DNA Methylation-based Classification Research Articles

Rapid DNA methylation-based classification of pediatric brain tumors from ultrasonic aspirate specimens.

Although cavitating ultrasonic aspirators are commonly used in neurosurgical procedures, the suitability of ultrasonic aspirator-derived tumor material for diagnostic procedures is still controversial. Here, we explore the feasibility of using ultrasonic aspirator-resected tumor tissue to classify otherwise discarded sample material by fast DNA methylation-based analysis using low pass nanopore whole genome sequencing. Ultrasonic aspirator-derived specimens from pediatric patients undergoing brain tumor resection were subjected to low-pass nanopore whole genome sequencing. DNA methylation-based classification using a neural network classifier and copy number variation analysis were performed. Tumor purity was estimated from copy number profiles.Results were compared to microarray (EPIC)-based routine neuropathological histomorphological and molecular evaluation. 19 samples with confirmed neuropathological diagnosis were evaluated. All samples were successfully sequenced and passed quality control for further analysis. DNA and sequencing characteristics from ultrasonic aspirator-derived specimens were comparable to routinely processed tumor tissue. Classification of both methods was concordant regarding methylation class in 17/19 (89%) cases. Application of a platform-specific threshold for nanopore-based classification ensured a specificity of 100%, whereas sensitivity was 79%. Copy number variation profiles were generated for all cases and matched EPIC results in 18/19 (95%) samples, even allowing the identification of diagnostically or therapeutically relevant genomic alterations. Methylation-based classification of pediatric CNS tumors based on ultrasonic aspirator-reduced and otherwise discarded tissue is feasible using time- and cost-efficient nanopore sequencing.

Tumor- and circulating-free DNA methylation identifies clinically relevant small cell lung cancer subtypes

Small cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, but implementing subtyping in the clinic has remained challenging, particularly due to limited tissue availability. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that subtype-specific patterns of DNA methylation could be detected in tumor or blood from SCLC patients. Using genomic-wide reduced-representation bisulfite sequencing (RRBS) in two cohorts totaling 179 SCLC patients and using machine learning approaches, we report a highly accurate DNA methylation-based classifier (SCLC-DMC) that can distinguish SCLC subtypes. We further adjust the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC), we demonstrate that SCLC phenotypes can evolve during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and might guide precision SCLC therapy.

Unclassifiable CNS tumors in DNA methylation-based classification: clinical challenges and prognostic impact.

DNA methylation analysis has become a powerful tool in neuropathology. Although DNA methylation-based classification usually shows high accuracy, certain samples cannot be classified and remain clinically challenging. We aimed to gain insight into these cases from a clinical perspective. To address, central nervous system (CNS) tumors were subjected to DNA methylation profiling and classified according to their calibrated score using the DKFZ brain tumor classifier (V11.4) as "≥ 0.84" (score ≥ 0.84), "0.3-0.84" (score 0.3-0.84), or "< 0.3" (score < 0.3). Histopathology, patient characteristics, DNA input amount, and tumor purity were correlated. Clinical outcome parameters were time to treatment decision, progression-free, and overall survival. In 1481 patients, the classifier identified 69 (4.6%) tumors with an unreliable score as "< 0.3". Younger age (P < 0.01) and lower tumor purity (P < 0.01) compromised accurate classification. A clinical impact was demonstrated as unclassifiable cases ("< 0.3") had a longer time to treatment decision (P < 0.0001). In a subset of glioblastomas, these cases experienced an increased time to adjuvant treatment start (P < 0.001) and unfavorable survival (P < 0.025). Although DNA methylation profiling adds an important contribution to CNS tumor diagnostics, clinicians should be aware of a potentially longer time to treatment initiation, especially in malignant brain tumors.

Decoding the DNA methylome of central nervous system tumors: An emerging modality for integrated diagnosis.

