved clinico-molecular stratification of the HIT2000 and I-HIT-MED cohorts

Molecular groups of medulloblastoma (MB) are well established. Novel risk stratification parameters include Group 3/4 (non-WNT/non-SHH) methylation subgroups I-VIII or whole-chromosomal aberration (WCA) phenotypes. This study investigates the integration of clinical and molecular parameters to improve risk stratification of non-WNT/non-SHH MB. Non-WNT/non-SHH MB from the HIT2000 study and the HIT-MED registries were selected based on availability of DNA-methylation profiling data. MYC or MYCN amplification and WCA of chromosomes 7, 8, and 11 were inferred from methylation array-based copy number profiles. In total, 403 non-WNT/non-SHH MB were identified, 346/403 (86%) had a methylation class family Group 3/4 methylation score (classifier v11b6) ≥ 0.9, and 294/346 (73%) were included in the risk stratification modeling based on Group 3 or 4 score (v11b6) ≥ 0.8 and subgroup I-VIII score (mb_g34) ≥ 0.8. Group 3 MB (5y-PFS, survival estimation ± standard deviation: 41.4 ± 4.6%; 5y-OS: 48.8 ± 5.0%) showed poorer survival compared to Group 4 (5y-PFS: 68.2 ± 3.7%; 5y-OS: 84.8 ± 2.8%). Subgroups II (5y-PFS: 27.6 ± 8.2%) and III (5y-PFS: 37.5 ± 7.9%) showed the poorest and subgroup VI (5y-PFS: 76.6 ± 7.9%), VII (5y-PFS: 75.9 ± 7.2%), and VIII (5y-PFS: 66.6 ± 5.8%) the best survival. Multivariate analysis revealed subgroup in combination with WCA phenotype to best predict risk of progression and death. The integration of clinical (age, M and R status) and molecular (MYC/N, subgroup, WCA phenotype) variables identified a low-risk stratum with a 5y-PFS of 94 ± 5.7 and a very high-risk stratum with a 5y-PFS of 29 ± 6.1%. Validation in an international MB cohort confirmed the combined stratification scheme with 82.1 ± 6.0% 5y-PFS in the low and 47.5 ± 4.1% in very high-risk groups, and outperformed the clinical model. These newly identified clinico-molecular low-risk and very high-risk strata, accounting for 6%, and 21% of non-WNT/non-SHH MB patients, respectively, may improve future treatment stratification

Targeting class I histone deacetylase 2 in MYC amplified group 3 medulloblastoma

Introduction: Medulloblastoma (MB) is the most frequent malignant brain tumor in children. Four subgroups with distinct genetic, epigenetic and clinical characteristics have been identified. Survival remains particularly poor in patients with Group 3 tumors harbouring a MYC amplification. We herein explore the molecular mechanisms and translational implications of class I histone deacetylase (HDAC) inhibition in MYC driven MBs. Material and Methods Expression of HDACs in primary MB subgroups was compared to normal brain tissue. A panel of MB cell lines, including Group 3 MYC amplified cell lines, were used as model systems. Cells were treated with HDAC inhibitors (HDACi) selectively targeting class I or IIa HDACs. Depletion of HDAC2 was performed. Intracellular HDAC activity, cellular viability, metabolic activity, caspase activity, cell cycle progression, RNA and protein expression were analyzed. Results: HDAC2 was found to be overexpressed in MB subgroups with poor prognosis (SHH, Group 3 and Group 4) compared to normal brain and the WNT subgroup. Inhibition of the enzymatic activity of the class I HDACs reduced metabolic activity, cell number, and viability in contrast to inhibition of class IIa HDACs. Increased sensitivity to HDACi was specifically observed in MYC amplified cells. Depletion of HDAC2 increased H4 acetylation and induced cell death. Simulation of clinical pharmacokinetics showed time-dependent on target activity that correlated with binding kinetics of HDACi compounds. Conclusions: We conclude that HDAC2 is a valid drug target in patients with MYC amplified MB. HDACi should cover HDAC2 in their inhibitory profile and timing and dosing regimen in clinical trials should take binding kinetics of compounds into consideration. Electronic supplementary material The online version of this article (doi:10.1186/s40478-015-0201-7) contains supplementary material, which is available to authorized users

