DIAGNOSTIC IMPACT OF THE CNS TUMOR METHYLATION PROFILING IN A NEUROPATHOLOGY CONSULT PRACTICE

Abstract DNA methylation profiling coupled with the application of CNS tumor methylation classifier has contributed to precise and accurate diagnostics for a range of tumor types involving the central nervous system. The impact and characteristics of methylation profiling on tumor diagnosis has not been fully assessed in the setting of neuropathology consultation practice. A consecutive series of 1,258 surgical neuropathology samples obtained primarily in a consultation practice were profiled over 2-year period and analyzed using the DKFZ/Heidelberg CNS tumor methylation classifier. Among the 1,045 cases received from outside institutions for consultation, the classifier was able to refine a histologically diagnosed entity (e.g. medulloblastoma) in 13.3% (n = 139) cases. A substantially new diagnosis was able to be rendered in an additional 17.9% (n = 187) cases, many of which could be confirmed using orthogonal methods. A “suggestive” (0.30-0.84) classifier score was found in 23% (242) cases and we found that complementary methods (UMAP, t-SNE and nearest-neighbors) were able to resolve this uncertainty in 118 cases. We found tumor purity significantly associated with varied classifier score (p = 1.53e-11). Computational tumor purity adjustment by deconvolution on a subset of gliomas provided a proof-of-concept to resolve diagnostics in the setting of low tumor purity. Overall, this work directly assesses the benefit of methylation classification in a set of diagnostically challenging CNS tumors, addresses tumor purity diminished methylation signal and provides complementary approaches to address diagnostics in cases of low-confidence classifier scores.