The definitive diagnosis and classification of individual cancers are crucial for patient care and cancer research. To achieve a robust diagnosis of central nervous system (CNS) tumors, a genotype-phenotype integrated diagnostic approach was introduced in recent versions of the World Health Organization classification, followed by the incorporation of a genome-wide DNA methylome-based classification. Microarray-based platforms are widely used to obtain DNA methylome data, and the German Cancer Research Center (Deutsches Krebsforschungszentrum [DKFZ]) has a webtool for a DNA methylation-based classifier (DKFZ classifier). Integration of DNA methylome will further enhance the precision of CNS tumor classification, especially in diagnostically challenging cases. However, in the clinical application of DNA methylome-based classification, challenges related to data interpretation persist, in addition to technical caveats, regulations, and limited accessibility. Dimensionality reduction (DMR) can complement integrated diagnosis by visualizing a profile and comparing it with other known samples. Therefore, DNA methylome-based classification is a highly useful research tool for auxiliary analysis in challenging diagnostic and rare disease cases, and for establishing novel tumor concepts. Decoding the DNA methylome, especially by DMR in addition to DKFZ classifier, emphasizes the capability of grasping the fundamental biological principles that provide new perspectives on CNS tumors.

Refinement of prognostication for IDH-mutant astrocytomas using DNA methylation-based classification.

The 2021 World Health Organization (WHO) grading system of isocitrate dehydrogenase (IDH)-mutant astrocytomas relies on histological features and the presence of homozygous deletion of the cyclin-dependent kinase inhibitor 2A and 2B (CDKN2A/B). DNA methylation profiling has become highly relevant in the diagnosis of central nervous system (CNS) tumors including gliomas, and it has been incorporated into routine clinical diagnostics in some countries. In this study, we, therefore, examined the value of DNA methylation-based classification for prognostication of patients with IDH-mutant astrocytomas. We analyzed histopathological diagnoses, genome-wide DNA methylation array data, and chromosomal copy number alteration profiles from a cohort of 385 adult-type IDH-mutant astrocytomas, including a local cohort of 127 cases and 258 cases from public repositories. Prognosis based on WHO 2021 CNS criteria (histological grade and CDKN2A/B homozygous deletion status), other relevant chromosomal/gene alterations in IDH-mutant astrocytomas and DNA methylation-based subclassification according to the molecular neuropathology classifier were assessed. We demonstrate that DNA methylation-based classification of IDH-mutant astrocytomas can be used to predict outcome of the patients equally well as WHO 2021 CNS criteria. In addition, methylation-based subclassification enabled the identification of IDH-mutant astrocytoma patients with poor survival among patients with grade 3 tumors and patients with grade 4 tumors with a more favorable outcome. In conclusion, DNA methylation-based subclassification adds prognostic information for IDH-mutant astrocytomas that can further refine the current WHO 2021 grading scheme for these patients.

DNA Methylation-Based Classification of Hairy Cell Leukemia and Splenic B Cell Lymphoma

