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000298356 1001_ $$0P:(DE-He78)f547f2d0863f048d6449706c72017a06$$aRitter, Michael$$b0$$eFirst author$$udkfz
000298356 245__ $$aSpatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics.
000298356 260__ $$aLondon$$bNature Research$$c2025
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000298356 500__ $$a#EA:B320#LA:B320# / 2025 Feb;6(2):292-306
000298356 520__ $$aThe diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tumor classification. A limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity, which restrict its feasibility. Here we demonstrate NePSTA (neuropathology spatial transcriptomic analysis) for comprehensive morphological and molecular neuropathological diagnostics from single 5-µm tissue sections. NePSTA uses spatial transcriptomics with graph neural networks for automated histological and molecular evaluations. Trained and evaluated across 130 participants with CNS malignancies and healthy donors across four medical centers, NePSTA predicts tissue histology and methylation-based subclasses with high accuracy. We demonstrate the ability to reconstruct immunohistochemistry and genotype profiling on tissue with minimal requirements, inadequate for conventional molecular diagnostics, demonstrating the potential to enhance tumor subtype identification with implications for fast and precise diagnostic workup.
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000298356 7001_ $$0P:(DE-He78)a4d58b3605bb0818ae8e3092b8c35182$$aBlume, Christina$$b1
000298356 7001_ $$0P:(DE-He78)885319b416123c7cae27903d64794cc0$$aTang, Yiheng$$b2$$udkfz
000298356 7001_ $$0P:(DE-He78)79506056c05e539af84edf040f0a93ad$$aPatel, Areeba Jamilkhan$$b3$$udkfz
000298356 7001_ $$0P:(DE-He78)90522f7479837e8152bce35f3f959191$$aPatel, Bhuvic$$b4
000298356 7001_ $$0P:(DE-He78)f83dff4ca396c46c049f1808e2f84000$$aBerghaus, Natalie$$b5$$udkfz
000298356 7001_ $$aKada Benotmane, Jasim$$b6
000298356 7001_ $$aKueckelhaus, Jan$$b7
000298356 7001_ $$00000-0002-1128-6038$$aYabo, Yahaya$$b8
000298356 7001_ $$00000-0002-0001-2971$$aZhang, Junyi$$b9
000298356 7001_ $$aGrabis, Elena$$b10
000298356 7001_ $$aVilla, Giulia$$b11
000298356 7001_ $$aZimmer, David Niklas$$b12
000298356 7001_ $$aKhriesh, Amir$$b13
000298356 7001_ $$0P:(DE-He78)8aad075b17d93a5636a34942bdbd7ee6$$aSievers, Philipp$$b14$$udkfz
000298356 7001_ $$0P:(DE-He78)813911a054caff29bc972432cffb790f$$aSeferbekova, Zaira$$b15$$udkfz
000298356 7001_ $$0P:(DE-He78)8126cfb1cc3fa7fa8fd5c20410c6d863$$aHinz, Felix$$b16$$udkfz
000298356 7001_ $$00000-0003-0062-2099$$aRavi, Vidhya M$$b17
000298356 7001_ $$aSeiz-Rosenhagen, Marcel$$b18
000298356 7001_ $$aRatliff, Miriam$$b19
000298356 7001_ $$aHerold-Mende, Christel$$b20
000298356 7001_ $$0P:(DE-HGF)0$$aSchnell, Oliver$$b21
000298356 7001_ $$00000-0002-7687-6098$$aBeck, Juergen$$b22
000298356 7001_ $$0P:(DE-He78)92e9783ca7025f36ce14e12cd348d2ee$$aWick, Wolfgang$$b23$$udkfz
000298356 7001_ $$0P:(DE-He78)a8a10626a848d31e70cfd96a133cc144$$avon Deimling, Andreas$$b24$$udkfz
000298356 7001_ $$0P:(DE-He78)bf8843f36606c8735a840f6278fa1e90$$aGerstung, Moritz$$b25$$udkfz
000298356 7001_ $$00000-0002-9258-3033$$aHeiland, Dieter Henrik$$b26
000298356 7001_ $$0P:(DE-He78)a1f4b408b9155beb2a8f7cba4d04fe88$$aSahm, Felix$$b27$$eLast author$$udkfz
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