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  • br We retrieved the dataset of translocation

    2020-08-28


    We retrieved the dataset of translocation breakpoints in cancer genomes from file CosmicBreakpointsExport.tsv.gz (release v85) available at COSMIC, https://cancer.sanger.ac.uk/cosmic/). These breakpoints were resolved at the bp level and mapped to specific hg38 genomic coordinates. We selected the translocations labeled “Interchromosomal unknown type” and “Interchromosomal reciprocal translocation”; the first entry represents frequent complex translocations between two or more chromosomes; the second defines rare reciprocal translocations between two sepa-rate chromosomes. Chromosome Y was excluded from the ana-lyses. We defined a translocation breakpoint at a G4 DNA-forming sequence if its coordinate was within 5 bases of a G4 DNA-forming sequence. To generate random genomic coordinates we used bedtools and toBitToFa to obtain a list of 50 bp-long sequences from which we excluded those containing any N; the middle po-sition was selected as the random genomic coordinate. The occurrence of pathologic mutations in cancer-related genes from patients with and without translocation breakpoints at G4 DNA was assessed from file CosmicMutantExportCensus.tsv.gz (release v85), also from COSMIC, after selecting the entries labeled as “pathogenic”.
    G4 DNA-forming sequences present within non-LTR trans-posons were retrieved based on the abundance of hits sharing identical sequence composition (including 5, 7, and 10 flanking bases). The localization of these hits within either LINE (L1) or Composite (SVA) transposable elements was confirmed by map-ping them onto hg38.
    We used the TCGA-Assembler suite (Wei et al., 2018) with the assayPlatform option set to gene.normalized_RNAseq to obtain the normalized Rsem RNA-seq gene expression data from TCGA (https://cancergenome.nih.gov) project. The TCGA-Assembler was also used to retrieve the clinical patient data and the somatic mu-tations specific to the tumor tissues, i.e. single SP 600125 substitutions and small insertions/deletions in exons genome-wide specific to the tumor but not the matched normal samples. A total of 32 datasets were analyzed; we were unable to examine the UCEC dataset because of inconsistencies in patient ID codes. Data were processed with in-house scripts (Cþþ and Bash) to obtain corre-lations between the expression of each gene and the number of somatic mutations in cancer patients. P-values were obtained from the cumulative distribution function (CDF) of the F distribution
    p2 ffiffiffiffiffiffiffiffiffiffiffi p2 ffiffiffiffiffiffiffiffiffiffiffiffiffi fi
    cient and n the number of observations, using the Cþþ BOOST li-brary. Kaplan-Meier survival curves were computed using the R libraries “dplyr”, “survival” and “survminer”. Gene enrichment analyses were conducted using the DAVID Bioinformatics Resource 6.8 (https://david.ncifcrf.gov/home.jsp) and the Ingenuity Pathway
    Analysis (https://www.qiagenbioinformatics.com/products/ ingenuity-pathway-analysis).
    3. Results
    3.1. G4 DNA elicits translocations in cancer genomes
    We reasoned that DNA secondary structure (and the consequent tertiary structure) is a probable factor in the differential sensitivity to genome stress during replication and transcription in cancer cells. One of the major types of non-B DNA structure involves the formation of G4 DNA; we therefore focused attention on experi-mentally and computationally examining G4 forming sequences in cancer genomes as exemplary regions of non-B-DNA.
    3.1.1. Translocations in cancer genomes are enriched at G4 DNA-forming sequences
    To assess whether G4 DNA structures are present at substantial
    Fig. 1. G4 DNA structures are readily detected in cell nuclei. Confocal microscopy of 293T, HAP1 and Hela cells stained with DAPI (blue) for nuclear DNA, a G4 DNA-structure specific antibody (red) and with Phalloidin for cytoplasmic cytoskeleton (green) display nuclear colocalization of G4 SP 600125 DNA structural foci with chromosomal DNA.
    Please cite this article as: Bacolla, A et al., Cancer mutational burden is shaped by G4 DNA, replication stress and mitochondrial dysfunction, Progress in Biophysics and Molecular Biology, https://doi.org/10.1016/j.pbiomolbio.2019.03.004
    4 A. Bacolla et al. / Progress in Biophysics and Molecular Biology xxx (xxxx) xxx
    Please cite this article as: Bacolla, A et al., Cancer mutational burden is shaped by G4 DNA, replication stress and mitochondrial dysfunction, Progress in Biophysics and Molecular Biology, https://doi.org/10.1016/j.pbiomolbio.2019.03.004
    A. Bacolla et al. / Progress in Biophysics and Molecular Biology xxx (xxxx) xxx 5