Improved classification of rare lymphoid neoplasms would be aided by a deeper understanding of their underlying molecular features and is important for diagnosis, prognosis and therapy. Tumor entities classified within the WHO category of splenic B cell lymphomas and leukemias often exhibit heterogenous, transecting features, and include hairy cell leukemia (HCL), splenic diffuse red pulp lymphoma (SDRPL), splenic marginal zone lymphoma (SMZL), and the newly described WHO entity, splenic B cell lymphoma/leukemia with prominent nucleoli (SBLPN); the latter including patients formerly classified as HCL-variant (HCL-V). Genome-wide epigenetic information provides a tumor cell fingerprint combining cell-of-origin and tumor-specific events. Here we used DNA methylation to perform an unbiased molecular subclassification and to explore novel biological aspects of these patients. Samples from patients with a pathological diagnosis of HCL, HCL-V, SDRPL and SMZL (made prior to the 5 th WHO revision and ICC classifications) were obtained from 19 institutions across 9 countries, totaling 367 patients. Cells were FACS-purified where necessary and DNA was analyzed by 450/850K Illumina DNA methylation arrays. Genetic mutations were assessed by whole-exome or targeted sequencing, IGHV-D-J sequences by Sanger sequencing, and copy number alterations (CNAs) by Illumina arrays. The 1000 most variable CpG methylation sites were used for k-means clustering. Recursive feature elimination/random forest algorithms were used to develop a classifier for DNA methylation-based subgroups with 98% accuracy. Unsupervised clustering of 197 patients diagnosed with HCL, HCL-V or SDRPL revealed 5 distinct DNA methylation (M) subgroups ( Figure 1). Subgroup assignment was stable throughout longitudinal sampling (including pre/post-treatment) and consistent between splenic, bone marrow and PBMC derived cells. A subgroup with universally clonal BRAF-V600E mutations and majority diagnosed as HCL was termed the M-HCL subgroup ( Table 1). Four other groups termed M-SBLPN1-4 contained all HCL-V and SDRPL diagnosed samples and were devoid of BRAF-V600E mutations. M-SBLPN1 comprised MAP2K1 mutations (91%) and was enriched for CREBBP, ARIDIA and TERT-promoter mutations. These patients displayed an HCL-like immunophenotype (64.3% CD25+) with 1/3 diagnosed as HCL. M-SBLPN2 exhibited the highest prevalence of TP53 mutations and concomitant genomic instability. Patients in M-SBLPN1,2 were enriched in unmutated IGHV4-34 rearrangements. M-SBLPN3,4 subgroups displayed an immunophenotype more dissimilar to HCL, mutated IGHV genes, and enrichment of IGLL5, SYK and BIRC3 mutations. M-SBLPN4 contained the most SDRPL samples, suggesting it may represent the SDRPL entity retained by the WHO. We next uncovered that 29/170 SMZL patients displayed DNA methylation patterns mapping to M-SBLPN2-4. These patients were phenotypically and molecularly similar to SBLPN (70% displaying villous morphology and depleted in IGHV1-2*04, NOTCH2, KLF2 mutations), likely representing SMZL patients suggested for reassignment to SBLPN in the updated WHO classification. To elucidate molecular pathways governing the biology of M-SBLPN subgroups, transcription factor motif enrichment analysis in hypomethylated genomic regions revealed selective activation of AP-1 in M-HCL along with ETS in M-SBLPN1,2. Both transcription factors are downstream of MAPK signaling, consistent with activating BRAF and MAP2K1 mutations in these subgroups. However, we observed strong ETS enrichment in the absence of MAP2K1 in M-SBLPN along with mutual exclusivity of MAP2K1 and TP53 mutations, suggesting TP53 mutations are driving ETS activation. Although lymphoid neoplasms rarely exhibit TERT promoter mutations, 83% of M-SBLPN1 patients showed the c.-124C&amp;gt;T mutation commonly observed in other cancers producing an ETS binding site and ectopic TERT activation. ETS activation and gain of an ETS site by mutation implies oncogenesis involves aberrant TERT activation in this subgroup. In summary, we have developed a DNA methylation-based classifier that resolves 4 SBLPN subgroups with distinct molecular features, and reclassifies a subset of SMZL and HCL patients, adding further information to the updated WHO/ICC entities. We reveal distinct biological pathways operating in M-SBLPN subgroups that may aid targeted therapy approaches.

MethylClass: an R package to construct DNA methylation-based classification models.

DNA methylation profiling is a useful tool to increase the accuracy of a cancer diagnosis. However, a comprehensive R package specially for it is lacking. Hence, we developed the R package methylClass for methylation-based classification. Within it, we provide the eSVM (ensemble-based support vector machine) model to achieve much higher accuracy in methylation data classification than the popular random forest model and overcome the time-consuming problem of the traditional SVM. In addition, some novel feature selection methods are included in the package to improve the classification. Furthermore, because methylation data can be converted to other omics, such as copy number variation data, we also provide functions for multi-omics studies. The testing of this package on four datasets shows the accurate performance of our package, especially eSVM, which can be used in both methylation and multi-omics models and outperforms other methods in both cases. methylClass is available at: https://github.com/yuabrahamliu/methylClass.

