Formalin-Fixed, Paraffin-Embedded-Targeted Locus Capture: A Next-Generation Sequencing Technology for Accurate DNA-Based Gene Fusion Detection in Bone and Soft Tissue Tumors.

Chromosomal rearrangements are important drivers in cancer, and their robust detection is essential for diagnosis, prognosis, and treatment selection, particularly for bone and soft tissue tumors. Current diagnostic methods are hindered by limitations, including difficulties with multiplexing targets and poor quality of RNA. A novel targeted DNA-based next-generation sequencing method, formalin-fixed, paraffin-embedded-targeted locus capture (FFPE-TLC), has shown advantages over current diagnostic methods when applied on FFPE lymphomas, including the ability to detect novel rearrangements. We evaluated the utility of FFPE-TLC in bone and soft tissue tumor diagnostics. FFPE-TLC sequencing was successfully applied on noncalcified and decalcified FFPE samples (n = 44) and control samples (n = 19). In total, 58 rearrangements were identified in 40 FFPE tumor samples, including three previously negative samples, and none was identified in the FFPE control samples. In all five discordant cases, FFPE-TLC could identify gene fusions where other methods had failed due to either detection limits or poor sample quality. FFPE-TLC achieved a high specificity and sensitivity (no false positives and negatives). These results indicate that FFPE-TLC is applicable in cancer diagnostics to simultaneously analyze many genes for their involvement in gene fusions. Similar to the observation in lymphomas, FFPE-TLC is a good DNA-based alternative to the conventional methods for detection of rearrangements in bone and soft tissue tumors.

Interplay between CTCF boundaries and a super enhancer controls cohesin extrusion trajectories and gene expression.

To understand how chromatin domains coordinate gene expression, we dissected select genetic elements organizing topology and transcription around the Prdm14 super enhancer in mouse embryonic stem cells. Taking advantage of allelic polymorphisms, we developed methods to sensitively analyze changes in chromatin topology, gene expression, and protein recruitment. We show that enhancer insulation does not rely strictly on loop formation between its flanking boundaries, that the enhancer activates the Slco5a1 gene beyond its prominent domain boundary, and that it recruits cohesin for loop extrusion. Upon boundary inversion, we find that oppositely oriented CTCF terminates extrusion trajectories but does not stall cohesin, while deleted or mutated CTCF sites allow cohesin to extend its trajectory. Enhancer-mediated gene activation occurs independent of paused loop extrusion near the gene promoter. We expand upon the loop extrusion model to propose that cohesin loading and extrusion trajectories originating at an enhancer contribute to gene activation.

Robust detection of translocations in lymphoma FFPE samples using targeted locus capture-based sequencing.

In routine diagnostic pathology, cancer biopsies are preserved by formalin-fixed, paraffin-embedding (FFPE) procedures for examination of (intra-) cellular morphology. Such procedures inadvertently induce DNA fragmentation, which compromises sequencing-based analyses of chromosomal rearrangements. Yet, rearrangements drive many types of hematolymphoid malignancies and solid tumors, and their manifestation is instructive for diagnosis, prognosis, and treatment. Here, we present FFPE-targeted locus capture (FFPE-TLC) for targeted sequencing of proximity-ligation products formed in FFPE tissue blocks, and PLIER, a computational framework that allows automated identification and characterization of rearrangements involving selected, clinically relevant, loci. FFPE-TLC, blindly applied to 149 lymphoma and control FFPE samples, identifies the known and previously uncharacterized rearrangement partners. It outperforms fluorescence in situ hybridization (FISH) in sensitivity and specificity, and shows clear advantages over standard capture-NGS methods, finding rearrangements involving repetitive sequences which they typically miss. FFPE-TLC is therefore a powerful clinical diagnostics tool for accurate targeted rearrangement detection in FFPE specimens.

Multi-contact 4C: long-molecule sequencing of complex proximity ligation products to uncover local cooperative and competitive chromatin topologies.

We present the experimental protocol and data analysis toolbox for multi-contact 4C (MC-4C), a new proximity ligation method tailored to study the higher-order chromatin contact patterns of selected genomic sites. Conventional chromatin conformation capture (3C) methods fragment proximity ligation products for efficient analysis of pairwise DNA contacts. By contrast, MC-4C is designed to preserve and collect large concatemers of proximity ligated fragments for long-molecule sequencing on an Oxford Nanopore or Pacific Biosciences platform. Each concatemer of proximity ligation products represents a snapshot topology of a different individual allele, revealing its multi-way chromatin interactions. By inverse PCR with primers specific for a fragment of interest (the viewpoint) and DNA size selection, sequencing is selectively targeted to thousands of different complex interactions containing this viewpoint. A tailored statistical analysis toolbox is able to generate background models and three-way interaction profiles from the same dataset. These profiles can be used to distinguish whether contacts between more than two regulatory sequences are mutually exclusive or, conversely, simultaneously occurring at chromatin hubs. The entire procedure can be completed in 2 w, and requires standard molecular biology and data analysis skills and equipment, plus access to a third-generation sequencing platform.

A data-driven interactome of synergistic genes improves network-based cancer outcome prediction.

