TY - JOUR A1 - Munchel, Sarah A1 - Hoang, Yen A1 - Zhao, Yue A1 - Cottrell, Joseph A1 - Klotzle, Brandy A1 - Godwin, Andrew K. A1 - Koestler, Devin A1 - Beyerlein, Peter A1 - Fan, Jian-Bing A1 - Bibikova, Marina A1 - Chien, Jeremy T1 - Targeted or whole genome sequencing of formalin fixed tissue samples: potential applications in cancer genomics JF - Oncotarget N2 - Current genomic studies are limited by the poor availability of fresh-frozen tissue samples. Although formalin-fixed diagnostic samples are in abundance, they are seldom used in current genomic studies because of the concern of formalin-fixation artifacts. Better characterization of these artifacts will allow the use of archived clinical specimens in translational and clinical research studies. To provide a systematic analysis of formalin-fixation artifacts on Illumina sequencing, we generated 26 DNA sequencing data sets from 13 pairs of matched formalin-fixed paraffin-embedded (FFPE) and fresh-frozen (FF) tissue samples. The results indicate high rate of concordant calls between matched FF/FFPE pairs at reference and variant positions in three commonly used sequencing approaches (whole genome, whole exome, and targeted exon sequencing). Global mismatch rates and C·G > T·A substitutions were comparable between matched FF/FFPE samples, and discordant rates were low (<0.26%) in all samples. Finally, low-pass whole genome sequencing produces similar pattern of copy number alterations between FF/FFPE pairs. The results from our studies suggest the potential use of diagnostic FFPE samples for cancer genomic studies to characterize and catalog variations in cancer genomes. KW - cancer genomics KW - FFPE DNA KW - whole exome sequencing KW - whole genome sequencing KW - copy number alteration Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-6307 SN - 1949-2553 VL - 6 IS - 28 SP - 25943 EP - 25961 ER - TY - JOUR A1 - Graw, Stefan A1 - Meier, Richard A1 - Minn, Kay A1 - Bloomer, Clark A1 - Godwin, Andrew K. A1 - Fridley, Brooke A1 - Vlad, Anda A1 - Beyerlein, Peter A1 - Chien, Jeremy T1 - Robust gene expression and mutation analyses of RNA-sequencing of formalin-fixed diagnostic tumor samples JF - Scientific Reports N2 - Current genomic studies are limited by the availability of fresh tissue samples. Here, we show that Illumina RNA sequencing of formalin-fixed diagnostic tumor samples produces gene expression that is strongly correlated with matched frozen tumor samples (r > 0.89). In addition, sequence variations identified from FFPE RNA show 99.67% concordance with that from exome sequencing of matched frozen tumor samples. Because FFPE is a routine diagnostic sample preparation, the feasibility results reported here will facilitate the setup of large-scale research and clinical studies in medical genomics that are currently limited by the availability of fresh frozen samples. Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-6319 SN - 2045-2322 VL - 5 ER - TY - JOUR A1 - Meier, Richard A1 - Graw, Stefan A1 - Beyerlein, Peter A1 - Koestler, Devin A1 - Molina, Julian R. A1 - Chien, Jeremy T1 - digit—a tool for detection and identification of genomic interchromosomal translocations JF - Nucleic Acids Research N2 - Structural variations (SVs) in genomic DNA can have profound effects on the evolution of living organisms, on phenotypic variations and on disease processes. A critical step in discovering the full extent of structural variations is the development of tools to characterize these variations accurately in next generation sequencing data. Toward this goal, we developed a software pipeline named digit that implements a novel measure of mapping ambiguity to discover interchromosomal SVs from mate-pair and pair-end sequencing data. The workflow robustly handles the high numbers of artifacts present in mate-pair sequencing and reduces the false positive rate while maintaining sensitivity. In the simulated data set, our workflow recovered 96% of simulated SVs. It generates a self-updating library of common translocations and allows for the investigation of patient- or group-specific events, making it suitable for discovering and cataloging chromosomal translocations associated with specific groups, traits, diseases or population structures. Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-9697 SN - 1362-4962 VL - 45 IS - 9 SP - e72 ER - TY - JOUR A1 - Schilling, Vincent A1 - Beyerlein, Peter A1 - Chien, Jeremy T1 - A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning JF - Algorithms N2 - The identification of biomarkers is crucial for cancer diagnosis, understanding the underlying biological mechanisms, and developing targeted therapies. In this study, we propose a machine learning approach to predict ovarian cancer patients’ outcomes and platinum resistance status using publicly available gene expression data. Six classical machine-learning algorithms are compared on their predictive performance. Those with the highest score are analyzed by their feature importance using the SHAP algorithm. We were able to select multiple genes that correlated with the outcome and platinum resistance status of the patients and validated those using Kaplan–Meier plots. In comparison to similar approaches, the performance of the models was higher, and different genes using feature importance analysis were identified. The most promising identified genes that could be used as biomarkers are TMEFF2, ACSM3, SLC4A1, and ALDH4A1. KW - ovarian cancer KW - machine learning KW - SHAP KW - diagnostic biomarkers KW - platinum resistance Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17751 VL - 16 IS - 7 PB - MDPI ER -