Lachance, Daniel Honore

Establishing causal links between inherited polymorphisms and cancer risk is challenging. Here, we focus on the single-nucleotide polymorphism rs55705857, which confers a sixfold greater risk of isocitrate dehydrogenase ( IDH) –mutant low-grade glioma (LGG). We reveal that rs55705857 itself is the causal variant and is associated with molecular pathways that drive LGG. Mechanistically, we show that rs55705857 resides within a brain-specific enhancer, where the risk allele disrupts OCT2/4 binding, allowing increased interaction with the Myc promoter and increased Myc expression. Mutating the orthologous mouse rs55705857 locus accelerated tumor development in an Idh1 R132H -driven LGG mouse model from 472 to 172 days and increased penetrance from 30% to 75%. Our work reveals mechanisms of the heritable predisposition to lethal glioma in ~40% of LGG patients.

Abstract Knowledge about inherited and acquired genetics of adult diffuse glioma has expanded significantly over the past decade. Genomewide association studies (GWAS) stratified by histologic subtype identified six germline variants that were associated specifically with glioblastoma (GBM) and 12 that were associated with lower grade glioma. A GWAS performed using the 2016 WHO criteria, stratifying patients by IDH mutation and 1p/19q codeletion (as well as TERT promoter mutation), discovered that many of the known variants are associated with specific WHO glioma subtypes. In addition, the GWAS stratified by molecular group identified two additional novel regions: variants in D2HGDH that were associated with tumors that had an IDH mutation and a variant near FAM20C that was associated with tumors that had both IDH mutation and 1p/19q codeletion. The results of these germline associations have been used to calculate polygenic risk scores, from which to estimate relative and absolute risk of overall glioma and risk of specific glioma subtypes. We will review the concept of polygenic risk models and their potential clinical utility, as well as discuss the published adult diffuse glioma polygenic risk models. To date, these prior genetic studies have been done on European populations. Using the published glioma polygenic risk model, we show that the genetic associations published to date do not generalize across genetic ancestries, demonstrating that genetic studies need to be done on more diverse populations.

Abstract Stroke-Like Migraine Attacks after Radiation Therapy (SMART) is a descriptive clinical entity consisting of transient hemispheric dysfunction. We were interested in pragmatic management patterns for patients with Recurrent Stroke-Like Episodes (R-SLE) of transient negative neurologic symptoms after cranial radiotherapy (RT) to define optimal management strategy and assess long-term outcomes. We conducted a retrospective review of all patients with recurrent negative neurologic symptoms after cranial RT who were treated at Mayo Clinic (Rochester), with follow-up extending until February 2021. Descriptive statistics and Chi-Square analysis was performed to assess for differences between patients with clinical cessation of symptoms, death, progressive encephalopathy and therapeutic class, patient and primary treatment characteristics (i.e. whole brain RT). We identified 27 patients with R-SLE after RT. 25 patients were included in analyses. Median age at diagnosis was 28.7 years (3.0-65.8 years, SD: 15.0 years). Median time from RT to symptom onset was 14.6 years (3.3-30.5 years, SD: 8.9 years). The most common presentations included hemiparesis (55.6%), hemisensory loss (22.2%), transient visual field loss (33.3%), encephalopathy (18.5%), and aphasia (22.2%). Antiseizure medications were most used for management of R-SLE (92%) followed by anti-platelets (68%), verapamil (52%), statins (48%), glucocorticoids (24%), antivirals (20%), and ACE inhibitors/angiotensin receptor blockers (16%). Beta blockers were not used. Verapamil use was frequently associated with clinical cessation of recurrent events with cessation being achieved in 64.7% of patients on verapamil versus 35.3% not on verapamil (p=0.0638). Other medical interventions did not approach clinical or statistical significance. Progressive encephalopathy was more common in patients without clinical cessation (87.5% vs. 23.5%, p=0.0026). All patients who died at last follow-up had progressive encephalopathy. We found cessation of recurrent negative neurologic symptoms after cranial RT in most patients. Verapamil use was associated with clinical cessation. Progressive encephalopathy was more common in patients without clinical cessation of recurrent events.

