Recent genomic approaches have suggested the existence of multiple distinct subtypes of medulloblastoma. We studied a large cohort of medulloblastomas to determine how many subgroups of the disease ...exist, how they differ, and the extent of overlap between subgroups.
We determined gene expression profiles and DNA copy number aberrations for 103 primary medulloblastomas. Bioinformatic tools were used for class discovery of medulloblastoma subgroups based on the most informative genes in the data set. Immunohistochemistry for subgroup-specific signature genes was used to determine subgroup affiliation for 294 nonoverlapping medulloblastomas on two independent tissue microarrays.
Multiple unsupervised analyses of transcriptional profiles identified the following four distinct, nonoverlapping molecular variants: WNT, SHH, group C, and group D. Supervised analysis of these four subgroups revealed significant subgroup-specific demographics, histology, metastatic status, and DNA copy number aberrations. Immunohistochemistry for DKK1 (WNT), SFRP1 (SHH), NPR3 (group C), and KCNA1 (group D) could reliably and uniquely classify formalin-fixed medulloblastomas in approximately 98% of patients. Group C patients (NPR3-positive tumors) exhibited a significantly diminished progression-free and overall survival irrespective of their metastatic status.
Our integrative genomics approach to a large cohort of medulloblastomas has identified four disparate subgroups with distinct demographics, clinical presentation, transcriptional profiles, genetic abnormalities, and clinical outcome. Medulloblastomas can be reliably assigned to subgroups through immunohistochemistry, thereby making medulloblastoma subclassification widely available. Future research on medulloblastoma and the development of clinical trials should take into consideration these four distinct types of medulloblastoma.
The World Health Organization (WHO) classification and grading system attempts to predict the clinical course of meningiomas based on morphological parameters. However, because of high interobserver ...variation of some criteria, more reliable prognostic markers are required. Here, we assessed the TERT promoter for mutations in the hotspot regions C228T and C250T in meningioma samples from 252 patients. Mutations were detected in 16 samples (6.4% across the cohort, 1.7%, 5.7%, and 20.0% of WHO grade I, II, and III cases, respectively). Data were analyzed by t test, Fisher's exact test, log-rank test, and Cox proportional hazard model. All statistical tests were two-sided. Within a mean follow-up time in surviving patients of 68.1 months, TERT promoter mutations were statistically significantly associated with shorter time to progression (P < .001). Median time to progression among mutant cases was 10.1 months compared with 179.0 months among wild-type cases. Our results indicate that the inclusion of molecular data (ie, analysis of TERT promoter status) into a histologically and genetically integrated classification and grading system for meningiomas increases prognostic power. Consequently, we propose to incorporate the assessment of TERT promoter status in upcoming grading schemes for meningioma.
The aim of this study was to analyze chromosomal aberrations in terms of frequency and impact on time to progression in patients with smoldering multiple myeloma (SMM) on the background of clinical ...prognostic factors.
The chromosomal abnormalities 1q21, 5p15/5q35, 9q34, 13q14.3, 15q22, 17p13, t(11;14)(q13;q32), and t(4;14)(p16.3;q32) were assessed in CD138-purified myeloma cells by interphase fluorescent in situ hybridization (iFISH) alongside clinical parameters in a consecutive series of 248 patients with SMM.
The high-risk aberrations in active myeloma (ie, del(17p13), t(4;14), and +1q21) present in 6.1%, 8.9%, and 29.8% of patients significantly confer adverse prognosis in SMM with hazard ratios (HRs) of 2.90 (95% CI, 1.56 to 5.40), 2.28 (95% CI, 1.33 to 3.91), and 1.66 (95% CI, 1.08 to 2.54), respectively. Contrary to the conditions in active myeloma, hyperdiploidy, present in 43.3% of patients, is an adverse prognostic factor (HR, 1.67; 95% CI, 1.10 to 2.54). Percentage of malignant bone marrow plasma cells assessed by iFISH and combination of M-protein and plasma cell infiltration as surrogates of tumor load significantly confer adverse prognosis with HRs of 4.37 (95% CI, 2.79 to 6.85) and 4.27 (95% CI, 2.77 to 6.56), respectively. In multivariate analysis, high-risk aberrations, hyperdiploidy, and surrogates of tumor load are independently prognostic.
