Faculty Bibliography

In contrast to adulthood, meningiomas are rare among children and adolescents. However, the molecular relations between both groups have not been elucidated in detail. We have analyzed 41 tumor samples from 37 pediatric meningioma patients (female: 17, male: 20; age range: 1-21 years). Atypical meningioma WHO grade II was the most frequent histological subtype (N=14, 38%). Most tumors were located at the convexity (N=18) or the skull base (N=15). Lack of SMO, AKT, KLF4/TRAF7 mutation in Sanger sequencing (n=22) prompted whole genome sequencing of a subset (n=7). All cases exhibited bi-allelic mutation of NF2 (combined large deletion and germline (5/7) or somatic (2/7) base exchanges/frameshifts). Subsequently, representative samples of all 37 patients were subjected to 450K DNA methylation profiling and remaining DNA to sequencing of a brain tumor specific gene panel. Loss of chromosome 22 was frequently detected (N=28, 76%), followed by loss of chromosome 1 (N=12, 32%) and chromosome 18 (N=7, 19%). Moreover, a separation into three groups was evident: One group covering all clearcell meningiomas with enrichment for SMARCE1 mutations, a second group dominated by atypical meningiomas, and a third group covering benign WHO grade I meningiomas, as well as rare subtypes such as rhabdoid meningiomas. Compared to adult tumors, the majority of pediatric meningiomas clustered in a separate group both by unsupervised hierarchical and clustering and t-stochastic nearest neighbor embedding. Analysis of four tumor recurrences did not reveal changes compared to the primary tumor. These data suggest that pediatric meningiomas are fundamentally different from adult counterparts

BACKGROUND: Up to 20% of meningiomas are aggressive tumors with high recurrence rates and poor prognosis. Biomarkers predicting the risk of an unfavorable clinical course are lacking although aberrations in NF2, increased copy number variations and a hypomethylated phenotype have been associated with more aggressive behavior. Mutational signatures (MS) are characteristic patterns of somatic mutations seen in cancer genomes associated with aging, exposure to certain mutagens, or defective DNA repair. We aimed to identify MS patterns in clinically aggressive meningiomas.
METHOD(S): We performed whole exome sequencing of 18 de novo meningiomas (locally invasive and recurrent WHO I, n=6; atypical WHO II, n=4; anaplastic WHO III, n=8). Median PFS was 18.9 months. Copy numbers and DNA methylation phenotype were assessed by DNA methylation array analysis. Mutational signatures were identified using published signature algorithms (COSMIC).
RESULT(S): MS1 and MS5 (aging) were found in 18 (100%) cases. MS associated with defective DNA MMR were highly prevalent: MS20 and MS26 were detected in 18 (100%) and MS6 in 2 (12%) cases. MS12 (unknown etiology) was present in 14 (82%) cases. Despite the association with defective DNA MMR, none (0%) of the MS6 cases harbored somatic mutations associated with DNA MMR while MS12 tumors were enriched for mutations in DNA MMR (43%), chromatin remodeling (36%) and other cancer-associated genes (7%). MS6 tumors had significantly lower indels compared to non-MS6 tumors (p=0.01). Tumors with mutations in chromatin remodeling genes had a significantly higher rate of single nucleotide variants (SNVs) compared to cases without such mutations (p=0.02).
CONCLUSION(S): MS associated with defective DNA MMR were highly prevalent in this set of aggressive meningiomas. However, despite the association with DNA MMR, MS6 meningiomas harbored no somatic mutations associated with DNA MMR while MS12 tumors were enriched for mutations in DNA MMR, chromatin remodeling and cancerassociated genes

BACKGROUND: Tumor heterogeneity presents a major challenge to cancer diagnosis and treatment. In addition to interpatient tumor variability, intratumoral heterogeneity characterized by distinct molecular and phenotypic profiles is increasingly recognized as a major cause of therapy resistance and cancer recurrence. Because DNA methylation patterns are largely responsible for determining cell-type-specific functioning, we hypothesized that distinct DNA methylation and proteomic alterations could be identified in histologically-defined invasive and proliferative tumor cell populations in human isocitrate dehydrogenase 1 (IDH1)- mutated and wild-type glioblastoma (GBM).
METHOD(S): Formalin-fixed paraffin-embedded tissue sections of human adult IDH1-mutated and wild-type GBM were laser-microdissected (LM) into perinecrotic pseudopalisading tumor cells (PPCs), non-pseudopalisading tumor core cells (NPPCs), invasive subpial spread (SPS) and perivascular satellitosis tumor cells and brain adjacent to tumor cells prior to analysis and compared to non-microdissected tumor (NMT) and/or germline DNA. Genomewide DNA methylation and chromosomal copy numbers were determined with Infinium MethylationEPIC 850K BeadChip and intratumoral DNA methylation patterns compared by unsupervised hierarchical clustering. Label-free quantitative liquid chromatography-mass spectrometry of proteins was performed and proteins differentially expressed across LM areas subjected to pathway enrichment analysis.
RESULT(S): Unsupervised hierarchical classification of DNA methylation patterns for each LM area and NMT demonstrated remarkable clustering for all patients, based on methylation probe and methylated gene patterns. Proteomics analysis showed upregulation of hypoxia-inducible factor-1 inducible proteins in hypoxic PPCs. Out of 1819 proteins quantified, 5 were overexpressed and 9 underexpressed more than 10-fold in SPS compared with NPPCs and associated with alterations in metabolism, transport, extracellular matrix and apoptosis. Correlation of protein expression and DNA methylation patterns was noted.
CONCLUSION(S): Compared to NPPCs, SPS cells migrating toward the invasive edge share a relatively consistent epigenetic and proteomic signature, suggesting potentially targetable common mechanism(s) of invasion shared among GBM

In the version of this Letter originally published, the competing interests statement was missing. The authors declare no competing interests; this statement has now been added in all online versions of the Letter.