Abstract Abnormalities on brain MRI may be identified in ~15% of individuals aged 50-66. It can be difficult to discriminate glioma, CNS lymphoma, Inflammatory Demyelinating Disease (CNSIDD) and solitary metastasis, and misdiagnosis may expose patients to unnecessary anxiety, surgery, or radiotherapy. CNS lymphoma requires only biopsy, solitary metastasis may be resected and radiated or radiated empirically, and high-grade glioma requires maximal safe resection followed by chemoradiation. CNSIDD should only be biopsied when diagnostic uncertainty requires it, and resection and radiotherapy are unnecessary, introducing unwarranted morbidity. Polygenic risk models can identify patients at the highest risk of developing glioma; we hypothesized that these polygenic models could help with differential diagnosis of indeterminate brain lesions. We also hypothesized that race would be an important contributing factor in the models. In the initial discovery and validation European (EUR) cohorts the mean probability of glioma for IDHmut non-codeleted glioma was 0.55 and 0.52, respectively, and in healthy controls was 0.19 and 0.21, respectively. To further evaluate sensitivity, we analyzed additional genotype data from 867 gliomas (764 EUR, 54 AFR, 24 AMR, 15 EAS, 10 SAS) from The Cancer Genome Atlas (TCGA). Across 764 EUR IDHmut non-codeleted glioma, the mean probability 0.53, whereas across 54 AFR and 24 AMR the mean was 0.22 and 0.32, respectively. To evaluate specificity, we analyzed 3200 TCGA patients with primary tumor types that commonly metastasize to the brain (2676 EUR, 365 AFR, 46 AMR, 95 EAS, 18 SAS), and 236 AFR healthy controls. For patients with non-CNS primary tumors, the mean probability ranged from 0.09-0.18 for EUR and 0.04-0.07 for AFR. For AFR healthy controls, the mean was 0.05. Overall, race is a significant factor for polygenic risk models. Further work entails evaluating polygenic risk models in IDHwt glioma, CNS lymphoma and CNSIDD cohorts as well as in additional races.

Abstract BACKGROUND Determination of the causation of germline single nucleotide polymorphisms (SNPs) located in non-coding regions of the genome is challenging. The genomic region of 8q24 has been identified as important in many kinds of cancer, linked to a topologically associated domain (TAD) encompassing MYC; this TAD contains a GWAS SNP (rs55705857) associated with IDH-mutant glioma. METHODS Germline genotyping data from 622 IDH-mutant glioma and 668 controls were used to fine map the rs55705857 locus by detailed haplotype analysis. Chromatin immunoprecipitation sequencing (ChIP-seq) of histone markers H3K4me1, H3K4me3, H3K27ac and H3K36me3 was performed on normal brain samples (n=8) and human glioma samples (n=11 IDH-wt and 52 IDH-mut). RNAseq from 9 normal and 83 brain tumors (n=26 IDH-wt and 55 IDH-mut) were used to assess differential gene expression. RESULTS Fine-mapping identified rs55705857 SNP as the most likely causative allele (OR=8.69; p<0.001) within 8q24 for the development of IDH-mutant glioma. At rs55705857, both H3K27ac and H3K4me1 in IDH-mutant vs IDH-wt tumors were increased 3.05- and 1.58-fold, respectively (DiffBind; p=5.81×10-7 and p=2.31×10-3). ChromHMM analysis of the marks indicated that promoter and enhancer functions were significantly increased, and the activity broadened at rs55705857 in IDH-mut gliomas compared to IDH-wt tumors and normal brain samples. This enhancement correlated with significant increased MYC expression in IDH-mut gliomas (p=3.1×10-13), as well as alterations of Myc signaling targets. Publicly available ATACseq, ChIPseq and long-range DNA interaction data demonstrated that the rs55705857 locus is open and interacts with the MYC promoter. CONCLUSIONS Fine-mapping of the 8q24 locus provided strong evidence that rs55705857 is the causative 8q24 locus associated with IDH-mut glioma. Functional experiments suggest that IDH mutation facilitates rs55705857 interaction with MYC to alter downstream MYC targets.

