Faculty Bibliography

Introduction: Gene rearrangements play a critical role in the development of brain tumors. RNA next-generation sequencing (NGS) panels cover a limited number of genes, are rarely successful in FFPE samples > 5 years old, and cannot detect rearrangements between genes and non-coding regulatory regions. We evaluated whole genome Hi-C NGS for detection of gene fusions and cryptic rearrangements.
Method(s): DNA was extracted from FFPE scrolls of 55 glial and non-glial brain tumors and processed using Arima-HiC+ FFPE Sample protocol, consisting of chromatin fragmentation, labeling, and re-ligation, followed by DNA purification and library preparation for paired-end Illumina sequencing with an average of 10X genome coverage (100M PE reads per sample). Data were analyzed using the Arima-SV pipeline using Juicer and HiCUP, SV detection using HiC-Breakfinder, loop calling using Juicer Tools, and integrative data visualization using Juicebox. Overexpression of putative driver genes was confirmed by immunohistochemistry.
Result(s): Hi-C libraries were prepared and sequenced from FFPE tissues including samples that failed RNA NGS. Hi-C successfully detected gene-gene fusions including actionable EML4-NTRK3, ETV6-NTRK3, fusions. We detected rearrangements missed by RNA NGS (i.e., complex MYBL1 rearrangement) or between non-coding regions and known cancer genes (i.e. PDL1, PAX5, NRAS, TERT, KAT6A, GATA6, and ARID1B). Since Hi-C data captures 3D genome structural features such as chromatin loops and topological domains, datasets were of high quality and capable of detecting up to 13,000 chromatin loops per tumor.
Conclusion(s): Genome-wide Hi-C NGS is successful in detecting gene fusions and cryptic rearrangements between coding and non-coding regions in archival FFPE tissue including degraded samples. Because Hi-C data captures 3D genome structures, these datasets simultaneously inform gene regulatory mechanisms that may play a role in oncogenesis or tumor progression. Whole-genome Hi-C NGS expands our ability to detect actionable and novel drivers, and potentially new therapeutic targets in a single NGS workflow

DNA methylation array profiling is an emerging modality in molecular diagnostics, which is particularly useful for cases with unclear or conflicting histology. Lesions with mixed subependymoma and ependymoma components have been well documented in the literature but grading and management of these lesions is unclear as they may represent unique entities with an intermediate prognosis to pure subependymoma and ependymomas or a histologic variant within one of these established diagnostic entities. We identified 10 cases between the years 2015-2021 that were classified as subependymoma by DNA methylation array profiling despite the given histologic diagnoses of low-grade ependymal neoplasm (1), ependymoma (5), mixed ependymomasubependymoma (2), or pilocytic astrocytoma (2). Eight of the surgical specimens were from men while 2 were from women, with the range of age at time of surgery being 30 - 75 years old (median = 57.5 years old). The majority of these tumors arose in the posterior fossa (8), including all of those with an original histologic diagnosis of ependymoma and mixed ependymoma-subependymoma, while the remaining 2 cases (1 pilocytic astrocytoma and 1 low grade ependymal neoplasm) were supratentorial. Laterality of the lesions was distributed between left (2), right (3), and midline (4). All but 2 cases were from initial resections, with one of the 2 recurrent cases having a PFS of 6 years before re-resection (unknown for the second recurrent case). Short-term follow-up data was available for 3 patients ranging from 13 - 21 months during which time none had any evidence of progression. These cases illustrate the value of methylation array profiling in characterizing ependymomas and related tumors, particularly in the posterior fossa

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. We assembled a cohort of 185 tumors classified as MPE based on DNA methylation from pediatric, adolescent, and adult patients. 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. Tumors in the methylation class MPE were histologically diagnosed as WHO grade I (59%), WHO grade II (37%), or WHO grade III tumors (4%). 75/77 analyzed tumors expressed HOXB13, which is a diagnostic feature not detected in other spinal ependymal tumors. Based on DNA methylation, our series split into two subtypes. MPE-A occurred in younger patients (median age 27 vs. 45 years, p=7.3e-05). They were enriched with WHO grade I tumors and associated with papillary morphology and MGMT promoter hypermethylation (all p<0.001). MPE-B included most tumors initially diagnosed as WHO grade II and cases with tanycytic morphology. 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. 15/30 MPE-A could not be totally resected compared to 1/58 MPE-B (p=6.3e-08), and progressionfree survival was significantly better for MPE-B (p=3.4e-06, 10-year relapse rate 33% vs. 85%). 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

