Uncovering the Genomic Landscape in Newly Diagnosed and Relapsed Pediatric Cytogenetically Normal FLT3-ITD AML.

Fms-like tyrosine kinase 3 (FLT3) internal tandem duplication (ITD) mutations, common in pediatric acute myeloid leukemia (AML), associate with early relapse and poor prognosis. Past studies have suggested additional cooperative mutations are required for leukemogenesis in FLT3-ITD+ AML. Using RNA sequencing and a next-generation targeted gene panel, we broadly characterize the co-occurring genomic alterations in pediatric cytogenetically normal (CN) FLT3-ITD+ AML to gain a deeper understanding of the clonal patterns and heterogeneity at diagnosis and relapse. We show that chimeric transcripts were present in 21 of 34 (62%) of de novo samples, 2 (6%) of these samples included a rare reoccurring fusion partner BCL11B. At diagnosis, the median number of mutations other than FLT3 per patient was 1 (range 0-3), which involved 8 gene pathways; WT1 and NPM1 mutations were frequently observed (35% and 24%, respectively). Fusion transcripts and high variant allele frequency (VAF) mutants, which included WT1, NPM1, SMARCA2, RAD21, and TYK2, were retained from diagnosis to relapse. We did observe reduction in VAF of simple or single mutation clones, but VAFs were preserved or expanded in more complex clones with multiple mutations. Our data provide the first insight into the genomic complexity of pediatric CN FLT3-ITD+ AML and could help stratify future targeted treatment strategies.

Mutational Landscape and Gene Expression Patterns in Adult Acute Myeloid Leukemias with Monosomy 7 as a Sole Abnormality.

Monosomy of chromosome 7 is the most frequent autosomal monosomy in acute myeloid leukemia (AML), where it associates with poor clinical outcomes. However, molecular features associated with this sole monosomy subtype (-7 AML), which may give insights into the basis for its poor prognosis, have not been characterized. In this study, we analyzed 36 cases of -7 AML for mutations in 81 leukemia/cancer-associated genes using a customized targeted next-generation sequencing panel (Miseq). Global gene and miRNA expression profiles were also determined using paired RNA and small RNA sequencing data. Notably, gene mutations were detected in all the major AML-associated functional groups, which include activated signaling, chromatin remodeling, cohesin complex, methylation, NPM1, spliceosome, transcription factors, and tumor suppressors. Gene mutations in the chromatin remodeling groups were relatively more frequent in patients <60 years of age, who also had less mutations in the methylation and spliceosome groups compared with patients ≥60 years of age. Novel recurrent mutational events in AML were identified in the SMARCA2 gene. In patients ≥60 years of age, the presence of spliceosome mutations associated with a lower complete remission rate (P = 0.03). RNA sequencing revealed distinct gene and miRNA expression patterns between the sole -7 and non -7 AML cases, with reduced expression, as expected, of many genes and miRNAs mapped to chromosome 7, and overexpression of ID1, MECOM, and PTPRM, among others. Overall, our findings illuminate a number of molecular features of the underlying aggressive pathobiology in -7 AML patients. Cancer Res; 77(1); 207-18. ©2016 AACR.

Complete Transcriptome RNA-Seq.

RNA-Seq is the leading technology for analyzing gene expression on a global scale across a broad spectrum of sample types. However, due to chemical modifications by fixation or degradation due to collection methods, samples often contain an abundance of RNA that is no longer intact, and the capability of current RNA-Seq protocols to accurately quantify such samples is often limited. We have developed an RNA-Seq protocol to address these key issues as well as quantify gene expression from the whole transcriptome. Furthermore, for compatibility with improved sequencing platforms, we use restructured adapter sequences to generate libraries for Illumina HiSeq, MiSeq, and NextSeq platforms. Our protocol utilizes duplex-specific nuclease (DSN) to remove abundant ribosomal RNA sequences while retaining other types of RNA for superior transcriptome profiling from low quantity input. We employ the Illumina sequencing platform, but this method is described in sufficient detail to adapt to other platforms.

MonoSeq Variant Caller Reveals Novel Mononucleotide Run Indel Mutations in Tumors with Defective DNA Mismatch Repair.

Next-generation sequencing has revolutionized cancer genetics, but accurately detecting mutations in repetitive DNA sequences, especially mononucleotide runs, remains a challenge. This is a particular concern for tumors with defective mismatch repair (MMR) that accumulate strand-slippage mutations. We developed MonoSeq to improve indel mutation detection in mononucleotide runs, and used MonoSeq to investigate strand-slippage mutations in endometrial cancers, a tumor type that has frequent loss of MMR. We performed extensive Sanger sequencing to validate both clonal and subclonal MonoSeq mutation calls. Eighty-one regions containing mononucleotide runs were sequenced in 540 primary endometrial cancers (223 with defective MMR). Our analyses revealed that the overall mutation rate in MMR-deficient tumors was 20-30-fold higher than in MMR-normal tumors. MonoSeq analysis identified several previously unreported mutations, including a novel hotspot in an A7 run in the terminal exon of ARID5B.The ARID5B indel mutations were seen in both MMR-deficient and MMR-normal tumors, suggesting biologic selection. The analysis of tumor mRNAs revealed the presence of mutant transcripts that could result in translation of neopeptides. Improved detection of mononucleotide run strand-slippage mutations has clear implications for comprehensive mutation detection in tumors with defective MMR. Indel frameshift mutations and the resultant antigenic peptides could help guide immunotherapy strategies.

MuCor: mutation aggregation and correlation.

MOTIVATION: There are many tools for variant calling and effect prediction, but little to tie together large sample groups. Aggregating, sorting and summarizing variants and effects across a cohort is often done with ad hoc scripts that must be re-written for every new project. In response, we have written MuCor, a tool to gather variants from a variety of input formats (including multiple files per sample), perform database lookups and frequency calculations, and write many types of reports. In addition to use in large studies with numerous samples, MuCor can also be employed to directly compare variant calls from the same sample across two or more platforms, parameters or pipelines. A companion utility, DepthGauge, measures coverage at regions of interest to increase confidence in calls. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/blachlylab/mucor and a Docker image is available at https://hub.docker.com/r/blachlylab/mucor/ CONTACT: james.blachly@osumc.eduSupplementary data: Supplementary data are available at Bioinformatics online.

Quality Control for RNA-Seq (QuaCRS): An Integrated Quality Control Pipeline.

QuaCRS (Quality Control for RNA-Seq) is an integrated, simplified quality control (QC) system for RNA-seq data that allows easy execution of several open-source QC tools, aggregation of their output, and the ability to quickly identify quality issues by performing meta-analyses on QC metrics across large numbers of samples in different studies. It comprises two main sections. First is the QC Pack wrapper, which executes three QC tools: FastQC, RNA-SeQC, and selected functions from RSeQC. Combining these three tools into one wrapper provides increased ease of use and provides a much more complete view of sample data quality than any individual tool. Second is the QC database, which displays the resulting metrics in a user-friendly web interface. It was designed to allow users with less computational experience to easily generate and view QC information for their data, to investigate individual samples and aggregate reports of sample groups, and to sort and search samples based on quality. The structure of the QuaCRS database is designed to enable expansion with additional tools and metrics in the future. The source code for not-for-profit use and a fully functional sample user interface with mock data are available at http://bioserv.mps.ohio-state.edu/QuaCRS/.