BACKGROUND AND PURPOSE/OBJECTIVE:Few studies have shown MR imaging features and ADC correlating with molecular markers and survival in patients with glioma. Our purpose was to correlate MR imaging features and ADC with molecular subtyping and survival in adult diffuse gliomas. MATERIALS AND METHODS/METHODS:promoter methylation, and overall survival. RESULTS:wild-type glioma. Other MR imaging features were not statistically significant predictors of survival. CONCLUSIONS:wild-type gliomas.

Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them-STK11, EGFR, FAT1, SETBP1, KRAS and TP53-can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .

A methionine substitution at lysine-27 on histone H3 variants (H3K27M) characterizes ~80% of diffuse intrinsic pontine gliomas (DIPG) and inhibits polycomb repressive complex 2 (PRC2) in a dominant-negative fashion. Yet, the mechanisms for this inhibition and abnormal epigenomic landscape have not been resolved. Using quantitative proteomics, we discovered that robust PRC2 inhibition requires levels of H3K27M greatly exceeding those of PRC2, seen in DIPG. While PRC2 inhibition requires interaction with H3K27M, we found that this interaction on chromatin is transient, with PRC2 largely being released from H3K27M. Unexpectedly, inhibition persisted even after PRC2 dissociated from H3K27M-containing chromatin, suggesting a lasting impact on PRC2. Furthermore, allosterically activated PRC2 is particularly sensitive to H3K27M, leading to the failure to spread H3K27me from PRC2 recruitment sites and consequently abrogating PRC2's ability to establish H3K27me2-3 repressive chromatin domains. In turn, levels of polycomb antagonists such as H3K36me2 are elevated, suggesting a more global, downstream effect on the epigenome. Together, these findings reveal the conditions required for H3K27M-mediated PRC2 inhibition and reconcile seemingly paradoxical effects of H3K27M on PRC2 recruitment and activity.

Visual inspection of histopathology slides is one of the main methods used by pathologists to assess the stage, type and subtype of lung tumors. Adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are the most prevalent subtypes of lung cancer, and their distinction requires visual inspection by an experienced pathologist. In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue. The performance of our method is comparable to that of pathologists, with an average area under the curve (AUC) of 0.97. Our model was validated on independent datasets of frozen tissues, formalin-fixed paraffin-embedded tissues and biopsies. Furthermore, we trained the network to predict the ten most commonly mutated genes in LUAD. We found that six of them-STK11, EGFR, FAT1, SETBP1, KRAS and TP53-can be predicted from pathology images, with AUCs from 0.733 to 0.856 as measured on a held-out population. These findings suggest that deep-learning models can assist pathologists in the detection of cancer subtype or gene mutations. Our approach can be applied to any cancer type, and the code is available at https://github.com/ncoudray/DeepPATH .

Patients with DICER1 predisposition syndrome have an increased risk to develop pleuropulmonary blastoma, cystic nephroma, embryonal rhabdomyosarcoma, and several other rare tumor entities. In this study, we identified 22 primary intracranial sarcomas, including 18 in pediatric patients, with a distinct methylation signature detected by array-based DNA-methylation profiling. In addition, two uterine rhabdomyosarcomas sharing identical features were identified. Gene panel sequencing of the 22 intracranial sarcomas revealed the almost unifying feature of DICER1 hotspot mutations (21/22; 95%) and a high frequency of co-occurring TP53 mutations (12/22; 55%). In addition, 17/22 (77%) sarcomas exhibited alterations in the mitogen-activated protein kinase pathway, most frequently affecting the mutational hotspots of KRAS (8/22; 36%) and mutations or deletions of NF1 (7/22; 32%), followed by mutations of FGFR4 (2/22; 9%), NRAS (2/22; 9%), and amplification of EGFR (1/22; 5%). A germline DICER1 mutation was detected in two of five cases with constitutional DNA available. Notably, none of the patients showed evidence of a cancer-related syndrome at the time of diagnosis. In contrast to the genetic findings, the morphological features of these tumors were less distinctive, although rhabdomyoblasts or rhabdomyoblast-like cells could retrospectively be detected in all cases. The identified combination of genetic events indicates a relationship between the intracranial tumors analyzed and DICER1 predisposition syndrome-associated sarcomas such as embryonal rhabdomyosarcoma or the recently described group of anaplastic sarcomas of the kidney. However, the intracranial tumors in our series were initially interpreted to represent various tumor types, but rhabdomyosarcoma was not among the typical differential diagnoses considered. Given the rarity of intracranial sarcomas, this molecularly clearly defined group comprises a considerable fraction thereof. We therefore propose the designation "spindle cell sarcoma with rhabdomyosarcoma-like features, DICER1 mutant" for this intriguing group.