Kollmeyer, Thomas M.

Abstract Telomere length variation is a hallmark of genomic instability, however, its role in shaping the structural variant (SV) landscape of diffuse astrocytoma remains poorly understood. In this study, we employed Oxford Nanopore long-read sequencing to investigate the association between SVs and chromosome arm-specific telomere length in a clinical patient cohort consisting of n = 20 individuals diagnosed with an IDH mutant astrocytoma. Oxford Nanopore long-read sequencing was performed to a depth of ~20-40× coverage. SVs were identified using Sniffles, copy number alterations were assessed using DNAcopy and chromosome arm-specific telomere length was estimated with Telogator. Statistical analyses were conducted to assess associations between telomere dynamics, structural variation, and copy number changes. Astrocytoma genomes exhibited a broad spectrum of structural variants, including deletions and insertions, with significant enrichment in the short arms of acrocentric chromosomes. A subset of samples demonstrated markedly shorter telomeres relative to the overall cohort, accompanied by an increased burden of structural variants, particularly duplications and inversions, suggesting enhanced genomic instability. Notably, a prominent accumulation of deletion and insertion SVs was observed near chromosome ends and centromeres, highlighting the (sub)telomeric and peri(centromeric) regions as SV hotspots. Statistical modeling revealed a significant association between telomere length and arm-specific variation, including telomeric deletions, inversions, and copy number losses. In summary, our findings reveal a previously underappreciated link between SVs and telomere length in astrocytomas. Genomic rearrangements caused by telomere shortening may represent critical drivers of genome instability and could serve as novel biomarkers for disease progression.

Abstract BACKGROUND IDH-mutant gliomas are diffusely infiltrating, slow-growing brain tumors that inevitably undergo malignant progression to grade IV astrocytoma - a highly aggressive and uniformly fatal disease with dismal prognosis. Despite extensive research, the molecular mechanisms underlying this transformation remain poorly understood. While alterations in canonical driver genes such as CDKN2A, PTEN, and PDGFRA have been implicated, the broader landscape of genetic drivers involved in IDH-mutant glioma progression is largely uncharacterized. In contrast to many other malignancies, IDH-mutant gliomas acquire relatively few somatic point mutations during progression - typically in the range of 30 to 40 - suggesting that alternative mechanisms may be driving tumor evolution. Notably, these tumors exhibit widespread chromosomal instability, resulting in a heterogeneous and highly prevalent pattern of copy number alterations (CNAs). This observation raises the possibility that gene dosage changes due to CNAs play a critical role in tumor progression. MATERIAL AND METHODS To investigate this hypothesis, we performed integrative analysis of IDH-mutant glioma cohorts from the Mayo Clinic and TCGA datasets (~1,500 patients), identifying twelve highly recurrent CNAs significantly associated with patient outcomes. We refer to these as malignant IDH-associated CNAs (mIDH CNAs). We hypothesized that these recurrent CNAs harbor driver genes that confer selective growth advantages, thereby contributing to their recurrence and prognostic relevance. To systematically assess the functional impact of genes within these regions, we developed a novel in vivo CRISPR-based screening platform, termed CRISPR-Knock Out and Activation Linked Assay (CRISPR-KOALA). This approach utilizes stereotaxic delivery of lentiviral gRNA libraries - targeting mouse orthologs of ~1,800 genes within recurrently deleted regions and ~1,200 genes within amplified regions - into the brains of a genetically engineered mouse model of IDH-mutant glioma (rs557mut;IDHmut;P53KO;AtrxKO;Cas9GFP: Yanchus et. al Science 2022). RESULTS Mice injected with mIDH CNA-targeting libraries developed tumors significantly more rapidly than control cohorts. Deconvolution of the CRISPR-KOALA screen identified several putative driver genes, including regulators of the NOTCH signaling pathway, lipid metabolism modulators such as THEM6, and the NMDA receptor modulator GRINA - implicating dysregulation of metabolism, immune response, and glutamatergic signaling in the malignant transformation of IDH-mutant gliomas. CONCLUSION Together, these findings provide critical insights into the genetic mechanisms driving the progression of IDH-mutant gliomas and identify new candidate genes and pathways for potential therapeutic targeting.

