Mapping Protein-DNA Interactions Using ChIP-exo and Illumina-Based Sequencing

Chromatin immunoprecipitation (ChIP) provides a means of enriching DNA associated with transcription factors, histone modifications, and indeed any other proteins for which suitably characterized antibodies are available. Over the years, sequence detection has progressed from quantitative real-time PCR and Southern blotting to microarrays (ChIP-chip) and now high-throughput sequencing (ChIP-seq). This progression has vastly increased the sequence coverage and data volumes generated. This in turn has enabled informaticians to predict the identity of multi-protein complexes on DNA based on the overrepresentation of sequence motifs in DNA enriched by ChIP with a single antibody against a single protein. In the course of the development of high-throughput sequencing, little has changed in the ChIP methodology until recently. In the last three years, a number of modifications have been made to the ChIP protocol with the goal of enhancing the sensitivity of the method and further reducing the levels of nonspecific background sequences in ChIPped samples. In this chapter, we provide a brief commentary on these methodological changes and describe a detailed ChIP-exo method able to generate narrower peaks and greater peak coverage from ChIPped material.

Inter-Laboratory Evaluation of a Next-Generation Sequencing Panel for Acute Myeloid Leukemia

INTRODUCTION: Acute myeloid leukemia (AML) is a heterogeneous clonal disorder often associated with dismal overall survival. The clinical diversity of AML is reflected in the range of recurrent somatic mutations in several genes, many of which have a prognostic and therapeutic value. Targeted next-generation sequencing (NGS) of these genes has the potential for translation into clinical practice. In order to assess this potential, an inter-laboratory evaluation of a commercially available AML gene panel across three diagnostic centres in the UK and Ireland was performed.

METHODS: DNA from six AML patient samples was distributed to each centre and processed using a standardised workflow, including a common sequencing platform, sequencing chips and bioinformatics pipeline. A duplicate sample in each centre was run to assess inter- and intra-laboratory performance.

RESULTS: An average sample read depth of 2725X (range 629-5600) was achieved using six samples per chip, with some variability observed in the depth of coverage generated for individual samples and between centres. A total of 16 somatic mutations were detected in the six AML samples, with a mean of 2.7 mutations per sample (range 1-4) representing nine genes on the panel. 15/16 mutations were identified by all three centres. Allelic frequencies of the mutations ranged from 5.6 to 53.3 % (median 44.4 %), with a high level of concordance of these frequencies between centres, for mutations detected.

CONCLUSION: In this inter-laboratory comparison, a high concordance, reproducibility and robustness was demonstrated using a commercially available NGS AML gene panel and platform.

Understanding next generation sequencing in oncology: A guide for oncologists

DNA sequencing is now faster and cheaper than ever before, due to the development of next generation sequencing (NGS) technologies. NGS is now widely used in the research setting and is becoming increasingly utilised in clinical practice. However, due to evolving clinical commitments, increased workload and lack of training opportunities, many oncologists may be unfamiliar with the terminology and technology involved. This can lead to oncologists feeling daunted by issues such as how to interpret the vast amounts of data generated by NGS and the differences between sequencing platforms. This review article explains common concepts and terminology, summarises the process of DNA sequencing (including data analysis) and discusses the main factors to consider when deciding on a sequencing method. This article aims to improve oncologists' understanding of the most commonly used sequencing platforms and the ongoing challenges faced in expanding the use of NGS into routine clinical practice.

Genetics and Prognostication in Splenic Marginal Zone Lymphoma: Revelations from Deep Sequencing

Purpose: Mounting evidence supports the clinical significance of gene mutations and immunogenetic features in common mature B-cell malignancies.

Experimental Design: We undertook a detailed characterization of the genetic background of splenic marginal zone lymphoma (SMZL), using targeted resequencing and explored potential clinical implications in a multinational cohort of 175 patients with SMZL.

Results: We identified recurrent mutations in TP53 (16%), KLF2 (12%), NOTCH2 (10%), TNFAIP3 (7%), MLL2 (11%), MYD88 (7%), and ARID1A (6%), all genes known to be targeted by somatic mutation in SMZL. KLF2 mutations were early, clonal events, enriched in patients with del(7q) and IGHV1-2*04 B-cell receptor immunoglobulins, and were associated with a short median time to first treatment (0.12 vs. 1.11 years; P = 0.01). In multivariate analysis, mutations in NOTCH2 [HR, 2.12; 95% confidence interval (CI), 1.02–4.4; P = 0.044] and 100% germline IGHV gene identity (HR, 2.19; 95% CI, 1.05–4.55; P = 0.036) were independent markers of short time to first treatment, whereas TP53 mutations were an independent marker of short overall survival (HR, 2.36; 95 % CI, 1.08–5.2; P = 0.03).

Conclusions: We identify key associations between gene mutations and clinical outcome, demonstrating for the first time that NOTCH2 and TP53 gene mutations are independent markers of reduced treatment-free and overall survival, respectively.