Development and validation of a 36-gene sequencing assay for hereditary cancer risk assessment.

The past two decades have brought many important advances in our understanding of the hereditary susceptibility to cancer. Numerous studies have provided convincing evidence that identification of germline mutations associated with hereditary cancer syndromes can lead to reductions in morbidity and mortality through targeted risk management options. Additionally, advances in gene sequencing technology now permit the development of multigene hereditary cancer testing panels. Here, we describe the 2016 revision of the Counsyl Inherited Cancer Screen for detecting single-nucleotide variants (SNVs), short insertions and deletions (indels), and copy number variants (CNVs) in 36 genes associated with an elevated risk for breast, ovarian, colorectal, gastric, endometrial, pancreatic, thyroid, prostate, melanoma, and neuroendocrine cancers. To determine test accuracy and reproducibility, we performed a rigorous analytical validation across 341 samples, including 118 cell lines and 223 patient samples. The screen achieved 100% test sensitivity across different mutation types, with high specificity and 100% concordance with conventional Sanger sequencing and multiplex ligation-dependent probe amplification (MLPA). We also demonstrated the screen's high intra-run and inter-run reproducibility and robust performance on blood and saliva specimens. Furthermore, we showed that pathogenic Alu element insertions can be accurately detected by our test. Overall, the validation in our clinical laboratory demonstrated the analytical performance required for collecting and reporting genetic information related to risk of developing hereditary cancers.

RNA Bind-n-Seq: Measuring the Binding Affinity Landscape of RNA-Binding Proteins.

RNA-binding proteins (RBPs) coordinate post-transcriptional control of gene expression, often through sequence-specific recognition of primary transcripts or mature messenger RNAs. Hundreds of RBPs are encoded in the human genome, most with undefined or incompletely defined biological roles. Understanding the function of these factors will require the identification of each RBP's distinct RNA binding specificity. RNA Bind-n-Seq (RBNS) is a high-throughput, cost-effective in vitro method capable of resolving sequence and secondary structure preferences of RBPs. Dissociation constants can also be inferred from RBNS data when provided with additional experimental information. Here, we describe the experimental procedures to perform RBNS and discuss important parameters of the method and ways that the experiment can be tailored to the specific RBP under study. Additionally, we present the conceptual framework and execution of the freely available RBNS computational pipeline and describe the outputs of the pipeline. Different approaches to quantify binding specificity, quality control metrics, and estimation of binding constants are also covered.

Global analyses of UPF1 binding and function reveal expanded scope of nonsense-mediated mRNA decay.

UPF1 is a DNA/RNA helicase with essential roles in nonsense-mediated mRNA decay (NMD) and embryonic development. How UPF1 regulates target abundance and the relationship between NMD and embryogenesis are not well understood. To explore how NMD shapes the embryonic transcriptome, we integrated genome-wide analyses of UPF1 binding locations, NMD-regulated gene expression, and translation in murine embryonic stem cells (mESCs). We identified over 200 direct UPF1 binding targets using crosslinking/immunoprecipitation-sequencing (CLIP-seq) and revealed a repression pathway that involves 3' UTR binding by UPF1 and translation but is independent of canonical targeting features involving 3' UTR length and stop codon placement. Interestingly, NMD targeting of this set of mRNAs occurs in other mouse tissues and is conserved in human. We also show, using ribosome footprint profiling, that actively translated upstream open reading frames (uORFs) are enriched in transcription factor mRNAs and predict mRNA repression by NMD, while poorly translated mRNAs escape repression. Together, our results identify novel NMD determinants and targets and provide context for understanding the impact of UPF1 and NMD on the mESC transcriptome.