Methylation-based Classification Research Articles

Abstract 2853: Comparison of methods for detection of hemispheric gliomas of infancy and their characteristic oncogenic tyrosine kinase gene fusions

Abstract Introduction High grade hemispheric gliomas in infancy (IHG) are characterized by a more favorable outcome compared to high grade gliomas in older children. We have shown in previous studies that these tumors are characterized by a stable genome. The occurrence of tyrosine kinase gene fusions in high-grade hemispheric gliomas of infancy may represent therapeutic targets. Methods: 33 high-grade hemispheric gliomas occurring in children younger than three years were retrieved from the archives of the Brain Tumor Reference Center, Institute of Neuropathology, University of Bonn. DNA and RNA were extracted from FFPE tumor samples. Methylation-based classification was done by hybridization to Illumina methylation arrays and subsequent use of the Heidelberg brain tumor classifier versions v11b4, v12.5 and v12.8. In addition UMAP plots were used to visualize similarities of methylation profiles. Gene fusions were identified on the DNA level by FISH using break-apart probes for ALK, NTRK1, -2, -3, ROS1 and MET and Molecular Inversion Probe (MIP) methodology. On the RNA level, fusion transcripts were detected by targeted RNA sequencing as well as Nanostring assay with fusion-specific probes. Results: 32 IHG occurred in the first year of life, one at the age of two years. 23/33 IHG were classified as such by methylation arrays; 6 further cases could be annotated to the IHG group by UMAP plots. 18/33 cases (54.5 %) showed fusions of ALK to different partners; all occurred in the first year of life. Fusions of ROS1 were found in 4, MET in 2, NTRK1 and -3 in 7 cases. 2 cases showed no fusions. The different methods for fusion detection led to comparable results. Only recurrent fusions with known fusion partners were detectable with Nanostring probes designed for the fusion sequence, and library construction for targeted RNA sequencing was not successful in a fraction of cases. Break-apart FISH led to reliable results on the next day, and MIP technology represented the most sensitive method for analysis of FFPE samples. Conclusions: Gene fusions involving the tyrosine kinase genes ALK, MET, ROS1 and NTRK1 and -3 occurred in 93 % of high grade hemispheric gliomas of infancy; the most frequent were ALK fusions occurring in children younger than 1 year. DNA-based MIP technology represented the most robust and sensitive assay. A combination of RNA- and DNA-based methods to detect these fusions with high reliability is recommended. Citation Format: Torsten Pietsch, Gerrit Gielen, Andreas Waha, Evelyn Dörner, Verena Dreschmann, Christof Kramm, Christian Vokuhl. Comparison of methods for detection of hemispheric gliomas of infancy and their characteristic oncogenic tyrosine kinase gene fusions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2853.

Abstract 3656: Target enrichment of methylated circulating tumor DNA for colorectal cancer detection

Abstract Aberrant DNA methylation changes are one of the earliest signatures of cancer development. Methylation profiling of circulating tumor DNA (ctDNA) from plasma has emerged as a promising approach for early cancer detection (ECD). However, the set of methylation targets that can successfully distinguish cancer through liquid biopsy will vary depending on cancer type and subtype. Here, we explored a methylation target enrichment workflow and developed a methylation-based classification model to distinguish between patients with colorectal cancer (CRC) and healthy individuals. First, CRC-specific CpG targets were identified by comparing the methylation landscape of CRC and normal samples (tissue and blood) available in The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. This analysis resulted in >800 CRC-specific CpG targets for initial evaluation. To develop and test the classification model, CRC patients and healthy individuals with plasma samples were included. A machine learning model was used to evaluate the highest performing CpG targets that effectively discriminated between CRC patients with detected ctDNA (“CRC ctDNA-positive”) and healthy individuals. Correlation between methylation levels of the high performing CpG targets and the variant allele frequency (VAF) among CRC ctDNA-positive patients was calculated. In total, 86 patients were included in this proof of concept study (mean age, 62 ±12 years; male, 55%). 50 CRC ctDNA-positive patients (24% stage I, 40% stage II, 24% stage III, and 12% stage IV) and 36 healthy normals were included in the analysis. We investigated this cohort to identify the highest performing CpG targets among the >800 potential CpG targets that provided effective discrimination between CRC patients and healthy individuals. Using the highest performing CpG targets, observed methylation level was correlated with VAF of single nucleotide variants detected by ctDNA testing (R2: 0.8). In summary, we developed a cost-effective methylation target enrichment workflow and utilized machine-learning algorithms to demonstrate the potential use of ctDNA methylation markers in early detection. Additional studies are needed to confirm that these and other markers can accurately distinguish between patients with CRC and healthy individuals. Citation Format: Hsiao-Yun Huang, Ravi Vijaya Satya, Wen-Ching Chan, Esha Atolia, Joshua Babiarz, Bernhard Zimmermann, Trupti Kawli. Target enrichment of methylated circulating tumor DNA for colorectal cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3656.

