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Vohringer, H.

Publications and source records attributed to Vohringer, H..

2 recordsLinked to original sources

Learning mutational signatures and their multidimensional genomic properties with TensorSignatures

Mutational signature analysis is an essential part of the cancer genome analysis toolkit. Conventionally, mutational signature analysis extracts patterns of different mutation types across many cancer genomes. Here we present TensorSignatures, an algorithm to learn mutational signatures jointly across all variant categories and their genomic context. The analysis of 2,778 primary and 3,824 metastatic cancer genomes of the PCAWG consortium and the HMF cohort shows that practically all signatures operate dynamically in response to various genomic and epigenomic states. The analysis pins differential spectra of UV mutagenesis found in active and inactive chromatin to global genome nucleotide excision repair. TensorSignatures accurately characterises transcription-associated mutagenesis, which is detected in 7 different cancer types. The analysis also unmasks replication- and double strand break repair-driven APOBEC mutagenesis, which manifests with differential numbers and length of mutation clusters indicating a differential processivity of the two triggers. As a fourth example, TensorSignatures detects a signature of somatic hypermutation generating highly clustered variants around the transcription start sites of active genes in lymphoid leukaemia, distinct from a more general and less clustered signature of Pol{eta}-driven translesion synthesis found in a broad range of cancer types. Key findingsO_LISimultaneous inference of mutational signatures across mutation types and genomic features refines signature spectra and defines their genomic determinants. C_LIO_LIAnalysis of 6,602 cancer genomes reveals pervasive intra-genomic variation of mutational processes. C_LIO_LIDistinct mutational signatures found in quiescent and active regions of the genome reveal differential repair and mutagenicity of UV- and tobacco-induced DNA damage. C_LIO_LIAPOBEC mutagenesis produces two signatures reflecting highly clustered, double strand break repair-initiated and lowly clustered replication-driven mutagenesis, respectively. C_LIO_LISomatic hypermutation in lymphoid cancers produces a strongly clustered mutational signature localised to transcription start sites, which is distinct from a weakly clustered translesion synthesis signature found in multiple tumour types. C_LI

cancer biology

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis

The diagnosis of cancer is typically based on histopathological assessment of tissue sections, and supplemented by genetic and other molecular tests1-6. Modern computer vision algorithms have high diagnostic accuracy and potential to augment histopathology workflows7-9. Here we use deep transfer learning to quantify histopathological patterns across 17,396 hematoxylin and eosin (H&E) stained histopathology slide images from 28 cancer types and correlate these with matched genomic, transcriptomic and survival data. This approach accurately classifies cancer types and provides spatially resolved tumor and normal distinction. Automatically learned computational histopathological features correlate with a large range of recurrent genetic aberrations pan-cancer. This includes whole genome duplications, which display universal features across cancer types, individual chromosomal aneuploidies, focal amplifications and deletions as well as driver gene mutations. There are wide-spread associations between bulk gene expression levels and histopathology, which reflect tumour composition and enables localising transcriptomically defined tumour infiltrating lymphocytes. Computational histopathology augments prognosis based on histopathological subtyping and grading and highlights prognostically relevant areas such as necrosis or lymphocytic aggregates. These findings demonstrate the large potential of computer vision to characterise the molecular basis of tumour histopathology and lay out a rationale for integrating molecular and histopathological data to augment diagnostic and prognostic workflows.

bioinformatics