Abstract Establishing causal links between genetic polymorphisms and increased heritable risk of developing brain cancer is a major challenge. The non-coding single nucleotide polymorphism rs55705857 (A >G) is associated with a ~6-fold increased risk to develop IDH-mutant low-grade glioma (LGG). The rs55705857 G allele has a minor allele frequency of only ~5% in the general population but is found in ~40% of patients with IDH-mutant LGG and patients carrying risk-alleles are diagnosed on average 7-12 years earlier than those carrying non-risk A alleles. This makes rs55705857 one of the highest reported genetic associations with cancer, comparable with inherited BRCA1 gene mutations and the risk of developing breast cancer or other familial glioma genes such as NF1/2, CDKN2A or p53. To generate a LGG mouse model, we combined clonal activation of IDH1R132H with mutations of Trp53 and Atrx, which resulted in the development of LGG-like brain tumors in 25% of mice. Mutating the highly conserved, orthologous mouse rs55705857 locus to mimic the human risk allele dramatically accelerated tumor development from 463 to 172 days and increased penetrance to 75%. The resulting tumors exhibit elevated R-2-hydroxyglutarate levels, well-differentiated fibrillary neoplastic histology and metabolic rewiring, recapitulating histopathological and molecular hallmarks of human LGG. Mechanistically, we show that the rs55705857 locus resides within a brain-specific enhancer, which shows enhanced activity in IDH-mutant tumors. In addition, we found that the risk allele disrupts OCT2/4 binding, allowing increased interaction with the Myc promoter and increased Myc expression. The hyperactive chromatin status combined with the tissue specificity of this enhancer explains the cooperativity between mutant IDH and rs55705857 and why rs55705857 is associated specifically with IDH-mutant glioma, but not other cancers. Overall, we generated new LGG mouse models, which provide insights into the pathophysiology of this deadly disease and shed light into the heritable predisposition to LGG development.

AbstractNeurolymphomatosis (NL) is a rare manifestation of lymphoma, with limited evidence for optimal management. The largest patient series, 50 cases of lymphoma and leukemia, was published in 2010 with limited rituximab exposure. This study aims to evaluate the clinical presentation, diagnostic testing, and outcomes of NL in the rituximab era. Forty biopsy-proven cases of NL, in association with non-Hodgkin lymphoma (NHL), at the Mayo Clinic were retrospectively evaluated. B-cell NHL was associated with 97% of NL cases, of which diffuse large B-cell lymphoma (DLBCL) was the most common (68%). Primary NL, defined as neural involvement present at the time of diagnosis of lymphoma, was noted in 52% cases. Seventy percent of patients presented with sensorimotor weakness and neuropathic pain. Magnetic resonance imaging (MRI) was positive in 100% patients. Overall survival (OS) was significantly better for primary NL and NL associated with indolent lymphomas. Relapses were seen in 60% (24/40) of patients; 75% involved the peripheral or central nervous system at relapse. The use of rituximab in the frontline setting significantly impacted progression-free survival (PFS). Transplant consolidation was noted to be associated with improved OS. This study adds to the available literature on NL in the rituximab era. The overall outcomes have improved in recent years. In our experience, MRI and positron emission tomography/computed tomography may be required for accurate assessment of the extent of disease involvement and identification of an optimal biopsy site. The use of rituximab was associated with improvement in PFS, and autologous stem cell transplant was associated with OS.

