Misha Movahed-Ezazi

Background: Pituitary neuroendocrine tumors (PitNETs) are the most common intracranial neuroendocrine tumors. PitNETs can be challenging to classify, and current recommendations include a large immunohistochemical panel to differentiate among 14 WHO-recognized categories. Methods: In this study, we analyzed clinical, immunohistochemical, and DNA methylation data of 118 PitNETs to develop a clinicomolecular approach to classifying PitNETs and identifying epigenetic classes. Results: CNS DNA methylation classifier has an excellent performance in recognizing PitNETs and distinguishing the 3 lineages when the calibrated score is ≥ 0.3. Unsupervised DNA methylation analysis separated PitNETs into 2 major clusters. The first was composed of silent gonadotrophs, which form a biologically distinct group of PitNETs characterized by clinical silencing, weak hormonal expression on immunohistochemistry, and simple copy number profile. The second major cluster was composed of corticotrophs and Pit1 lineage PitNETs, which could be further classified using DNA methylation into distinct subclusters that corresponded to clinically functioning and silent tumors and are consistent with transcription factor expression. Analysis of promoter methylation patterns correlated with lineage for corticotrophs and Pit1 lineage subtypes. However, the gonadotrophic genes did not show a distinct promoter methylation pattern in gonadotroph tumors compared to other lineages. Promoter of the NR5A1 gene, which encodes SF1, was hypermethylated across all PitNETs clinical and molecular subtypes including gonadotrophs with strong SF1 protein expression indicating alternative epigenetic regulation. Conclusion: Our findings suggest that classification of PitNETs may benefit from DNA methylation for clinicopathological stratification.

Standard techniques for detecting genomic rearrangements in formalin-fixed paraffin-embedded (FFPE) biopsies have important limitations. We performed FFPE-compatible Hi-C on 44 clinical biopsies comprising large B-cell lymphomas (n=18), plasma cell neoplasms (n=14), and other diverse lymphoid cancers, identifying consistent topological differences between malignant B cell and plasma cell states. Hi-C detected expected oncogene rearrangements at high concordance with fluorescence in-situ hybridization (FISH) and supported enhancer-hijacking in recurrent rearrangements of BCL2 , CCND1 , and MYC , plus unanticipated variants involving homologous loci. Hi-C identified unanticipated non-coding rearrangements involving PD-1 ligand genes and other loci of potential therapeutic relevance, distinguished between functionally divergent classes of BCL6 rearrangements, and provided topological information supporting the interpretation of atypical MYC rearrangements. In biopsies lacking MYC -activating rearrangements, Hi-C revealed differential interactions with functionally-validated disease-specific native MYC locus enhancers. FFPE-compatible Hi-C detects oncogene rearrangements and their topological consequences at genome-wide scale, finding clinically-relevant drivers that are missed by standard approaches.

In many cancers, specific subtypes are more prevalent in specific racial backgrounds. However, little is known about the racial distribution of specific molecular types of brain tumors. Public data repositories lack data on many brain tumor subtypes as well as diagnostic annotation using the current World Health Organization classification. A better understanding of the prevalence of brain tumors in different racial backgrounds may provide insight into tumor predisposition and development, and improve prevention. We retrospectively analyzed the racial distribution of 1709 primary brain tumors classified by their methylation profiles using clinically validated whole genome DNA methylation. Self-reported race was obtained from medical records. Our cohort included 82% White, 10% Black, and 8% Asian patients with 74% of patients reporting their race. There was a significant difference in the racial distribution of specific types of brain tumors. Blacks were overrepresented in pituitary adenomas (35%, P < .001), with the largest proportion of FSH/LH subtype. Whites were underrepresented at 47% of all pituitary adenoma patients (P < .001). Glioblastoma (GBM) IDH wild-type showed an enrichment of Whites, at 90% (P < .001), and a significantly smaller percentage of Blacks, at 3% (P < .001). Molecularly classified brain tumor groups and subgroups show different distributions among the three main racial backgrounds suggesting the contribution of race to brain tumor development.

Identifying gene fusions in tumor biopsies is critical for understanding disease etiology; however, clinical NGS panels often fail to yield clear genetic drivers. A key challenge is that RNA-seq does not perform well in FFPE tissue blocks due to RNA degradation and/or RNA panel design, and cannot detect breakpoints outside of the gene body, which are established clinical biomarkers with mechanistic significance and clinical utility in solid hematological cancers. We developed a novel DNA-based partner-agnostic approach for identifying fusions from FFPE tumors using 3D genomics based on Arima-HiC technology and NGS. We profiled 184 FFPE tumors across various tumor types. This cohort includes 33 tumors with known gene fusions detected by RNA panels for clinical concordance analysis. It also includes 151 FFPE tumors, including 62 CNS tumors, 59 gynecological sarcomas, and 22 solid heme tumors, with no detectable genetic drivers from prior DNA and RNA panel CLIA assays. For clinical concordance, Arima-HiC technology detected 33/33 fusions previously detected by RNA panels. For clinical validation and utility studies in our driver-negative cohort, Arima-HiC detected 1 or more fusions in 72% (109/151) of tumors. To attribute clinical significance, we compared the genes implicated in our fusion calls with NCCN and WHO guidelines, and OncoKB, and assigned which tumors had an FDA-approved therapeutic level biomarker (“Tier 1”), a biomarker targeted in an ongoing clinical trial (“Tier 2”), or a diagnostic/prognostic biomarker (“Tier 3”). Arima-HiC analysis found 33.8% (51/151) of tumors had Tier 1 biomarkers, 4.0% (6/151) had Tier 2 biomarkers, and a further 14.6% (22/151) had Tier 3 biomarkers, indicating an overall yield of clinically actionable biomarkers at 52.3%. Several cases with Tier 1-3 fusions underwent confirmatory immunohistochemistry testing for oncoprotein expression, and 91.6% (11/12) showed diffuse or focally positive staining. To highlight examples from prospectively analyzed cases from this cohort, we identified a novel PD-L1 rearrangement in a pediatric glioma tumor that was not detected by DNA or RNA panels, which we confirmed by PD-L1 IHC, and the patient was administered pembrolizumab after tumor recurrence and has since exhibited stable disease. We also identified a MYBL1 fusion in a glioma that spared the patient unnecessary chemotherapy post-resection. Our findings provide evidence of clinical concordance, validation, and utility, and underscore the need for 3D genome profiling to increase diagnostic yield by finding clinically actionable fusions in FFPE solid tumors. Citation Format: Anthony Schmitt, Kristin Sikkink, Kristyn Galbraith, Misha Movahed-Ezazi, George Jour, Matija Snuderl. Uncovering gene fusions with 3D genomics: from clinical validation to actionable insights for undiagnosable solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB291.