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

Glioneuronal tumor with ATRX alteration, kinase fusion and anaplastic features (GTAKA): a molecularly distinct brain tumor type with recurrent NTRK gene fusions

Glioneuronal tumors are a heterogenous group of CNS neoplasms that can be challenging to accurately diagnose. Molecular methods are highly useful in classifying these tumors-distinguishing precise classes from their histological mimics and identifying previously unrecognized types of tumors. Using an unsupervised visualization approach of DNA methylation data, we identified a novel group of tumors (n = 20) that formed a cluster separate from all established CNS tumor types. Molecular analyses revealed ATRX alterations (in 16/16 cases by DNA sequencing and/or immunohistochemistry) as well as potentially targetable gene fusions involving receptor tyrosine-kinases (RTK; mostly NTRK1-3) in all of these tumors (16/16; 100%). In addition, copy number profiling showed homozygous deletions of CDKN2A/B in 55% of cases. Histological and immunohistochemical investigations revealed glioneuronal tumors with isomorphic, round and often condensed nuclei, perinuclear clearing, high mitotic activity and microvascular proliferation. Tumors were mainly located supratentorially (84%) and occurred in patients with a median age of 19 years. Survival data were limited (n = 18) but point towards a more aggressive biology as compared to other glioneuronal tumors (median progression-free survival 12.5 months). Given their molecular characteristics in addition to anaplastic features, we suggest the term glioneuronal tumor with ATRX alteration, kinase fusion and anaplastic features (GTAKA) to describe these tumors. In summary, our findings highlight a novel type of glioneuronal tumor driven by different RTK fusions accompanied by recurrent alterations in ATRX and homozygous deletions of CDKN2A/B. Targeted approaches such as NTRK inhibition might represent a therapeutic option for patients suffering from these tumors

Sarcoma classification by DNA methylation profiling

Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications

Effects of tozasertib on histone H3 phosphorylation (indicating aurora kinase A and B activity), cell cycle distribution, and apoptosis (indicated by BAX activation) in neuroblastoma cells.

<p>A) Numbers of cells expressing phosphorylated histone H3 were determined flow cytometry; * P<0.05 relative to untreated control; B) Representative histograms indicating cell cycle distribution in neuroblastoma cells after tozasertib treatment, arrows indicate additional peaks with high DNA content, possibly indicating endoreduplication; C) Numbers of cells with activated BAX expressed as fold change relative to control as determined by flow cytometry using an antibody specific for activated BAX after 24 h of tozasertib treatment. * P<0.05 relative to untreated control.</p

Role of p53 signalling in response to aurora kinase inhibition.

<p>A) tozasertib-induced p53 and p21 expression as indicated by Western blot after 24 h of incubation; B) tozasertib- and alisertib-induced expression of p53 target genes in UKF-NB-3 cells in which p53 was depleted using a lentiviral vector encoding shRNA directed against p53 (UKF-NB-3^p53shRNA) or in UKF-NB-3 cells transduced with a control vector encoding non-targeting scramble shRNA (UKF-NB-3^scr) as indicated by qPCR after 24 h. Expression levels are presented as fold change relative to non-treated controls. * P<0.05 relative to non-treated control; C) tozasertib and alisertib concentrations that reduce UKF-NB-3, UKF-NB-3^scr, and UKF-NB-3^p53shRNA viability by 50% (IC₅₀). * P<0.05 relative to UKF-NB-3 cells.</p

Effects of tozasertib and alisertib on neuroblastoma cell viability.

<p>A) IC₅₀ values determined after 120 h of incubation by MTT assay; B) tozasertib IC₅₀ values in the presence of the ABCB1 inhibitor zosuquidar (5 µM); C) Average IC₅₀ values in high and low ABCB1-expressing cells, * P<0.05 compared to low ABCB1-expressing cells; D) IC₅₀ values in UKF-NB-3 cells, UKF-NB-3 cells transduced with a lentiviral vector encoding for ABCB1 (UKF-NB-3^ABCB1), and UKF-NB-3 cells transduced with a control vector (UKF-NB-3^control), * P<0.05 compared to UKF-NB-3.</p