Background: Tumefactive demyelination (TD) is a rare variant of multiple sclerosis (MS) characterized by tumor-like lesions that often require aggressive management. Genome-wide association studies (GWAS) identified variants associated with MS; similar analyses in TD are lacking. Objective: A GWAS was performed to identify variants associated with TD. Methods: The case–control study included 142 TD cases and 293 controls. TD patients were required to have a demyelinating event and magnetic resonance imaging (MRI) showing one or more lesions. Controls were patients without a neurologic or systemic inflammatory disease or cancer. Logistic regression was used to compare cases versus controls for each variant; age, sex, and principal components were included as covariates. A p -value threshold of 5 × 10 −8 was GWAS significant and 5 × 10 −6 nominally significant. A polygenic risk score (PRS) was compared across TD and controls. Results: Variants on chromosome 14 (rs117797734, p = 2.06 × 10 −11 , odds ratio (OR) = 13.14) and chromosome 6 (most significant rs6936540, p = 5.5 × 10 −7 , OR = 2.61) near DCBLD1 were significant. Seven non-MHC and two MHC variants associated with MS were associated with TD. The PRS was significantly higher in TD versus controls. Conclusion: We identified novel regions associated with TD, demonstrating the importance of performing GWAS in homogeneous subtypes of MS. Further validation and functional experiments are necessary.

Abstract Background The CCDC26 germline variant rs55705857 is causal for development of IDH mutant (IDHmut) adult glioma. However, ~60% of IDHmut patients do not carry the rs55705857 risk allele. We aimed to identify variants associated with developing IDHmut glioma among patients that do not have the rs55705857 risk allele and to further understand development of IDHwt glioma. Methods We used three datasets that included 1216 IDHmut and 1442 IDHwt glioma patients and a case–case design to perform genome-wide association (GWAS) analyses comparing IDHmut versus IDHwt glioma. Analyses were performed overall and stratified by rs55705857 genotype and sex. Multivariable logistic regression and regression trees were used to develop models to predict IDH status using germline variants, age, and contrast enhancement on MRI. Results Three regions were identified comparing IDHmut versus IDHwt: rs55705857 (meta P-value [P] = 1.35 × 10−43), PHLDB1 (rs7125115, P = 3.46 × 10−17), and D2HGDH (rs71430382, P = 2.43 × 10−12). When analyzing only patients that do not have the rs55705857 risk allele, PHLDB1 (rs7125115, P = 1.73 × 10−13) and D2HGDH (rs71430382, P = 8.86 × 10−10) were identified. Among patients who have the rs55705857 risk allele, four variants between ROBO1 and ROBO2 (rs4680975, P = 4.65 × 10−7) increased the likelihood of having an IDHwt tumor. Tumor expression of ROBO1 was associated with rs4680975 genotype in IDHwt patients that have the rs55705857 risk allele (P = 0.034). Multivariable logistic analysis demonstrated that rs55705857, rs71430382 (D2HGDH), and age predicted IDH mutation status. Conclusions To understand the development of adult glioma, we demonstrate that D2HGDH and PHLDB1 should be prioritized for functional studies in IDHmut tumors. The ROBO1 region warrants further investigation in IDHwt tumors.

Abstract Low-grade gliomas (LGG) are slowly growing brain cancers that ultimately undergo a malignant progression to lethal secondary glioblastomas with a dismal prognosis. Despite intensive research, very little progress has been made in our understanding of this transformation. Hence, outside of known alternations in well-characterized driver genes CDKN2A, PTEN and PDGFRA, genetic drivers that underlie LGG transformation are largely unknown. Contrary to other types of cancer, LGGs exhibit very few driver mutations (30-40 mutations) as they progress. Interestingly, LGGs also exhibit a highly prevalent and heterogenous pattern of copy-number-alterations (CNAs) suggesting that they might be driven by changes in gene dosages that result from widespread chromosomal instability observed within these tumors. Through analysis of the human Mayo Clinic and TCGA LGG datasets (~1500 patients), we have identified eleven highly recurrent CNAs affecting LGG patients, many of which correlate with patient survival. We hypothesized that these CNAs contain driver genes that confer a growth advantage resulting in the highly recurrent CNA pattern and prognostic associations. To investigate the tumor suppressive potential of the ~1800 genes located in recurrently deleted regions and the oncogenic potential of the ~1200 genes located in recurrently amplified regions, our lab developed a novel in vivo ‘CRISPR-Knock Out and Activation Linked Assay’ (CRISPR-KOALA). Stereotaxic delivery of arm-level CNA lentiviral gRNA libraries targeting the mouse orthologs of genes in CNAs into the brain of our animal model (rs557mut;IDHmut;P53KO;AtrxKO;Cas9GFP: Yanchus et. al Science 2022) resulted in rapid formation of tumors compared to animals injected with control libraries. Deconvoluting the screen revealed enrichment of immune modulators such as Them6 as well as a modulator of NMDA receptors (Grina) implicating immune system and glutamate dysregulation in LGG malignant transformation. Taken together, our study provides insights into mechanisms underlying LGG progression which may serve to inform intervention strategies for this lethal disease.