EPCO-24. DNA METHYLATION-BASED SUBCLASS PREDICTS THE EFFECT OF THE EXTENT OF RESECTION ON SURVIVAL IN IDH-WILDTYPE GLIOBLASTOMA

Abstract BACKGROUND DNA methylation-based classification of diffuse glioma is increasingly used in daily practice. Yet, little is known regarding the prognostic and predictive value of DNA methylation-based subclasses of IDH-wildtype glioblastoma. A recent study found a predictive value for the effect of extent or resection (EOR) on survival for DNA methylation-based subclasses using the v11.4 classifier. We set out to validate these findings in an independent cohort and to test if these results are similar using a more recent version (v12.5) of the classifier. METHODS This concerns a single-center retrospective cohort study of all patients with a glioblastoma, IDH-wildtype of whom DNA methylation was obtained. Patient and tumor characteristics, pre-operative Karnofsky performance status and EOR (complete, subtotal, partial, biopsy) were retrieved. DNA methylation-based classification and MGMT-promoter status were obtained using version 11.4 and 12.5 of the Heidelberg classifier. Progression-free survival (PFS) and overall survival (OS) were collected and validated using the national brain tumor registry. RESULTS Of 149 patients, the subtype was Mesenchymal in 57 (38%), RTK1 in 41 (28%), and RTK2 in 51 patients (34%). PFS and OS did not differ between the three subclasses, also not after pooling RTK1 and RTK2 into one Classical subclass. Complete or subtotal resection resulted in a longer OS than partial resection or biopsy in the entire cohort. This was similar for the pooled Classical subclass. In the Mesenchymal subclass, however, there was no survival advantage for complete or subtotal resection over partial resection or biopsy. These results were similar with the 11.4 and 12.5 classifier version. CONCLUSION In IDH-wildtype glioblastoma of the Mesenchymal subclass complete or subtotal resection does not result in a survival benefit. Our findings evoke the need for pre- or intraoperative assessment of the DNA-methylation based subclass of IDH-wildtype glioblastoma.

P11.47.B DNA METHYLATION-BASED SUBCLASS PREDICTS THE EFFECT OF THE EXTENT OF RESECTION ON SURVIVAL IN IDH-WILDTYPE GLIOBLASTOMA

Abstract BACKGROUND DNA methylation-based classification of diffuse glioma is increasingly used in daily practice. Yet, little is known regarding the prognostic and predictive value of DNA methylation-based subclasses of IDH-wildtype glioblastoma. A recent study found a predictive value for the effect of extent or resection (EOR) on survival for DNA methylation-based subclasses using the v11.4 classifier. We set out to validate these findings in an independent cohort and to test if these results are similar using the newest v12.5 classifier. MATERIAL AND METHODS A single-center retrospective cohort study of all patients with a glioblastoma, IDH-wildtype of whom DNA methylation was obtained. Patient and tumor characteristics, pre-operative Karnofsky performance status, and EOR (complete, subtotal, partial, biopsy) were retrieved. DNA methylation-based classification and MGMT promotor status were obtained using version 11.4 and 12.5 of the Heidelberg classifier. Progression-free survival (PFS) and overall survival (OS) were collected and validated using the national brain tumor registry. RESULTS Of 149 patients, the subtype was Mesenchymal in 57 (38.27%), RTK1 in 41 (27.52%), and RTK2 in 51 patients (34.23%). PFS and OS did not differ between the three subclasses, also not after pooling RTK1 and RTK2 into one Classical subclass. Complete or subtotal resection resulted in a longer OS than partial resection or biopsy in the entire cohort. This was similar for the pooled Classical subclass. In the Mesenchymal subclass, however, there was no survival advantage for complete or subtotal resection over partial resection or biopsy. These results were similar with the 11.4 and 12.5 classifier version. CONCLUSION In IDH-wildtype glioblastoma of the Mesenchymal subclass complete or subtotal resection does not result in a survival benefit. A less aggressive surgical strategy should be considered in these patients. Also, there is need for pre- or intraoperative assessment of the DNA methylation-based subclass of IDH-wildtype glioblastoma.