The high-risk chromosomal aberrations del(17p13), t(4;14), and +1q21 are adverse prognostic factors in SMM just as they are in active myeloma, independent of tumor mass. Hyperdiploidy is the first example for an adverse prognostic factor in SMM of opposite predictiveness in active myeloma. Risk association of chromosomal aberrations is not only a priori treatment dependent (predictive) but is also an intrinsic property of myeloma cells (prognostic).
With whole-body magnetic resonance imaging (wb-MRI), almost the whole bone marrow compartment can be examined in patients with monoclonal plasma cell disease. Focal lesions (FLs) detected by spinal ...MRI have been of prognostic significance in symptomatic multiple myeloma (sMM). In this study, we investigated the prognostic significance of FLs in wb-MRI in patients with asymptomatic multiple myeloma (aMM).
Wb-MRI was performed in 149 patients with aMM. The prognostic significance of the presence and absence, as well as the number, of FLs for progression into sMM was analyzed.
FLs were present in 28% of patients. The presence per se of FLs and a number of greater than one FL were the strongest adverse prognostic factors for progression into sMM (P < .001) in multivariate analysis. A diffuse infiltration pattern in MRI, a monoclonal protein of 40 g/L or greater, and a plasma cell infiltration in bone marrow of 20% or greater were other adverse prognostic factors for progression-free survival in univariate analysis.
We recommend use of wb-MRI for risk stratification of patients with asymptomatic multiple myeloma.
Meningiomas are classified based on histological features, but genetic and epigenetic features are emerging as relevant biomarkers for outcome prediction and may supplement histomorphological ...evaluation. We investigated meningioma‐relevant mutations and their correlation with DNA methylation clusters and patient survival times. Formalin‐fixed and paraffin‐embedded samples of 126 meningioma patients (WHO grade I 52/126; 41.3%; WHO grade II: 48/126; 38.1%; WHO grade III: 26/126; 20.6%) were investigated. We analyzed NF2, TRAF7, KLF4, ARID, SMO, AKT, TERT promotor, PIK3CA, and SUFU mutations using panel sequencing and correlated them to DNA methylation classes (MC) determined using 850k EPIC arrays. The TRAKL mutation genotype was characterized by the presence of any of the following mutations: TRAF7, AKT1, and KLF4. Survival data including progression‐free survival (PFS) and overall survival (OS) was retrieved from chart review. Mutations were evident in 90/126 (71.4%) specimens with mutations in NF2 (39/126; 31.0%), TRAF7 (39/126; 31.0%) and KLF4 (25/126; 19.8%) being the most frequent ones. Two or more mutations were observed in 35/126 (27.8%) specimens. While TRAKL was predominantly found in benign MC, NF2 was associated with malign MC (p < 0.05). TRAF7, KLF4, and TRAKL mutation genotype were associated with improved PFS and OS (p < 0.05). TERT promotor methylation, intermediate, and malign MC were associated with impaired PFS and OS (p < 0.05). Methylation cluster showed better prognostic discrimination for PFS and OS (c‐index 0.77/0.75) than each of the individual mutations (c‐index 0.63/0.68). In multivariate analysis correcting for age, gender, MC, and WHO grade, none of the individual mutations except TERT remained an independent significant prognostic factor for PFS. Molecular profiling including mutational analysis and DNA methylation classification may facilitate more precise prognostic assessment and identification of potential targets for personalized therapy in meningioma patients.
Molecular profiling including meningioma relevant mutations and DNA methylation classification may facilitate more precise prognostic assessment and identification of potential targets for personalized therapy in meningioma patients.
We have developed the R package c060 with the aim of improving R software func- tionality for high-dimensional risk prediction modeling, e.g., for prognostic modeling of survival data using ...high-throughput genomic data. Penalized regression models provide a statistically appealing way of building risk prediction models from high-dimensional data. The popular CRAN package glmnet implements an efficient algorithm for fitting penalized Cox and generalized linear models. However, in practical applications the data analysis will typically not stop at the point where the model has been fitted. One is for example often interested in the stability of selected features and in assessing the prediction performance of a model and we provide functions to deal with both of these tasks. Our R functions are computationally efficient and offer the possibility of speeding up computing time through parallel computing. Another feature which can drastically reduce computing time is an efficient interval-search algorithm, which we have implemented for selecting the optimal parameter combination for elastic net penalties. These functions have been useful in our daily work at the Biostatistics department (C060) of the German Cancer Research Center where prognostic modeling of patient survival data is of particular interest. Although we focus on a survival data application of penalized Cox models in this article, the functions in our R package are in general applicable to all types of regression models implemented in the glmnet package, with the exception of prediction error curves, which are specific to time-to-event data.