Abstract Acquired tumor alterations are part of the WHO diagnosis of glioma. However, germline predisposition to these alterations is unknown. GWAS in European and Asian populations identified germline variants in 30 regions, many in or near genes that also acquire alterations, suggesting that interactions between inherited and acquired genetics are involved in glioma development. To evaluate such interactions, we used 3686 glioma cases and 1889 controls. A case-control design and multinomial logistic regression were used to identify germline variants associated with development of TERT-promoter mutant gliomas, generating two interesting observations. First, TERT germline variants were associated with predisposition to IDHwt glioma that have a TERT promoter mutation, but not with IDH-mutant codeleted gliomas that have a TERT promoter mutation. Second, two novel regions were observed in this more homogeneous subgroup of TERT mutated tumors: variants in TXNDC11 (OR=2.66, p=5.7x10-8) and RUVBL2 (OR=3.1, p=5.8x10-8). High tumor expression of TXNDC11 is associated with poor prognosis in glioma and RUVBL2 interacts with TERT to affect telomerase activity. Similar analyses are being performed on additional clinically relevant acquired alterations. In parallel, case-case GWAS analyses were performed to evaluate germline variants associated with other tumor alterations. Comparing IDH-mutant versus IDHwt stratified by rs55705857 genotype, the most significant variants were PHLDB1 (p=1.2x10-13) and D2HGDH (p=2.1x10-10), highlighting the importance of these two variants independent of CCDC26 for the development of IDH-mutant glioma. As a parallel approach, we utilized recursive partitioning and regression trees (rpart) to classify IDH mutation status using the 30 known variants. The top four branches of the regression tree included CCDC26, D2HGDH, PHLDB1, and MAML2, as well as two Asian variants (RAB27A, CYP4F12). This highlights the importance of utilizing variants discovered in non-European populations in analyses. Overall, understanding these interactions are necessary to develop therapeutics, stratify clinical trials, and to develop relevant tumor models.

Abstract Histologic features, presence of CDKN2A/B homozygous deletion and methylation subtype are strong determinants of outcome and of therapy in IDH-mutant gliomas. We were interested in learning if there are additional markers of outcome in these tumors. To address this question, we assembled a cohort of 963 IDH-mutant gliomas with overall survival data (median follow-up alive 2.7 years, median follow-up deceased 3.9 years). Array-based copy number data, targeted NGS, germline genotyping data and Illumina EPIC array methylation data were available on 872, 226, 542, and 401 patients, respectively. Morphologic type (oligodendroglioma versus astrocytoma, grade (2/3 vs 4) and methylation subtype (A_IDH_LG vs A_IDH_HG) were all significantly associated with survival (p <0.001). There was no significant difference in survival between grade 2 vs 3 IDH-mutant astrocytomas (p=0.53 adjusting for age and sex). However, subjects with the risk allele (G) for rs55705857 had a better overall survival than subjects with the non-risk allele (A) in IDH-mutant oligodendroglioma (AG/GG vs AA, median overall survival 21.6 vs 15.7 years, p=0.048 after adjusting for grade, age, and sex). Conversely, rs55705857 was associated with a markedly worse survival in grade 2 IDH-mutant astrocytomas (AG/GG vs AA, median overall survival 8.6 vs 19.6 years, p=0.007 after adjusting for age and sex). Despite similar sample sizes and number of deaths, there was no significant difference in overall survival by rs55705857 genotype in grade 3 or 4 IDH-mutant astrocytomas. Upon further functional analyses, TLX1 and GALNT17 were found to be differentially methylated in IDH1-mutant astrocytomas and oligodendrogliomas, respectively, when stratified by rs55705857 risk allele. This is the first observation of a germline characteristic (e.g. rs55705857) predicting survival in low-grade IDH-mutant astrocytoma. While this observation requires validation, it suggests that germline genotype may be practice changing and inform results of trials such as CATNON and CODEL.

