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.</p

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

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.</p

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