Abstract Introduction Tumefactive multiple sclerosis (MS) and glioblastoma (GBM) can be difficult to differentiate. Clinical presentations are nonspecific and may occur in patients with both diseases. Magnetic resonance imaging (MRI) scans are routinely used to generate an initial differential diagnosis. Although imaging features can help distinguish these entities, there are no pathognomonic signs, neither on traditional nor advanced MRI. Moreover, the ability to differentiate these entities depends on the radiologists’ experience. Ensuring the correct diagnosis has important therapeutic and prognostic implications, as the management of these diseases is very different. To date, a brain biopsy often represents the only option to obtain a diagnosis, but it may be inconclusive and could add unnecessary morbidity. We hypothesize that a machine-learning tool based on routinely performed MRI scans could guarantee a more reliable, fast, and reproducible diagnosis. Methods Only patients with a histological diagnosis were included in the study. Patients that underwent any form of radiation therapy before the surgery were excluded to avoid confounding factors due to treatment-related effects. A total of 99 MS and 257 GBMs were included, and preoperative T2-weighted images were collected. For each patient, the lesions' segmentation was performed, including both the contrast-enhancing and non-enhancing areas of the lesions. The segmentation masks were used to extract radiomic features from the T2-weighted sequences. All MRI images were standardized to the same mean and standard deviation before feature extraction and resampled to a voxel size of 1.0 × 1.0 × 1.0 mm. Features extraction was performed using Pyradiomics (v. 3.0.1) and included first order, shape, and texture features, totaling 100 features. The classifier consisted of a support vector machine (SVM) model. Patients were randomly assigned to the training and test sets with an 80-20% proportion for each class. To select the best hyperparameters for the SVM classifier, a grid-search analysis was performed on the training set using a nested cross-validation scheme (k=5). All experiments were performed using Python (v. 3.5) and the Scikit-learn library (v. 0.23.1). Results The mean accuracy and area under the ROC curve for the best set of hyperparameters on the validation set were 0.83 and 0.86, respectively. The weighted-average precision, recall, and F1 scores on the test set were 0.78, 0.76, and 0.77, respectively. Conclusions Radiomic features extracted from a routinely acquired MRI sequence showed promising results in differentiating tumefactive multiple sclerosis from glioblastoma, opening the possibility to obtain a reliable diagnosis without the need for a brain biopsy. Future experiments Future experiments will focus on using post-contrast T1-weighted images for the radiomic features extractions, testing multiple machine learning models and features selection algorithms, and including more diagnostic classes (i.e., metastasis, lymphomas). Citation Format: Gian Marco Conte, Jeanette E. Eckel-Passow, W. Oliver Tobin, Paul Decker, Daniel H. Lachance, Robert B. Jenkins, Bradley J. Erickson. Differentiation of tumefactive multiple sclerosis and glioblastoma using radiomics features extracted from magnetic resonance imaging and machine learning [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-023.

Abstract BACKGROUND Molecular parameters have been incorporated into the 2016 WHO Classification of CNS Tumors and subsequent cIMPACT-NOW updates to facilitate clinical management of glioma patients. However, there have been few reports of the overall clinical utility of comprehensive genetic testing in adult glioma patients. We report the results of sequencing and chromosomal microarray analysis of adult gliomas seen at the Mayo Clinic and through the Mayo Clinic Laboratories (MCL) reference practice. METHODS A consecutive series of 379 Mayo Clinic adult glioma patients were consented to receive targeted next generation sequencing and chromosomal microarray analysis, regardless of standard clinical ordering practices. Known diagnostic, prognostic, and predictive alterations were annotated for each case. These results were compared to the larger MCL reference practice of 2400 adult glioma samples that received one or both tests clinically. RESULTS Of the consecutive Mayo Clinic cases, 67% had alterations that fell into at least one of the diagnostic, prognostic, and/or predictive categories. Molecular testing generated diagnostic, prognostic, and predictive information in 44%, 34%, and 36% of cases, respectively. Diagnostically, molecular testing mainly aided in arriving at a final integrated diagnosis, and only a small number of cases (n= 5; 1%) had a change in diagnosis. The consecutive cases represent the typical distribution of adult glioma types (47% IDH-wildtype glioblastoma/astrocytoma, 22% IDH-mutant astrocytoma/glioblastoma, 13% IDH-mutant 1p/19q-codeleted oligodendroglioma, and 18% other tumor types). The MCL practice is enriched in tumors with atypical molecular patterns (37% IDH-wildtype glioblastoma/astrocytoma, 17% IDH-mutant astrocytoma/glioblastoma, 18% IDH-mutant 1p/19q-codeleted oligodendroglioma, and 29% other tumor types). CONCLUSIONS Molecular testing provides useful diagnostic, prognostic, and predictive information for the clinical management of adult glioma patients. While the current guidelines for ordering molecular testing are effective for diagnostic purposes, additional prognostic and predictive information can be gleaned from comprehensive molecular testing of adult gliomas.