Pulmonary inflammatory leiomyosarcoma represents a potential diagnostic pitfall of DNA methylation-based classification of sarcomas: a case report

BackgroundPulmonary inflammatory leiomyosarcoma (PILMS) is a rare type of myogenic tumor with prominent lymphohistiocytic infiltration. Despite their histological similarities, PILMS is immunohistochemically and genetically distinct from soft tissue inflammatory leiomyosarcoma, and its clinicopathological picture including DNA methylome data remains still unknown.Case presentationHere we present a case of PILMS in an 18-year-old male who underwent lobectomy. As reported previously, the current case demonstrated spindle myoid cell proliferation with smooth muscle differentiation within a prominent lymphohistiocytic infiltration and a diploid genome with a MUC3A gene alteration. DNA methylation analysis predicted this case to be an “inflammatory myofibroblastic tumor” (IMT) according to the Deutsches Krebsforschungszentrum (DKFZ) classifier. The data was analyzed by t-distributed stochastic neighbor embedding, which plotted the case tumor in the vicinity of IMT, however, there were no IMT histological features. These discordant results could be due to background non-neoplastic inflammatory cells.ConclusionsAs the DNA methylation classification of PILMS might be a potential diagnostic pitfall, an integrative histological and genetic approach is required for its accurate diagnosis.

DNA methylation-based classifier differentiates intrahepatic pancreato-biliary tumours.

Differentiating intrahepatic cholangiocarcinomas (iCCA) from hepatic metastases of pancreatic ductal adenocarcinoma (PAAD) is challenging. Both tumours have similar morphological and immunohistochemical pattern and share multiple driver mutations. We hypothesised that DNA methylation-based machine-learning algorithms may help perform this task. We assembled genome-wide DNA methylation data for iCCA (n=259), PAAD (n=431), and normal bile duct (n=70) from publicly available sources. We split this cohort into a reference (n=399) and a validation set (n=361). Using the reference cohort, we trained three machine learning models to differentiate between these entities. Furthermore, we validated the classifiers on the technical validation set and used an internal cohort (n=72) to test our classifier. On the validation cohort, the neural network, support vector machine, and the random forest classifiers reached accuracies of 97.68%, 95.62%, and 96.5%, respectively. Filtering by anomaly detection and thresholds improved the accuracy to 99.07% (37 samples excluded by filtering), 96.22% (17 samples excluded), and 100% (44 samples excluded) for the neural network, support vector machine and random forest, respectively. Because of best balance between accuracy and number of predictable cases we tested the neural network with applied filters on the in-house cohort, obtaining an accuracy of 95.45%. We developed a classifier that can differentiate between iCCAs, intrahepatic metastases of a PAAD, and normal bile duct tissue with high accuracy. This tool can be used for improving the diagnosis of pancreato-biliary cancers of the liver. This work was supported by Berlin Institute of Health (JCS Program), DKTK Berlin (Young Investigator Grant 2022), German Research Foundation (493697503 and 314905040 - SFB/TRR 209 Liver Cancer B01), and German Cancer Aid (70113922).

Buffy coat signatures of breast cancer risk in a prospective cohort study

BackgroundEpigenetic alterations are a near-universal feature of human malignancy and have been detected in malignant cells as well as in easily accessible specimens such as blood and urine. These findings offer promising applications in cancer detection, subtyping, and treatment monitoring. However, much of the current evidence is based on findings in retrospective studies and may reflect epigenetic patterns that have already been influenced by the onset of the disease.MethodsStudying breast cancer, we established genome-scale DNA methylation profiles of prospectively collected buffy coat samples (n = 702) from a case–control study nested within the EPIC-Heidelberg cohort using reduced representation bisulphite sequencing (RRBS).ResultsWe observed cancer-specific DNA methylation events in buffy coat samples. Increased DNA methylation in genomic regions associated with SURF6 and REXO1/CTB31O20.3 was linked to the length of time to diagnosis in the prospectively collected buffy coat DNA from individuals who subsequently developed breast cancer. Using machine learning methods, we piloted a DNA methylation-based classifier that predicted case–control status in a held-out validation set with 76.5% accuracy, in some cases up to 15 years before clinical diagnosis of the disease.ConclusionsTaken together, our findings suggest a model of gradual accumulation of cancer-associated DNA methylation patterns in peripheral blood, which may be detected long before clinical manifestation of cancer. Such changes may provide useful markers for risk stratification and, ultimately, personalized cancer prevention.

