Biology subjects

Fitch, J. R.

Publications and source records attributed to Fitch, J. R..

4 recordsLinked to original sources

bioRxiv2021

Discovery of Clinically Relevant Fusions in Pediatric Cancer

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSPediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions. ResultsOur ensemble fusion detection approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and AWS serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a "known fusion list" prevents failure to report known pathogenic events. We have employed the ensemble fusion-calling pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patients medical record, both known and novel fusions provided medically meaningful information. ConclusionsOur ensemble fusion detection pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

genomics↗

bioRxiv2020

Pioneer activity of an oncogenic fusion transcription factor at inaccessible chromatin

Recent characterizations of pioneer transcription factors have led to new insights into their structures and patterns of chromatin recognition that are instructive for understanding their role in cell fate commitment and transformation. Intersecting with these basic science concepts, the identification of pioneer factors (PFs) fused together as driver translocations in childhood cancers raises questions of whether these fusions retain the fundamental ability to invade repressed chromatin, consistent with their monomeric PF constituents. In this study, we define the cellular and chromatin localization of the translocation, PAX3-FOXO1, an oncogenic driver of childhood rhabdomyosarcoma (RMS), derived from a genetic fusion of PFs. To quantitatively define its chromatin-targeting functions and capacity to drive epigenetic reprogramming, we developed a new method for ChIP-seq with per-cell normalization (pc-ChIP-seq). Our quantitative localization studies address structural variation in RMS genomes and reveal novel insights into heterochromatin localization of PAX3-FOXO1. From these studies, we report novel pioneer function for the major driver oncogene in RMS, with repressed chromatin binding and nucleosome-motif targeting in human cells.

genomics↗

bioRxiv2019

Global Analysis of Human mRNA Folding Disruptions in Synonymous Variants Demonstrates Significant Population Constraint

BackgroundIn most organisms the structure of an mRNA molecule is crucial in determining speed of translation, half-life, splicing propensities and final protein configuration. Synonymous variants which distort this wildtype mRNA structure may be pathogenic as a consequence. However, current clinical guidelines classify synonymous or "silent" single nucleotide variants (sSNVs) as largely benign unless a role in RNA splicing can be demonstrated. ResultsWe developed novel software to conduct a global transcriptome study in which RNA folding statistics were computed for 469 million SNVs in 45,800 transcripts using an Apache Spark implementation of ViennaRNA in the cloud. Focusing our analysis on the subset of 17.9 million sSNVs, we discover that variants predicted to disrupt mRNA structure have lower rates of incidence in the human population. Given that the community lacks tools to evaluate the potential pathogenic impact of sSNVs, we introduce a "Structural Predictivity Index" (SPI) to quantify this constraint due to mRNA structure. ConclusionsOur findings support the hypothesis that sSNVs may play a role in genetic disorders due to their effects on mRNA structure. Our RNA-folding scores provide a means of gauging the structural constraint operating on any sSNV in the human genome. Given that the majority of patients with rare or as yet to be diagnosed disease lack a molecular diagnosis, these scores have the potential to enable discovery of novel genetic etiologies. Our RNA Stability Pipeline as well as ViennaRNA structural metrics and SPI scores for all human synonymous variants can be downloaded from GitHub https://github.com/nch-igm/rna-stability.

genomics↗

bioRxiv2019

Samovar: Single-sample mosaic SNV calling with linked reads

We present Samovar, a mosaic single-nucleotide variant (SNV) caller for linked-read whole-genome shotgun sequencing data. Samovar scores candidate sites using a random forest model trained using the input dataset that considers read quality, phasing, and linked-read characteristics. We show Samovar calls mosaic SNVs within a single sample with accuracy comparable to what previously required trios or matched tumor/normal pairs and outperform single-sample mosaic variant callers at MAF 5%-50% with at least 30x coverage. Furthermore, we use Samovar to find somatic variants in whole genome sequencing of both tumor and normal from 13 pediatric cancer cases that can be corroborated with high recall with whole exome sequencing. Samovar is available open-source at https://github.com/cdarby/samovar under the MIT license.

genomics↗