Accurate CNS tumor diagnosis can be challenging, and methylation profiling can serve as an adjunct to classify diagnostically difficult cases. An integrated diagnostic approach was employed for a consecutive series of 1258 surgical neuropathology samples obtained primarily in a consultation practice over 2-year period. DNA methylation profiling and classification using the DKFZ/Heidelberg CNS tumor classifier was performed, as well as unsupervised analyses of methylation data. Ancillary testing, where relevant, was performed. Among the received cases in consultation, a high-confidence methylation classifier score (>0.84) was reached in 66.4% of cases. The classifier impacted the diagnosis in 46.7% of these high-confidence classifier score cases, including a substantially new diagnosis in 26.9% cases. Among the 289 cases received with only a descriptive diagnosis, methylation was able to resolve approximately half (144, 49.8%) with high-confidence scores. Additional methods were able to resolve diagnostic uncertainty in 41.6% of the low-score cases. Tumor purity was significantly associated with classifier score (P = 1.15e−11). Deconvolution demonstrated that suspected glioblastomas (GBMs) matching as control/inflammatory brain tissue could be resolved into GBM methylation profiles, which provided a proof-of-concept approach to resolve tumor classification in the setting of low tumor purity. This work assesses the impact of a methylation classifier and additional methods in a consultative practice by defining the proportions with concordant vs change in diagnosis in a set of diagnostically challenging CNS tumors. We address approaches to low-confidence scores and confounding issues of low tumor purity.

Brain metastases (BMs) are the most common and among the deadliest brain tumors. Currently, there are no reliable predictors of BM development from primary cancer, which limits early intervention. Lung adenocarcinoma (LUAD) is the most common BM source and here we obtained 402 tumor and plasma samples from a large cohort of patients with LUAD with or without BM (n = 346). LUAD DNA methylation signatures were evaluated to build and validate an accurate model predicting BM development from LUAD, which was integrated with clinical factors to provide comprehensive patient-specific BM risk probabilities in a nomogram. Additionally, immune and cell interaction gene sets were differentially methylated at promoters in BM versus paired primary LUAD and had aligning dysregulation in the proteome. Immune cells were differentially abundant in BM versus LUAD. Finally, liquid biomarkers identified from methylated cell-free DNA sequenced in plasma were used to generate and validate accurate classifiers for early BM detection. Overall, LUAD methylomes can be leveraged to predict and noninvasively identify BM, moving toward improved patient outcomes with personalized treatment.

Malignant peripheral nerve sheath tumor (MPNST) is a highly aggressive sarcoma, and a lethal neurofibromatosis type 1-related malignancy, with little progress made on treatment strategies. Here, we apply a multiplatform integrated molecular analysis on 108 tumors spanning the spectrum of peripheral nerve sheath tumors to identify candidate drivers of MPNST that can serve as therapeutic targets. Unsupervised analyses of methylome and transcriptome profiles identify two distinct subgroups of MPNSTs with unique targetable oncogenic programs. We establish two subgroups of MPNSTs: SHH pathway activation in MPNST-G1 and WNT/ß-catenin/CCND1 pathway activation in MPNST-G2. Single nuclei RNA sequencing characterizes the complex cellular architecture and demonstrate that malignant cells from MPNST-G1 and MPNST-G2 have neural crest-like and Schwann cell precursor-like cell characteristics, respectively. Further, in pre-clinical models of MPNST we confirm that inhibiting SHH pathway in MPNST-G1 prevent growth and malignant progression, providing the rational for investigating these treatments in clinical trials.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths worldwide. Only a fraction of NSCLC harbor actionable driver mutations and there is an urgent need for patient-derived model systems that will enable the development of new targeted therapies. NSCLC and other cancers display profound proteome remodeling compared to normal tissue that is not predicted by DNA or RNA analyses. Here, we generate 137 NSCLC patient-derived xenografts (PDXs) that recapitulate the histology and molecular features of primary NSCLC. Proteome analysis of the PDX models reveals 3 adenocarcinoma and 2 squamous cell carcinoma proteotypes that are associated with different patient outcomes, protein-phosphotyrosine profiles, signatures of activated pathways and candidate targets, and in adenocarcinoma, stromal immune features. These findings portend proteome-based NSCLC classification and treatment and support the PDX resource as a viable model for the development of new targeted therapies.

Chordomas are rare malignant bone cancers of the skull-base and spine. Patient survival is variable and not reliably predicted using clinical factors or molecular features. This study identifies prognostic epigenetic chordoma subtypes that are detected noninvasively using plasma methylomes. Methylation profiles of 68 chordoma surgical samples were obtained between 1996 and 2018 across three international centers along with matched plasma methylomes where available. Consensus clustering identified two stable tissue clusters with a disease-specific survival difference that was independent of clinical factors in a multivariate Cox analysis (HR = 14.2, 95%CI: 2.1–94.8, P = 0.0063). Immune-related pathways with genes hypomethylated at promoters and increased immune cell abundance were observed in the poor-performing “Immune-infiltrated” subtype. Cell-to-cell interaction plus extracellular matrix pathway hypomethylation and higher tumor purity were observed in the better-performing “Cellular” subtype. The findings were validated in additional DNA methylation and RNA sequencing datasets as well as with immunohistochemical staining. Plasma methylomes distinguished chordomas from other clinical differential diagnoses by applying fifty chordoma-versus-other binomial generalized linear models in random 20% testing sets (mean AUROC = 0.84, 95%CI: 0.52–1.00). Tissue-based and plasma-based methylation signals were highly correlated in both prognostic clusters. Additionally, leave-one-out models accurately classified all tumors into their correct cluster based on plasma methylation data. Here, we show the first identification of prognostic epigenetic chordoma subtypes and first use of plasma methylome-based biomarkers to noninvasively diagnose and subtype chordomas. These results may transform patient management by allowing treatment aggressiveness to be balanced with patient risk according to prognosis.