Differences and homologies of chromosomal alterations within and between breast cancer cell lines: a clustering analysis

Rondón-Lagos, Milena; Cantogno, Ludovica Verdun di; Marchiò, Caterina; Rangel, Nelson; Payán‐Gómez, César; Gugliotta, Patrizia; Botta, C.; Bussolati, Gianni; Ramírez-Clavijo, Sandra; Pasini, Barbara; Sapino, Anna

doi:10.1186/1755-8166-7-8

Cited by 41 publications

(39 citation statements)

References 30 publications

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“…(breast) cancer cell lines identified several translocations, against which we compare our calls (Takahashi et al 1989;Davidson et al 2000;Espino et al 2009;Peng et al 2010;Rondon-Lagos et al 2014). We find that 30 out of our 90 translocations were previously reported (Supp.…”

Section: Evaluation Of Translocation Calls From Hictransmentioning

confidence: 68%

Identification of copy number variations and translocations in cancer cells from Hi-C data

Chakraborty

2017

Preprint

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Motivation: Eukaryotic chromosomes adapt a complex and highly dynamic three-dimensional (3D) structure, which profoundly affects different cellular functions and outcomes including changes in epigenetic landscape and in gene expression. Making the scenario even more complex, cancer cells harbor chromosomal abnormalities (e.g., copy number variations (CNVs) and translocations) altering their genomes both at the sequence level and at the level of 3D organization. High-throughput chromosome conformation capture techniques (e.g., Hi-C), which are originally developed for decoding the 3D structure of the chromatin, provide a great opportunity to simultaneously identify the locations of genomic rearrangements and to investigate the 3D genome organization in cancer cells. Even though Hi-C data has been used for validating known rearrangements, computational methods that can distinguish rearrangement signals from the inherent biases of Hi-C data and from the actual 3D conformation of chromatin, and can precisely detect rearrangement locations de novo have been missing. Results:In this work, we characterize how intra and inter-chromosomal Hi-C contacts are distributed for normal and rearranged chromosomes to devise a new set of algorithms (i) to identify genomic segments that correspond to CNV regions such as amplifications and deletions (HiCnv), (ii) to call inter-chromosomal translocations and their boundaries (HiCtrans) from Hi-C experiments, and (iii) to simulate Hi-C data from genomes with desired rearrangements and abnormalities (AveSim) in order to select optimal parameters for and to benchmark the accuracy of our methods. Our results on 10 different cancer cell lines with Hi-C data show that we identify a total number of 105 amplifications and 45 deletions together with 90 translocations, whereas we identify virtually no such events for two karyotypically normal cell lines. Our CNV predictions correlate very well with whole genome sequencing (WGS) data among chromosomes with CNV events for a breast cancer cell line (r=0.89) and capture most of the CNVs we simulate using Avesim. For HiCtrans predictions, we report evidence from the literature for 30 out of 90 translocations for eight of our cancer cell lines. Furthermore, we show that our tools identify and correctly classify relatively understudied rearrangements such as double minutes (DMs) and homogeneously staining regions (HSRs). Conclusions:Considering the inherent limitations of existing techniques for karyotyping (i.e., missing balanced rearrangements and those near repetitive regions), the accurate identification of CNVs and translocations in a cost-effective and high-throughput setting is still a challenge. Our results show that the set of tools we develop effectively utilize moderately sequenced Hi-C libraries (100-300 million reads) to identify known and de novo chromosomal rearrangements/abnormalities in well-established cancer cell lines. With the decrease in required number of cells and the increase in attainable resolution, we believe that ...

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Section: Evaluation Of Translocation Calls From Hictransmentioning

confidence: 68%

Identification of copy number variations and translocations in cancer cells from Hi-C data

Chakraborty

2017

Preprint

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“…Most common laboratory cancer cell lines exhibit changes in ploidy, with many lines carrying four or more copies of a chromosome (Paulsson et al, 2013; Rondon-Lagos et al, 2014). In these cases, true knockout phenotypes may only emerge once every functional copy of the target gene is mutated.…”

Section: Choosing the Right Tool For The Jobmentioning

confidence: 99%

Choosing the Right Tool for the Job: RNAi, TALEN, or CRISPR

2015

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The most widely used approach for defining a genes’ function is to reduce or completely disrupt its normal expression. For over a decade, RNAi has ruled the lab, offering a magic bullet to disrupt gene expression in many organisms. However, new biotechnological tools - specifically CRISPR-based technologies - have become available and are squeezing out RNAi dominance in mammalian cell studies. These seemingly competing technologies leave research investigators with the question: ‘Which technology should I use in my experiment?’ This review offers a practical resource to compare and contrast these technologies, guiding the investigator when and where to use this fantastic array of powerful tools.

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“…Further, a disparate number of reads were noted between cell lines which can be attributed to both (1) efforts made to keep the DNA as intact as possible, as the long strands of DNA may make it harder to get a uniform quantification; as well as (2) the aneuploidy which is known to exist in these immortalized breast cell lines. MDA-MB-231 and MCF-7 are near-triploid and hypotetraploid, with modal chromosomal numbers of 64 and 82, respectively 14,15 .…”

Section: Figure 1bmentioning

confidence: 99%

Targeted Nanopore Sequencing with Cas9 for studies of methylation, structural variants, and mutations

Gilpatrick

Lee

Graham

et al. 2019

Preprint

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Nanopore sequencing technology can rapidly and directly interrogate native DNA molecules. Often we are interested only in interrogating specific areas at high depth, but conventional enrichment methods have thus far proved unsuitable for long reads 1 . Existing strategies are currently limited by high input DNA requirements, low yield, short (<5kb) reads, time-intensive protocols, and/or amplification or cloning (losing base modification information). In this paper, we describe a technique utilizing the ability of Cas9 to introduce cuts at specific locations and ligating nanopore sequencing adaptors directly to those sites, a method we term 'nanopore Cas9 Targeted-Sequencing' (nCATS).We have demonstrated this using an Oxford Nanopore MinION flow cell (Capacity >10Gb+) to generate a median 165X coverage at 10 genomic loci with a median length of 18kb, representing a several hundred-fold improvement over the 2-3X coverage achieved without enrichment. We performed a pilot run on the smaller Flongle flow cell (Capacity ~1Gb), generating a median coverage of 30X at 11 genomic loci with a median length of 18kb. Using panels of guide RNAs, we show that the high coverage data from this method enables us to (1) profile DNA methylation patterns at cancer driver genes, (2) detect structural variations at known hot spots, and (3) survey for the presence of single nucleotide mutations. Together, this provides a low-cost method that can be applied even in low resource settings to directly examine cellular DNA. This technique has extensive clinical applications for assessing medically relevant genes and has the versatility to be a rapid and comprehensive diagnostic tool. We demonstrate applications of this technique by examining the well-characterized GM12878 cell line as well as three breast cell lines (MCF-10A, MCF-7, MDA-MB-231) with varying tumorigenic potential as a model for cancer. ContributionsTG and WT constructed the study. TG performed the experiments. TG, IL, and FS analyzed the data. TG, JG, ER, RB and AH and developed the method. TG and WT wrote the paper

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Differences and homologies of chromosomal alterations within and between breast cancer cell lines: a clustering analysis

Cited by 41 publications

References 30 publications

Identification of copy number variations and translocations in cancer cells from Hi-C data

Identification of copy number variations and translocations in cancer cells from Hi-C data

Choosing the Right Tool for the Job: RNAi, TALEN, or CRISPR

Targeted Nanopore Sequencing with Cas9 for studies of methylation, structural variants, and mutations

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