Robustly predicting outcome for cancer patients from gene expression is an important challenge on the road to better personalized treatment. Network-based outcome predictors (NOPs), which considers the cellular wiring diagram in the classification, hold much promise to improve performance, stability and interpretability of identified marker genes. Problematically, reports on the efficacy of NOPs are conflicting and for instance suggest that utilizing random networks performs on par to networks that describe biologically relevant interactions. In this paper we turn the prediction problem around: instead of using a given biological network in the NOP, we aim to identify the network of genes that truly improves outcome prediction. To this end, we propose SyNet, a gene network constructed ab initio from synergistic gene pairs derived from survival-labelled gene expression data. To obtain SyNet, we evaluate synergy for all 69 million pairwise combinations of genes resulting in a network that is specific to the dataset and phenotype under study and can be used to in a NOP model. We evaluated SyNet and 11 other networks on a compendium dataset of >4000 survival-labelled breast cancer samples. For this purpose, we used cross-study validation which more closely emulates real world application of these outcome predictors. We find that SyNet is the only network that truly improves performance, stability and interpretability in several existing NOPs. We show that SyNet overlaps significantly with existing gene networks, and can be confidently predicted (~85% AUC) from graph-topological descriptions of these networks, in particular the breast tissue-specific network. Due to its data-driven nature, SyNet is not biased to well-studied genes and thus facilitates post-hoc interpretation. We find that SyNet is highly enriched for known breast cancer genes and genes related to e.g. histological grade and tamoxifen resistance, suggestive of a role in determining breast cancer outcome.

Enhancer hubs and loop collisions identified from single-allele topologies.

Chromatin folding contributes to the regulation of genomic processes such as gene activity. Existing conformation capture methods characterize genome topology through analysis of pairwise chromatin contacts in populations of cells but cannot discern whether individual interactions occur simultaneously or competitively. Here we present multi-contact 4C (MC-4C), which applies Nanopore sequencing to study multi-way DNA conformations of individual alleles. MC-4C distinguishes cooperative from random and competing interactions and identifies previously missed structures in subpopulations of cells. We show that individual elements of the ß-globin superenhancer can aggregate into an enhancer hub that can simultaneously accommodate two genes. Neighboring chromatin domain loops can form rosette-like structures through collision of their CTCF-bound anchors, as seen most prominently in cells lacking the cohesin-unloading factor WAPL. Here, massive collision of CTCF-anchored chromatin loops is believed to reflect 'cohesin traffic jams'. Single-allele topology studies thus help us understand the mechanisms underlying genome folding and functioning.

Gamma-Retrovirus Integration Marks Cell Type-Specific Cancer Genes: A Novel Profiling Tool in Cancer Genomics.

Retroviruses have been foundational in cancer research since early studies identified proto-oncogenes as targets for insertional mutagenesis. Integration of murine gamma-retroviruses into the host genome favours promoters and enhancers and entails interaction of viral integrase with host BET/bromodomain factors. We report that this integration pattern is conserved in feline leukaemia virus (FeLV), a gamma-retrovirus that infects many human cell types. Analysis of FeLV insertion sites in the MCF-7 mammary carcinoma cell line revealed strong bias towards active chromatin marks with no evidence of significant post-integration growth selection. The most prominent FeLV integration targets had little overlap with the most abundantly expressed transcripts, but were strongly enriched for annotated cancer genes. A meta-analysis based on several gamma-retrovirus integration profiling (GRIP) studies in human cells (CD34+, K562, HepG2) revealed a similar cancer gene bias but also remarkable cell-type specificity, with prominent exceptions including a universal integration hotspot at the long non-coding RNA MALAT1. Comparison of GRIP targets with databases of super-enhancers from the same cell lines showed that these have only limited overlap and that GRIP provides unique insights into the upstream drivers of cell growth. These observations elucidate the oncogenic potency of the gamma-retroviruses and support the wider application of GRIP to identify the genes and growth regulatory circuits that drive distinct cancer types.

FERAL: network-based classifier with application to breast cancer outcome prediction.

MOTIVATION: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed. In spite of the initial claims, recent studies revealed that neither performance nor consistency can be improved using these methods. NOPs typically rely on the construction of meta-genes by averaging the expression of several genes connected in a network that encodes protein interactions or pathway information. In this article, we expose several fundamental issues in NOPs that impede on the prediction power, consistency of discovered markers and obscures biological interpretation. RESULTS: To overcome these issues, we propose FERAL, a network-based classifier that hinges upon the Sparse Group Lasso which performs simultaneous selection of marker genes and training of the prediction model. An important feature of FERAL, and a significant departure from existing NOPs, is that it uses multiple operators to summarize genes into meta-genes. This gives the classifier the opportunity to select the most relevant meta-gene for each gene set. Extensive evaluation revealed that the discovered markers are markedly more stable across independent datasets. Moreover, interpretation of the marker genes detected by FERAL reveals valuable mechanistic insight into the etiology of breast cancer. AVAILABILITY AND IMPLEMENTATION: All code is available for download at: http://homepage.tudelft.nl/53a60/resources/FERAL/FERAL.zip.