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.

Predicting recurrence using a tumor-uninformed ctDNA assay detecting MRD in patients with resected stage II or III colorectal cancer: Subset analysis from the GALAXY study in CIRCULATE-Japan.

21 Background: The presence of circulating tumor DNA (ctDNA) after curative-intent surgery can identify patients with minimal residual disease (MRD) who are at risk of recurrence and aid with postsurgical risk stratification. ~80% of stage II and only 50% of stage III colorectal cancer (CRC) patients are cured by surgery alone. A tumor-uninformed plasma-only approach for MRD assessment accelerates the turnaround time, enabling rapid adjuvant chemotherapy (ACT) treatment decision making, optimizing the impact on patient outcomes. Methods: A subset of samples from GALAXY, an observational arm of the CIRCULATE-Japan study (UMIN000039205), were analyzed for postsurgical ctDNA detection at a landmark time point (LMT) defined as 4 weeks after curative surgery in pathological stage II or III CRC patients. In a prospective lead-in to clinical validation, 80 patients meeting pre-specified eligibility criteria were randomly selected with enrichment for recurrence to 50% while maintaining the stage II:III recurrent (R)/nonrecurrent (NR) ratio to that observed in GALAXY. Residual plasma samples were analyzed with the Tempus xM assay (xM), a tumor-uninformed ctDNA MRD assay that integrates methylation and genomic variant MRD classifiers to deliver a binary MRD call. All calls were blinded to clinical outcomes. The methylation-based classifier detects fragments with CRC methylation signatures in differentially methylated regions trained by sequencing CRC and presumed-healthy samples on a 6Mbp panel. The variant-based MRD classifier detects highly prevalent CRC variants. Artifacts, CHIP, and germline variant filtering algorithms were also applied. Results: Of the 80 patients (pts), 70 [Stage II n= 29 (41%), Stage III n= 41 (59%)] were evaluable (36 R and 34 NR). At the LMT and with at least 1-year follow-up, 18/36 R pts had detectable ctDNA (xM MRD+) and 30/34 NR pts had undetectable ctDNA (xM MRD-), providing a sensitivity of 50% and specificity of 88%. Additionally, xM MRD+/- status strongly correlated with disease-free survival (DFS) with a hazard ratio (HR) of 5.09, adjusted to account for a 24% recurrence rate in GALAXY. In an updated, ongoing longitudinal analysis of the 4 false positive pts, 2 were Stage III, MRD+ from both the methylation and genomic classifiers, received ACT, and have likely cleared their ctDNA without any sign of clinical recurrence. An adjusted sensitivity and specificity analysis resulted in xM clinical performance of 53% (20/38 R) and 94% (30/32 NR), respectively. Conclusions: xM is a novel tumor-uninformed assay that demonstrated remarkable performance to detect clinical recurrence at a LMT, and a clinically meaningful correlation with DFS. A larger clinical validation study including longitudinal analysis, correlation with ACT, and outcomes from the GALAXY study is currently underway.

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.

10111-MPC-11 CLINICAL AND MOLECULAR NEUROPATHOLOGICAL FEATURES OF EPITHELIOID GLIOBLASTOMA

Abstract BACKGROUND Epithelioid Glioblastoma (E-GBM) has been identified as a pathological subtype of Glioblastoma. It is a rare glioblastoma and is mainly known in case reports, and the details of its clinical features and genetic characteristics are not clear. Here, we retrospecivly studied the results of clinical course and genetic features of E-GBM. METHODS Four patients who underwent surgery at our institution and were diagnosed with E-GBM were included in this study. Histological diagnosis was obtained by surgical excision in all cases. We extracted DNA from the resected tissues and analyzed glioma-related genes using various methods such as high resolution melting (HRM), MGMT methylation-specific HRM, digital PCR, and Multiple ligation-dependent probe amplification (MLPA) analysis. Moreover, we assayed methylation EPIC BeadChip arrays for each sample. RESULTS E-GBM tended to develop at a slightly younger age than other general glioblastoma. All patients underwent surgical resection followed by radiation therapy and concurrent temozolomide therapy. However, none of the patients were able to transition to temozolomide maintenance therapy due to disease progression, and the overall survival period was 8 to 19 weeks, indicating an extremely poor prognosis. Genetic analysis revealed both TERT promoter mutations and BRAFV600E mutation in all cases. In addition, IDH mutation was not found, and MGMT promoter methylation was unmethylated in all cases. EGFR amplification, CDKN2A/2B homozygous deletion and 1p/19q co-deletion were not found in any cases. Methylation-based classification did not reach E-GBM in all samples. CONCLUSION The simultaneous mutations of TERT promoter with BRAFV600E could serve as diagnostic molecular markers for the diagnosis of E-GBM. Standard therapy for glioblastoma was not expected to be effective in treating this tumor. It was considered necessary to introduce new therapeutic strategies such as early administration of BRAF and MEK inhibitors.