Abstract Background The optimal diagnosis and management of patients with brain tumors currently uses the 2021 WHO integrated diagnosis of histomorphologic and molecular features. However, neuro-oncology practice in resource-limited settings usually relies solely on histomorphology. This study aimed to classify glioma cases diagnosed in the Department of Anatomic and Molecular Pathology, Lagos University Teaching Hospital, using the 2021 WHO CNS tumor classification. Methods Fifty-six brain tumors from 55 patients diagnosed with glioma between 2013 and 2021 were reevaluated for morphologic diagnosis. Molecular features were determined from formalin-fixed paraffin-embedded (FFPE) tissue using immunohistochemistry (IHC) for IDH1-R132H, ATRX, BRAF-V600E, p53, Ki67, and H3-K27M, OncoScan chromosomal microarray for copy number, targeted next generation sequencing for mutation and fusion and methylation array profiling. Results Of 55 central nervous system tumors, 3 were excluded from histomorphologic reevaluation for not being of glial or neuroepithelial origin. Of the remaining 52 patients, the median age was 20.5 years (range: 1 to 60 years), 38(73%) were males and 14(27%) were females. Seventy-one percent of the gliomas evaluated provided adequate DNA from archival FFPE tissue blocks. After applying the 2021 WHO diagnostic criteria the initial morphologic diagnosis changed for 35% (18/52) of cases. Diagnoses of 5 (9.6%) gliomas were upgraded, and 7 (14%) were downgraded. Conclusions This study shows that the incorporation of molecular testing can considerably improve brain tumor diagnoses in Nigeria. Furthermore, this study highlights the diagnostic challenges in resource-limited settings and what is at stake in the global disparities of brain tumor diagnosis.

Abstract Clinical management of IDH wildtype glioblastoma (GBM) and tumefactive multiple sclerosis (tMS) is drastically different. GBM requires maximal safe resection followed by chemoradiation, while tMS outcome is worsened by surgery and radiotherapy. Misdiagnosis of brain lesions may expose patients to unnecessary anxiety, surgery, or radiotherapy. Noninvasive methods to help with accurate diagnosis of tumor and non-tumor etiologies are needed. tMS subjects were identified by the Mayo Clinic Center for Multiple Sclerosis and Autoimmune Neurology (CMSAN). GBM subjects were matched to tMS by age at diagnosis, index MRI date, and sex. Inclusion criteria included one cm minimal lesion size and pre-operative post-contrast T1 and T2 images available for analysis. To identify potential experimental design bias, MRI technology and clinical characteristics were compared across groups. A 3D-DenseNet121 was used to develop a classification model using prespecified parameters: 600 epochs, batch size 16, learning rate 10-3, cross entropy loss, and AdamW optimizer. The stopping rule was defined as three sequential epoch cross entropy loss < 0.02. Five-fold cross validation (CV) was utilized to obtain an unbiased estimate of predictive performance (overfitting) and sensitivity of the 3D-DenseNet121 algorithm was evaluated. 224 subjects (114 GBM, 110 tMS) were analyzed: 40% male, median age 46. After one round of CV, the average unbiased estimate of sensitivity and specificity for predicting GBM from the final classification model was 0.86 and 0.73, respectively. To assess the reproducibility of the deep learning algorithm (3D-DenseNet121), the exact same CV folds were rerun resulting in average sensitivity and specificity of 0.82 and 0.75, respectively. Obtaining unbiased estimates of model performance using internal validation is critical before evaluating a final model on an external dataset. Understanding sensitivity of a deep learning algorithm is important for model validity. Differentiation of GBM and tMS appears reasonable with MRI; external validation is in progress.