DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

DNA methylation profiling to predict recurrence risk in meningioma: development and validation of a nomogram to optimize clinical management

Abstract Background Variability in standard-of-care classifications precludes accurate predictions of early tumor recurrence for individual patients with meningioma, limiting the appropriate selection of patients who would benefit from adjuvant radiotherapy to delay recurrence. We aimed to develop an individualized prediction model of early recurrence risk combining clinical and molecular factors in meningioma. Methods DNA methylation profiles of clinically annotated tumor samples across multiple institutions were used to develop a methylome model of 5-year recurrence-free survival (RFS). Subsequently, a 5-year meningioma recurrence score was generated using a nomogram that integrated the methylome model with established prognostic clinical factors. Performance of both models was evaluated and compared with standard-of-care models using multiple independent cohorts. Results The methylome-based predictor of 5-year RFS performed favorably compared with a grade-based predictor when tested using the 3 validation cohorts (ΔAUC = 0.10, 95% CI: 0.03–0.018) and was independently associated with RFS after adjusting for histopathologic grade, extent of resection, and burden of copy number alterations (hazard ratio 3.6, 95% CI: 1.8–7.2, P &lt; 0.001). A nomogram combining the methylome predictor with clinical factors demonstrated greater discrimination than a nomogram using clinical factors alone in 2 independent validation cohorts (ΔAUC = 0.25, 95% CI: 0.22–0.27) and resulted in 2 groups with distinct recurrence patterns (hazard ratio 7.7, 95% CI: 5.3–11.1, P &lt; 0.001) with clinical implications. Conclusions The models developed and validated in this study provide important prognostic information not captured by previously established clinical and molecular factors which could be used to individualize decisions regarding postoperative therapeutic interventions, in particular whether to treat patients with adjuvant radiotherapy versus observation alone. </jats:sec

Molecular Classification and Therapeutic Targets in Ependymoma

Ependymoma is a biologically diverse tumor wherein molecular classification has superseded traditional histological grading based on its superior ability to characterize behavior, prognosis, and possible targeted therapies. The current, updated molecular classification of ependymoma consists of ten distinct subgroups spread evenly among the spinal, infratentorial, and supratentorial compartments, each with its own distinct clinical and molecular characteristics. In this review, the history, histopathology, standard of care, prognosis, oncogenic drivers, and hypothesized molecular targets for all subgroups of ependymoma are explored. This review emphasizes that despite the varied behavior of the ependymoma subgroups, it remains clear that research must be performed to further elucidate molecular targets for these tumors. Although not all ependymoma subgroups are oncologically aggressive, development of targeted therapies is essential, particularly for cases where surgical resection is not an option without causing significant morbidity. The development of molecular therapies must rely on building upon our current understanding of ependymoma oncogenesis, as well as cultivating transfer of knowledge based on malignancies with similar genomic alterations

Identification of histological correlates of overall survival in lower grade gliomas using a bag-of-words paradigm: A preliminary analysis based on hematoxylin & eosin stained slides from the lower grade glioma cohort of the cancer genome Atlas

Background: Glioma, the most common primary brain neoplasm, describes a heterogeneous tumor of multiple histologic subtypes and cellular origins. At clinical presentation, gliomas are graded according to the World Health Organization guidelines (WHO), which reflect the malignant characteristics of the tumor based on histopathological and molecular features. Lower grade diffuse gliomas (LGGs) (WHO Grade II–III) have fewer malignant characteristics than high-grade gliomas (WHO Grade IV), and a better clinical prognosis, however, accurate discrimination of overall survival (OS) remains a challenge. In this study, we aimed to identify tissue-derived image features using a machine learning approach to predict OS in a mixed histology and grade cohort of lower grade glioma patients. To achieve this aim, we used H and E stained slides from the public LGG cohort of The Cancer Genome Atlas (TCGA) to create a machine learned dictionary of “image-derived visual words” associated with OS. We then evaluated the combined efficacy of using these visual words in predicting short versus long OS by training a generalized machine learning model. Finally, we mapped these predictive visual words back to molecular signaling cascades to infer potential drivers of the machine learned survival-associated phenotypes. Methods: We analyzed digitized histological sections downloaded from the LGG cohort of TCGA using a bag-of-words approach. This method identified a diverse set of histological patterns that were further correlated with OS, histology, and molecular signaling activity using Cox regression, analysis of variance, and Spearman correlation, respectively. A support vector machine (SVM) model was constructed to discriminate patients into short and long OS groups dichotomized at 24-month. Results: This method identified disease-relevant phenotypes associated with OS, some of which are correlated with disease-associated molecular pathways. From these image-derived phenotypes, a generalized SVM model which could discriminate 24-month OS (area under the curve, 0.76) was obtained. Conclusion: Here, we demonstrated one potential strategy to incorporate image features derived from H and E stained slides into predictive models of OS. In addition, we showed how these image-derived phenotypic characteristics correlate with molecular signaling activity underlying the etiology or behavior of LGG