- starks@ inf.ethz.ch
- +41 44 632 23 74
Department of Computer Science
Biomedical Informatics Group Universitätsstrasse 6
- CAB F52.1
Abstract The recent adoption of Electronic Health Records (EHRs) by health care providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opportunities for research and clinical care. However, many methods require the patient representations to be in structured formats, while the information in the EHR is often locked in unstructured texts designed for human readability. In this work, we develop the methodology to automatically extract clinical features from clinical narratives from large EHR corpora without the need for prior knowledge. We consider medical terms and sentences appearing in clinical narratives as atomic information units. We propose an efficient clustering strategy suitable for the analysis of large text corpora and to utilize the clusters to represent information about the patient compactly. To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4,007 cancer patients and their tumors. We apply the proposed algorithm to a dataset consisting of about 65 thousand documents with a total of about 3.2 million sentences. We identify 341 significant statistical associations between the presence of somatic mutations and clinical features. We annotated these associations according to their novelty, and report several known associations. We also propose 32 testable hypotheses where the underlying biological mechanism does not appear to be known but plausible. These results illustrate that the …
Authors Stefan G Stark, Stephanie L Hyland, Melanie F Pradier, Kjong-Van Lehmann, Andreas Wicki, Fernando Perez Cruz, Julia E Vogt, Gunnar Rätsch
Abstract Our comprehensive analysis of alternative splicing across 32 The Cancer Genome Atlas cancer types from 8,705 patients detects alternative splicing events and tumor variants by reanalyzing RNA and whole-exome sequencing data. Tumors have up to 30% more alternative splicing events than normal samples. Association analysis of somatic variants with alternative splicing events confirmed known trans associations with variants in SF3B1 and U2AF1 and identified additional trans-acting variants (e.g., TADA1, PPP2R1A). Many tumors have thousands of alternative splicing events not detectable in normal samples; on average, we identified ≈930 exon-exon junctions (“neojunctions”) in tumors not typically found in GTEx normals. From Clinical Proteomic Tumor Analysis Consortium data available for breast and ovarian tumor samples, we confirmed ≈1.7 neojunction- and ≈0.6 single nucleotide variant-derived peptides per tumor sample that are also predicted major histocompatibility complex-I binders (“putative neoantigens”).
Authors Andre Kahles, Kjong-Van Lehmann, Nora C. Toussaint, Matthias Hüser, Stefan Stark, Timo Sachsenberg, Oliver Stegle, Oliver Kohlbacher, Chris Sander, Gunnar Rätsch, The Cancer Genome Atlas Research Network
Submitted Cancer Cell
Abstract We present the most comprehensive catalogue of cancer-associated gene alterations through characterization of tumor transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes project. Using matched whole-genome sequencing data, we attributed RNA alterations to germline and somatic DNA alterations, revealing likely genetic mechanisms. We identified 444 associations of gene expression with somatic non-coding single-nucleotide variants. We found 1,872 splicing alterations associated with somatic mutation in intronic regions, including novel exonization events associated with Alu elements. Somatic copy number alterations were the major driver of total gene and allele-specific expression (ASE) variation. Additionally, 82% of gene fusions had structural variant support, including 75 of a novel class called "bridged" fusions, in which a third genomic location bridged two different genes. Globally, we observe transcriptomic alteration signatures that differ between cancer types and have associations with DNA mutational signatures. Given this unique dataset of RNA alterations, we also identified 1,012 genes significantly altered through both DNA and RNA mechanisms. Our study represents an extensive catalog of RNA alterations and reveals new insights into the heterogeneous molecular mechanisms of cancer gene alterations.
Authors Claudia Calabrese, Natalie R Davidson, Nuno A Fonseca, Yao He, André Kahles, Kjong-Van Lehmann, Fenglin Liu, Yuichi Shiraishi, Cameron M Soulette, Lara Urban, Deniz Demircioğlu, Liliana Greger, Siliang Li, Dongbing Liu, Marc D Perry, Linda Xiang, Fan Zhang, Junjun Zhang, Peter Bailey, Serap Erkek, Katherine A Hoadley, Yong Hou, Helena Kilpinen, Jan O Korbel, Maximillian G Marin, Julia Markowski, Tannistha Nandi, Qiang Pan-Hammarström, Chandra S Pedamallu, Reiner Siebert, Stefan G Stark, Hong Su, Patrick Tan, Sebastian M Waszak, Christina Yung, Shida Zhu, Philip Awadalla, Chad J Creighton, Matthew Meyerson, B Francis F Ouellette, Kui Wu, Huanming Yang, Alvis Brazma, Angela N Brooks, Jonathan Göke, Gunnar Rätsch, Roland F Schwarz, Oliver Stegle, Zemin Zhang