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.

Methylation Biology of a Blood-Based MDS Risk Stratification Test

Background: Myelodysplastic syndromes (MDS) are a heterogeneous group of myeloid malignancies associated with a myriad of deleterious outcomes and a 5-year relative survival of 37%. MDS risk stratification is key for optimal treatment decisions. We previously demonstrated that methylation-based markers may help stratify MDS risk and achieved comparable performance relative to IPSS-R (the Revised International Prognostic Scoring System) by assessing the risk prediction ability of a classifier trained with features from either Bone Marrow (BM) whole-genome bisulfite sequencing (WGBS) or serum targeted methylation (TM) sequencing. Here, we elucidate the biological underpinnings of the methylation-based classifier and explore the survival stratification performance of our previously reported classifier. Methods: Utilizing datasets from BM WGBS and matched Serum TM sequencing of 127 patients with MDS (N=104) and secondary AML (N=23), we delineated the features used by the classifier by identifying differentially methylated regions (DMRs) that distinguished patients with overall survival of &amp;gt;3 years from those with &amp;lt;3 years. Our approach also included a functional enrichment and driver gene analysis associated with these DMRs, as well as cluster analysis across patients. We performed further evaluations of the survival outcomes of our BM WGBS and matched Serum TM prognostic classifier using concordance indices and log-rank tests. Results: Our analysis revealed 7,742 DMRs in BM WGBS and 14,093 DMRs in the Serum TM that significantly separated the long and short-term survival groups. Unsupervised hierarchical clustering of differential methylation patterns in both BM WGBS and Serum TM highlighted three distinctive subgroups of samples: short-term survivors, long-term survivors, and a group with intermediate survival outcomes. Notably, the short-term survivor group exhibited extensive hypermethylation across a majority of DMRs, which were linked to key biological pathways including calcium signaling, cAMP, MAPK, Rap1, and PI3K-Akt. Enrichment analysis of the BM WGBS DMRs between individuals with &amp;lt;3 and &amp;gt;3 years of survival underscored the differential hypermethylation of promoters, CpG islands, and CpG shores in association with worse survival outcomes. Our analysis of BM WGBS DMR driver genes via Hypergeometric Optimization of Motif EnRichment (HOMER) revealed potential contributors to differential survival outcomes, including known genes associated with MDS/AML, such as EWS/FLI-1, ERG, Pu.1, and RUNX1. Both pathway and driver gene analysis recapitulated previously known drivers of MDS to AML progression. The methylation classifier achieved comparable risk stratification performance for both BM WGBS comparing with IPSS-R (C-index: 0.69 [95% CI: 0.61-0.77] for methylation vs 0.71 [95% CI: 0.64-0.77] for IPSS-R) and Serum TM (C-index: 0.68 [95% CI: 0.60-0.75] for methylation vs. 0.70 [95% CI: 0.63-0.78] for IPSS-R). At a specificity threshold of 0.8, both BM WGBS and Serum TM prognostic classifiers were able to stratify between predicted short and predicted long survivors (BM WGBS HR: 2.83, p&amp;lt;0.0001; Serum TM HR: 3.01, p=0.0008). Conclusions: Our findings generated biological insights into the methylation-based risk stratification of MDS, shedding light on the specific genomic features and biological pathways that underlie these methylation patterns. We also demonstrated that methylation-based MDS risk stratification performs comparably with IPSS-R. These insights not only expand our understanding of the complexity of MDS, but also reinforce the potential of methylation-based sequencing as a viable, possibly less-invasive, alternative for MDS risk stratification compared to traditional IPSS-R methods.

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.

Rapid Classification of Sarcomas Using Methylation Fingerprint: A Pilot Study.

Sarcoma classification is challenging and can lead to treatment delays. Previous studies used DNA aberrations and machine-learning classifiers based on methylation profiles for diagnosis. We aimed to classify sarcomas by analyzing methylation signatures obtained from low-coverage whole-genome sequencing, which also identifies copy-number alterations. DNA was extracted from 23 suspected sarcoma samples and sequenced on an Oxford Nanopore sequencer. The methylation-based classifier, applied in the nanoDx pipeline, was customized using a reference set based on processed Illumina-based methylation data. Classification analysis utilized the Random Forest algorithm and t-distributed stochastic neighbor embedding, while copy-number alterations were detected using a designated R package. Out of the 23 samples encompassing a restricted range of sarcoma types, 20 were successfully sequenced, but two did not contain tumor tissue, according to the pathologist. Among the 18 tumor samples, 14 were classified as reported in the pathology results. Four classifications were discordant with the pathological report, with one compatible and three showing discrepancies. Improving tissue handling, DNA extraction methods, and detecting point mutations and translocations could enhance accuracy. We envision that rapid, accurate, point-of-care sarcoma classification using nanopore sequencing could be achieved through additional validation in a diverse tumor cohort and the integration of methylation-based classification and other DNA aberrations.

