Biology subjects

Song, F.

Publications and source records attributed to Song, F..

3 recordsLinked to original sources

bioRxiv2017

Genome-wide and high-density CRISPR-Cas9 screens identify point mutations in PARP1 causing PARP inhibitor resistance

PARP inhibitors (PARPi) target homologous recombination defective tumour cells via synthetic lethality. Genome-wide and high-density CRISPR-Cas9 \"tag, mutate and enrich\" mutagenesis screens identified single amino acid mutations in PARP1 that cause profound PARPi-resistance. These included PARP1 mutations outside of the DNA interacting regions of the protein, such as mutations in solvent exposed regions of the catalytic domain and clusters of mutations around points of contact between ZnF, WGR and HD domains. These mutations altered PARP1 trapping, as did a mutation found in a clinical case of PARPi resistance. These genetic studies reinforce the importance of trapped PARP1 as a key cytotoxic DNA lesion and suggest that interactions between non-DNA binding domains of PARP1 influence cytotoxicity. Finally, different mechanisms of PARPi resistance (BRCA1 reversion, PARP1, 53BP1, REV7 mutation) had differing effects on chemotherapy sensitivity, suggesting that the underlying mechanism of PARPi resistance likely influences the success of subsequent therapies.

cancer biology↗

bioRxiv2017

Measuring the reproducibility and quality of Hi-C data

Hi-C is currently the most widely used assay to investigate the 3D organization of the genome and to study its role in gene regulation, DNA replication, and disease. However, Hi-C experiments are costly to perform and involve multiple complex experimental steps; thus, accurate methods for measuring the quality and reproducibility of Hi-C data are essential to determine whether the output should be used further in a study. Using real and simulated data, we profile the performance of several recently proposed methods for assessing reproducibility of population Hi-C data, including HiCRep, GenomeDISCO, HiC-Spector and QuASAR-Rep. By explicitly controlling noise and sparsity through simulations, we demonstrate the deficiencies of performing simple correlation analysis on pairs of matrices, and we show that methods developed specifically for Hi-C data produce better measures of reproducibility. We also show how to use established (e.g., ratio of intra to interchromosomal interactions) and novel (e.g., QuASAR-QC) measures to identify low quality experiments. In this work, we assess reproducibility and quality measures by varying sequencing depth, resolution and noise levels in Hi-C data from 13 cell lines, with two biological replicates each, as well as 176 simulated matrices. Through this extensive validation and benchmarking of Hi-C data, we describe best practices for reproducibility and quality assessment of Hi-C experiments. We make all software publicly available at http://github.com/kundajelab/3DChromatin_ReplicateQC to facilitate adoption in the community.

genomics↗

bioRxiv2017

An Integrative Framework For Detecting Structural Variations In Cancer Genomes

Structural variants can contribute to oncogenesis through a variety of mechanisms, yet, despite their importance, the identification of structural variants in cancer genomes remains challenging. Here, we present an integrative framework for comprehensively identifying structural variation in cancer genomes. For the first time, we apply next-generation optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole genome sequencing to systematically detect SVs in a variety of cancer cells.\n\nUsing this approach, we identify and characterize structural variants in up to 29 commonly used normal and cancer cell lines. We find that each method has unique strengths in identifying different classes of structural variants and at different scales, suggesting that integrative approaches are likely the only way to comprehensively identify structural variants in the genome. Studying the impact of the structural variants in cancer cell lines, we identify widespread structural variation events affecting the functions of non-coding sequences in the genome, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel 3D chromatin structural domains.\n\nThese results underscore the importance of comprehensive structural variant identification and indicate that non-coding structural variation may be an underappreciated mutational process in cancer genomes.

genomics↗