Pediatric low-grade gliomas (pLGG) show heterogeneous responses to MAPK inhibitors (MAPKi) in clinical trials. Thus, more complex stratification biomarkers are needed to identify patients likely to ...benefit from MAPKi therapy. Here, we identify MAPK-related genes enriched in MAPKi-sensitive cell lines using the GDSC dataset and apply them to calculate class-specific MAPKi sensitivity scores (MSSs) via single-sample gene set enrichment analysis. The MSSs discriminate MAPKi-sensitive and non-sensitive cells in the GDSC dataset and significantly correlate with response to MAPKi in an independent PDX dataset. The MSSs discern gliomas with varying MAPK alterations and are higher in pLGG compared to other pediatric CNS tumors. Heterogenous MSSs within pLGGs with the same MAPK alteration identify proportions of potentially sensitive patients. The MEKi MSS predicts treatment response in a small set of pLGG patients treated with trametinib. High MSSs correlate with a higher immune cell infiltration, with high expression in the microglia compartment in single-cell RNA sequencing data, while low MSSs correlate with low immune infiltration and increased neuronal score. The MSSs represent predictive tools for the stratification of pLGG patients and should be prospectively validated in clinical trials. Our data supports a role for microglia in the response to MAPKi.
A hypoxic microenvironment induces resistance to alkylating agents by activating targets in the mammalian target of rapamycin (mTOR) pathway. The molecular mechanisms involved in this mTOR-mediated ...hypoxia-induced chemoresistance, however, are unclear. Here we identify the mTOR target N -myc downstream regulated gene 1 (NDRG1) as a key determinant of resistance toward alkylating chemotherapy, driven by hypoxia but also by therapeutic measures such as irradiation, corticosteroids, and chronic exposure to alkylating agents via distinct molecular routes involving hypoxia-inducible factor (HIF)-1alpha, p53, and the mTOR complex 2 (mTORC2)/serum glucocorticoid-induced protein kinase 1 (SGK1) pathway. Resistance toward alkylating chemotherapy but not radiotherapy was dependent on NDRG1 expression and activity. In posttreatment tumor tissue of patients with malignant gliomas, NDRG1 was induced and predictive of poor response to alkylating chemotherapy. On a molecular level, NDRG1 bound and stabilized methyltransferases, chiefly O ⁶-methylguanine-DNA methyltransferase (MGMT), a key enzyme for resistance to alkylating agents in glioblastoma patients. In patients with glioblastoma, MGMT promoter methylation in tumor tissue was not more predictive for response to alkylating chemotherapy in patients who received concomitant corticosteroids.
Medulloblastoma is a rare primary brain tumor in adults, whereas it constitutes the most common malignant brain tumor in children. Integrated genomics approaches revealed at least four distinct ...disease variants in children. The aim of this study was to investigate molecular subtypes and their prognostic implication in a large cohort of adult medulloblastomas as the biology in this age group remains poorly understood.
We combined transcriptome and DNA copy number analyses for 28 adult medulloblastomas. Statistical and bioinformatic tools were applied to discover distinct molecular variants. Clinical and molecular characteristics of each biologic subtype were validated using immunohistochemistry on a tissue microarray derived from an independent patient cohort of adult medulloblastomas (n = 103).
Gene expression profiles revealed three distinct molecular variants with stable subtype separation using the 300 most varying transcripts. Distinct demographics, genetics, transcriptome, and prognosis were noted for each subtype of adult medulloblastoma. Immunohistochemistry revealed aberrant activation of the sonic hedgehog (SHH) pathway in over half of adult medulloblastomas constituting a promising molecular therapeutic target. In contrast, subtype C tumors, which comprise a robust subtype in childhood medulloblastoma are only exceptionally seen in adult cohorts. Notably, adult subtype D and Wnt/wingless tumors were associated with worse prognosis than pediatric cohorts, whereas survival for SHH tumors was similar for both age groups.
The transcriptome of adult medulloblastomas differs considerably from pediatric counterparts, both in terms of tumor biology and prognostic impact. Therefore, age-specific classification is required and must be adapted for use in clinical trials of adult medulloblastoma.
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