Analysis of tumor- and circulating-free DNA methylation to identify clinically relevant small cell lung cancer subtypes.

8577 Background: Small-cell lung cancer (SCLC) is an aggressive malignancy composed of distinct transcriptional subtypes, defined by the predominant expression of one of the three transcription factors ASCL1 (SCLC-A), NEUROD1 (SCLC-N) and POU2F3 (SCLC-P) as well as an inflamed subtype (SCLC-I; see Gay et al. Cancer Cell. 2021), each with potential therapeutic vulnerabilities. Implementing subtyping in the clinic has remained challenging due to limited tissue availability, particularly for longitudinal monitoring. Given the known epigenetic regulation of critical SCLC transcriptional programs, we hypothesized that there would be subtype-specific patterns of DNA methylation that could be detected in tumor or blood from SCLC patients. Methods: We included 179 patients with SCLC and performed RNA sequencing and genomic-wide reduced-representation bisulfite sequencing (RRBS). We further analyzed DNA methylation in 68 plasma samples including longitudinal samples to track SCLC subtype evolution over time. Results: Using machine learning approaches, we developed a highly accurate DNA methylation-based classifier (SCLC-DMC) that could distinguish SCLC subtypes using clinical tumor samples with 95.8% accuracy in the testing set compared to mRNA-based profiling. We further adjusted the classifier for circulating-free DNA (cfDNA) to subtype SCLC from plasma. Using the cfDNA classifier (cfDMC), we could demonstrate that SCLC subtypes evolve frequently during disease progression, highlighting the need for longitudinal tracking of SCLC during clinical treatment. Furthermore, methylation-based subtyping predicted response to a wide variety of drugs in preclinical models like CDK and AURK inhibitors, and clinical outcomes were indistinguishable in cohorts of patients subtyped using mRNA or SCLC-DMC (p = 0.95). Conclusions: These data establish that tumor and cfDNA methylation can be used to identify SCLC subtypes and guide precision SCLC therapy.

Abstract 6551: HiTAIC: Hierarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation data

Abstract Background: Human cancers are heterogenous by their cell composition and origination site. Cancer metastasis generates the conundrum of the unknown origin of migrated tumor cells. Tracing tissue of origin and tumor type in primary and metastasized cancer is vital for clinical significance. Researchers have demonstrated the high performance of DNA methylation-based machine learning models tracing the tissue origin of tumor cells. However, previous models were devised based on tissue site instead of tumor type, generating potential problems of indistinguishable tumor subtypes from the same site, e.g., esophageal squamous cell carcinoma versus esophageal adenocarcinoma. Methods: we employed a novel tumor-type-specific hierarchical model using DNA methylation data to develop a multilayer perceptron model (MLP) to trace tumor tissue of origin and subtype. DNA methylation data on 7735 tumor samples from 27 cancer types were downloaded from GEO and TCGA to develop tumor-type-specific libraries. The discovery data set was split into 80% training and 20 % testing. The tumor classifier hierarchy was established with two layers for 27 cancer types. In each layer of the hierarchy, the MLP model was trained using the selected cancer-type discriminatory CpGs. HiTAIC was established based on the four hierarchical MLP models. To validate HiTAIC, we applied the model to the external validation data sets computing stratified and overall precision, recall, and f1-score to evaluate the performance. Next, we applied the model to the application data sets and used stratified and overall precision, recall, and f1-score to evaluate model performance in metastasized cancers. Results: We observed high accuracy of HiTAIC's performance in primary cancer tracing with 99% accuracy and 99% weighted average F1-score in the testing dataset. We observed 93% accuracy and 93% weighted average F1-score for external validation across 25 cancer types. In metastasized cancer, HiTAIC demonstrated 96% accuracy and 98% weighted average F1-score across five cancer types with six different metastatic locations. HiTAIC traces tumor tissue of origin and cancer subtype with high accuracy in primary and metastasized cancer. Conclusion: We developed HiTAIC, a DNA methylation-based classifier, to trace tissue of origin and tumor type in primary and metastasized tumors. The model's capability to trace the tumor origin and subtype with high resolution and accuracy promises potential clinical use in identifying cancer of unknown origin. HiTAIC can be easily deployed in a web-based application, transforming computational sophistication into a user-friendly public tool. Citation Format: Ze Zhang, Brock Christensen, Lucas Salas. HiTAIC: Hierarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation data. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6551.