We analyzed the contributions of structural variants (SVs) to gliomagenesis across 179 pediatric high-grade gliomas (pHGGs). The most recurrent SVs targeted MYC isoforms and receptor tyrosine kinases (RTKs), including an SV amplifying a MYC enhancer in 12% of diffuse midline gliomas (DMG), indicating an underappreciated role for MYC in pHGG. SV signature analysis revealed that tumors with simple signatures were TP53 wild type (TP53^WT) but showed alterations in TP53 pathway members PPM1D and MDM4. Complex signatures were associated with direct aberrations in TP53, CDKN2A and RB1 early in tumor evolution and with later-occurring extrachromosomal amplicons. All pHGGs exhibited at least one simple-SV signature, but complex-SV signatures were primarily restricted to subsets of H3.3^K27M DMGs and hemispheric pHGGs. Importantly, DMGs with complex-SV signatures were associated with shorter overall survival independent of histone mutation and TP53 status. These data provide insight into the impact of SVs on gliomagenesis and the mechanisms that shape them.

PURPOSE/OBJECTIVE:Lung adenocarcinoma (LUAD) is a clinically heterogenous disease, which is highlighted by the unpredictable recurrence in low-stage tumors and highly variable responses observed in patients treated with immunotherapies, which cannot be explained by mutational profiles. DNA methylation-based classification and understanding of microenviromental heterogeneity may allow stratification into clinically relevant molecular subtypes of LUADs. EXPERIMENTAL DESIGN/METHODS:We characterize the genome-wide DNA methylation landscape of 88 resected LUAD tumors. Exome sequencing focusing on a panel of cancer-related genes was used to genotype these adenocarcinoma samples. Bioinformatic and statistical tools, the immune cell composition, DNA methylation age (DNAm age), and DNA methylation clustering were used to identify clinically relevant subgroups. RESULTS:Deconvolution of DNA methylation data identified immunologically hot and cold subsets of lung adenocarcinomas. Additionally, concurrent factors were analyzed that could affect the immune microenvironment, such as smoking history, ethnicity, or presence of KRAS or TP53 mutations. When the DNAm age was calculated, a lower DNAm age was correlated with the presence of a set of oncogenic drivers, poor overall survival, and specific immune cell populations. Unsupervised DNA methylation clustering identified 6 molecular subgroups of LUAD tumors with distinct clinical and microenvironmental characteristics. CONCLUSIONS:Our results demonstrate that DNA methylation signatures can stratify lung adenocarcinoma into clinically relevant subtypes, and thus such classification of LUAD at the time of resection may lead to better methods in predicting tumor recurrence and therapy responses.

Paired-like homeobox 2b (PHOX2B) is an established immunomarker for peripheral neuroblastoma and autonomic nervous system cells. We aimed to evaluate the utility of PHOX2B immunostaining in central nervous system (CNS) tumors with embryonal morphology. Fifty-one tumors were stained with PHOX2B and submitted for whole slide image analysis: 35 CNS tumors with embryonal morphology (31 CNS embryonal tumors and four gliomas); and 16 peripheral neuroblastomas were included for comparison. Diffuse nuclear immunopositivity was observed in all (16/16) neuroblastomas (primary and metastatic). Among CNS embryonal tumors, focal immunoreactivity for PHOX2B was observed in most (5/7) embryonal tumors with multilayered rosettes (ETMR) and a single high-grade neuroepithelial tumor (HGNET) with PLAGL2 amplification; the remaining 27 CNS tumors were essentially immunonegative (<0.05% positive). Among ETMR, PHOX2B expression was observed in a small overall proportion (0.04%-4.94%) of neoplastic cells but focally reached up to 39% in 1 mm 'hot spot' areas. In the PLAGL2-amplified case, 0.09% of the total neoplastic population was immunoreactive, with 0.53% in the 'hot spot' area. Care should be taken in interpreting PHOX2B immunopositivity in a differential diagnosis that includes metastatic neuroblastoma and CNS tumors; focal or patchy expression should not be considered definitively diagnostic of metastatic peripheral neuroblastoma.

Molecular classification has improved diagnosis and treatment for patients with malignant gliomas. However, classification has relied on individual assays that are both costly and slow, leading to frequent delays in treatment. Here, we propose the use of DNA methylation, as an emerging clinical diagnostic platform, to classify gliomas based on major genomic alterations and provide insight into subtype characteristics. We show that using machine learning models, DNA methylation signatures can accurately predict somatic alterations and show improvement over existing classifiers. The established Unified Diagnostic Pipeline (UniD) we develop is rapid and cost-effective for genomic alterations and gene expression subtypes diagnostic at early clinical phase and improves over individual assays currently in clinical use. The significant relationship between genetic alteration and epigenetic signature indicates broad applicability of our approach to other malignancies.