Faculty Bibliography

The immature physiology of cardiomyocytes derived from human induced pluripotent stem cells (hiPSCs) limits their utility for drug screening and disease modelling. Here we show that suitable combinations of mechanical stimuli and metabolic cues can enhance the maturation of hiPSC-derived cardiomyocytes, and that the maturation-inducing cues have phenotype-dependent effects on the cells' action-potential morphology and calcium handling. By using microfluidic chips that enhanced the alignment and extracellular-matrix production of cardiac microtissues derived from genetically distinct sources of hiPSC-derived cardiomyocytes, we identified fatty-acid-enriched maturation media that improved the cells' mitochondrial structure and calcium handling, and observed divergent cell-source-dependent effects on action-potential duration (APD). Specifically, in the presence of maturation media, tissues with abnormally prolonged APDs exhibited shorter APDs, and tissues with aberrantly short APDs displayed prolonged APDs. Regardless of cell source, tissue maturation reduced variabilities in spontaneous beat rate and in APD, and led to converging cell phenotypes (with APDs within the 300-450 ms range characteristic of human left ventricular cardiomyocytes) that improved the modelling of the effects of pro-arrhythmic drugs on cardiac tissue.

BACKGROUND:Myxopapillary ependymoma (MPE) is a heterogeneous disease regarding histopathology and outcome. The underlying molecular biology is poorly understood, and markers that reliably predict the patients' clinical course are unknown. METHODS:We assembled a cohort of 185 tumors classified as MPE based on DNA methylation. Methylation patterns, copy number profiles, and MGMT promoter methylation were analyzed for all tumors, 106 tumors were evaluated histomorphologically, and RNA sequencing was performed for 37 cases. Based on methylation profiling, we defined two subtypes MPE-A and MPEB, and explored associations with epidemiological, clinical, pathological, and molecular characteristics of these tumors. RESULTS:MPE-A occurred at a median age of 27 years and were enriched with tumors demonstrating papillary morphology and MGMT promoter hypermethylation. Half of these tumors could not be totally resected, and 85% relapsed within 10 years. Copy number alterations were more common in MPE-A. RNA sequencing revealed an enrichment for extracellular matrix and immune system-related signatures in MPE-A. MPE-B occurred at a median age of 45 years and included many tumors with a histological diagnosis of WHO grade II and tanycytic morphology. Patients within this subtype had a significantly better outcome with a relapse rate of 33% in 10 years (p=3.4e-06). CONCLUSIONS:We unraveled the morphological and clinical heterogeneity of MPE by identifying two molecularly distinct subtypes. These subtypes significantly differed in progression-free survival and will likely need different protocols for surveillance and treatment.

BACKGROUND:Survival for patients with high-risk neuroblastoma (HRNB) remains poor despite aggressive multimodal therapies. AIMS/OBJECTIVE:To study the feasibility and safety of incorporating a genomic-based targeted agent to induction therapy for HRNB as well as the feasibility and safety of adding difluoromethylornithine (DFMO) to anti-GD2 immunotherapy. METHODS:twice daily) was added to maintenance with dinutuximab and isotretinoin, followed by continuation of DFMO alone for 2 years. DNA methylation analysis was performed retrospectively and compared to RNA expression. RESULTS:Of the 20 subjects enrolled, 19 started targeted therapy during cycle 3 and 1 started during cycle 5. Eighty-five percent of subjects met feasibility criteria (receiving 75% of targeted agent doses). Addition of targeted agents did not result in toxicities requiring dose reduction of chemotherapy or permanent discontinuation of targeted agent. Following standard consolidation, 15 subjects continued onto immunotherapy with DFMO. This combination was well-tolerated and resulted in no unexpected adverse events related to DFMO. CONCLUSION/CONCLUSIONS:This study demonstrates the safety and feasibility of adding targeted agents to standard induction therapy and adding DFMO to immunotherapy for HRNB. This treatment regimen has been expanded to a Phase II trial to evaluate efficacy.

