Faculty Bibliography

OBJECTIVE: It is intuitive to suggest that knowledge of the variation in the anatomy of the aortic root may influence the outcomes of transcatheter implantation of the aortic valve (TAVI). We have now assessed such variation. METHODS: We used 26 specimens of normal hearts and 78 CT data sets of adults with a mean age of 64+/-15 years to measure the dimensions of the membranous septum and to assess any influence played by rotation of the aortic root, inferring the relationship to the atrioventricular conduction axis. RESULTS: The aortic root was positioned centrally in the majority of both cohorts, although with significant variability. For the cadaveric hearts, 14 roots were central (54%), 4 clockwise-rotated (15%) and 8 counterclockwise-rotated (31%). In the adult CT cohort, 44 were central (56%), 21 clockwise-rotated (27%) and 13 counterclockwise-rotated (17%). A mean angle of 15.5 degrees was measured relative to the right fibrous trigone in the adult CT cohort, with a range of -32 degrees to 44.7 degrees . The dimensions of the membranous septum were independent of rotation. Fibrous continuity between the membranous septum and the right fibrous trigone increased with counterclockwise to clockwise rotation, implying variation in the relationship to the atrioventricular conduction axis. CONCLUSIONS: The central fibrous body is wider, providing greater fibrous support, in the setting of clockwise rotation of the aortic root. Individuals with this pattern may be more vulnerable to conduction damage following TAVI. Knowledge of such variation may prove invaluable for risk stratification.

Background: Osteosarcoma is one of the most common bone malignancies in the pediatric population, although it affects a wide age range. While pathognomonic genomic alterations have been identified in other pediatric bone and soft tissue tumors such as Ewing sarcoma and synovial sarcoma, no such alterations are seen in osteosarcoma. Epigenetic modifications such as global or specific changes in DNA methylation are gaining recognition as a primary mechanism of oncogenesis in pediatric and adult cancers. Identifying unique epigenetic modifications in tumors lacking known fusions could contribute to both diagnosis and selection of potential therapeutic targets. Methods: Using the Illumina Infinium Human Methylation450 BeadChip Array (450K array) platform, we performed genome-wide DNA methylation analysis on 15 osteosarcomas with tissue meeting criteria for methylation analysis, including formalin-fixed paraffin-embedded, frozen, and fresh tissue obtained from NYU and Memorial Sloan Kettering Cancer Center (mean age = 26 years; range 6-80 years). Comparison was made to 10 Ewing sarcomas and 11 synovial sarcomas in the same pilot cohort. Diagnosis was based on histologic criteria and, where available, absence of a known non-osteosarcoma genomic fusion. Unsupervised hierarchical clustering analysis was performed to classify tumor type and to assess for differentially methylated target regions. Results: Osteosarcomas formed a unique subtype on unsupervised hierarchical clustering analysis of DNA methylation. Of the 15 tumors profiled, molecular testing confirming the absence of a known fusion was previously done on 5, and fusion status did not impact clustering. Pathway analysis through MSig

Background: Bone sarcomas present a unique diagnostic challenge because of the considerable morphologic overlap between different entities. The choice of optimal treatment, however, is dependent upon accurate diagnosis. Genome-wide DNA methylation profiling has emerged as a new approach to aid in the diagnosis of brain tumors, with diagnostic accuracy exceeding standard histopathology. In this work we developed and validated a methylation based classifier to differentiate between osteosarcoma, Ewing's sarcoma, and synovial sarcoma. Methods: DNA methylation status of 482,421 CpG sites in 15 osteosarcoma, 10 Ewing's sarcoma, and 11 synovial sarcoma samples were measured using the Illumina HumanMethylation450 array. From this training set of 36 samples we developed a random forest classifier using the 400 most differentially methylated CpG sites (FDR q value < 0.001). This classifier was then validated on 10 synovial sarcoma samples from TCGA, 86 osteosarcoma samples from TARGET-OS, and 15 Ewing's sarcoma from a recently published series (Huertas-Martinez et al., Cancer Letters 2016). Results: Methylation profiling revealed three distinct molecular clusters, each enriched with a single sarcoma subtype. Within the validation cohorts, all samples from TCGA were correctly classified as synovial sarcoma (10/10, sensitivity and specificity 100%). All but one sample from TARGET-OS were classified as osteosarcoma (85/86, sensitivity 98%, specificity 100%) and all but one sample from the Ewing's sarcoma series was classified as Ewing's sarcoma (14/15, sensitivity 93%, specificity 100%). The single misclassified osteosarcoma sample was classified as Ewing's sarcoma, and was later determined to be a misdiagnosed Ewing's sarcoma based on RNA-Seq demonstrating high EWRS1 and ETV1 expression. An additional clinical sample that was misdiagnosed as a synovial sarcoma by initial histolopathology, was accurately recognized as osteosarcoma by the methylation classifier. Conclusions: Osteosarcoma, Ewing's sarcoma and synovial sarcoma have distinct epigenetic profiles. Our validated methylation-based classifier can be used to provide an accurate diagnosis when histological and standard techniques are inconclusive

Purpose/UNASSIGNED:Pediatric sarcomas provide a unique diagnostic challenge. There is considerable morphologic overlap between entities, increasing the importance of molecular studies in the diagnosis, treatment, and identification of therapeutic targets. We developed and validated a genome-wide DNA methylation based classifier to differentiate between osteosarcoma, Ewing's sarcoma, and synovial sarcoma. Materials and Methods/UNASSIGNED:DNA methylation status of 482,421 CpG sites in 10 Ewing's sarcoma, 11 synovial sarcoma, and 15 osteosarcoma samples were determined using the Illumina Infinium HumanMethylation450 array. We developed a random forest classifier trained from the 400 most differentially methylated CpG sites within the training set of 36 sarcoma samples. This classifier was validated on data drawn from The Cancer Genome Atlas (TCGA) synovial sarcoma, TARGET Osteosarcoma, and a recently published series of Ewing's sarcoma. Results/UNASSIGNED:Methylation profiling revealed three distinct patterns, each enriched with a single sarcoma subtype. Within the validation cohorts, all samples from TCGA were accurately classified as synovial sarcoma (10/10, sensitivity and specificity 100%), all but one sample from TARGET-OS were classified as osteosarcoma (85/86, sensitivity 98%, specificity 100%) and 14/15 Ewing's sarcoma samples classified correctly (sensitivity 93%, specificity 100%). The single misclassified osteosarcoma sample demonstrated high EWSR1 and ETV1 expression on RNA-seq although no fusion was found on manual curation of the transcript sequence. Two additional clinical samples, that were difficult to classify by morphology and molecular methods, were classified as osteosarcoma when previously suspected to be a synovial sarcoma and Ewing's sarcoma on initial diagnosis, respectively. Conclusion/UNASSIGNED:Osteosarcoma, synovial sarcoma, and Ewing's sarcoma have distinct epigenetic profiles. Our validated methylation-based classifier can be used to provide diagnostic assistance when histological and standard techniques are inconclusive.

Chronic granulomatous disease (CGD) is a rare, inherited immunodeficiency disorder that reduces the superoxide generation ability of phagocytes, leading to recurrent infections and granulomatous inflammation. We report the case of a previously healthy 3-year-old boy who presented with classic features of Crohn's disease. Suspicion from histopathological assessment allowed early diagnosis and treatment for CGD before the onset of infections.

Background: PNETs (Pancreatic Neuroendocrine tumors) are infrequent in the context of tuberous sclerosis complex (TSC) in children. There is marked disease heterogeneity, challenging clinical presentation and a limited knowledge of underlying molecular mechanisms. Inactivating mutations in the two genes TSC1 and TSC2, which correspondingly encode for hamartin and tuberin, are pathognomonic for the disease. The upregulation of the phosphoinositide 3-kinase (PI3K)/AKT (protein kinase B) with cascade activation of mitogen-activated protein kinase (MAPK) and the mammalian target of rapamycin (mTOR) leads to uncontrolled cell proliferation and tumorigenesis. The association of TSC and PNET is not clearly defined. We describe a case of PNET in a toddler with TSC1. Case presentation: An asymptomatic male aged 3 years 10 months with a TSC1 was identified with a pancreatic mass. Clinical sequence of events: diagnosed at 3 months with epilepsy and 3 hypomelanotic lesions on trunk; delayed speech development; at 3 years: kidney angiomyolipomas/left cortical cyst; at 3 years 7 months: multiple small rhabdomyomas in the right/left ventricles, at 3 years 9 months: a subependymal giant cell astrocytoma (SGCA)/excision and at 4 years of age open enucleation of 1 cm pancreatic mass located near junction of body and tail. Endocrine study: Test NameValue Reference Range Gastrin, pg/mL 83 Fasting 3-4 hrs: 2-168 Neuron-Specific Enolase (NSE), Serum, ng/mL 8.9 0.0-12.5 Chromogranin A, nmol/L 2 0-5 Glucose, plasma, mg/dL 81 65-99 Insulin, ulU/mL 4 2.6-24.9 IGF-1, ng/mL 59 20-141 IGFBP3, ng/mL 1960 972-4123 Diagnostic study: Molecular study: exon 10 of the TSC1 gene has a heterozygous basechange mutation (c.989dupT, an abnormal TSC1 protein, hamartin - p.Ser331fs). Brain MRI: SGCA (2.3 cm). Histology: Grade I, single mitoses, immunostains (+) for GFAP (glial fibrillary acidic protein) and synaptophysin (neuroendocrine marker), increased MIB-1 proliferation index (10%) indicative of high activity of cell proliferation. SGCA in the setting of TSC1 is able to express GFAP. MRI abdomen with contrast: pancreatic body lesion 1.1 cm in diameter (interval change from 4 mm in 5 months). Pathology: 1.0 x 0.8 x 0.6 cm; pale tan-pink tumor. Histology: PNET, grade 2; 2 mitotic figures/10 hpf, the Ki-67 proliferation index is about 20%; diffuse positivity with chromogranin, synaptophysin and CD56 (membranous). Conclusions: Our case demonstrates that patients with TSC1 may develop SGCA and pancreatic nonsecretory tumor at an early age along with the typical clinical stigmata of this entity. PNETs express neuroendocrine markers, which are useful to differentiate functional vs nonfunctional tumors. Semi-annual/annual evaluation with abdominal MRI will be beneficial for identification of PNETs. Some evidence exists to support that PNETs are an associated feature of TSC and require further investigation