Faculty Bibliography

BACKGROUND:Extraneural metastases are relatively rare manifestations of medulloblastoma. CASE PRESENTATION/METHODS:We present the case of a young boy with group three MYCN-amplified medulloblastoma. He received multimodal chemotherapy consisting of gross total resection followed by postoperative craniospinal radiation and adjuvant chemotherapy. The patient developed extraneural metastases 4 months after the end of therapy. Literature review identifies the poor prognosis of MYCN-amplified medulloblastomas as well as extraneural metastases; we review the current limitations and future directions of medulloblastoma treatment options. CONCLUSION/CONCLUSIONS:To the best of our knowledge, this is the first molecularly characterized report of extraneural metastases of medulloblastoma in a child.

Accurate histopathologic diagnosis is essential for providing optimal surgical management of pediatric brain tumors. Current methods for intraoperative histology are time- and labor-intensive and often introduce artifacts that limit interpretation. Stimulated Raman histology (SRH) is a novel label-free imaging technique that provides intraoperative histologic images of fresh, unprocessed surgical specimens. Here we evaluate the capacity of SRH for use in the intraoperative diagnosis of pediatric type brain tumors. SRH revealed key diagnostic features in fresh tissue specimens collected from 33 prospectively enrolled pediatric type brain tumor patients, preserving tumor cytology and histoarchitecture in all specimens. We simulated an intraoperative consultation for 25 patients with specimens imaged using both SRH and standard hematoxylin and eosin histology. SRH-based diagnoses achieved near-perfect diagnostic concordance (Cohen's kappa, kappa > 0.90) and an accuracy of 92-96%. We then developed a quantitative histologic method using SRH images based on rapid image feature extraction. Nuclear density, tumor-associated macrophage infiltration, and nuclear morphology parameters from 3337 SRH fields of view were used to develop and validate a decision-tree machine-learning model. Using SRH image features, our model correctly classified 25 fresh pediatric type surgical specimens into normal versus lesional tissue and low-grade versus high-grade tumors with 100% accuracy. Our results provide insight into how SRH can deliver rapid diagnostic histologic data that could inform the surgical management of pediatric brain tumors.

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

Posterior fossa ependymomas (EPN_PF) in children comprise two morphologically identical, but biologically distinct tumor entities. Group-A (EPN_PFA) tumors have a poor prognosis and require intensive therapy. In contrast, group-B tumors (EPN_PFB) exhibit excellent prognosis and the current consensus opinion recommends future clinical trials to test the possibility of treatment de-escalation in these patients. Therefore, distinguishing these two tumor subtypes is critical. EPN_PFA and EPN_PFB can be distinguished based on DNA methylation signatures, but these assays are not routinely available. We have previously shown that a subset of poorly prognostic childhood EPN_PF exhibits global reduction in H3K27me3. Therefore, we set out to determine whether a simple immunohistochemical assay for H3K27me3 could be used to segregate EPN_PFA from EPN_PFB tumors. We assembled a cohort of 230 childhood ependymomas and H3K27me3 immunohistochemistry was assessed as positive or negative in a blinded manner. H3K27me3 staining results were compared with DNA methylation-based subgroup information available in 112 samples [EPN_PFA (n = 72) and EPN_PFB tumors (n = 40)]. H3K27me3 staining was globally reduced in EPN_PFA tumors and immunohistochemistry showed 99% sensitivity and 100% specificity in segregating EPN_PFA from EPN_PFB tumors. Moreover, H3K27me3 immunostaining was sufficient to delineate patients with worse prognosis in two independent, non-overlapping cohorts (n = 133 and n = 97). In conclusion, immunohistochemical evaluation of H3K27me3 global reduction is an economic, easily available and readily adaptable method for defining high-risk EPN_PFA from low-risk posterior fossa EPN_PFB tumors to inform prognosis and to enable the design of future clinical trials.

Conventional methods for intraoperative histopathologic diagnosis are labour- and time-intensive, and may delay decision-making during brain-tumour surgery. Stimulated Raman scattering (SRS) microscopy, a label-free optical process, has been shown to rapidly detect brain-tumour infiltration in fresh, unprocessed human tissues. Here, we demonstrate the first application of SRS microscopy in the operating room by using a portable fibre-laser-based microscope and unprocessed specimens from 101 neurosurgical patients. We also introduce an image-processing method - stimulated Raman histology (SRH) - which leverages SRS images to create virtual haematoxylin-and-eosin-stained slides, revealing essential diagnostic features. In a simulation of intraoperative pathologic consultation in 30 patients, we found a remarkable concordance of SRH and conventional histology for predicting diagnosis (Cohen's kappa, κ > 0.89), with accuracy exceeding 92%. We also built and validated a multilayer perceptron based on quantified SRH image attributes that predicts brain-tumour subtype with 90% accuracy. Our findings provide insight into how SRH can now be used to improve the surgical care of brain tumour patients.

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.