Sudden unexplained death in childhood (SUDC) is death of a child over 1 year of age that is unexplained after review of clinical history, circumstances of death, and complete autopsy with ancillary testing. Multiple etiologies may cause SUDC. SUDC and sudden unexpected death in epilepsy (SUDEP) share clinical and pathological features, suggesting some similarities in mechanism of death and possible abnormalities in hippocampus and cortex. To identify molecular signaling pathways, we performed label-free quantitative mass spectrometry on microdissected frontal cortex, hippocampal dentate gyrus (DG), and cornu ammonis (CA1-3) in SUDC (n = 19) and pediatric control cases (n = 19) with an explained cause of death. At a 5% false discovery rate (FDR), we found differential expression of 660 proteins in frontal cortex, 170 in DG, and 57 in CA1-3. Pathway analysis of altered proteins identified top signaling pathways associated with activated oxidative phosphorylation (p = 6.3 × 10^-15, z = 4.08) and inhibited EIF2 signaling (p = 2.0 × 10^-21, z = - 2.56) in frontal cortex, and activated acute phase response in DG (p = 8.5 × 10^-6, z = 2.65) and CA1-3 (p = 4.7 × 10^-6, z = 2.00). Weighted gene correlation network analysis (WGCNA) of clinical history indicated that SUDC-positive post-mortem virology (n = 4/17) had the most significant module in each brain region, with the top most significant associated with decreased mRNA metabolic processes (p = 2.8 × 10^-5) in frontal cortex. Additional modules were associated with clinical history, including fever within 24 h of death (top: increased mitochondrial fission in DG, p = 1.8 × 10^-3) and febrile seizure history (top: decreased small molecule metabolic processes in frontal cortex, p = 8.8 × 10^-5) in all brain regions, neuropathological hippocampal findings in the DG (top: decreased focal adhesion, p = 1.9 × 10^-3). Overall, cortical and hippocampal protein changes were present in SUDC cases and some correlated with clinical features. Our studies support that proteomic studies of SUDC cohorts can advance our understanding of the pathogenesis of these tragedies and may inform the development of preventive strategies.

PURPOSE/OBJECTIVE:-truncating mutations. Conversely, the molecular underpinnings of the rare juvenile granulosa cell tumor (JGCT) have not been well elucidated. To this end, we applied a tumor-only integrated approach to investigate the genomic, transcriptomic, and epigenomic landscape of 31 JGCTs to identify putative oncogenic drivers. EXPERIMENTAL DESIGN/METHODS:Multipronged analyses of 31 JGCTs were performed utilizing a clinically validated next-generation sequencing (NGS)-panel targeting 580 cancer-related genes for genomic interrogation, in addition to targeted RNA NGS for transcriptomic exploration. Genome-wide DNA methylation profiling was conducted using an Infinium Methylation EPIC array targeting 866,562 CpG methylation sites. RESULTS:non-rearranged JGCTs under direct promoter control. Genome-wide DNA methylation rendered a clear delineation between AGCTs and JGCTs at the epigenomic level further supporting its diagnostic utility in distinguishing among these tumors. CONCLUSIONS:rearrangements in a subset of tumors. Our findings further offer insights into possible targeted therapies in a rare entity.

Uterine perivascular epithelioid cell tumor (PEComa) is a rare mesenchymal neoplasm that occasionally shares morphologic and immunohistochemical overlap with low- and high-grade endometrial stromal sarcoma (LGESS and HGESS). In this study, we sought to characterize the clinical, morphologic, genetic, and epigenetic features of five uterine sarcomas that display histologic features of LGESS, HGESS, and PEComa. All tumors demonstrated epithelioid cells often associated with a low-grade spindled component resembling LGESS, with both regions expressing CD10, ER, PR, variable HMB45, and Melan-A immunoreactivity, and strong cathepsin K and pS6 expression. Targeted massively parallel sequencing analysis revealed the presence of somatic TSC2 mutations in all five cases, of which four harbored concurrent or consecutive JAZF1-SUZ12 gene fusions. Unsupervised hierarchical clustering analysis of methylation profiles of TSC2-mutant uterine sarcomas (n = 4), LGESS (n = 10), and HGESS (n = 12) demonstrated two clusters consisting of (1) all LGESS and TSC2-mutant uterine sarcomas and (2) all HGESS. KEGG pathway analysis detected methylation differences in genes involved in PI3K/AKT, calcium, and Rap1 signaling. TSC2-mutant uterine sarcomas were responsive to hormone suppression, and mTOR inhibition demonstrated clinical benefit in four patients with these neoplasms. Our results suggest that these tumors represent histologically distinctive LGESS with TSC2 mutations. TSC2 mutations and JAZF1-SUZ12 fusion may help diagnose these tumors and possibly direct effective treatment.

Image-based analysis as a method for mutation detection can be advantageous in settings when tumor tissue is limited or unavailable for direct testing. Here, we utilize two distinct and complementary machine learning methods of analyzing whole slide images (WSI) for predicting mutated BRAF. In the first method, WSI of melanomas from 256 patients were used to train a deep convolutional neural network (CNN) in order to develop a fully automated model that first selects for tumor-rich areas (Area Under the Curve AUC=0.96) then predicts for mutated BRAF (AUC=0.71). Saliency mapping was performed and revealed that pixels corresponding to nuclei were the most relevant to network learning. In the second method, WSI were analyzed using a pathomics pipeline that first annotates nuclei and then quantifies nuclear features, demonstrating that mutated BRAF nuclei were significantly larger and rounder nuclei compared to BRAF WT nuclei. Lastly, we developed a model that combines clinical information, deep learning, and pathomics that improves the predictive performance for mutated BRAF to AUC=0.89. Not only does this provide additional insights on how BRAF mutations affect tumor structural characteristics, machine learning-based analysis of WSI has the potential to be integrated into higher order models for understanding tumor biology.