Multiomic neuropathology improves diagnostic accuracy in pediatric neuro-oncology

The large diversity of central nervous system (CNS) tumor types in children and adolescents results in disparate patient outcomes and renders accurate diagnosis challenging. In this study, we prospectively integrated DNA methylation profiling and targeted gene panel sequencing with blinded neuropathological reference diagnostics for a population-based cohort of more than 1,200 newly diagnosed pediatric patients with CNS tumors, to assess their utility in routine neuropathology. We show that the multi-omic integration increased diagnostic accuracy in a substantial proportion of patients through annotation to a refining DNA methylation class (50%), detection of diagnostic or therapeutically relevant genetic alterations (47%) or identification of cancer predisposition syndromes (10%). Discrepant results by neuropathological WHO-based and DNA methylation-based classification (30%) were enriched in histological high-grade gliomas, implicating relevance for current clinical patient management in 5% of all patients. Follow-up (median 2.5 years) suggests improved survival for patients with histological high-grade gliomas displaying lower-grade molecular profiles. These results provide preliminary evidence of the utility of integrating multi-omics in neuropathology for pediatric neuro-oncology.

Longitudinal DNA methylation analysis of adult-type IDH-mutant gliomas

Diffuse gliomas are the most prevalent malignant primary brain tumors in adults and remain incurable despite standard therapy. Tumor recurrence is currently inevitable, which contributes to a persistent high morbidity and mortality in these patients. In this study, we examined the genome-wide DNA methylation profiles of primary and recurrent adult-type IDH-mutant gliomas to elucidate DNA methylation changes associated with tumor progression (with or without malignant transformation). We analyzed DNA methylation profiles of 37 primary IDH-mutant gliomas and 42 paired recurrences using the DNA methylation EPIC beadChip array. DNA methylation-based classification reflected the tumor progression over time. We observed a methylation subtype switch in a proportion of IDH-mutant astrocytomas; the primary tumors were subclassified as low-grade astrocytomas, which progressed to high-grade astrocytomas in the recurrent tumors. The CNS WHO grade 4 IDH-mutant astrocytomas did not always resemble methylation subclasses of higher grades. The number of differentially methylated CpG sites increased over time, and astrocytomas accumulated more differentially methylated CpG sites than oligodendrogliomas during tumor progression. Few differentially methylated CpG sites were shared between patients. We demonstrated that DNA methylation profiles are mostly maintained during IDH-mutant glioma progression, but CpG site-specific methylation alterations can occur.

Predicting methylation class from diffusely infiltrating adult gliomas using multimodality MRI data.

Radiogenomic studies of adult-type diffuse gliomas have used magnetic resonance imaging (MRI) data to infer tumor attributes, including abnormalities such as IDH-mutation status and 1p19q deletion. This approach is effective but does not generalize to tumor types that lack highly recurrent alterations. Tumors have intrinsic DNA methylation patterns and can be grouped into stable methylation classes even when lacking recurrent mutations or copy number changes. The purpose of this study was to prove the principle that a tumor's DNA-methylation class could be used as a predictive feature for radiogenomic modeling. Using a custom DNA methylation-based classification model, molecular classes were assigned to diffuse gliomas in The Cancer Genome Atlas (TCGA) dataset. We then constructed and validated machine learning models to predict a tumor's methylation family or subclass from matched multisequence MRI data using either extracted radiomic features or directly from MRI images. For models using extracted radiomic features, we demonstrated top accuracies above 90% for predicting IDH-glioma and GBM-IDHwt methylation families, IDH-mutant tumor methylation subclasses, or GBM-IDHwt molecular subclasses. Classification models utilizing MRI images directly demonstrated average accuracies of 80.6% for predicting methylation families, compared to 87.2% and 89.0% for differentiating IDH-mutated astrocytomas from oligodendrogliomas and glioblastoma molecular subclasses, respectively. These findings demonstrate that MRI-based machine learning models can effectively predict the methylation class of brain tumors. Given appropriate datasets, this approach could generalize to most brain tumor types, expanding the number and types of tumors that could be used to develop radiomic or radiogenomic models.