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.

NKX3.1 immunohistochemistry and methylome profiling in mesenchymal chondrosarcoma: additional diagnostic value for a well-defined disease?

Mesenchymal chondrosarcoma (MCS) is a rare and highly aggressive tumour of soft tissue and bone that is defined by an underlying and highly specific fusion transcript involving HEY1 and NCOA2. Histologically, the tumours show a biphasic appearance consisting of an undifferentiated blue and round cell component as well as islands of highly differentiated cartilage. Particularly in core needle biopsies, the chondromatous component can be missed and the non-specific morphology and immunophenotype of the round cell component can cause diagnostic challenges. We applied NKX3.1 immunohistochemistry which was recently reported as a highly specific marker as well as methylome and copy number profiling to a set of 45 well characterised MCS cases to evaluate their potential diagnostic value. Methylome profiling revealed a highly distinct cluster for MCS. Notably, the findings were reproducible also when analysing the round cell and cartilaginous component separately. Furthermore, four outliers were identified by methylome profiling for which the diagnosis had to be revised. NKX3.1 immunohistochemistry showed positivity in 36% of tumours, the majority of which was rather focal and weak. Taken together, NKX3.1 expression showed a low sensitivity but a high specificity in our analysis. Methylome profiling on the other hand represents a sensitive, specific and reliable tool to support the diagnosis of MCS, particularly if only the round cell component is obtained in a biopsy and the diagnosis is not suspected. Furthermore, it can aid in confirming the diagnosis in case RNA sequencing for the HEY1::NCOA2 fusion transcript is not available.

Abstract 979: Development of a methylation-based classifier to identify pancreatic adenocarcinoma subtype

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) demonstrates variable prognosis and response to first-line FOLFIRINOX therapy depending on molecular subtype; patients with classical tumors showed a greater response compared to basal-like tumors. Identifying molecular subtype may inform treatment approaches, but current subtyping schemas rely on tumor biopsies which are invasive and can be technically challenging. Blood based epigenetic assays are commercially available, and identification of subtype-associated epigenetic modifications may provide a path toward blood-based PDAC molecular subtyping. We analyzed methylation levels and developed a subtype classifier using methylation data on PDAC tumors. Using this classifier, we then developed a methylation-specific qPCR assay to subtype PDAC tumors by analyzing methylation at particular sites in tumor DNA which may now be further adapted to more facile liquid biopsy approaches. Methods: Using methylation data (n=150) from TCGA PAAD, we applied a random forest (RF) model followed by a k-Top Scoring Pairs (k-TSP) algorithm to develop a methylation site-based classifier that allows prediction of subtype by comparing methylation levels between paired sites. The classifier was validated on two independent datasets, including ICGC (n=82) and patient-derived xenografts (PDXs) (n=21). We designed methylation-specific qPCR primers for these sites and validated their specificity with fully methylated or fully unmethylated DNA. Using genomic DNA extracted from 10 PDAC PDXs of known molecular subtype, we obtained methylation levels at sites identified by the classifier via a SYBR Green-based qPCR assay. We then used our classifier to predict subtype of the PDX tumors. Results: We validated our RF-kTSP methylation subtype classifier on two independent cohorts and predicted subtype using 20 methylation sites. In ICGC, we found balanced accuracy=0.94, AUROC=0.98, AU-PR=0.89. In PDX PDAC tumors, we found balanced accuracy=1, AUROC=1, AU-PR=1. We designed 14/20 primers for qPCR and confirmed specificity for methylated DNA. Using 14/20 methylation sites, we achieved 80% correct subtype prediction of 10 PDX tumors (5/5 basal-like, 3/5 classical). Conclusion: We have developed a methylation-based classifier that accurately and replicably predicts PDAC subtype. We continue to develop a qPCR assay for PDAC subtype that so far has 80% accuracy using 14/20 probes. We are completing validation of the remaining 6 primers and correlating methylation levels at each site with those obtained from EPIC array methylation analysis. The long-term goal is to apply our complete set of primers in a digital droplet PCR assay that will be amenable for subtyping using circulating tumor DNA (ctDNA). Citation Format: Zachary Schrank, Des Weighill, Hannah Thel, Hannah Trembath, Ashley Morrison, Jen Jen Yeh. Development of a methylation-based classifier to identify pancreatic adenocarcinoma subtype [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 979.