Events in CNS Tumor Pathology Post-2016 WHO CNS: cIMPACT-NOW Updates and Other Advancements: A Comprehensive Review Plus a Summary of the Salient Features of 2021 WHO CNS 5.

The 2016 World Health Organization Classification (WHO) of Tumors of the Central Nervous System (CNS) represented a major change. It recommended an "integrated diagnosis" comprising histologic and molecular information facilitating a more precise diagnosis of specific CNS tumors. Its goal was to provide greater diagnostic precision and reproducibility resulting in more clinical relevance and predictive value, ultimately leading to better patient care. Advances in molecular classification, mostly resulting from DNA methylation array profiling of CNS tumors, were occurring at a very rapid pace and required more rapid integration into clinical practice. cIMPACT-NOW updates and other recent papers plus salient features of 2021 WHO CNS5 in this comprehensive write-up were reviewed. CNS tumor classification needs to be updated at a rapid pace and mechanisms put into place to guide diagnosticians and clinicians in the interim period if major changes in the classification of tumor types came to light. Recognizing the need to integrate these into clinical practice more rapidly and without inordinate delay, the International Society of Neuropathology (ISN) 2016 sponsored an initiative called cIMPACT-NOW. Goal of cIMPACT-NOW was to provide clarification regarding contentious issues arising in the wake of the 2016 WHO CNS update as well as report new advancements in molecular classification of CNS tumors and new tumor entities emerging as a result of these advancements. cIMPACT-NOW updates: It thus laid the foundation for the 5th edition of the WHO Classification of CNS tumors (2021 WHO CNS 5). We have discussed cIMPACT updates in detail in this review. In addition, molecular diagnostics including DNA methylation-based classification of CNS tumors and the practical use of molecular classification in the prognostication and treatment of CNS tumors is discussed. Finally, the salient features of the new CNS tumor classification are summarized.

Genomic markers of recurrence risk in atypical meningioma following gross total resection.

Meningiomas are the most common primary central nervous system (CNS) tumor in adults and CNS World Health Organization grade 2 (atypical) meningiomas show an intermediate risk of recurrence/progression. Molecular parameters are needed to better inform management following gross total resection (GTR). We performed comprehensive genomic analysis of tumor tissue from 63 patients who underwent radiologically confirmed GTR of a primary grade 2 meningioma, including a CLIA-certified target next-generation sequencing panel (n = 61), chromosomal microarray (n = 63), genome-wide methylation profiling (n = 62), H3K27me3 immunohistochemistry (n = 62), and RNA-sequencing (n = 19). Genomic features were correlated with long-term clinical outcomes (median follow-up: 10 years) using Cox proportional hazards regression modeling and published molecular prognostic signatures were evaluated. The presence of specific copy number variants (CNVs), including -1p, -10q, -7p, and -4p, was the strongest predictor of decreased recurrence-free survival (RFS) within our cohort (P < .05). NF2 mutations were frequent (51%) but did not show a significant association with RFS. DNA methylation-based classification assigned tumors to DKFZ Heidelberg benign (52%) or intermediate (47%) meningioma subclasses and was not associated with RFS. H3K27 trimethylation (H3K27me3) was unequivocally lost in 4 tumors, insufficient for RFS analysis. Application of published integrated histologic/molecular grading systems did not improve prediction of recurrence risk over the presence of -1p or -10q alone. CNVs are strong predictors of RFS in grade 2 meningiomas following GTR. Our study supports incorporation of CNV profiling into clinical evaluation to better guide postoperative patient management, which can be readily implemented using existing, clinically validated technologies.

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs.